postgresql/src/backend/executor/nodeAgg.c

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/*-------------------------------------------------------------------------
*
* nodeAgg.c
* Routines to handle aggregate nodes.
*
* ExecAgg normally evaluates each aggregate in the following steps:
*
* transvalue = initcond
* foreach input_tuple do
* transvalue = transfunc(transvalue, input_value(s))
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* result = finalfunc(transvalue, direct_argument(s))
*
* If a finalfunc is not supplied then the result is just the ending
* value of transvalue.
*
* Other behaviors can be selected by the "aggsplit" mode, which exists
* to support partial aggregation. It is possible to:
* * Skip running the finalfunc, so that the output is always the
* final transvalue state.
* * Substitute the combinefunc for the transfunc, so that transvalue
* states (propagated up from a child partial-aggregation step) are merged
* rather than processing raw input rows. (The statements below about
* the transfunc apply equally to the combinefunc, when it's selected.)
* * Apply the serializefunc to the output values (this only makes sense
* when skipping the finalfunc, since the serializefunc works on the
* transvalue data type).
* * Apply the deserializefunc to the input values (this only makes sense
* when using the combinefunc, for similar reasons).
* It is the planner's responsibility to connect up Agg nodes using these
* alternate behaviors in a way that makes sense, with partial aggregation
* results being fed to nodes that expect them.
*
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* If a normal aggregate call specifies DISTINCT or ORDER BY, we sort the
* input tuples and eliminate duplicates (if required) before performing
* the above-depicted process. (However, we don't do that for ordered-set
* aggregates; their "ORDER BY" inputs are ordinary aggregate arguments
* so far as this module is concerned.) Note that partial aggregation
* is not supported in these cases, since we couldn't ensure global
* ordering or distinctness of the inputs.
*
* If transfunc is marked "strict" in pg_proc and initcond is NULL,
* then the first non-NULL input_value is assigned directly to transvalue,
* and transfunc isn't applied until the second non-NULL input_value.
* The agg's first input type and transtype must be the same in this case!
*
* If transfunc is marked "strict" then NULL input_values are skipped,
* keeping the previous transvalue. If transfunc is not strict then it
* is called for every input tuple and must deal with NULL initcond
* or NULL input_values for itself.
*
* If finalfunc is marked "strict" then it is not called when the
* ending transvalue is NULL, instead a NULL result is created
* automatically (this is just the usual handling of strict functions,
* of course). A non-strict finalfunc can make its own choice of
* what to return for a NULL ending transvalue.
*
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* Ordered-set aggregates are treated specially in one other way: we
* evaluate any "direct" arguments and pass them to the finalfunc along
Allow polymorphic aggregates to have non-polymorphic state data types. Before 9.4, such an aggregate couldn't be declared, because its final function would have to have polymorphic result type but no polymorphic argument, which CREATE FUNCTION would quite properly reject. The ordered-set-aggregate patch found a workaround: allow the final function to be declared as accepting additional dummy arguments that have types matching the aggregate's regular input arguments. However, we failed to notice that this problem applies just as much to regular aggregates, despite the fact that we had a built-in regular aggregate array_agg() that was known to be undeclarable in SQL because its final function had an illegal signature. So what we should have done, and what this patch does, is to decouple the extra-dummy-arguments behavior from ordered-set aggregates and make it generally available for all aggregate declarations. We have to put this into 9.4 rather than waiting till later because it slightly alters the rules for declaring ordered-set aggregates. The patch turned out a bit bigger than I'd hoped because it proved necessary to record the extra-arguments option in a new pg_aggregate column. I'd thought we could just look at the final function's pronargs at runtime, but that didn't work well for variadic final functions. It's probably just as well though, because it simplifies life for pg_dump to record the option explicitly. While at it, fix array_agg() to have a valid final-function signature, and add an opr_sanity test to notice future deviations from polymorphic consistency. I also marked the percentile_cont() aggregates as not needing extra arguments, since they don't.
2014-04-24 01:17:31 +02:00
* with the transition value.
*
* A finalfunc can have additional arguments beyond the transvalue and
* any "direct" arguments, corresponding to the input arguments of the
* aggregate. These are always just passed as NULL. Such arguments may be
* needed to allow resolution of a polymorphic aggregate's result type.
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* We compute aggregate input expressions and run the transition functions
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* in a temporary econtext (aggstate->tmpcontext). This is reset at least
* once per input tuple, so when the transvalue datatype is
* pass-by-reference, we have to be careful to copy it into a longer-lived
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* memory context, and free the prior value to avoid memory leakage. We
* store transvalues in another set of econtexts, aggstate->aggcontexts
* (one per grouping set, see below), which are also used for the hashtable
* structures in AGG_HASHED mode. These econtexts are rescanned, not just
* reset, at group boundaries so that aggregate transition functions can
* register shutdown callbacks via AggRegisterCallback.
*
* The node's regular econtext (aggstate->ss.ps.ps_ExprContext) is used to
* run finalize functions and compute the output tuple; this context can be
* reset once per output tuple.
*
* The executor's AggState node is passed as the fmgr "context" value in
* all transfunc and finalfunc calls. It is not recommended that the
* transition functions look at the AggState node directly, but they can
* use AggCheckCallContext() to verify that they are being called by
* nodeAgg.c (and not as ordinary SQL functions). The main reason a
* transition function might want to know this is so that it can avoid
* palloc'ing a fixed-size pass-by-ref transition value on every call:
* it can instead just scribble on and return its left input. Ordinarily
* it is completely forbidden for functions to modify pass-by-ref inputs,
* but in the aggregate case we know the left input is either the initial
* transition value or a previous function result, and in either case its
* value need not be preserved. See int8inc() for an example. Notice that
* the EEOP_AGG_PLAIN_TRANS step is coded to avoid a data copy step when
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
* the previous transition value pointer is returned. It is also possible
* to avoid repeated data copying when the transition value is an expanded
* object: to do that, the transition function must take care to return
* an expanded object that is in a child context of the memory context
* returned by AggCheckCallContext(). Also, some transition functions want
* to store working state in addition to the nominal transition value; they
* can use the memory context returned by AggCheckCallContext() to do that.
*
* Note: AggCheckCallContext() is available as of PostgreSQL 9.0. The
* AggState is available as context in earlier releases (back to 8.1),
* but direct examination of the node is needed to use it before 9.0.
*
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* As of 9.4, aggregate transition functions can also use AggGetAggref()
* to get hold of the Aggref expression node for their aggregate call.
* This is mainly intended for ordered-set aggregates, which are not
* supported as window functions. (A regular aggregate function would
* need some fallback logic to use this, since there's no Aggref node
* for a window function.)
*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Grouping sets:
*
* A list of grouping sets which is structurally equivalent to a ROLLUP
* clause (e.g. (a,b,c), (a,b), (a)) can be processed in a single pass over
* ordered data. We do this by keeping a separate set of transition values
* for each grouping set being concurrently processed; for each input tuple
* we update them all, and on group boundaries we reset those states
* (starting at the front of the list) whose grouping values have changed
* (the list of grouping sets is ordered from most specific to least
* specific).
*
* Where more complex grouping sets are used, we break them down into
* "phases", where each phase has a different sort order (except phase 0
* which is reserved for hashing). During each phase but the last, the
* input tuples are additionally stored in a tuplesort which is keyed to the
* next phase's sort order; during each phase but the first, the input
* tuples are drawn from the previously sorted data. (The sorting of the
* data for the first phase is handled by the planner, as it might be
* satisfied by underlying nodes.)
*
* Hashing can be mixed with sorted grouping. To do this, we have an
* AGG_MIXED strategy that populates the hashtables during the first sorted
* phase, and switches to reading them out after completing all sort phases.
* We can also support AGG_HASHED with multiple hash tables and no sorting
* at all.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* From the perspective of aggregate transition and final functions, the
* only issue regarding grouping sets is this: a single call site (flinfo)
* of an aggregate function may be used for updating several different
* transition values in turn. So the function must not cache in the flinfo
* anything which logically belongs as part of the transition value (most
* importantly, the memory context in which the transition value exists).
* The support API functions (AggCheckCallContext, AggRegisterCallback) are
* sensitive to the grouping set for which the aggregate function is
* currently being called.
*
* Plan structure:
*
* What we get from the planner is actually one "real" Agg node which is
* part of the plan tree proper, but which optionally has an additional list
* of Agg nodes hung off the side via the "chain" field. This is because an
* Agg node happens to be a convenient representation of all the data we
* need for grouping sets.
*
* For many purposes, we treat the "real" node as if it were just the first
* node in the chain. The chain must be ordered such that hashed entries
* come before sorted/plain entries; the real node is marked AGG_MIXED if
* there are both types present (in which case the real node describes one
* of the hashed groupings, other AGG_HASHED nodes may optionally follow in
* the chain, followed in turn by AGG_SORTED or (one) AGG_PLAIN node). If
* the real node is marked AGG_HASHED or AGG_SORTED, then all the chained
* nodes must be of the same type; if it is AGG_PLAIN, there can be no
* chained nodes.
*
* We collect all hashed nodes into a single "phase", numbered 0, and create
* a sorted phase (numbered 1..n) for each AGG_SORTED or AGG_PLAIN node.
* Phase 0 is allocated even if there are no hashes, but remains unused in
* that case.
*
* AGG_HASHED nodes actually refer to only a single grouping set each,
* because for each hashed grouping we need a separate grpColIdx and
* numGroups estimate. AGG_SORTED nodes represent a "rollup", a list of
* grouping sets that share a sort order. Each AGG_SORTED node other than
* the first one has an associated Sort node which describes the sort order
* to be used; the first sorted node takes its input from the outer subtree,
* which the planner has already arranged to provide ordered data.
*
* Memory and ExprContext usage:
*
* Because we're accumulating aggregate values across input rows, we need to
* use more memory contexts than just simple input/output tuple contexts.
* In fact, for a rollup, we need a separate context for each grouping set
* so that we can reset the inner (finer-grained) aggregates on their group
* boundaries while continuing to accumulate values for outer
* (coarser-grained) groupings. On top of this, we might be simultaneously
* populating hashtables; however, we only need one context for all the
* hashtables.
*
* So we create an array, aggcontexts, with an ExprContext for each grouping
* set in the largest rollup that we're going to process, and use the
* per-tuple memory context of those ExprContexts to store the aggregate
* transition values. hashcontext is the single context created to support
* all hash tables.
*
* Transition / Combine function invocation:
*
* For performance reasons transition functions, including combine
* functions, aren't invoked one-by-one from nodeAgg.c after computing
* arguments using the expression evaluation engine. Instead
* ExecBuildAggTrans() builds one large expression that does both argument
* evaluation and transition function invocation. That avoids performance
* issues due to repeated uses of expression evaluation, complications due
* to filter expressions having to be evaluated early, and allows to JIT
* the entire expression into one native function.
*
* Portions Copyright (c) 1996-2019, PostgreSQL Global Development Group
* Portions Copyright (c) 1994, Regents of the University of California
*
* IDENTIFICATION
2010-09-20 22:08:53 +02:00
* src/backend/executor/nodeAgg.c
*
*-------------------------------------------------------------------------
*/
#include "postgres.h"
#include "access/htup_details.h"
#include "catalog/objectaccess.h"
#include "catalog/pg_aggregate.h"
#include "catalog/pg_proc.h"
#include "catalog/pg_type.h"
#include "executor/executor.h"
#include "executor/nodeAgg.h"
#include "miscadmin.h"
#include "nodes/makefuncs.h"
#include "nodes/nodeFuncs.h"
#include "optimizer/clauses.h"
#include "optimizer/tlist.h"
#include "parser/parse_agg.h"
#include "parser/parse_coerce.h"
#include "utils/acl.h"
#include "utils/builtins.h"
#include "utils/lsyscache.h"
#include "utils/memutils.h"
1999-07-16 07:00:38 +02:00
#include "utils/syscache.h"
#include "utils/tuplesort.h"
#include "utils/datum.h"
static void select_current_set(AggState *aggstate, int setno, bool is_hash);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
static void initialize_phase(AggState *aggstate, int newphase);
static TupleTableSlot *fetch_input_tuple(AggState *aggstate);
static void initialize_aggregates(AggState *aggstate,
AggStatePerGroup *pergroups,
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
int numReset);
static void advance_transition_function(AggState *aggstate,
AggStatePerTrans pertrans,
AggStatePerGroup pergroupstate);
static void advance_aggregates(AggState *aggstate);
static void process_ordered_aggregate_single(AggState *aggstate,
AggStatePerTrans pertrans,
2010-02-26 03:01:40 +01:00
AggStatePerGroup pergroupstate);
static void process_ordered_aggregate_multi(AggState *aggstate,
AggStatePerTrans pertrans,
2010-02-26 03:01:40 +01:00
AggStatePerGroup pergroupstate);
static void finalize_aggregate(AggState *aggstate,
AggStatePerAgg peragg,
2003-08-04 02:43:34 +02:00
AggStatePerGroup pergroupstate,
Datum *resultVal, bool *resultIsNull);
static void finalize_partialaggregate(AggState *aggstate,
2016-06-10 00:02:36 +02:00
AggStatePerAgg peragg,
AggStatePerGroup pergroupstate,
Datum *resultVal, bool *resultIsNull);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
static void prepare_projection_slot(AggState *aggstate,
2015-05-24 03:35:49 +02:00
TupleTableSlot *slot,
int currentSet);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
static void finalize_aggregates(AggState *aggstate,
2015-05-24 03:35:49 +02:00
AggStatePerAgg peragg,
AggStatePerGroup pergroup);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
static TupleTableSlot *project_aggregates(AggState *aggstate);
static Bitmapset *find_unaggregated_cols(AggState *aggstate);
static bool find_unaggregated_cols_walker(Node *node, Bitmapset **colnos);
static void build_hash_table(AggState *aggstate);
static TupleHashEntryData *lookup_hash_entry(AggState *aggstate);
static void lookup_hash_entries(AggState *aggstate);
static TupleTableSlot *agg_retrieve_direct(AggState *aggstate);
static void agg_fill_hash_table(AggState *aggstate);
static TupleTableSlot *agg_retrieve_hash_table(AggState *aggstate);
static Datum GetAggInitVal(Datum textInitVal, Oid transtype);
static void build_pertrans_for_aggref(AggStatePerTrans pertrans,
AggState *aggstate, EState *estate,
Aggref *aggref, Oid aggtransfn, Oid aggtranstype,
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
Oid aggserialfn, Oid aggdeserialfn,
Datum initValue, bool initValueIsNull,
Oid *inputTypes, int numArguments);
static int find_compatible_peragg(Aggref *newagg, AggState *aggstate,
int lastaggno, List **same_input_transnos);
static int find_compatible_pertrans(AggState *aggstate, Aggref *newagg,
bool shareable,
Oid aggtransfn, Oid aggtranstype,
Oid aggserialfn, Oid aggdeserialfn,
Datum initValue, bool initValueIsNull,
List *transnos);
2003-06-25 23:30:34 +02:00
/*
* Select the current grouping set; affects current_set and
* curaggcontext.
*/
static void
select_current_set(AggState *aggstate, int setno, bool is_hash)
{
/* when changing this, also adapt ExecInterpExpr() and friends */
if (is_hash)
aggstate->curaggcontext = aggstate->hashcontext;
else
aggstate->curaggcontext = aggstate->aggcontexts[setno];
aggstate->current_set = setno;
}
/*
* Switch to phase "newphase", which must either be 0 or 1 (to reset) or
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* current_phase + 1. Juggle the tuplesorts accordingly.
*
* Phase 0 is for hashing, which we currently handle last in the AGG_MIXED
* case, so when entering phase 0, all we need to do is drop open sorts.
*/
static void
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
initialize_phase(AggState *aggstate, int newphase)
{
Assert(newphase <= 1 || newphase == aggstate->current_phase + 1);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Whatever the previous state, we're now done with whatever input
* tuplesort was in use.
*/
if (aggstate->sort_in)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
tuplesort_end(aggstate->sort_in);
aggstate->sort_in = NULL;
}
if (newphase <= 1)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Discard any existing output tuplesort.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->sort_out)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
tuplesort_end(aggstate->sort_out);
aggstate->sort_out = NULL;
}
}
else
{
/*
* The old output tuplesort becomes the new input one, and this is the
* right time to actually sort it.
*/
aggstate->sort_in = aggstate->sort_out;
aggstate->sort_out = NULL;
Assert(aggstate->sort_in);
tuplesort_performsort(aggstate->sort_in);
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
2015-05-24 03:35:49 +02:00
* If this isn't the last phase, we need to sort appropriately for the
* next phase in sequence.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
if (newphase > 0 && newphase < aggstate->numphases - 1)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
2015-05-24 03:35:49 +02:00
Sort *sortnode = aggstate->phases[newphase + 1].sortnode;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
PlanState *outerNode = outerPlanState(aggstate);
TupleDesc tupDesc = ExecGetResultType(outerNode);
aggstate->sort_out = tuplesort_begin_heap(tupDesc,
sortnode->numCols,
sortnode->sortColIdx,
sortnode->sortOperators,
sortnode->collations,
sortnode->nullsFirst,
work_mem,
NULL, false);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
aggstate->current_phase = newphase;
aggstate->phase = &aggstate->phases[newphase];
}
/*
* Fetch a tuple from either the outer plan (for phase 1) or from the sorter
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* populated by the previous phase. Copy it to the sorter for the next phase
* if any.
*
* Callers cannot rely on memory for tuple in returned slot remaining valid
* past any subsequently fetched tuple.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
static TupleTableSlot *
fetch_input_tuple(AggState *aggstate)
{
TupleTableSlot *slot;
if (aggstate->sort_in)
{
/* make sure we check for interrupts in either path through here */
CHECK_FOR_INTERRUPTS();
if (!tuplesort_gettupleslot(aggstate->sort_in, true, false,
aggstate->sort_slot, NULL))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
return NULL;
slot = aggstate->sort_slot;
}
else
slot = ExecProcNode(outerPlanState(aggstate));
if (!TupIsNull(slot) && aggstate->sort_out)
tuplesort_puttupleslot(aggstate->sort_out, slot);
return slot;
}
/*
* (Re)Initialize an individual aggregate.
*
* This function handles only one grouping set, already set in
* aggstate->current_set.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static void
initialize_aggregate(AggState *aggstate, AggStatePerTrans pertrans,
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
AggStatePerGroup pergroupstate)
{
/*
* Start a fresh sort operation for each DISTINCT/ORDER BY aggregate.
*/
if (pertrans->numSortCols > 0)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
/*
* In case of rescan, maybe there could be an uncompleted sort
* operation? Clean it up if so.
*/
if (pertrans->sortstates[aggstate->current_set])
tuplesort_end(pertrans->sortstates[aggstate->current_set]);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* We use a plain Datum sorter when there's a single input column;
* otherwise sort the full tuple. (See comments for
* process_ordered_aggregate_single.)
*/
if (pertrans->numInputs == 1)
{
Form_pg_attribute attr = TupleDescAttr(pertrans->sortdesc, 0);
pertrans->sortstates[aggstate->current_set] =
tuplesort_begin_datum(attr->atttypid,
pertrans->sortOperators[0],
pertrans->sortCollations[0],
pertrans->sortNullsFirst[0],
work_mem, NULL, false);
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
else
pertrans->sortstates[aggstate->current_set] =
tuplesort_begin_heap(pertrans->sortdesc,
pertrans->numSortCols,
pertrans->sortColIdx,
pertrans->sortOperators,
pertrans->sortCollations,
pertrans->sortNullsFirst,
work_mem, NULL, false);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* (Re)set transValue to the initial value.
*
2015-05-24 03:35:49 +02:00
* Note that when the initial value is pass-by-ref, we must copy it (into
* the aggcontext) since we will pfree the transValue later.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
if (pertrans->initValueIsNull)
pergroupstate->transValue = pertrans->initValue;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
else
{
MemoryContext oldContext;
oldContext = MemoryContextSwitchTo(
aggstate->curaggcontext->ecxt_per_tuple_memory);
pergroupstate->transValue = datumCopy(pertrans->initValue,
pertrans->transtypeByVal,
pertrans->transtypeLen);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
MemoryContextSwitchTo(oldContext);
}
pergroupstate->transValueIsNull = pertrans->initValueIsNull;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
2015-05-24 03:35:49 +02:00
* If the initial value for the transition state doesn't exist in the
* pg_aggregate table then we will let the first non-NULL value returned
* from the outer procNode become the initial value. (This is useful for
* aggregates like max() and min().) The noTransValue flag signals that we
* still need to do this.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
pergroupstate->noTransValue = pertrans->initValueIsNull;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
/*
* Initialize all aggregate transition states for a new group of input values.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* If there are multiple grouping sets, we initialize only the first numReset
* of them (the grouping sets are ordered so that the most specific one, which
* is reset most often, is first). As a convenience, if numReset is 0, we
* reinitialize all sets.
*
* NB: This cannot be used for hash aggregates, as for those the grouping set
* number has to be specified from further up.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static void
initialize_aggregates(AggState *aggstate,
AggStatePerGroup *pergroups,
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
int numReset)
{
int transno;
2015-05-24 03:35:49 +02:00
int numGroupingSets = Max(aggstate->phase->numsets, 1);
int setno = 0;
int numTrans = aggstate->numtrans;
AggStatePerTrans transstates = aggstate->pertrans;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (numReset == 0)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
numReset = numGroupingSets;
for (setno = 0; setno < numReset; setno++)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
AggStatePerGroup pergroup = pergroups[setno];
select_current_set(aggstate, setno, false);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
for (transno = 0; transno < numTrans; transno++)
{
AggStatePerTrans pertrans = &transstates[transno];
AggStatePerGroup pergroupstate = &pergroup[transno];
initialize_aggregate(aggstate, pertrans, pergroupstate);
}
}
}
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Given new input value(s), advance the transition function of one aggregate
* state within one grouping set only (already set in aggstate->current_set)
*
* The new values (and null flags) have been preloaded into argument positions
* 1 and up in pertrans->transfn_fcinfo, so that we needn't copy them again to
* pass to the transition function. We also expect that the static fields of
* the fcinfo are already initialized; that was done by ExecInitAgg().
*
* It doesn't matter which memory context this is called in.
*/
static void
advance_transition_function(AggState *aggstate,
AggStatePerTrans pertrans,
AggStatePerGroup pergroupstate)
{
FunctionCallInfo fcinfo = &pertrans->transfn_fcinfo;
MemoryContext oldContext;
2006-10-04 02:30:14 +02:00
Datum newVal;
if (pertrans->transfn.fn_strict)
{
/*
2006-10-04 02:30:14 +02:00
* For a strict transfn, nothing happens when there's a NULL input; we
* just keep the prior transValue.
*/
int numTransInputs = pertrans->numTransInputs;
int i;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
for (i = 1; i <= numTransInputs; i++)
{
if (fcinfo->argnull[i])
return;
}
if (pergroupstate->noTransValue)
{
/*
2005-10-15 04:49:52 +02:00
* transValue has not been initialized. This is the first non-NULL
* input value. We use it as the initial value for transValue. (We
* already checked that the agg's input type is binary-compatible
* with its transtype, so straight copy here is OK.)
*
* We must copy the datum into aggcontext if it is pass-by-ref. We
* do not need to pfree the old transValue, since it's NULL.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
oldContext = MemoryContextSwitchTo(
aggstate->curaggcontext->ecxt_per_tuple_memory);
pergroupstate->transValue = datumCopy(fcinfo->arg[1],
pertrans->transtypeByVal,
pertrans->transtypeLen);
pergroupstate->transValueIsNull = false;
pergroupstate->noTransValue = false;
MemoryContextSwitchTo(oldContext);
return;
}
if (pergroupstate->transValueIsNull)
{
/*
* Don't call a strict function with NULL inputs. Note it is
2005-10-15 04:49:52 +02:00
* possible to get here despite the above tests, if the transfn is
* strict *and* returned a NULL on a prior cycle. If that happens
* we will propagate the NULL all the way to the end.
*/
return;
}
}
/* We run the transition functions in per-input-tuple memory context */
oldContext = MemoryContextSwitchTo(aggstate->tmpcontext->ecxt_per_tuple_memory);
/* set up aggstate->curpertrans for AggGetAggref() */
aggstate->curpertrans = pertrans;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
* OK to call the transition function
*/
fcinfo->arg[0] = pergroupstate->transValue;
fcinfo->argnull[0] = pergroupstate->transValueIsNull;
fcinfo->isnull = false; /* just in case transfn doesn't set it */
2003-06-25 23:30:34 +02:00
newVal = FunctionCallInvoke(fcinfo);
aggstate->curpertrans = NULL;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
2005-10-15 04:49:52 +02:00
* If pass-by-ref datatype, must copy the new value into aggcontext and
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
* free the prior transValue. But if transfn returned a pointer to its
* first input, we don't need to do anything. Also, if transfn returned a
* pointer to a R/W expanded object that is already a child of the
* aggcontext, assume we can adopt that value without copying it.
*/
if (!pertrans->transtypeByVal &&
2005-10-15 04:49:52 +02:00
DatumGetPointer(newVal) != DatumGetPointer(pergroupstate->transValue))
{
if (!fcinfo->isnull)
{
MemoryContextSwitchTo(aggstate->curaggcontext->ecxt_per_tuple_memory);
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
if (DatumIsReadWriteExpandedObject(newVal,
false,
pertrans->transtypeLen) &&
MemoryContextGetParent(DatumGetEOHP(newVal)->eoh_context) == CurrentMemoryContext)
/* do nothing */ ;
else
newVal = datumCopy(newVal,
pertrans->transtypeByVal,
pertrans->transtypeLen);
}
if (!pergroupstate->transValueIsNull)
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
{
if (DatumIsReadWriteExpandedObject(pergroupstate->transValue,
false,
pertrans->transtypeLen))
DeleteExpandedObject(pergroupstate->transValue);
else
pfree(DatumGetPointer(pergroupstate->transValue));
}
}
pergroupstate->transValue = newVal;
pergroupstate->transValueIsNull = fcinfo->isnull;
MemoryContextSwitchTo(oldContext);
}
/*
* Advance each aggregate transition state for one input tuple. The input
* tuple has been stored in tmpcontext->ecxt_outertuple, so that it is
* accessible to ExecEvalExpr.
*
* We have two sets of transition states to handle: one for sorted aggregation
* and one for hashed; we do them both here, to avoid multiple evaluation of
* the inputs.
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static void
advance_aggregates(AggState *aggstate)
{
bool dummynull;
ExecEvalExprSwitchContext(aggstate->phase->evaltrans,
aggstate->tmpcontext,
&dummynull);
}
/*
* Run the transition function for a DISTINCT or ORDER BY aggregate
* with only one input. This is called after we have completed
* entering all the input values into the sort object. We complete the
* sort, read out the values in sorted order, and run the transition
* function on each value (applying DISTINCT if appropriate).
*
* Note that the strictness of the transition function was checked when
* entering the values into the sort, so we don't check it again here;
* we just apply standard SQL DISTINCT logic.
*
* The one-input case is handled separately from the multi-input case
* for performance reasons: for single by-value inputs, such as the
* common case of count(distinct id), the tuplesort_getdatum code path
* is around 300% faster. (The speedup for by-reference types is less
* but still noticeable.)
*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* This function handles only one grouping set (already set in
* aggstate->current_set).
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static void
process_ordered_aggregate_single(AggState *aggstate,
AggStatePerTrans pertrans,
AggStatePerGroup pergroupstate)
{
Datum oldVal = (Datum) 0;
2010-02-26 03:01:40 +01:00
bool oldIsNull = true;
bool haveOldVal = false;
MemoryContext workcontext = aggstate->tmpcontext->ecxt_per_tuple_memory;
MemoryContext oldContext;
bool isDistinct = (pertrans->numDistinctCols > 0);
Datum newAbbrevVal = (Datum) 0;
Datum oldAbbrevVal = (Datum) 0;
FunctionCallInfo fcinfo = &pertrans->transfn_fcinfo;
2006-10-04 02:30:14 +02:00
Datum *newVal;
bool *isNull;
Assert(pertrans->numDistinctCols < 2);
tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
/* Load the column into argument 1 (arg 0 will be transition value) */
newVal = fcinfo->arg + 1;
isNull = fcinfo->argnull + 1;
/*
2005-10-15 04:49:52 +02:00
* Note: if input type is pass-by-ref, the datums returned by the sort are
* freshly palloc'd in the per-query context, so we must be careful to
* pfree them when they are no longer needed.
*/
while (tuplesort_getdatum(pertrans->sortstates[aggstate->current_set],
true, newVal, isNull, &newAbbrevVal))
{
/*
2005-10-15 04:49:52 +02:00
* Clear and select the working context for evaluation of the equality
* function and transition function.
*/
MemoryContextReset(workcontext);
oldContext = MemoryContextSwitchTo(workcontext);
/*
* If DISTINCT mode, and not distinct from prior, skip it.
*
* Note: we assume equality functions don't care about collation.
*/
if (isDistinct &&
haveOldVal &&
((oldIsNull && *isNull) ||
(!oldIsNull && !*isNull &&
oldAbbrevVal == newAbbrevVal &&
DatumGetBool(FunctionCall2(&pertrans->equalfnOne,
oldVal, *newVal)))))
{
/* equal to prior, so forget this one */
if (!pertrans->inputtypeByVal && !*isNull)
pfree(DatumGetPointer(*newVal));
}
else
{
advance_transition_function(aggstate, pertrans, pergroupstate);
/* forget the old value, if any */
if (!oldIsNull && !pertrans->inputtypeByVal)
pfree(DatumGetPointer(oldVal));
/* and remember the new one for subsequent equality checks */
oldVal = *newVal;
oldAbbrevVal = newAbbrevVal;
oldIsNull = *isNull;
haveOldVal = true;
}
MemoryContextSwitchTo(oldContext);
}
if (!oldIsNull && !pertrans->inputtypeByVal)
pfree(DatumGetPointer(oldVal));
tuplesort_end(pertrans->sortstates[aggstate->current_set]);
pertrans->sortstates[aggstate->current_set] = NULL;
}
/*
* Run the transition function for a DISTINCT or ORDER BY aggregate
* with more than one input. This is called after we have completed
* entering all the input values into the sort object. We complete the
* sort, read out the values in sorted order, and run the transition
* function on each value (applying DISTINCT if appropriate).
*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* This function handles only one grouping set (already set in
* aggstate->current_set).
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static void
process_ordered_aggregate_multi(AggState *aggstate,
AggStatePerTrans pertrans,
AggStatePerGroup pergroupstate)
{
ExprContext *tmpcontext = aggstate->tmpcontext;
FunctionCallInfo fcinfo = &pertrans->transfn_fcinfo;
TupleTableSlot *slot1 = pertrans->sortslot;
TupleTableSlot *slot2 = pertrans->uniqslot;
int numTransInputs = pertrans->numTransInputs;
int numDistinctCols = pertrans->numDistinctCols;
Datum newAbbrevVal = (Datum) 0;
Datum oldAbbrevVal = (Datum) 0;
2010-02-26 03:01:40 +01:00
bool haveOldValue = false;
TupleTableSlot *save = aggstate->tmpcontext->ecxt_outertuple;
2010-02-26 03:01:40 +01:00
int i;
tuplesort_performsort(pertrans->sortstates[aggstate->current_set]);
ExecClearTuple(slot1);
if (slot2)
ExecClearTuple(slot2);
while (tuplesort_gettupleslot(pertrans->sortstates[aggstate->current_set],
true, true, slot1, &newAbbrevVal))
{
CHECK_FOR_INTERRUPTS();
tmpcontext->ecxt_outertuple = slot1;
tmpcontext->ecxt_innertuple = slot2;
if (numDistinctCols == 0 ||
!haveOldValue ||
newAbbrevVal != oldAbbrevVal ||
!ExecQual(pertrans->equalfnMulti, tmpcontext))
{
/*
* Extract the first numTransInputs columns as datums to pass to
* the transfn.
*/
slot_getsomeattrs(slot1, numTransInputs);
/* Load values into fcinfo */
/* Start from 1, since the 0th arg will be the transition value */
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
for (i = 0; i < numTransInputs; i++)
{
fcinfo->arg[i + 1] = slot1->tts_values[i];
fcinfo->argnull[i + 1] = slot1->tts_isnull[i];
}
advance_transition_function(aggstate, pertrans, pergroupstate);
if (numDistinctCols > 0)
{
/* swap the slot pointers to retain the current tuple */
TupleTableSlot *tmpslot = slot2;
slot2 = slot1;
slot1 = tmpslot;
/* avoid ExecQual() calls by reusing abbreviated keys */
oldAbbrevVal = newAbbrevVal;
haveOldValue = true;
}
}
/* Reset context each time */
ResetExprContext(tmpcontext);
ExecClearTuple(slot1);
}
if (slot2)
ExecClearTuple(slot2);
tuplesort_end(pertrans->sortstates[aggstate->current_set]);
pertrans->sortstates[aggstate->current_set] = NULL;
/* restore previous slot, potentially in use for grouping sets */
tmpcontext->ecxt_outertuple = save;
}
/*
* Compute the final value of one aggregate.
*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* This function handles only one grouping set (already set in
* aggstate->current_set).
*
* The finalfunction will be run, and the result delivered, in the
* output-tuple context; caller's CurrentMemoryContext does not matter.
*
* The finalfn uses the state as set in the transno. This also might be
* being used by another aggregate function, so it's important that we do
* nothing destructive here.
*/
static void
finalize_aggregate(AggState *aggstate,
AggStatePerAgg peragg,
AggStatePerGroup pergroupstate,
Datum *resultVal, bool *resultIsNull)
{
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
FunctionCallInfoData fcinfo;
bool anynull = false;
MemoryContext oldContext;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
int i;
ListCell *lc;
AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
* Evaluate any direct arguments. We do this even if there's no finalfn
* (which is unlikely anyway), so that side-effects happen as expected.
Allow polymorphic aggregates to have non-polymorphic state data types. Before 9.4, such an aggregate couldn't be declared, because its final function would have to have polymorphic result type but no polymorphic argument, which CREATE FUNCTION would quite properly reject. The ordered-set-aggregate patch found a workaround: allow the final function to be declared as accepting additional dummy arguments that have types matching the aggregate's regular input arguments. However, we failed to notice that this problem applies just as much to regular aggregates, despite the fact that we had a built-in regular aggregate array_agg() that was known to be undeclarable in SQL because its final function had an illegal signature. So what we should have done, and what this patch does, is to decouple the extra-dummy-arguments behavior from ordered-set aggregates and make it generally available for all aggregate declarations. We have to put this into 9.4 rather than waiting till later because it slightly alters the rules for declaring ordered-set aggregates. The patch turned out a bit bigger than I'd hoped because it proved necessary to record the extra-arguments option in a new pg_aggregate column. I'd thought we could just look at the final function's pronargs at runtime, but that didn't work well for variadic final functions. It's probably just as well though, because it simplifies life for pg_dump to record the option explicitly. While at it, fix array_agg() to have a valid final-function signature, and add an opr_sanity test to notice future deviations from polymorphic consistency. I also marked the percentile_cont() aggregates as not needing extra arguments, since they don't.
2014-04-24 01:17:31 +02:00
* The direct arguments go into arg positions 1 and up, leaving position 0
* for the transition state value.
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*/
i = 1;
foreach(lc, peragg->aggdirectargs)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
{
ExprState *expr = (ExprState *) lfirst(lc);
fcinfo.arg[i] = ExecEvalExpr(expr,
aggstate->ss.ps.ps_ExprContext,
&fcinfo.argnull[i]);
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
anynull |= fcinfo.argnull[i];
i++;
}
/*
* Apply the agg's finalfn if one is provided, else return transValue.
*/
if (OidIsValid(peragg->finalfn_oid))
{
int numFinalArgs = peragg->numFinalArgs;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/* set up aggstate->curperagg for AggGetAggref() */
aggstate->curperagg = peragg;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
InitFunctionCallInfoData(fcinfo, &peragg->finalfn,
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
numFinalArgs,
pertrans->aggCollation,
(void *) aggstate, NULL);
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/* Fill in the transition state value */
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
fcinfo.arg[0] = MakeExpandedObjectReadOnly(pergroupstate->transValue,
pergroupstate->transValueIsNull,
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
pertrans->transtypeLen);
fcinfo.argnull[0] = pergroupstate->transValueIsNull;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
anynull |= pergroupstate->transValueIsNull;
/* Fill any remaining argument positions with nulls */
Allow polymorphic aggregates to have non-polymorphic state data types. Before 9.4, such an aggregate couldn't be declared, because its final function would have to have polymorphic result type but no polymorphic argument, which CREATE FUNCTION would quite properly reject. The ordered-set-aggregate patch found a workaround: allow the final function to be declared as accepting additional dummy arguments that have types matching the aggregate's regular input arguments. However, we failed to notice that this problem applies just as much to regular aggregates, despite the fact that we had a built-in regular aggregate array_agg() that was known to be undeclarable in SQL because its final function had an illegal signature. So what we should have done, and what this patch does, is to decouple the extra-dummy-arguments behavior from ordered-set aggregates and make it generally available for all aggregate declarations. We have to put this into 9.4 rather than waiting till later because it slightly alters the rules for declaring ordered-set aggregates. The patch turned out a bit bigger than I'd hoped because it proved necessary to record the extra-arguments option in a new pg_aggregate column. I'd thought we could just look at the final function's pronargs at runtime, but that didn't work well for variadic final functions. It's probably just as well though, because it simplifies life for pg_dump to record the option explicitly. While at it, fix array_agg() to have a valid final-function signature, and add an opr_sanity test to notice future deviations from polymorphic consistency. I also marked the percentile_cont() aggregates as not needing extra arguments, since they don't.
2014-04-24 01:17:31 +02:00
for (; i < numFinalArgs; i++)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
{
fcinfo.arg[i] = (Datum) 0;
fcinfo.argnull[i] = true;
anynull = true;
}
if (fcinfo.flinfo->fn_strict && anynull)
{
/* don't call a strict function with NULL inputs */
*resultVal = (Datum) 0;
*resultIsNull = true;
}
else
{
*resultVal = FunctionCallInvoke(&fcinfo);
*resultIsNull = fcinfo.isnull;
}
aggstate->curperagg = NULL;
}
else
{
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
/* Don't need MakeExpandedObjectReadOnly; datumCopy will copy it */
*resultVal = pergroupstate->transValue;
*resultIsNull = pergroupstate->transValueIsNull;
}
/*
* If result is pass-by-ref, make sure it is in the right context.
*/
if (!peragg->resulttypeByVal && !*resultIsNull &&
2001-03-22 05:01:46 +01:00
!MemoryContextContains(CurrentMemoryContext,
DatumGetPointer(*resultVal)))
*resultVal = datumCopy(*resultVal,
peragg->resulttypeByVal,
peragg->resulttypeLen);
MemoryContextSwitchTo(oldContext);
}
/*
* Compute the output value of one partial aggregate.
*
* The serialization function will be run, and the result delivered, in the
* output-tuple context; caller's CurrentMemoryContext does not matter.
*/
static void
finalize_partialaggregate(AggState *aggstate,
AggStatePerAgg peragg,
AggStatePerGroup pergroupstate,
Datum *resultVal, bool *resultIsNull)
{
2016-06-10 00:02:36 +02:00
AggStatePerTrans pertrans = &aggstate->pertrans[peragg->transno];
MemoryContext oldContext;
oldContext = MemoryContextSwitchTo(aggstate->ss.ps.ps_ExprContext->ecxt_per_tuple_memory);
/*
* serialfn_oid will be set if we must serialize the transvalue before
* returning it
*/
if (OidIsValid(pertrans->serialfn_oid))
{
/* Don't call a strict serialization function with NULL input. */
if (pertrans->serialfn.fn_strict && pergroupstate->transValueIsNull)
{
*resultVal = (Datum) 0;
*resultIsNull = true;
}
else
{
FunctionCallInfo fcinfo = &pertrans->serialfn_fcinfo;
2016-06-10 00:02:36 +02:00
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
fcinfo->arg[0] = MakeExpandedObjectReadOnly(pergroupstate->transValue,
pergroupstate->transValueIsNull,
pertrans->transtypeLen);
fcinfo->argnull[0] = pergroupstate->transValueIsNull;
*resultVal = FunctionCallInvoke(fcinfo);
*resultIsNull = fcinfo->isnull;
}
}
else
{
Improve speed of aggregates that use array_append as transition function. In the previous coding, if an aggregate's transition function returned an expanded array, nodeAgg.c and nodeWindowAgg.c would always copy it and thus force it into the flat representation. This led to ping-ponging between flat and expanded formats, which costs a lot. For an aggregate using array_append as transition function, I measured about a 15X slowdown compared to the pre-9.5 code, when working on simple int[] arrays. Of course, the old code was already O(N^2) in this usage due to copying flat arrays all the time, but it wasn't quite this inefficient. To fix, teach nodeAgg.c and nodeWindowAgg.c to allow expanded transition values without copying, so long as the transition function takes care to return the transition value already properly parented under the aggcontext. That puts a bit of extra responsibility on the transition function, but doing it this way allows us to not need any extra logic in the fast path of advance_transition_function (ie, with a pass-by-value transition value, or with a modified-in-place pass-by-reference value). We already know that that's a hot spot so I'm loath to add any cycles at all there. Also, while only array_append currently knows how to follow this convention, this solution allows other transition functions to opt-in without needing to have a whitelist in the core aggregation code. (The reason we would need a whitelist is that currently, if you pass a R/W expanded-object pointer to an arbitrary function, it's allowed to do anything with it including deleting it; that breaks the core agg code's assumption that it should free discarded values. Returning a value under aggcontext is the transition function's signal that it knows it is an aggregate transition function and will play nice. Possibly the API rules for expanded objects should be refined, but that would not be a back-patchable change.) With this fix, an aggregate using array_append is no longer O(N^2), so it's much faster than pre-9.5 code rather than much slower. It's still a bit slower than the bespoke infrastructure for array_agg, but the differential seems to be only about 10%-20% rather than orders of magnitude. Discussion: <6315.1477677885@sss.pgh.pa.us>
2016-10-30 17:27:41 +01:00
/* Don't need MakeExpandedObjectReadOnly; datumCopy will copy it */
*resultVal = pergroupstate->transValue;
*resultIsNull = pergroupstate->transValueIsNull;
}
/* If result is pass-by-ref, make sure it is in the right context. */
if (!peragg->resulttypeByVal && !*resultIsNull &&
!MemoryContextContains(CurrentMemoryContext,
2016-06-10 00:02:36 +02:00
DatumGetPointer(*resultVal)))
*resultVal = datumCopy(*resultVal,
peragg->resulttypeByVal,
peragg->resulttypeLen);
MemoryContextSwitchTo(oldContext);
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Prepare to finalize and project based on the specified representative tuple
* slot and grouping set.
*
* In the specified tuple slot, force to null all attributes that should be
* read as null in the context of the current grouping set. Also stash the
* current group bitmap where GroupingExpr can get at it.
*
* This relies on three conditions:
*
* 1) Nothing is ever going to try and extract the whole tuple from this slot,
* only reference it in evaluations, which will only access individual
* attributes.
*
* 2) No system columns are going to need to be nulled. (If a system column is
* referenced in a group clause, it is actually projected in the outer plan
* tlist.)
*
* 3) Within a given phase, we never need to recover the value of an attribute
* once it has been set to null.
*
* Poking into the slot this way is a bit ugly, but the consensus is that the
* alternative was worse.
*/
static void
prepare_projection_slot(AggState *aggstate, TupleTableSlot *slot, int currentSet)
{
if (aggstate->phase->grouped_cols)
{
2015-05-24 03:35:49 +02:00
Bitmapset *grouped_cols = aggstate->phase->grouped_cols[currentSet];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->grouped_cols = grouped_cols;
if (TTS_EMPTY(slot))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
/*
* Force all values to be NULL if working on an empty input tuple
* (i.e. an empty grouping set for which no input rows were
* supplied).
*/
ExecStoreAllNullTuple(slot);
}
else if (aggstate->all_grouped_cols)
{
ListCell *lc;
/* all_grouped_cols is arranged in desc order */
slot_getsomeattrs(slot, linitial_int(aggstate->all_grouped_cols));
foreach(lc, aggstate->all_grouped_cols)
{
2015-05-24 03:35:49 +02:00
int attnum = lfirst_int(lc);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (!bms_is_member(attnum, grouped_cols))
slot->tts_isnull[attnum - 1] = true;
}
}
}
}
/*
* Compute the final value of all aggregates for one group.
*
* This function handles only one grouping set at a time, which the caller must
* have selected. It's also the caller's responsibility to adjust the supplied
* pergroup parameter to point to the current set's transvalues.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* Results are stored in the output econtext aggvalues/aggnulls.
*/
static void
finalize_aggregates(AggState *aggstate,
AggStatePerAgg peraggs,
AggStatePerGroup pergroup)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
Datum *aggvalues = econtext->ecxt_aggvalues;
bool *aggnulls = econtext->ecxt_aggnulls;
int aggno;
int transno;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* If there were any DISTINCT and/or ORDER BY aggregates, sort their
* inputs and run the transition functions.
*/
for (transno = 0; transno < aggstate->numtrans; transno++)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
AggStatePerTrans pertrans = &aggstate->pertrans[transno];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
AggStatePerGroup pergroupstate;
pergroupstate = &pergroup[transno];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (pertrans->numSortCols > 0)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
Assert(aggstate->aggstrategy != AGG_HASHED &&
aggstate->aggstrategy != AGG_MIXED);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (pertrans->numInputs == 1)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
process_ordered_aggregate_single(aggstate,
pertrans,
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
pergroupstate);
else
process_ordered_aggregate_multi(aggstate,
pertrans,
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
pergroupstate);
}
}
/*
* Run the final functions.
*/
for (aggno = 0; aggno < aggstate->numaggs; aggno++)
{
AggStatePerAgg peragg = &peraggs[aggno];
int transno = peragg->transno;
AggStatePerGroup pergroupstate;
pergroupstate = &pergroup[transno];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
finalize_partialaggregate(aggstate, peragg, pergroupstate,
&aggvalues[aggno], &aggnulls[aggno]);
else
finalize_aggregate(aggstate, peragg, pergroupstate,
&aggvalues[aggno], &aggnulls[aggno]);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
}
/*
* Project the result of a group (whose aggs have already been calculated by
* finalize_aggregates). Returns the result slot, or NULL if no row is
* projected (suppressed by qual).
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
static TupleTableSlot *
project_aggregates(AggState *aggstate)
{
ExprContext *econtext = aggstate->ss.ps.ps_ExprContext;
/*
2015-05-24 03:35:49 +02:00
* Check the qual (HAVING clause); if the group does not match, ignore it.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
Faster expression evaluation and targetlist projection. This replaces the old, recursive tree-walk based evaluation, with non-recursive, opcode dispatch based, expression evaluation. Projection is now implemented as part of expression evaluation. This both leads to significant performance improvements, and makes future just-in-time compilation of expressions easier. The speed gains primarily come from: - non-recursive implementation reduces stack usage / overhead - simple sub-expressions are implemented with a single jump, without function calls - sharing some state between different sub-expressions - reduced amount of indirect/hard to predict memory accesses by laying out operation metadata sequentially; including the avoidance of nearly all of the previously used linked lists - more code has been moved to expression initialization, avoiding constant re-checks at evaluation time Future just-in-time compilation (JIT) has become easier, as demonstrated by released patches intended to be merged in a later release, for primarily two reasons: Firstly, due to a stricter split between expression initialization and evaluation, less code has to be handled by the JIT. Secondly, due to the non-recursive nature of the generated "instructions", less performance-critical code-paths can easily be shared between interpreted and compiled evaluation. The new framework allows for significant future optimizations. E.g.: - basic infrastructure for to later reduce the per executor-startup overhead of expression evaluation, by caching state in prepared statements. That'd be helpful in OLTPish scenarios where initialization overhead is measurable. - optimizing the generated "code". A number of proposals for potential work has already been made. - optimizing the interpreter. Similarly a number of proposals have been made here too. The move of logic into the expression initialization step leads to some backward-incompatible changes: - Function permission checks are now done during expression initialization, whereas previously they were done during execution. In edge cases this can lead to errors being raised that previously wouldn't have been, e.g. a NULL array being coerced to a different array type previously didn't perform checks. - The set of domain constraints to be checked, is now evaluated once during expression initialization, previously it was re-built every time a domain check was evaluated. For normal queries this doesn't change much, but e.g. for plpgsql functions, which caches ExprStates, the old set could stick around longer. The behavior around might still change. Author: Andres Freund, with significant changes by Tom Lane, changes by Heikki Linnakangas Reviewed-By: Tom Lane, Heikki Linnakangas Discussion: https://postgr.es/m/20161206034955.bh33paeralxbtluv@alap3.anarazel.de
2017-03-14 23:45:36 +01:00
if (ExecQual(aggstate->ss.ps.qual, econtext))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
/*
* Form and return projection tuple using the aggregate results and
* the representative input tuple.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
return ExecProject(aggstate->ss.ps.ps_ProjInfo);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
else
InstrCountFiltered1(aggstate, 1);
return NULL;
}
/*
* find_unaggregated_cols
* Construct a bitmapset of the column numbers of un-aggregated Vars
* appearing in our targetlist and qual (HAVING clause)
*/
static Bitmapset *
find_unaggregated_cols(AggState *aggstate)
{
Agg *node = (Agg *) aggstate->ss.ps.plan;
2006-10-04 02:30:14 +02:00
Bitmapset *colnos;
colnos = NULL;
(void) find_unaggregated_cols_walker((Node *) node->plan.targetlist,
&colnos);
(void) find_unaggregated_cols_walker((Node *) node->plan.qual,
&colnos);
return colnos;
}
static bool
find_unaggregated_cols_walker(Node *node, Bitmapset **colnos)
{
if (node == NULL)
return false;
if (IsA(node, Var))
{
Var *var = (Var *) node;
/* setrefs.c should have set the varno to OUTER_VAR */
Assert(var->varno == OUTER_VAR);
Assert(var->varlevelsup == 0);
*colnos = bms_add_member(*colnos, var->varattno);
return false;
}
2015-05-24 03:35:49 +02:00
if (IsA(node, Aggref) ||IsA(node, GroupingFunc))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
/* do not descend into aggregate exprs */
return false;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
return expression_tree_walker(node, find_unaggregated_cols_walker,
(void *) colnos);
}
/*
* Initialize the hash table(s) to empty.
*
* To implement hashed aggregation, we need a hashtable that stores a
* representative tuple and an array of AggStatePerGroup structs for each
* distinct set of GROUP BY column values. We compute the hash key from the
* GROUP BY columns. The per-group data is allocated in lookup_hash_entry(),
* for each entry.
*
* We have a separate hashtable and associated perhash data structure for each
* grouping set for which we're doing hashing.
*
* The hash tables always live in the hashcontext's per-tuple memory context
* (there is only one of these for all tables together, since they are all
* reset at the same time).
*/
static void
build_hash_table(AggState *aggstate)
{
2003-08-04 02:43:34 +02:00
MemoryContext tmpmem = aggstate->tmpcontext->ecxt_per_tuple_memory;
Size additionalsize;
int i;
Assert(aggstate->aggstrategy == AGG_HASHED || aggstate->aggstrategy == AGG_MIXED);
additionalsize = aggstate->numtrans * sizeof(AggStatePerGroupData);
for (i = 0; i < aggstate->num_hashes; ++i)
{
AggStatePerHash perhash = &aggstate->perhash[i];
Assert(perhash->aggnode->numGroups > 0);
perhash->hashtable = BuildTupleHashTable(&aggstate->ss.ps,
perhash->hashslot->tts_tupleDescriptor,
perhash->numCols,
perhash->hashGrpColIdxHash,
perhash->eqfuncoids,
perhash->hashfunctions,
perhash->aggnode->numGroups,
additionalsize,
aggstate->hashcontext->ecxt_per_tuple_memory,
tmpmem,
DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
}
}
/*
* Compute columns that actually need to be stored in hashtable entries. The
* incoming tuples from the child plan node will contain grouping columns,
* other columns referenced in our targetlist and qual, columns used to
* compute the aggregate functions, and perhaps just junk columns we don't use
* at all. Only columns of the first two types need to be stored in the
* hashtable, and getting rid of the others can make the table entries
* significantly smaller. The hashtable only contains the relevant columns,
* and is packed/unpacked in lookup_hash_entry() / agg_retrieve_hash_table()
* into the format of the normal input descriptor.
*
* Additional columns, in addition to the columns grouped by, come from two
* sources: Firstly functionally dependent columns that we don't need to group
* by themselves, and secondly ctids for row-marks.
*
* To eliminate duplicates, we build a bitmapset of the needed columns, and
* then build an array of the columns included in the hashtable. Note that
* the array is preserved over ExecReScanAgg, so we allocate it in the
* per-query context (unlike the hash table itself).
*/
static void
find_hash_columns(AggState *aggstate)
{
Bitmapset *base_colnos;
List *outerTlist = outerPlanState(aggstate)->plan->targetlist;
int numHashes = aggstate->num_hashes;
EState *estate = aggstate->ss.ps.state;
int j;
/* Find Vars that will be needed in tlist and qual */
base_colnos = find_unaggregated_cols(aggstate);
for (j = 0; j < numHashes; ++j)
{
AggStatePerHash perhash = &aggstate->perhash[j];
Bitmapset *colnos = bms_copy(base_colnos);
AttrNumber *grpColIdx = perhash->aggnode->grpColIdx;
List *hashTlist = NIL;
TupleDesc hashDesc;
int i;
perhash->largestGrpColIdx = 0;
/*
* If we're doing grouping sets, then some Vars might be referenced in
* tlist/qual for the benefit of other grouping sets, but not needed
* when hashing; i.e. prepare_projection_slot will null them out, so
* there'd be no point storing them. Use prepare_projection_slot's
* logic to determine which.
*/
if (aggstate->phases[0].grouped_cols)
{
Bitmapset *grouped_cols = aggstate->phases[0].grouped_cols[j];
ListCell *lc;
foreach(lc, aggstate->all_grouped_cols)
{
int attnum = lfirst_int(lc);
if (!bms_is_member(attnum, grouped_cols))
colnos = bms_del_member(colnos, attnum);
}
}
/* Add in all the grouping columns */
for (i = 0; i < perhash->numCols; i++)
colnos = bms_add_member(colnos, grpColIdx[i]);
perhash->hashGrpColIdxInput =
palloc(bms_num_members(colnos) * sizeof(AttrNumber));
perhash->hashGrpColIdxHash =
palloc(perhash->numCols * sizeof(AttrNumber));
/*
* First build mapping for columns directly hashed. These are the
* first, because they'll be accessed when computing hash values and
* comparing tuples for exact matches. We also build simple mapping
* for execGrouping, so it knows where to find the to-be-hashed /
* compared columns in the input.
*/
for (i = 0; i < perhash->numCols; i++)
{
perhash->hashGrpColIdxInput[i] = grpColIdx[i];
perhash->hashGrpColIdxHash[i] = i + 1;
perhash->numhashGrpCols++;
/* delete already mapped columns */
bms_del_member(colnos, grpColIdx[i]);
}
/* and add the remaining columns */
while ((i = bms_first_member(colnos)) >= 0)
{
perhash->hashGrpColIdxInput[perhash->numhashGrpCols] = i;
perhash->numhashGrpCols++;
}
/* and build a tuple descriptor for the hashtable */
for (i = 0; i < perhash->numhashGrpCols; i++)
{
int varNumber = perhash->hashGrpColIdxInput[i] - 1;
hashTlist = lappend(hashTlist, list_nth(outerTlist, varNumber));
perhash->largestGrpColIdx =
Max(varNumber + 1, perhash->largestGrpColIdx);
}
Remove WITH OIDS support, change oid catalog column visibility. Previously tables declared WITH OIDS, including a significant fraction of the catalog tables, stored the oid column not as a normal column, but as part of the tuple header. This special column was not shown by default, which was somewhat odd, as it's often (consider e.g. pg_class.oid) one of the more important parts of a row. Neither pg_dump nor COPY included the contents of the oid column by default. The fact that the oid column was not an ordinary column necessitated a significant amount of special case code to support oid columns. That already was painful for the existing, but upcoming work aiming to make table storage pluggable, would have required expanding and duplicating that "specialness" significantly. WITH OIDS has been deprecated since 2005 (commit ff02d0a05280e0). Remove it. Removing includes: - CREATE TABLE and ALTER TABLE syntax for declaring the table to be WITH OIDS has been removed (WITH (oids[ = true]) will error out) - pg_dump does not support dumping tables declared WITH OIDS and will issue a warning when dumping one (and ignore the oid column). - restoring an pg_dump archive with pg_restore will warn when restoring a table with oid contents (and ignore the oid column) - COPY will refuse to load binary dump that includes oids. - pg_upgrade will error out when encountering tables declared WITH OIDS, they have to be altered to remove the oid column first. - Functionality to access the oid of the last inserted row (like plpgsql's RESULT_OID, spi's SPI_lastoid, ...) has been removed. The syntax for declaring a table WITHOUT OIDS (or WITH (oids = false) for CREATE TABLE) is still supported. While that requires a bit of support code, it seems unnecessary to break applications / dumps that do not use oids, and are explicit about not using them. The biggest user of WITH OID columns was postgres' catalog. This commit changes all 'magic' oid columns to be columns that are normally declared and stored. To reduce unnecessary query breakage all the newly added columns are still named 'oid', even if a table's column naming scheme would indicate 'reloid' or such. This obviously requires adapting a lot code, mostly replacing oid access via HeapTupleGetOid() with access to the underlying Form_pg_*->oid column. The bootstrap process now assigns oids for all oid columns in genbki.pl that do not have an explicit value (starting at the largest oid previously used), only oids assigned later by oids will be above FirstBootstrapObjectId. As the oid column now is a normal column the special bootstrap syntax for oids has been removed. Oids are not automatically assigned during insertion anymore, all backend code explicitly assigns oids with GetNewOidWithIndex(). For the rare case that insertions into the catalog via SQL are called for the new pg_nextoid() function can be used (which only works on catalog tables). The fact that oid columns on system tables are now normal columns means that they will be included in the set of columns expanded by * (i.e. SELECT * FROM pg_class will now include the table's oid, previously it did not). It'd not technically be hard to hide oid column by default, but that'd mean confusing behavior would either have to be carried forward forever, or it'd cause breakage down the line. While it's not unlikely that further adjustments are needed, the scope/invasiveness of the patch makes it worthwhile to get merge this now. It's painful to maintain externally, too complicated to commit after the code code freeze, and a dependency of a number of other patches. Catversion bump, for obvious reasons. Author: Andres Freund, with contributions by John Naylor Discussion: https://postgr.es/m/20180930034810.ywp2c7awz7opzcfr@alap3.anarazel.de
2018-11-21 00:36:57 +01:00
hashDesc = ExecTypeFromTL(hashTlist);
execTuplesHashPrepare(perhash->numCols,
perhash->aggnode->grpOperators,
&perhash->eqfuncoids,
&perhash->hashfunctions);
perhash->hashslot =
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
ExecAllocTableSlot(&estate->es_tupleTable, hashDesc,
&TTSOpsMinimalTuple);
list_free(hashTlist);
bms_free(colnos);
}
bms_free(base_colnos);
}
/*
* Estimate per-hash-table-entry overhead for the planner.
*
* Note that the estimate does not include space for pass-by-reference
* transition data values, nor for the representative tuple of each group.
* Nor does this account of the target fill-factor and growth policy of the
* hash table.
*/
Size
hash_agg_entry_size(int numAggs)
{
Size entrysize;
/* This must match build_hash_table */
entrysize = sizeof(TupleHashEntryData) +
numAggs * sizeof(AggStatePerGroupData);
entrysize = MAXALIGN(entrysize);
return entrysize;
}
/*
* Find or create a hashtable entry for the tuple group containing the current
* tuple (already set in tmpcontext's outertuple slot), in the current grouping
* set (which the caller must have selected - note that initialize_aggregate
* depends on this).
*
* When called, CurrentMemoryContext should be the per-query context.
*/
static TupleHashEntryData *
lookup_hash_entry(AggState *aggstate)
{
TupleTableSlot *inputslot = aggstate->tmpcontext->ecxt_outertuple;
AggStatePerHash perhash = &aggstate->perhash[aggstate->current_set];
TupleTableSlot *hashslot = perhash->hashslot;
TupleHashEntryData *entry;
bool isnew;
int i;
/* transfer just the needed columns into hashslot */
slot_getsomeattrs(inputslot, perhash->largestGrpColIdx);
ExecClearTuple(hashslot);
for (i = 0; i < perhash->numhashGrpCols; i++)
{
int varNumber = perhash->hashGrpColIdxInput[i] - 1;
hashslot->tts_values[i] = inputslot->tts_values[varNumber];
hashslot->tts_isnull[i] = inputslot->tts_isnull[varNumber];
}
ExecStoreVirtualTuple(hashslot);
/* find or create the hashtable entry using the filtered tuple */
entry = LookupTupleHashEntry(perhash->hashtable, hashslot, &isnew);
if (isnew)
{
AggStatePerGroup pergroup;
int transno;
pergroup = (AggStatePerGroup)
MemoryContextAlloc(perhash->hashtable->tablecxt,
sizeof(AggStatePerGroupData) * aggstate->numtrans);
entry->additional = pergroup;
/*
* Initialize aggregates for new tuple group, lookup_hash_entries()
* already has selected the relevant grouping set.
*/
for (transno = 0; transno < aggstate->numtrans; transno++)
{
AggStatePerTrans pertrans = &aggstate->pertrans[transno];
AggStatePerGroup pergroupstate = &pergroup[transno];
initialize_aggregate(aggstate, pertrans, pergroupstate);
}
}
return entry;
}
/*
* Look up hash entries for the current tuple in all hashed grouping sets,
* returning an array of pergroup pointers suitable for advance_aggregates.
*
* Be aware that lookup_hash_entry can reset the tmpcontext.
*/
static void
lookup_hash_entries(AggState *aggstate)
{
int numHashes = aggstate->num_hashes;
AggStatePerGroup *pergroup = aggstate->hash_pergroup;
int setno;
for (setno = 0; setno < numHashes; setno++)
{
select_current_set(aggstate, setno, true);
pergroup[setno] = lookup_hash_entry(aggstate)->additional;
}
}
/*
* ExecAgg -
*
* ExecAgg receives tuples from its outer subplan and aggregates over
* the appropriate attribute for each aggregate function use (Aggref
* node) appearing in the targetlist or qual of the node. The number
* of tuples to aggregate over depends on whether grouped or plain
* aggregation is selected. In grouped aggregation, we produce a result
* row for each group; in plain aggregation there's a single result row
* for the whole query. In either case, the value of each aggregate is
* stored in the expression context to be used when ExecProject evaluates
* the result tuple.
*/
static TupleTableSlot *
ExecAgg(PlanState *pstate)
{
AggState *node = castNode(AggState, pstate);
TupleTableSlot *result = NULL;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
CHECK_FOR_INTERRUPTS();
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (!node->agg_done)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* Dispatch based on strategy */
switch (node->phase->aggstrategy)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
case AGG_HASHED:
if (!node->table_filled)
agg_fill_hash_table(node);
/* FALLTHROUGH */
case AGG_MIXED:
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
result = agg_retrieve_hash_table(node);
break;
case AGG_PLAIN:
case AGG_SORTED:
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
result = agg_retrieve_direct(node);
break;
}
if (!TupIsNull(result))
return result;
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
return NULL;
}
/*
* ExecAgg for non-hashed case
*/
static TupleTableSlot *
agg_retrieve_direct(AggState *aggstate)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Agg *node = aggstate->phase->aggnode;
ExprContext *econtext;
ExprContext *tmpcontext;
AggStatePerAgg peragg;
AggStatePerGroup *pergroups;
TupleTableSlot *outerslot;
TupleTableSlot *firstSlot;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
TupleTableSlot *result;
bool hasGroupingSets = aggstate->phase->numsets > 0;
int numGroupingSets = Max(aggstate->phase->numsets, 1);
int currentSet;
int nextSetSize;
int numReset;
int i;
/*
* get state info from node
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* econtext is the per-output-tuple expression context
*
* tmpcontext is the per-input-tuple expression context
*/
econtext = aggstate->ss.ps.ps_ExprContext;
tmpcontext = aggstate->tmpcontext;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
peragg = aggstate->peragg;
pergroups = aggstate->pergroups;
firstSlot = aggstate->ss.ss_ScanTupleSlot;
/*
2001-03-22 05:01:46 +01:00
* We loop retrieving groups until we find one matching
* aggstate->ss.ps.qual
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* For grouping sets, we have the invariant that aggstate->projected_set
* is either -1 (initial call) or the index (starting from 0) in
* gset_lengths for the group we just completed (either by projecting a
* row or by discarding it in the qual).
*/
while (!aggstate->agg_done)
{
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Clear the per-output-tuple context for each group, as well as
* aggcontext (which contains any pass-by-ref transvalues of the old
* group). Some aggregate functions store working state in child
* contexts; those now get reset automatically without us needing to
* do anything special.
*
* We use ReScanExprContext not just ResetExprContext because we want
* any registered shutdown callbacks to be called. That allows
* aggregate functions to ensure they've cleaned up any non-memory
* resources.
*/
ReScanExprContext(econtext);
/*
* Determine how many grouping sets need to be reset at this boundary.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->projected_set >= 0 &&
aggstate->projected_set < numGroupingSets)
numReset = aggstate->projected_set + 1;
else
numReset = numGroupingSets;
/*
* numReset can change on a phase boundary, but that's OK; we want to
* reset the contexts used in _this_ phase, and later, after possibly
* changing phase, initialize the right number of aggregates for the
* _new_ phase.
*/
for (i = 0; i < numReset; i++)
{
ReScanExprContext(aggstate->aggcontexts[i]);
}
/*
* Check if input is complete and there are no more groups to project
* in this phase; move to next phase or mark as done.
*/
if (aggstate->input_done == true &&
aggstate->projected_set >= (numGroupingSets - 1))
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->current_phase < aggstate->numphases - 1)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
initialize_phase(aggstate, aggstate->current_phase + 1);
aggstate->input_done = false;
aggstate->projected_set = -1;
numGroupingSets = Max(aggstate->phase->numsets, 1);
node = aggstate->phase->aggnode;
numReset = numGroupingSets;
}
else if (aggstate->aggstrategy == AGG_MIXED)
{
/*
* Mixed mode; we've output all the grouped stuff and have
* full hashtables, so switch to outputting those.
*/
initialize_phase(aggstate, 0);
aggstate->table_filled = true;
ResetTupleHashIterator(aggstate->perhash[0].hashtable,
&aggstate->perhash[0].hashiter);
select_current_set(aggstate, 0, true);
return agg_retrieve_hash_table(aggstate);
}
else
{
aggstate->agg_done = true;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
break;
}
}
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Get the number of columns in the next grouping set after the last
* projected one (if any). This is the number of columns to compare to
* see if we reached the boundary of that set too.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->projected_set >= 0 &&
aggstate->projected_set < (numGroupingSets - 1))
nextSetSize = aggstate->phase->gset_lengths[aggstate->projected_set + 1];
else
nextSetSize = 0;
/*----------
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* If a subgroup for the current grouping set is present, project it.
*
* We have a new group if:
2015-05-24 03:35:49 +02:00
* - we're out of input but haven't projected all grouping sets
* (checked above)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* OR
2015-05-24 03:35:49 +02:00
* - we already projected a row that wasn't from the last grouping
* set
* AND
* - the next grouping set has at least one grouping column (since
* empty grouping sets project only once input is exhausted)
* AND
* - the previous and pending rows differ on the grouping columns
* of the next grouping set
*----------
*/
tmpcontext->ecxt_innertuple = econtext->ecxt_outertuple;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->input_done ||
(node->aggstrategy != AGG_PLAIN &&
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->projected_set != -1 &&
aggstate->projected_set < (numGroupingSets - 1) &&
nextSetSize > 0 &&
!ExecQualAndReset(aggstate->phase->eqfunctions[nextSetSize - 1],
tmpcontext)))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
aggstate->projected_set += 1;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Assert(aggstate->projected_set < numGroupingSets);
Assert(nextSetSize > 0 || aggstate->input_done);
}
else
{
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* We no longer care what group we just projected, the next
* projection will always be the first (or only) grouping set
* (unless the input proves to be empty).
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->projected_set = 0;
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* If we don't already have the first tuple of the new group,
* fetch it from the outer plan.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->grp_firstTuple == NULL)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
outerslot = fetch_input_tuple(aggstate);
if (!TupIsNull(outerslot))
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Make a copy of the first input tuple; we will use this
* for comparisons (in group mode) and for projection.
*/
Make TupleTableSlots extensible, finish split of existing slot type. This commit completes the work prepared in 1a0586de36, splitting the old TupleTableSlot implementation (which could store buffer, heap, minimal and virtual slots) into four different slot types. As described in the aforementioned commit, this is done with the goal of making tuple table slots extensible, to allow for pluggable table access methods. To achieve runtime extensibility for TupleTableSlots, operations on slots that can differ between types of slots are performed using the TupleTableSlotOps struct provided at slot creation time. That includes information from the size of TupleTableSlot struct to be allocated, initialization, deforming etc. See the struct's definition for more detailed information about callbacks TupleTableSlotOps. I decided to rename TTSOpsBufferTuple to TTSOpsBufferHeapTuple and ExecCopySlotTuple to ExecCopySlotHeapTuple, as that seems more consistent with other naming introduced in recent patches. There's plenty optimization potential in the slot implementation, but according to benchmarking the state after this commit has similar performance characteristics to before this set of changes, which seems sufficient. There's a few changes in execReplication.c that currently need to poke through the slot abstraction, that'll be repaired once the pluggable storage patchset provides the necessary infrastructure. Author: Andres Freund and Ashutosh Bapat, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-17 01:35:11 +01:00
aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
else
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* outer plan produced no tuples at all */
if (hasGroupingSets)
{
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* If there was no input at all, we need to project
* rows only if there are grouping sets of size 0.
* Note that this implies that there can't be any
* references to ungrouped Vars, which would otherwise
* cause issues with the empty output slot.
*
* XXX: This is no longer true, we currently deal with
* this in finalize_aggregates().
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->input_done = true;
while (aggstate->phase->gset_lengths[aggstate->projected_set] > 0)
{
aggstate->projected_set += 1;
if (aggstate->projected_set >= numGroupingSets)
{
/*
* We can't set agg_done here because we might
* have more phases to do, even though the
* input is empty. So we need to restart the
* whole outer loop.
*/
break;
}
}
if (aggstate->projected_set >= numGroupingSets)
continue;
}
else
{
aggstate->agg_done = true;
/* If we are grouping, we should produce no tuples too */
if (node->aggstrategy != AGG_PLAIN)
return NULL;
}
}
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Initialize working state for a new input tuple group.
*/
initialize_aggregates(aggstate, pergroups, numReset);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggstate->grp_firstTuple != NULL)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Store the copied first input tuple in the tuple table slot
* reserved for it. The tuple will be deleted when it is
* cleared from the slot.
*/
Make TupleTableSlots extensible, finish split of existing slot type. This commit completes the work prepared in 1a0586de36, splitting the old TupleTableSlot implementation (which could store buffer, heap, minimal and virtual slots) into four different slot types. As described in the aforementioned commit, this is done with the goal of making tuple table slots extensible, to allow for pluggable table access methods. To achieve runtime extensibility for TupleTableSlots, operations on slots that can differ between types of slots are performed using the TupleTableSlotOps struct provided at slot creation time. That includes information from the size of TupleTableSlot struct to be allocated, initialization, deforming etc. See the struct's definition for more detailed information about callbacks TupleTableSlotOps. I decided to rename TTSOpsBufferTuple to TTSOpsBufferHeapTuple and ExecCopySlotTuple to ExecCopySlotHeapTuple, as that seems more consistent with other naming introduced in recent patches. There's plenty optimization potential in the slot implementation, but according to benchmarking the state after this commit has similar performance characteristics to before this set of changes, which seems sufficient. There's a few changes in execReplication.c that currently need to poke through the slot abstraction, that'll be repaired once the pluggable storage patchset provides the necessary infrastructure. Author: Andres Freund and Ashutosh Bapat, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-17 01:35:11 +01:00
ExecForceStoreHeapTuple(aggstate->grp_firstTuple,
firstSlot);
Phase 2 of pgindent updates. Change pg_bsd_indent to follow upstream rules for placement of comments to the right of code, and remove pgindent hack that caused comments following #endif to not obey the general rule. Commit e3860ffa4dd0dad0dd9eea4be9cc1412373a8c89 wasn't actually using the published version of pg_bsd_indent, but a hacked-up version that tried to minimize the amount of movement of comments to the right of code. The situation of interest is where such a comment has to be moved to the right of its default placement at column 33 because there's code there. BSD indent has always moved right in units of tab stops in such cases --- but in the previous incarnation, indent was working in 8-space tab stops, while now it knows we use 4-space tabs. So the net result is that in about half the cases, such comments are placed one tab stop left of before. This is better all around: it leaves more room on the line for comment text, and it means that in such cases the comment uniformly starts at the next 4-space tab stop after the code, rather than sometimes one and sometimes two tabs after. Also, ensure that comments following #endif are indented the same as comments following other preprocessor commands such as #else. That inconsistency turns out to have been self-inflicted damage from a poorly-thought-through post-indent "fixup" in pgindent. This patch is much less interesting than the first round of indent changes, but also bulkier, so I thought it best to separate the effects. Discussion: https://postgr.es/m/E1dAmxK-0006EE-1r@gemulon.postgresql.org Discussion: https://postgr.es/m/30527.1495162840@sss.pgh.pa.us
2017-06-21 21:18:54 +02:00
aggstate->grp_firstTuple = NULL; /* don't keep two pointers */
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* set up for first advance_aggregates call */
tmpcontext->ecxt_outertuple = firstSlot;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Process each outer-plan tuple, and then fetch the next one,
* until we exhaust the outer plan or cross a group boundary.
*/
for (;;)
{
/*
* During phase 1 only of a mixed agg, we need to update
* hashtables as well in advance_aggregates.
*/
if (aggstate->aggstrategy == AGG_MIXED &&
aggstate->current_phase == 1)
{
lookup_hash_entries(aggstate);
}
/* Advance the aggregates (or combine functions) */
advance_aggregates(aggstate);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* Reset per-input-tuple context after each tuple */
ResetExprContext(tmpcontext);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
outerslot = fetch_input_tuple(aggstate);
if (TupIsNull(outerslot))
{
/* no more outer-plan tuples available */
if (hasGroupingSets)
{
aggstate->input_done = true;
break;
}
else
{
aggstate->agg_done = true;
break;
}
}
/* set up for next advance_aggregates call */
tmpcontext->ecxt_outertuple = outerslot;
/*
* If we are grouping, check whether we've crossed a group
* boundary.
*/
if (node->aggstrategy != AGG_PLAIN)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
tmpcontext->ecxt_innertuple = firstSlot;
if (!ExecQual(aggstate->phase->eqfunctions[node->numCols - 1],
tmpcontext))
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
Make TupleTableSlots extensible, finish split of existing slot type. This commit completes the work prepared in 1a0586de36, splitting the old TupleTableSlot implementation (which could store buffer, heap, minimal and virtual slots) into four different slot types. As described in the aforementioned commit, this is done with the goal of making tuple table slots extensible, to allow for pluggable table access methods. To achieve runtime extensibility for TupleTableSlots, operations on slots that can differ between types of slots are performed using the TupleTableSlotOps struct provided at slot creation time. That includes information from the size of TupleTableSlot struct to be allocated, initialization, deforming etc. See the struct's definition for more detailed information about callbacks TupleTableSlotOps. I decided to rename TTSOpsBufferTuple to TTSOpsBufferHeapTuple and ExecCopySlotTuple to ExecCopySlotHeapTuple, as that seems more consistent with other naming introduced in recent patches. There's plenty optimization potential in the slot implementation, but according to benchmarking the state after this commit has similar performance characteristics to before this set of changes, which seems sufficient. There's a few changes in execReplication.c that currently need to poke through the slot abstraction, that'll be repaired once the pluggable storage patchset provides the necessary infrastructure. Author: Andres Freund and Ashutosh Bapat, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-17 01:35:11 +01:00
aggstate->grp_firstTuple = ExecCopySlotHeapTuple(outerslot);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
break;
}
}
}
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Use the representative input tuple for any references to
* non-aggregated input columns in aggregate direct args, the node
* qual, and the tlist. (If we are not grouping, and there are no
2015-05-24 03:35:49 +02:00
* input rows at all, we will come here with an empty firstSlot
* ... but if not grouping, there can't be any references to
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* non-aggregated input columns, so no problem.)
*/
econtext->ecxt_outertuple = firstSlot;
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Assert(aggstate->projected_set >= 0);
currentSet = aggstate->projected_set;
prepare_projection_slot(aggstate, econtext->ecxt_outertuple, currentSet);
select_current_set(aggstate, currentSet, false);
finalize_aggregates(aggstate,
peragg,
pergroups[currentSet]);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
2015-05-24 03:35:49 +02:00
* If there's no row to project right now, we must continue rather
* than returning a null since there might be more groups.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
result = project_aggregates(aggstate);
if (result)
return result;
}
/* No more groups */
return NULL;
}
/*
* ExecAgg for hashed case: read input and build hash table
*/
static void
agg_fill_hash_table(AggState *aggstate)
{
TupleTableSlot *outerslot;
ExprContext *tmpcontext = aggstate->tmpcontext;
/*
2005-10-15 04:49:52 +02:00
* Process each outer-plan tuple, and then fetch the next one, until we
* exhaust the outer plan.
*/
for (;;)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
outerslot = fetch_input_tuple(aggstate);
if (TupIsNull(outerslot))
break;
/* set up for lookup_hash_entries and advance_aggregates */
tmpcontext->ecxt_outertuple = outerslot;
/* Find or build hashtable entries */
lookup_hash_entries(aggstate);
/* Advance the aggregates (or combine functions) */
advance_aggregates(aggstate);
/*
* Reset per-input-tuple context after each tuple, but note that the
* hash lookups do this too
*/
ResetExprContext(aggstate->tmpcontext);
}
aggstate->table_filled = true;
/* Initialize to walk the first hash table */
select_current_set(aggstate, 0, true);
ResetTupleHashIterator(aggstate->perhash[0].hashtable,
&aggstate->perhash[0].hashiter);
}
/*
* ExecAgg for hashed case: retrieving groups from hash table
*/
static TupleTableSlot *
agg_retrieve_hash_table(AggState *aggstate)
{
ExprContext *econtext;
AggStatePerAgg peragg;
AggStatePerGroup pergroup;
TupleHashEntryData *entry;
TupleTableSlot *firstSlot;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
TupleTableSlot *result;
AggStatePerHash perhash;
/*
* get state info from node.
*
* econtext is the per-output-tuple expression context.
*/
econtext = aggstate->ss.ps.ps_ExprContext;
peragg = aggstate->peragg;
firstSlot = aggstate->ss.ss_ScanTupleSlot;
/*
* Note that perhash (and therefore anything accessed through it) can
* change inside the loop, as we change between grouping sets.
*/
perhash = &aggstate->perhash[aggstate->current_set];
/*
* We loop retrieving groups until we find one satisfying
* aggstate->ss.ps.qual
*/
while (!aggstate->agg_done)
{
TupleTableSlot *hashslot = perhash->hashslot;
int i;
CHECK_FOR_INTERRUPTS();
/*
* Find the next entry in the hash table
*/
entry = ScanTupleHashTable(perhash->hashtable, &perhash->hashiter);
if (entry == NULL)
{
int nextset = aggstate->current_set + 1;
if (nextset < aggstate->num_hashes)
{
/*
* Switch to next grouping set, reinitialize, and restart the
* loop.
*/
select_current_set(aggstate, nextset, true);
perhash = &aggstate->perhash[aggstate->current_set];
ResetTupleHashIterator(perhash->hashtable, &perhash->hashiter);
continue;
}
else
{
/* No more hashtables, so done */
aggstate->agg_done = true;
return NULL;
}
}
/*
* Clear the per-output-tuple context for each group
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* We intentionally don't use ReScanExprContext here; if any aggs have
* registered shutdown callbacks, they mustn't be called yet, since we
* might not be done with that agg.
*/
ResetExprContext(econtext);
/*
* Transform representative tuple back into one with the right
* columns.
*/
ExecStoreMinimalTuple(entry->firstTuple, hashslot, false);
slot_getallattrs(hashslot);
ExecClearTuple(firstSlot);
memset(firstSlot->tts_isnull, true,
firstSlot->tts_tupleDescriptor->natts * sizeof(bool));
for (i = 0; i < perhash->numhashGrpCols; i++)
{
int varNumber = perhash->hashGrpColIdxInput[i] - 1;
firstSlot->tts_values[varNumber] = hashslot->tts_values[i];
firstSlot->tts_isnull[varNumber] = hashslot->tts_isnull[i];
}
ExecStoreVirtualTuple(firstSlot);
pergroup = (AggStatePerGroup) entry->additional;
/*
* Use the representative input tuple for any references to
* non-aggregated input columns in the qual and tlist.
*/
econtext->ecxt_outertuple = firstSlot;
prepare_projection_slot(aggstate,
econtext->ecxt_outertuple,
aggstate->current_set);
finalize_aggregates(aggstate, peragg, pergroup);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
result = project_aggregates(aggstate);
if (result)
return result;
}
/* No more groups */
return NULL;
}
/* -----------------
* ExecInitAgg
*
* Creates the run-time information for the agg node produced by the
* planner and initializes its outer subtree.
*
* -----------------
*/
AggState *
ExecInitAgg(Agg *node, EState *estate, int eflags)
{
AggState *aggstate;
AggStatePerAgg peraggs;
AggStatePerTrans pertransstates;
AggStatePerGroup *pergroups;
Plan *outerPlan;
ExprContext *econtext;
TupleDesc scanDesc;
int numaggs,
transno,
aggno;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
int phase;
int phaseidx;
ListCell *l;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Bitmapset *all_grouped_cols = NULL;
int numGroupingSets = 1;
int numPhases;
int numHashes;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
int i = 0;
int j = 0;
bool use_hashing = (node->aggstrategy == AGG_HASHED ||
node->aggstrategy == AGG_MIXED);
1999-05-25 18:15:34 +02:00
/* check for unsupported flags */
Assert(!(eflags & (EXEC_FLAG_BACKWARD | EXEC_FLAG_MARK)));
/*
* create state structure
*/
aggstate = makeNode(AggState);
aggstate->ss.ps.plan = (Plan *) node;
aggstate->ss.ps.state = estate;
aggstate->ss.ps.ExecProcNode = ExecAgg;
aggstate->aggs = NIL;
aggstate->numaggs = 0;
aggstate->numtrans = 0;
aggstate->aggstrategy = node->aggstrategy;
aggstate->aggsplit = node->aggsplit;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->maxsets = 0;
aggstate->projected_set = -1;
aggstate->current_set = 0;
aggstate->peragg = NULL;
aggstate->pertrans = NULL;
aggstate->curperagg = NULL;
aggstate->curpertrans = NULL;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->input_done = false;
aggstate->agg_done = false;
aggstate->pergroups = NULL;
aggstate->grp_firstTuple = NULL;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->sort_in = NULL;
aggstate->sort_out = NULL;
/*
* phases[0] always exists, but is dummy in sorted/plain mode
*/
numPhases = (use_hashing ? 1 : 2);
numHashes = (use_hashing ? 1 : 0);
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Calculate the maximum number of grouping sets in any phase; this
* determines the size of some allocations. Also calculate the number of
* phases, since all hashed/mixed nodes contribute to only a single phase.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (node->groupingSets)
{
numGroupingSets = list_length(node->groupingSets);
foreach(l, node->chain)
{
2015-05-24 03:35:49 +02:00
Agg *agg = lfirst(l);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
numGroupingSets = Max(numGroupingSets,
list_length(agg->groupingSets));
/*
* additional AGG_HASHED aggs become part of phase 0, but all
* others add an extra phase.
*/
if (agg->aggstrategy != AGG_HASHED)
++numPhases;
else
++numHashes;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
}
aggstate->maxsets = numGroupingSets;
aggstate->numphases = numPhases;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->aggcontexts = (ExprContext **)
palloc0(sizeof(ExprContext *) * numGroupingSets);
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Create expression contexts. We need three or more, one for
* per-input-tuple processing, one for per-output-tuple processing, one
* for all the hashtables, and one for each grouping set. The per-tuple
* memory context of the per-grouping-set ExprContexts (aggcontexts)
* replaces the standalone memory context formerly used to hold transition
* values. We cheat a little by using ExecAssignExprContext() to build
* all of them.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*
* NOTE: the details of what is stored in aggcontexts and what is stored
* in the regular per-query memory context are driven by a simple
* decision: we want to reset the aggcontext at group boundaries (if not
* hashing) and in ExecReScanAgg to recover no-longer-wanted space.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
ExecAssignExprContext(estate, &aggstate->ss.ps);
aggstate->tmpcontext = aggstate->ss.ps.ps_ExprContext;
for (i = 0; i < numGroupingSets; ++i)
{
ExecAssignExprContext(estate, &aggstate->ss.ps);
aggstate->aggcontexts[i] = aggstate->ss.ps.ps_ExprContext;
}
if (use_hashing)
{
ExecAssignExprContext(estate, &aggstate->ss.ps);
aggstate->hashcontext = aggstate->ss.ps.ps_ExprContext;
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
ExecAssignExprContext(estate, &aggstate->ss.ps);
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* Initialize child nodes.
*
2006-10-04 02:30:14 +02:00
* If we are doing a hashed aggregation then the child plan does not need
* to handle REWIND efficiently; see ExecReScanAgg.
*/
if (node->aggstrategy == AGG_HASHED)
eflags &= ~EXEC_FLAG_REWIND;
outerPlan = outerPlan(node);
outerPlanState(aggstate) = ExecInitNode(outerPlan, estate, eflags);
/*
* initialize source tuple type.
*/
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
ExecCreateScanSlotFromOuterPlan(estate, &aggstate->ss,
ExecGetResultSlotOps(outerPlanState(&aggstate->ss), NULL));
scanDesc = aggstate->ss.ss_ScanTupleSlot->tts_tupleDescriptor;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (node->chain)
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
aggstate->sort_slot = ExecInitExtraTupleSlot(estate, scanDesc,
&TTSOpsMinimalTuple);
/*
* Initialize result type, slot and projection.
*/
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
ExecInitResultTupleSlotTL(&aggstate->ss.ps, &TTSOpsVirtual);
ExecAssignProjectionInfo(&aggstate->ss.ps, NULL);
/*
* initialize child expressions
*
* We expect the parser to have checked that no aggs contain other agg
* calls in their arguments (and just to be sure, we verify it again while
* initializing the plan node). This would make no sense under SQL
* semantics, and it's forbidden by the spec. Because it is true, we
* don't need to worry about evaluating the aggs in any particular order.
*
* Note: execExpr.c finds Aggrefs for us, and adds their AggrefExprState
* nodes to aggstate->aggs. Aggrefs in the qual are found here; Aggrefs
* in the targetlist are found during ExecAssignProjectionInfo, below.
*/
aggstate->ss.ps.qual =
ExecInitQual(node->plan.qual, (PlanState *) aggstate);
/*
Faster expression evaluation and targetlist projection. This replaces the old, recursive tree-walk based evaluation, with non-recursive, opcode dispatch based, expression evaluation. Projection is now implemented as part of expression evaluation. This both leads to significant performance improvements, and makes future just-in-time compilation of expressions easier. The speed gains primarily come from: - non-recursive implementation reduces stack usage / overhead - simple sub-expressions are implemented with a single jump, without function calls - sharing some state between different sub-expressions - reduced amount of indirect/hard to predict memory accesses by laying out operation metadata sequentially; including the avoidance of nearly all of the previously used linked lists - more code has been moved to expression initialization, avoiding constant re-checks at evaluation time Future just-in-time compilation (JIT) has become easier, as demonstrated by released patches intended to be merged in a later release, for primarily two reasons: Firstly, due to a stricter split between expression initialization and evaluation, less code has to be handled by the JIT. Secondly, due to the non-recursive nature of the generated "instructions", less performance-critical code-paths can easily be shared between interpreted and compiled evaluation. The new framework allows for significant future optimizations. E.g.: - basic infrastructure for to later reduce the per executor-startup overhead of expression evaluation, by caching state in prepared statements. That'd be helpful in OLTPish scenarios where initialization overhead is measurable. - optimizing the generated "code". A number of proposals for potential work has already been made. - optimizing the interpreter. Similarly a number of proposals have been made here too. The move of logic into the expression initialization step leads to some backward-incompatible changes: - Function permission checks are now done during expression initialization, whereas previously they were done during execution. In edge cases this can lead to errors being raised that previously wouldn't have been, e.g. a NULL array being coerced to a different array type previously didn't perform checks. - The set of domain constraints to be checked, is now evaluated once during expression initialization, previously it was re-built every time a domain check was evaluated. For normal queries this doesn't change much, but e.g. for plpgsql functions, which caches ExprStates, the old set could stick around longer. The behavior around might still change. Author: Andres Freund, with significant changes by Tom Lane, changes by Heikki Linnakangas Reviewed-By: Tom Lane, Heikki Linnakangas Discussion: https://postgr.es/m/20161206034955.bh33paeralxbtluv@alap3.anarazel.de
2017-03-14 23:45:36 +01:00
* We should now have found all Aggrefs in the targetlist and quals.
*/
numaggs = aggstate->numaggs;
Assert(numaggs == list_length(aggstate->aggs));
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* For each phase, prepare grouping set data and fmgr lookup data for
* compare functions. Accumulate all_grouped_cols in passing.
*/
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
aggstate->phases = palloc0(numPhases * sizeof(AggStatePerPhaseData));
aggstate->num_hashes = numHashes;
if (numHashes)
{
aggstate->perhash = palloc0(sizeof(AggStatePerHashData) * numHashes);
aggstate->phases[0].numsets = 0;
aggstate->phases[0].gset_lengths = palloc(numHashes * sizeof(int));
aggstate->phases[0].grouped_cols = palloc(numHashes * sizeof(Bitmapset *));
}
phase = 0;
for (phaseidx = 0; phaseidx <= list_length(node->chain); ++phaseidx)
{
2015-05-24 03:35:49 +02:00
Agg *aggnode;
Sort *sortnode;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (phaseidx > 0)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
aggnode = list_nth_node(Agg, node->chain, phaseidx - 1);
sortnode = castNode(Sort, aggnode->plan.lefttree);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
else
{
aggnode = node;
sortnode = NULL;
}
Assert(phase <= 1 || sortnode);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (aggnode->aggstrategy == AGG_HASHED
|| aggnode->aggstrategy == AGG_MIXED)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
AggStatePerPhase phasedata = &aggstate->phases[0];
AggStatePerHash perhash;
Bitmapset *cols = NULL;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Assert(phase == 0);
i = phasedata->numsets++;
perhash = &aggstate->perhash[i];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* phase 0 always points to the "real" Agg in the hash case */
phasedata->aggnode = node;
phasedata->aggstrategy = node->aggstrategy;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* but the actual Agg node representing this hash is saved here */
perhash->aggnode = aggnode;
phasedata->gset_lengths[i] = perhash->numCols = aggnode->numCols;
for (j = 0; j < aggnode->numCols; ++j)
cols = bms_add_member(cols, aggnode->grpColIdx[j]);
phasedata->grouped_cols[i] = cols;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
all_grouped_cols = bms_add_members(all_grouped_cols, cols);
continue;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
else
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
AggStatePerPhase phasedata = &aggstate->phases[++phase];
int num_sets;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
phasedata->numsets = num_sets = list_length(aggnode->groupingSets);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (num_sets)
{
phasedata->gset_lengths = palloc(num_sets * sizeof(int));
phasedata->grouped_cols = palloc(num_sets * sizeof(Bitmapset *));
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
i = 0;
foreach(l, aggnode->groupingSets)
{
int current_length = list_length(lfirst(l));
Bitmapset *cols = NULL;
/* planner forces this to be correct */
for (j = 0; j < current_length; ++j)
cols = bms_add_member(cols, aggnode->grpColIdx[j]);
phasedata->grouped_cols[i] = cols;
phasedata->gset_lengths[i] = current_length;
++i;
}
all_grouped_cols = bms_add_members(all_grouped_cols,
phasedata->grouped_cols[0]);
}
else
{
Assert(phaseidx == 0);
phasedata->gset_lengths = NULL;
phasedata->grouped_cols = NULL;
}
/*
* If we are grouping, precompute fmgr lookup data for inner loop.
*/
if (aggnode->aggstrategy == AGG_SORTED)
{
int i = 0;
Assert(aggnode->numCols > 0);
/*
* Build a separate function for each subset of columns that
* need to be compared.
*/
phasedata->eqfunctions =
(ExprState **) palloc0(aggnode->numCols * sizeof(ExprState *));
/* for each grouping set */
for (i = 0; i < phasedata->numsets; i++)
{
int length = phasedata->gset_lengths[i];
if (phasedata->eqfunctions[length - 1] != NULL)
continue;
phasedata->eqfunctions[length - 1] =
execTuplesMatchPrepare(scanDesc,
length,
aggnode->grpColIdx,
aggnode->grpOperators,
(PlanState *) aggstate);
}
/* and for all grouped columns, unless already computed */
if (phasedata->eqfunctions[aggnode->numCols - 1] == NULL)
{
phasedata->eqfunctions[aggnode->numCols - 1] =
execTuplesMatchPrepare(scanDesc,
aggnode->numCols,
aggnode->grpColIdx,
aggnode->grpOperators,
(PlanState *) aggstate);
}
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
phasedata->aggnode = aggnode;
phasedata->aggstrategy = aggnode->aggstrategy;
phasedata->sortnode = sortnode;
}
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* Convert all_grouped_cols to a descending-order list.
*/
i = -1;
while ((i = bms_next_member(all_grouped_cols, i)) >= 0)
aggstate->all_grouped_cols = lcons_int(i, aggstate->all_grouped_cols);
/*
2005-10-15 04:49:52 +02:00
* Set up aggregate-result storage in the output expr context, and also
* allocate my private per-agg working storage
*/
econtext = aggstate->ss.ps.ps_ExprContext;
econtext->ecxt_aggvalues = (Datum *) palloc0(sizeof(Datum) * numaggs);
econtext->ecxt_aggnulls = (bool *) palloc0(sizeof(bool) * numaggs);
peraggs = (AggStatePerAgg) palloc0(sizeof(AggStatePerAggData) * numaggs);
pertransstates = (AggStatePerTrans) palloc0(sizeof(AggStatePerTransData) * numaggs);
aggstate->peragg = peraggs;
aggstate->pertrans = pertransstates;
aggstate->all_pergroups =
(AggStatePerGroup *) palloc0(sizeof(AggStatePerGroup)
* (numGroupingSets + numHashes));
pergroups = aggstate->all_pergroups;
if (node->aggstrategy != AGG_HASHED)
{
for (i = 0; i < numGroupingSets; i++)
{
pergroups[i] = (AggStatePerGroup) palloc0(sizeof(AggStatePerGroupData)
* numaggs);
}
aggstate->pergroups = pergroups;
pergroups += numGroupingSets;
}
/*
* Hashing can only appear in the initial phase.
*/
if (use_hashing)
{
/* this is an array of pointers, not structures */
aggstate->hash_pergroup = pergroups;
find_hash_columns(aggstate);
build_hash_table(aggstate);
aggstate->table_filled = false;
}
/*
* Initialize current phase-dependent values to initial phase. The initial
* phase is 1 (first sort pass) for all strategies that use sorting (if
* hashing is being done too, then phase 0 is processed last); but if only
* hashing is being done, then phase 0 is all there is.
*/
if (node->aggstrategy == AGG_HASHED)
{
aggstate->current_phase = 0;
initialize_phase(aggstate, 0);
select_current_set(aggstate, 0, true);
}
else
{
aggstate->current_phase = 1;
initialize_phase(aggstate, 1);
select_current_set(aggstate, 0, false);
}
/* -----------------
* Perform lookups of aggregate function info, and initialize the
* unchanging fields of the per-agg and per-trans data.
*
* We try to optimize by detecting duplicate aggregate functions so that
* their state and final values are re-used, rather than needlessly being
* re-calculated independently. We also detect aggregates that are not
* the same, but which can share the same transition state.
*
* Scenarios:
*
* 1. Identical aggregate function calls appear in the query:
*
2016-06-10 00:02:36 +02:00
* SELECT SUM(x) FROM ... HAVING SUM(x) > 0
*
* Since these aggregates are identical, we only need to calculate
* the value once. Both aggregates will share the same 'aggno' value.
*
* 2. Two different aggregate functions appear in the query, but the
* aggregates have the same arguments, transition functions and
* initial values (and, presumably, different final functions):
*
* SELECT AVG(x), STDDEV(x) FROM ...
*
2016-06-10 00:02:36 +02:00
* In this case we must create a new peragg for the varying aggregate,
* and we need to call the final functions separately, but we need
* only run the transition function once. (This requires that the
* final functions be nondestructive of the transition state, but
* that's required anyway for other reasons.)
*
* For either of these optimizations to be valid, all aggregate properties
* used in the transition phase must be the same, including any modifiers
* such as ORDER BY, DISTINCT and FILTER, and the arguments mustn't
* contain any volatile functions.
* -----------------
*/
aggno = -1;
transno = -1;
foreach(l, aggstate->aggs)
{
AggrefExprState *aggrefstate = (AggrefExprState *) lfirst(l);
Faster expression evaluation and targetlist projection. This replaces the old, recursive tree-walk based evaluation, with non-recursive, opcode dispatch based, expression evaluation. Projection is now implemented as part of expression evaluation. This both leads to significant performance improvements, and makes future just-in-time compilation of expressions easier. The speed gains primarily come from: - non-recursive implementation reduces stack usage / overhead - simple sub-expressions are implemented with a single jump, without function calls - sharing some state between different sub-expressions - reduced amount of indirect/hard to predict memory accesses by laying out operation metadata sequentially; including the avoidance of nearly all of the previously used linked lists - more code has been moved to expression initialization, avoiding constant re-checks at evaluation time Future just-in-time compilation (JIT) has become easier, as demonstrated by released patches intended to be merged in a later release, for primarily two reasons: Firstly, due to a stricter split between expression initialization and evaluation, less code has to be handled by the JIT. Secondly, due to the non-recursive nature of the generated "instructions", less performance-critical code-paths can easily be shared between interpreted and compiled evaluation. The new framework allows for significant future optimizations. E.g.: - basic infrastructure for to later reduce the per executor-startup overhead of expression evaluation, by caching state in prepared statements. That'd be helpful in OLTPish scenarios where initialization overhead is measurable. - optimizing the generated "code". A number of proposals for potential work has already been made. - optimizing the interpreter. Similarly a number of proposals have been made here too. The move of logic into the expression initialization step leads to some backward-incompatible changes: - Function permission checks are now done during expression initialization, whereas previously they were done during execution. In edge cases this can lead to errors being raised that previously wouldn't have been, e.g. a NULL array being coerced to a different array type previously didn't perform checks. - The set of domain constraints to be checked, is now evaluated once during expression initialization, previously it was re-built every time a domain check was evaluated. For normal queries this doesn't change much, but e.g. for plpgsql functions, which caches ExprStates, the old set could stick around longer. The behavior around might still change. Author: Andres Freund, with significant changes by Tom Lane, changes by Heikki Linnakangas Reviewed-By: Tom Lane, Heikki Linnakangas Discussion: https://postgr.es/m/20161206034955.bh33paeralxbtluv@alap3.anarazel.de
2017-03-14 23:45:36 +01:00
Aggref *aggref = aggrefstate->aggref;
AggStatePerAgg peragg;
AggStatePerTrans pertrans;
int existing_aggno;
int existing_transno;
List *same_input_transnos;
Oid inputTypes[FUNC_MAX_ARGS];
2006-10-04 02:30:14 +02:00
int numArguments;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
int numDirectArgs;
HeapTuple aggTuple;
Form_pg_aggregate aggform;
AclResult aclresult;
Oid transfn_oid,
finalfn_oid;
bool shareable;
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
Oid serialfn_oid,
deserialfn_oid;
Expr *finalfnexpr;
Oid aggtranstype;
Datum textInitVal;
Datum initValue;
bool initValueIsNull;
/* Planner should have assigned aggregate to correct level */
Assert(aggref->agglevelsup == 0);
/* ... and the split mode should match */
Assert(aggref->aggsplit == aggstate->aggsplit);
/* 1. Check for already processed aggs which can be re-used */
existing_aggno = find_compatible_peragg(aggref, aggstate, aggno,
&same_input_transnos);
if (existing_aggno != -1)
{
/*
* Existing compatible agg found. so just point the Aggref to the
* same per-agg struct.
*/
aggrefstate->aggno = existing_aggno;
continue;
}
/* Mark Aggref state node with assigned index in the result array */
peragg = &peraggs[++aggno];
peragg->aggref = aggref;
aggrefstate->aggno = aggno;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/* Fetch the pg_aggregate row */
aggTuple = SearchSysCache1(AGGFNOID,
ObjectIdGetDatum(aggref->aggfnoid));
if (!HeapTupleIsValid(aggTuple))
elog(ERROR, "cache lookup failed for aggregate %u",
aggref->aggfnoid);
aggform = (Form_pg_aggregate) GETSTRUCT(aggTuple);
/* Check permission to call aggregate function */
aclresult = pg_proc_aclcheck(aggref->aggfnoid, GetUserId(),
ACL_EXECUTE);
if (aclresult != ACLCHECK_OK)
aclcheck_error(aclresult, OBJECT_AGGREGATE,
get_func_name(aggref->aggfnoid));
InvokeFunctionExecuteHook(aggref->aggfnoid);
Fix handling of argument and result datatypes for partial aggregation. When doing partial aggregation, the args list of the upper (combining) Aggref node is replaced by a Var representing the output of the partial aggregation steps, which has either the aggregate's transition data type or a serialized representation of that. However, nodeAgg.c blindly continued to use the args list as an indication of the user-level argument types. This broke resolution of polymorphic transition datatypes at executor startup (though it accidentally failed to fail for the ANYARRAY case, which is likely the only one anyone had tested). Moreover, the constructed FuncExpr passed to the finalfunc contained completely wrong information, which would have led to bogus answers or crashes for any case where the finalfunc examined that information (which is only likely to be with polymorphic aggregates using a non-polymorphic transition type). As an independent bug, apply_partialaggref_adjustment neglected to resolve a polymorphic transition datatype before assigning it as the output type of the lower-level Aggref node. This again accidentally failed to fail for ANYARRAY but would be unlikely to work in other cases. To fix the first problem, record the user-level argument types in a separate OID-list field of Aggref, and look to that rather than the args list when asking what the argument types were. (It turns out to be convenient to include any "direct" arguments in this list too, although those are not currently subject to being overwritten.) Rather than adding yet another resolve_aggregate_transtype() call to fix the second problem, add an aggtranstype field to Aggref, and store the resolved transition type OID there when the planner first computes it. (By doing this in the planner and not the parser, we can allow the aggregate's transition type to change from time to time, although no DDL support yet exists for that.) This saves nothing of consequence for simple non-polymorphic aggregates, but for polymorphic transition types we save a catalog lookup during executor startup as well as several planner lookups that are new in 9.6 due to parallel query planning. In passing, fix an error that was introduced into count_agg_clauses_walker some time ago: it was applying exprTypmod() to something that wasn't an expression node at all, but a TargetEntry. exprTypmod silently returned -1 so that there was not an obvious failure, but this broke the intended sensitivity of aggregate space consumption estimates to the typmod of varchar and similar data types. This part needs to be back-patched. Catversion bump due to change of stored Aggref nodes. Discussion: <8229.1466109074@sss.pgh.pa.us>
2016-06-18 03:44:37 +02:00
/* planner recorded transition state type in the Aggref itself */
aggtranstype = aggref->aggtranstype;
Assert(OidIsValid(aggtranstype));
/*
* If this aggregation is performing state combines, then instead of
* using the transition function, we'll use the combine function
*/
if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
{
transfn_oid = aggform->aggcombinefn;
/* If not set then the planner messed up */
if (!OidIsValid(transfn_oid))
elog(ERROR, "combinefn not set for aggregate function");
}
else
transfn_oid = aggform->aggtransfn;
/* Final function only required if we're finalizing the aggregates */
if (DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit))
peragg->finalfn_oid = finalfn_oid = InvalidOid;
else
peragg->finalfn_oid = finalfn_oid = aggform->aggfinalfn;
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
/*
* If finalfn is marked read-write, we can't share transition states;
* but it is okay to share states for AGGMODIFY_SHAREABLE aggs. Also,
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
* if we're not executing the finalfn here, we can share regardless.
*/
shareable = (aggform->aggfinalmodify != AGGMODIFY_READ_WRITE) ||
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
(finalfn_oid == InvalidOid);
peragg->shareable = shareable;
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
serialfn_oid = InvalidOid;
deserialfn_oid = InvalidOid;
/*
* Check if serialization/deserialization is required. We only do it
* for aggregates that have transtype INTERNAL.
*/
if (aggtranstype == INTERNALOID)
{
/*
* The planner should only have generated a serialize agg node if
* every aggregate with an INTERNAL state has a serialization
* function. Verify that.
*/
if (DO_AGGSPLIT_SERIALIZE(aggstate->aggsplit))
{
/* serialization only valid when not running finalfn */
Assert(DO_AGGSPLIT_SKIPFINAL(aggstate->aggsplit));
if (!OidIsValid(aggform->aggserialfn))
elog(ERROR, "serialfunc not provided for serialization aggregation");
serialfn_oid = aggform->aggserialfn;
}
/* Likewise for deserialization functions */
if (DO_AGGSPLIT_DESERIALIZE(aggstate->aggsplit))
{
/* deserialization only valid when combining states */
Assert(DO_AGGSPLIT_COMBINE(aggstate->aggsplit));
if (!OidIsValid(aggform->aggdeserialfn))
elog(ERROR, "deserialfunc not provided for deserialization aggregation");
deserialfn_oid = aggform->aggdeserialfn;
}
}
/* Check that aggregate owner has permission to call component fns */
{
HeapTuple procTuple;
2005-10-15 04:49:52 +02:00
Oid aggOwner;
procTuple = SearchSysCache1(PROCOID,
ObjectIdGetDatum(aggref->aggfnoid));
if (!HeapTupleIsValid(procTuple))
elog(ERROR, "cache lookup failed for function %u",
aggref->aggfnoid);
aggOwner = ((Form_pg_proc) GETSTRUCT(procTuple))->proowner;
ReleaseSysCache(procTuple);
aclresult = pg_proc_aclcheck(transfn_oid, aggOwner,
ACL_EXECUTE);
if (aclresult != ACLCHECK_OK)
aclcheck_error(aclresult, OBJECT_FUNCTION,
get_func_name(transfn_oid));
InvokeFunctionExecuteHook(transfn_oid);
if (OidIsValid(finalfn_oid))
{
aclresult = pg_proc_aclcheck(finalfn_oid, aggOwner,
ACL_EXECUTE);
if (aclresult != ACLCHECK_OK)
aclcheck_error(aclresult, OBJECT_FUNCTION,
get_func_name(finalfn_oid));
InvokeFunctionExecuteHook(finalfn_oid);
}
if (OidIsValid(serialfn_oid))
{
aclresult = pg_proc_aclcheck(serialfn_oid, aggOwner,
ACL_EXECUTE);
if (aclresult != ACLCHECK_OK)
aclcheck_error(aclresult, OBJECT_FUNCTION,
get_func_name(serialfn_oid));
InvokeFunctionExecuteHook(serialfn_oid);
}
if (OidIsValid(deserialfn_oid))
{
aclresult = pg_proc_aclcheck(deserialfn_oid, aggOwner,
ACL_EXECUTE);
if (aclresult != ACLCHECK_OK)
aclcheck_error(aclresult, OBJECT_FUNCTION,
get_func_name(deserialfn_oid));
InvokeFunctionExecuteHook(deserialfn_oid);
}
}
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
* Get actual datatypes of the (nominal) aggregate inputs. These
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* could be different from the agg's declared input types, when the
* agg accepts ANY or a polymorphic type.
*/
numArguments = get_aggregate_argtypes(aggref, inputTypes);
/* Count the "direct" arguments, if any */
numDirectArgs = list_length(aggref->aggdirectargs);
/* Detect how many arguments to pass to the finalfn */
if (aggform->aggfinalextra)
peragg->numFinalArgs = numArguments + 1;
else
peragg->numFinalArgs = numDirectArgs + 1;
/* Initialize any direct-argument expressions */
peragg->aggdirectargs = ExecInitExprList(aggref->aggdirectargs,
(PlanState *) aggstate);
/*
* build expression trees using actual argument & result types for the
* finalfn, if it exists and is required.
*/
if (OidIsValid(finalfn_oid))
{
build_aggregate_finalfn_expr(inputTypes,
peragg->numFinalArgs,
aggtranstype,
aggref->aggtype,
aggref->inputcollid,
finalfn_oid,
&finalfnexpr);
fmgr_info(finalfn_oid, &peragg->finalfn);
fmgr_info_set_expr((Node *) finalfnexpr, &peragg->finalfn);
}
/* get info about the output value's datatype */
get_typlenbyval(aggref->aggtype,
&peragg->resulttypeLen,
&peragg->resulttypeByVal);
/*
2005-10-15 04:49:52 +02:00
* initval is potentially null, so don't try to access it as a struct
* field. Must do it the hard way with SysCacheGetAttr.
*/
textInitVal = SysCacheGetAttr(AGGFNOID, aggTuple,
Anum_pg_aggregate_agginitval,
&initValueIsNull);
if (initValueIsNull)
initValue = (Datum) 0;
else
initValue = GetAggInitVal(textInitVal, aggtranstype);
/*
* 2. Build working state for invoking the transition function, or
* look up previously initialized working state, if we can share it.
*
* find_compatible_peragg() already collected a list of shareable
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
* per-Trans's with the same inputs. Check if any of them have the
* same transition function and initial value.
*/
existing_transno = find_compatible_pertrans(aggstate, aggref,
shareable,
transfn_oid, aggtranstype,
serialfn_oid, deserialfn_oid,
initValue, initValueIsNull,
same_input_transnos);
if (existing_transno != -1)
{
/*
* Existing compatible trans found, so just point the 'peragg' to
* the same per-trans struct, and mark the trans state as shared.
*/
pertrans = &pertransstates[existing_transno];
pertrans->aggshared = true;
peragg->transno = existing_transno;
}
else
{
pertrans = &pertransstates[++transno];
build_pertrans_for_aggref(pertrans, aggstate, estate,
aggref, transfn_oid, aggtranstype,
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
serialfn_oid, deserialfn_oid,
initValue, initValueIsNull,
inputTypes, numArguments);
peragg->transno = transno;
}
ReleaseSysCache(aggTuple);
}
/*
* Update aggstate->numaggs to be the number of unique aggregates found.
* Also set numstates to the number of unique transition states found.
*/
aggstate->numaggs = aggno + 1;
aggstate->numtrans = transno + 1;
/*
* Last, check whether any more aggregates got added onto the node while
* we processed the expressions for the aggregate arguments (including not
* only the regular arguments and FILTER expressions handled immediately
* above, but any direct arguments we might've handled earlier). If so,
* we have nested aggregate functions, which is semantically nonsensical,
* so complain. (This should have been caught by the parser, so we don't
* need to work hard on a helpful error message; but we defend against it
* here anyway, just to be sure.)
*/
if (numaggs != list_length(aggstate->aggs))
ereport(ERROR,
(errcode(ERRCODE_GROUPING_ERROR),
errmsg("aggregate function calls cannot be nested")));
/*
* Build expressions doing all the transition work at once. We build a
* different one for each phase, as the number of transition function
* invocation can differ between phases. Note this'll work both for
* transition and combination functions (although there'll only be one
* phase in the latter case).
*/
for (phaseidx = 0; phaseidx < aggstate->numphases; phaseidx++)
{
AggStatePerPhase phase = &aggstate->phases[phaseidx];
bool dohash = false;
bool dosort = false;
/* phase 0 doesn't necessarily exist */
if (!phase->aggnode)
continue;
if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 1)
{
/*
* Phase one, and only phase one, in a mixed agg performs both
* sorting and aggregation.
*/
dohash = true;
dosort = true;
}
else if (aggstate->aggstrategy == AGG_MIXED && phaseidx == 0)
{
/*
* No need to compute a transition function for an AGG_MIXED phase
* 0 - the contents of the hashtables will have been computed
* during phase 1.
*/
continue;
}
else if (phase->aggstrategy == AGG_PLAIN ||
phase->aggstrategy == AGG_SORTED)
{
dohash = false;
dosort = true;
}
else if (phase->aggstrategy == AGG_HASHED)
{
dohash = true;
dosort = false;
}
else
Assert(false);
phase->evaltrans = ExecBuildAggTrans(aggstate, phase, dosort, dohash);
}
return aggstate;
}
/*
* Build the state needed to calculate a state value for an aggregate.
*
* This initializes all the fields in 'pertrans'. 'aggref' is the aggregate
* to initialize the state for. 'aggtransfn', 'aggtranstype', and the rest
* of the arguments could be calculated from 'aggref', but the caller has
* calculated them already, so might as well pass them.
*/
static void
build_pertrans_for_aggref(AggStatePerTrans pertrans,
AggState *aggstate, EState *estate,
Aggref *aggref,
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
Oid aggtransfn, Oid aggtranstype,
Oid aggserialfn, Oid aggdeserialfn,
Datum initValue, bool initValueIsNull,
Oid *inputTypes, int numArguments)
{
int numGroupingSets = Max(aggstate->maxsets, 1);
Expr *serialfnexpr = NULL;
Expr *deserialfnexpr = NULL;
ListCell *lc;
int numInputs;
int numDirectArgs;
List *sortlist;
int numSortCols;
int numDistinctCols;
int i;
/* Begin filling in the pertrans data */
pertrans->aggref = aggref;
pertrans->aggshared = false;
pertrans->aggCollation = aggref->inputcollid;
pertrans->transfn_oid = aggtransfn;
pertrans->serialfn_oid = aggserialfn;
pertrans->deserialfn_oid = aggdeserialfn;
pertrans->initValue = initValue;
pertrans->initValueIsNull = initValueIsNull;
/* Count the "direct" arguments, if any */
numDirectArgs = list_length(aggref->aggdirectargs);
/* Count the number of aggregated input columns */
pertrans->numInputs = numInputs = list_length(aggref->args);
pertrans->aggtranstype = aggtranstype;
/* Detect how many arguments to pass to the transfn */
if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
pertrans->numTransInputs = numInputs;
else
pertrans->numTransInputs = numArguments;
/*
* When combining states, we have no use at all for the aggregate
* function's transfn. Instead we use the combinefn. In this case, the
* transfn and transfn_oid fields of pertrans refer to the combine
* function rather than the transition function.
*/
if (DO_AGGSPLIT_COMBINE(aggstate->aggsplit))
{
Expr *combinefnexpr;
build_aggregate_combinefn_expr(aggtranstype,
aggref->inputcollid,
aggtransfn,
&combinefnexpr);
fmgr_info(aggtransfn, &pertrans->transfn);
fmgr_info_set_expr((Node *) combinefnexpr, &pertrans->transfn);
InitFunctionCallInfoData(pertrans->transfn_fcinfo,
&pertrans->transfn,
2,
pertrans->aggCollation,
(void *) aggstate, NULL);
/*
* Ensure that a combine function to combine INTERNAL states is not
* strict. This should have been checked during CREATE AGGREGATE, but
* the strict property could have been changed since then.
*/
if (pertrans->transfn.fn_strict && aggtranstype == INTERNALOID)
ereport(ERROR,
(errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
errmsg("combine function with transition type %s must not be declared STRICT",
format_type_be(aggtranstype))));
}
else
{
Expr *transfnexpr;
/*
* Set up infrastructure for calling the transfn. Note that invtrans
* is not needed here.
*/
build_aggregate_transfn_expr(inputTypes,
numArguments,
numDirectArgs,
aggref->aggvariadic,
aggtranstype,
aggref->inputcollid,
aggtransfn,
InvalidOid,
&transfnexpr,
NULL);
fmgr_info(aggtransfn, &pertrans->transfn);
fmgr_info_set_expr((Node *) transfnexpr, &pertrans->transfn);
InitFunctionCallInfoData(pertrans->transfn_fcinfo,
&pertrans->transfn,
pertrans->numTransInputs + 1,
pertrans->aggCollation,
(void *) aggstate, NULL);
/*
* If the transfn is strict and the initval is NULL, make sure input
* type and transtype are the same (or at least binary-compatible), so
* that it's OK to use the first aggregated input value as the initial
* transValue. This should have been checked at agg definition time,
* but we must check again in case the transfn's strictness property
* has been changed.
*/
if (pertrans->transfn.fn_strict && pertrans->initValueIsNull)
{
if (numArguments <= numDirectArgs ||
!IsBinaryCoercible(inputTypes[numDirectArgs],
aggtranstype))
ereport(ERROR,
(errcode(ERRCODE_INVALID_FUNCTION_DEFINITION),
errmsg("aggregate %u needs to have compatible input type and transition type",
aggref->aggfnoid)));
}
}
/* get info about the state value's datatype */
get_typlenbyval(aggtranstype,
&pertrans->transtypeLen,
&pertrans->transtypeByVal);
if (OidIsValid(aggserialfn))
{
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
build_aggregate_serialfn_expr(aggserialfn,
&serialfnexpr);
fmgr_info(aggserialfn, &pertrans->serialfn);
fmgr_info_set_expr((Node *) serialfnexpr, &pertrans->serialfn);
InitFunctionCallInfoData(pertrans->serialfn_fcinfo,
&pertrans->serialfn,
1,
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
InvalidOid,
(void *) aggstate, NULL);
}
if (OidIsValid(aggdeserialfn))
{
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
build_aggregate_deserialfn_expr(aggdeserialfn,
&deserialfnexpr);
fmgr_info(aggdeserialfn, &pertrans->deserialfn);
fmgr_info_set_expr((Node *) deserialfnexpr, &pertrans->deserialfn);
InitFunctionCallInfoData(pertrans->deserialfn_fcinfo,
&pertrans->deserialfn,
Fix type-safety problem with parallel aggregate serial/deserialization. The original specification for this called for the deserialization function to have signature "deserialize(serialtype) returns transtype", which is a security violation if transtype is INTERNAL (which it always would be in practice) and serialtype is not (which ditto). The patch blithely overrode the opr_sanity check for that, which was sloppy-enough work in itself, but the indisputable reason this cannot be allowed to stand is that CREATE FUNCTION will reject such a signature and thus it'd be impossible for extensions to create parallelizable aggregates. The minimum fix to make the signature type-safe is to add a second, dummy argument of type INTERNAL. But to lock it down a bit more and make misuse of INTERNAL-accepting functions less likely, let's get rid of the ability to specify a "serialtype" for an aggregate and just say that the only useful serialtype is BYTEA --- which, in practice, is the only interesting value anyway, due to the usefulness of the send/recv infrastructure for this purpose. That means we only have to allow "serialize(internal) returns bytea" and "deserialize(bytea, internal) returns internal" as the signatures for these support functions. In passing fix bogus signature of int4_avg_combine, which I found thanks to adding an opr_sanity check on combinefunc signatures. catversion bump due to removing pg_aggregate.aggserialtype and adjusting signatures of assorted built-in functions. David Rowley and Tom Lane Discussion: <27247.1466185504@sss.pgh.pa.us>
2016-06-22 22:52:41 +02:00
2,
InvalidOid,
(void *) aggstate, NULL);
}
/*
* If we're doing either DISTINCT or ORDER BY for a plain agg, then we
* have a list of SortGroupClause nodes; fish out the data in them and
* stick them into arrays. We ignore ORDER BY for an ordered-set agg,
* however; the agg's transfn and finalfn are responsible for that.
*
* Note that by construction, if there is a DISTINCT clause then the ORDER
* BY clause is a prefix of it (see transformDistinctClause).
*/
if (AGGKIND_IS_ORDERED_SET(aggref->aggkind))
{
sortlist = NIL;
numSortCols = numDistinctCols = 0;
}
else if (aggref->aggdistinct)
{
sortlist = aggref->aggdistinct;
numSortCols = numDistinctCols = list_length(sortlist);
Assert(numSortCols >= list_length(aggref->aggorder));
}
else
{
sortlist = aggref->aggorder;
numSortCols = list_length(sortlist);
numDistinctCols = 0;
}
pertrans->numSortCols = numSortCols;
pertrans->numDistinctCols = numDistinctCols;
/*
* If we have either sorting or filtering to do, create a tupledesc and
* slot corresponding to the aggregated inputs (including sort
* expressions) of the agg.
*/
if (numSortCols > 0 || aggref->aggfilter)
{
Remove WITH OIDS support, change oid catalog column visibility. Previously tables declared WITH OIDS, including a significant fraction of the catalog tables, stored the oid column not as a normal column, but as part of the tuple header. This special column was not shown by default, which was somewhat odd, as it's often (consider e.g. pg_class.oid) one of the more important parts of a row. Neither pg_dump nor COPY included the contents of the oid column by default. The fact that the oid column was not an ordinary column necessitated a significant amount of special case code to support oid columns. That already was painful for the existing, but upcoming work aiming to make table storage pluggable, would have required expanding and duplicating that "specialness" significantly. WITH OIDS has been deprecated since 2005 (commit ff02d0a05280e0). Remove it. Removing includes: - CREATE TABLE and ALTER TABLE syntax for declaring the table to be WITH OIDS has been removed (WITH (oids[ = true]) will error out) - pg_dump does not support dumping tables declared WITH OIDS and will issue a warning when dumping one (and ignore the oid column). - restoring an pg_dump archive with pg_restore will warn when restoring a table with oid contents (and ignore the oid column) - COPY will refuse to load binary dump that includes oids. - pg_upgrade will error out when encountering tables declared WITH OIDS, they have to be altered to remove the oid column first. - Functionality to access the oid of the last inserted row (like plpgsql's RESULT_OID, spi's SPI_lastoid, ...) has been removed. The syntax for declaring a table WITHOUT OIDS (or WITH (oids = false) for CREATE TABLE) is still supported. While that requires a bit of support code, it seems unnecessary to break applications / dumps that do not use oids, and are explicit about not using them. The biggest user of WITH OID columns was postgres' catalog. This commit changes all 'magic' oid columns to be columns that are normally declared and stored. To reduce unnecessary query breakage all the newly added columns are still named 'oid', even if a table's column naming scheme would indicate 'reloid' or such. This obviously requires adapting a lot code, mostly replacing oid access via HeapTupleGetOid() with access to the underlying Form_pg_*->oid column. The bootstrap process now assigns oids for all oid columns in genbki.pl that do not have an explicit value (starting at the largest oid previously used), only oids assigned later by oids will be above FirstBootstrapObjectId. As the oid column now is a normal column the special bootstrap syntax for oids has been removed. Oids are not automatically assigned during insertion anymore, all backend code explicitly assigns oids with GetNewOidWithIndex(). For the rare case that insertions into the catalog via SQL are called for the new pg_nextoid() function can be used (which only works on catalog tables). The fact that oid columns on system tables are now normal columns means that they will be included in the set of columns expanded by * (i.e. SELECT * FROM pg_class will now include the table's oid, previously it did not). It'd not technically be hard to hide oid column by default, but that'd mean confusing behavior would either have to be carried forward forever, or it'd cause breakage down the line. While it's not unlikely that further adjustments are needed, the scope/invasiveness of the patch makes it worthwhile to get merge this now. It's painful to maintain externally, too complicated to commit after the code code freeze, and a dependency of a number of other patches. Catversion bump, for obvious reasons. Author: Andres Freund, with contributions by John Naylor Discussion: https://postgr.es/m/20180930034810.ywp2c7awz7opzcfr@alap3.anarazel.de
2018-11-21 00:36:57 +01:00
pertrans->sortdesc = ExecTypeFromTL(aggref->args);
pertrans->sortslot =
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
&TTSOpsMinimalTuple);
}
if (numSortCols > 0)
{
/*
* We don't implement DISTINCT or ORDER BY aggs in the HASHED case
* (yet)
*/
Assert(aggstate->aggstrategy != AGG_HASHED && aggstate->aggstrategy != AGG_MIXED);
/* If we have only one input, we need its len/byval info. */
if (numInputs == 1)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
{
get_typlenbyval(inputTypes[numDirectArgs],
&pertrans->inputtypeLen,
&pertrans->inputtypeByVal);
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
}
else if (numDistinctCols > 0)
{
/* we will need an extra slot to store prior values */
pertrans->uniqslot =
Introduce notion of different types of slots (without implementing them). Upcoming work intends to allow pluggable ways to introduce new ways of storing table data. Accessing those table access methods from the executor requires TupleTableSlots to be carry tuples in the native format of such storage methods; otherwise there'll be a significant conversion overhead. Different access methods will require different data to store tuples efficiently (just like virtual, minimal, heap already require fields in TupleTableSlot). To allow that without requiring additional pointer indirections, we want to have different structs (embedding TupleTableSlot) for different types of slots. Thus different types of slots are needed, which requires adapting creators of slots. The slot that most efficiently can represent a type of tuple in an executor node will often depend on the type of slot a child node uses. Therefore we need to track the type of slot is returned by nodes, so parent slots can create slots based on that. Relatedly, JIT compilation of tuple deforming needs to know which type of slot a certain expression refers to, so it can create an appropriate deforming function for the type of tuple in the slot. But not all nodes will only return one type of slot, e.g. an append node will potentially return different types of slots for each of its subplans. Therefore add function that allows to query the type of a node's result slot, and whether it'll always be the same type (whether it's fixed). This can be queried using ExecGetResultSlotOps(). The scan, result, inner, outer type of slots are automatically inferred from ExecInitScanTupleSlot(), ExecInitResultSlot(), left/right subtrees respectively. If that's not correct for a node, that can be overwritten using new fields in PlanState. This commit does not introduce the actually abstracted implementation of different kind of TupleTableSlots, that will be left for a followup commit. The different types of slots introduced will, for now, still use the same backing implementation. While this already partially invalidates the big comment in tuptable.h, it seems to make more sense to update it later, when the different TupleTableSlot implementations actually exist. Author: Ashutosh Bapat and Andres Freund, with changes by Amit Khandekar Discussion: https://postgr.es/m/20181105210039.hh4vvi4vwoq5ba2q@alap3.anarazel.de
2018-11-16 07:00:30 +01:00
ExecInitExtraTupleSlot(estate, pertrans->sortdesc,
&TTSOpsMinimalTuple);
}
/* Extract the sort information for use later */
pertrans->sortColIdx =
(AttrNumber *) palloc(numSortCols * sizeof(AttrNumber));
pertrans->sortOperators =
(Oid *) palloc(numSortCols * sizeof(Oid));
pertrans->sortCollations =
(Oid *) palloc(numSortCols * sizeof(Oid));
pertrans->sortNullsFirst =
(bool *) palloc(numSortCols * sizeof(bool));
i = 0;
foreach(lc, sortlist)
{
SortGroupClause *sortcl = (SortGroupClause *) lfirst(lc);
TargetEntry *tle = get_sortgroupclause_tle(sortcl, aggref->args);
/* the parser should have made sure of this */
Assert(OidIsValid(sortcl->sortop));
pertrans->sortColIdx[i] = tle->resno;
pertrans->sortOperators[i] = sortcl->sortop;
pertrans->sortCollations[i] = exprCollation((Node *) tle->expr);
pertrans->sortNullsFirst[i] = sortcl->nulls_first;
i++;
}
Assert(i == numSortCols);
}
if (aggref->aggdistinct)
{
Oid *ops;
Assert(numArguments > 0);
Assert(list_length(aggref->aggdistinct) == numDistinctCols);
ops = palloc(numDistinctCols * sizeof(Oid));
i = 0;
foreach(lc, aggref->aggdistinct)
ops[i++] = ((SortGroupClause *) lfirst(lc))->eqop;
/* lookup / build the necessary comparators */
if (numDistinctCols == 1)
fmgr_info(get_opcode(ops[0]), &pertrans->equalfnOne);
else
pertrans->equalfnMulti =
execTuplesMatchPrepare(pertrans->sortdesc,
numDistinctCols,
pertrans->sortColIdx,
ops,
&aggstate->ss.ps);
pfree(ops);
}
pertrans->sortstates = (Tuplesortstate **)
palloc0(sizeof(Tuplesortstate *) * numGroupingSets);
}
static Datum
GetAggInitVal(Datum textInitVal, Oid transtype)
{
Oid typinput,
typioparam;
char *strInitVal;
Datum initVal;
getTypeInputInfo(transtype, &typinput, &typioparam);
strInitVal = TextDatumGetCString(textInitVal);
initVal = OidInputFunctionCall(typinput, strInitVal,
typioparam, -1);
pfree(strInitVal);
return initVal;
}
/*
* find_compatible_peragg - search for a previously initialized per-Agg struct
*
* Searches the previously looked at aggregates to find one which is compatible
* with this one, with the same input parameters. If no compatible aggregate
* can be found, returns -1.
*
* As a side-effect, this also collects a list of existing, shareable per-Trans
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
* structs with matching inputs. If no identical Aggref is found, the list is
* passed later to find_compatible_pertrans, to see if we can at least reuse
* the state value of another aggregate.
*/
static int
find_compatible_peragg(Aggref *newagg, AggState *aggstate,
int lastaggno, List **same_input_transnos)
{
int aggno;
AggStatePerAgg peraggs;
*same_input_transnos = NIL;
/* we mustn't reuse the aggref if it contains volatile function calls */
if (contain_volatile_functions((Node *) newagg))
return -1;
peraggs = aggstate->peragg;
/*
* Search through the list of already seen aggregates. If we find an
* existing identical aggregate call, then we can re-use that one. While
* searching, we'll also collect a list of Aggrefs with the same input
* parameters. If no matching Aggref is found, the caller can potentially
* still re-use the transition state of one of them. (At this stage we
* just compare the parsetrees; whether different aggregates share the
* same transition function will be checked later.)
*/
for (aggno = 0; aggno <= lastaggno; aggno++)
{
AggStatePerAgg peragg;
Aggref *existingRef;
peragg = &peraggs[aggno];
existingRef = peragg->aggref;
/* all of the following must be the same or it's no match */
if (newagg->inputcollid != existingRef->inputcollid ||
Fix handling of argument and result datatypes for partial aggregation. When doing partial aggregation, the args list of the upper (combining) Aggref node is replaced by a Var representing the output of the partial aggregation steps, which has either the aggregate's transition data type or a serialized representation of that. However, nodeAgg.c blindly continued to use the args list as an indication of the user-level argument types. This broke resolution of polymorphic transition datatypes at executor startup (though it accidentally failed to fail for the ANYARRAY case, which is likely the only one anyone had tested). Moreover, the constructed FuncExpr passed to the finalfunc contained completely wrong information, which would have led to bogus answers or crashes for any case where the finalfunc examined that information (which is only likely to be with polymorphic aggregates using a non-polymorphic transition type). As an independent bug, apply_partialaggref_adjustment neglected to resolve a polymorphic transition datatype before assigning it as the output type of the lower-level Aggref node. This again accidentally failed to fail for ANYARRAY but would be unlikely to work in other cases. To fix the first problem, record the user-level argument types in a separate OID-list field of Aggref, and look to that rather than the args list when asking what the argument types were. (It turns out to be convenient to include any "direct" arguments in this list too, although those are not currently subject to being overwritten.) Rather than adding yet another resolve_aggregate_transtype() call to fix the second problem, add an aggtranstype field to Aggref, and store the resolved transition type OID there when the planner first computes it. (By doing this in the planner and not the parser, we can allow the aggregate's transition type to change from time to time, although no DDL support yet exists for that.) This saves nothing of consequence for simple non-polymorphic aggregates, but for polymorphic transition types we save a catalog lookup during executor startup as well as several planner lookups that are new in 9.6 due to parallel query planning. In passing, fix an error that was introduced into count_agg_clauses_walker some time ago: it was applying exprTypmod() to something that wasn't an expression node at all, but a TargetEntry. exprTypmod silently returned -1 so that there was not an obvious failure, but this broke the intended sensitivity of aggregate space consumption estimates to the typmod of varchar and similar data types. This part needs to be back-patched. Catversion bump due to change of stored Aggref nodes. Discussion: <8229.1466109074@sss.pgh.pa.us>
2016-06-18 03:44:37 +02:00
newagg->aggtranstype != existingRef->aggtranstype ||
newagg->aggstar != existingRef->aggstar ||
newagg->aggvariadic != existingRef->aggvariadic ||
newagg->aggkind != existingRef->aggkind ||
!equal(newagg->args, existingRef->args) ||
!equal(newagg->aggorder, existingRef->aggorder) ||
!equal(newagg->aggdistinct, existingRef->aggdistinct) ||
!equal(newagg->aggfilter, existingRef->aggfilter))
continue;
/* if it's the same aggregate function then report exact match */
if (newagg->aggfnoid == existingRef->aggfnoid &&
newagg->aggtype == existingRef->aggtype &&
newagg->aggcollid == existingRef->aggcollid &&
equal(newagg->aggdirectargs, existingRef->aggdirectargs))
{
list_free(*same_input_transnos);
*same_input_transnos = NIL;
return aggno;
}
/*
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
* Not identical, but it had the same inputs. If the final function
* permits sharing, return its transno to the caller, in case we can
* re-use its per-trans state. (If there's already sharing going on,
* we might report a transno more than once. find_compatible_pertrans
* is cheap enough that it's not worth spending cycles to avoid that.)
*/
if (peragg->shareable)
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
*same_input_transnos = lappend_int(*same_input_transnos,
peragg->transno);
}
return -1;
}
/*
* find_compatible_pertrans - search for a previously initialized per-Trans
* struct
*
* Searches the list of transnos for a per-Trans struct with the same
* transition function and initial condition. (The inputs have already been
* verified to match.)
*/
static int
find_compatible_pertrans(AggState *aggstate, Aggref *newagg, bool shareable,
Oid aggtransfn, Oid aggtranstype,
Oid aggserialfn, Oid aggdeserialfn,
Datum initValue, bool initValueIsNull,
List *transnos)
{
ListCell *lc;
Explicitly track whether aggregate final functions modify transition state. Up to now, there's been hard-wired assumptions that normal aggregates' final functions never modify their transition states, while ordered-set aggregates' final functions always do. This has always been a bit limiting, and in particular it's getting in the way of improving the built-in ordered-set aggregates to allow merging of transition states. Therefore, let's introduce catalog and CREATE AGGREGATE infrastructure that lets the finalfn's behavior be declared explicitly. There are now three possibilities for the finalfn behavior: it's purely read-only, it trashes the transition state irrecoverably, or it changes the state in such a way that no more transfn calls are possible but the state can still be passed to other, compatible finalfns. There are no examples of this third case today, but we'll shortly make the built-in OSAs act like that. This change allows user-defined aggregates to explicitly disclaim support for use as window functions, and/or to prevent transition state merging, if their implementations cannot handle that. While it was previously possible to handle the window case with a run-time error check, there was not any way to prevent transition state merging, which in retrospect is something commit 804163bc2 should have provided for. But better late than never. In passing, split out pg_aggregate.c's extern function declarations into a new header file pg_aggregate_fn.h, similarly to what we've done for some other catalog headers, so that pg_aggregate.h itself can be safe for frontend files to include. This lets pg_dump use the symbolic names for relevant constants. Discussion: https://postgr.es/m/4834.1507849699@sss.pgh.pa.us
2017-10-14 21:21:39 +02:00
/* If this aggregate can't share transition states, give up */
if (!shareable)
return -1;
foreach(lc, transnos)
{
int transno = lfirst_int(lc);
AggStatePerTrans pertrans = &aggstate->pertrans[transno];
/*
* if the transfns or transition state types are not the same then the
* state can't be shared.
*/
if (aggtransfn != pertrans->transfn_oid ||
aggtranstype != pertrans->aggtranstype)
continue;
/*
* The serialization and deserialization functions must match, if
* present, as we're unable to share the trans state for aggregates
2016-06-10 00:02:36 +02:00
* which will serialize or deserialize into different formats.
* Remember that these will be InvalidOid if they're not required for
* this agg node.
*/
if (aggserialfn != pertrans->serialfn_oid ||
aggdeserialfn != pertrans->deserialfn_oid)
continue;
/*
* Check that the initial condition matches, too.
*/
if (initValueIsNull && pertrans->initValueIsNull)
return transno;
if (!initValueIsNull && !pertrans->initValueIsNull &&
datumIsEqual(initValue, pertrans->initValue,
pertrans->transtypeByVal, pertrans->transtypeLen))
return transno;
}
return -1;
}
void
ExecEndAgg(AggState *node)
{
PlanState *outerPlan;
int transno;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
int numGroupingSets = Max(node->maxsets, 1);
int setno;
/* Make sure we have closed any open tuplesorts */
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
if (node->sort_in)
tuplesort_end(node->sort_in);
if (node->sort_out)
tuplesort_end(node->sort_out);
for (transno = 0; transno < node->numtrans; transno++)
{
AggStatePerTrans pertrans = &node->pertrans[transno];
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
for (setno = 0; setno < numGroupingSets; setno++)
{
if (pertrans->sortstates[setno])
tuplesort_end(pertrans->sortstates[setno]);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
}
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/* And ensure any agg shutdown callbacks have been called */
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
for (setno = 0; setno < numGroupingSets; setno++)
ReScanExprContext(node->aggcontexts[setno]);
if (node->hashcontext)
ReScanExprContext(node->hashcontext);
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* We don't actually free any ExprContexts here (see comment in
* ExecFreeExprContext), just unlinking the output one from the plan node
* suffices.
*/
ExecFreeExprContext(&node->ss.ps);
2001-03-22 05:01:46 +01:00
/* clean up tuple table */
ExecClearTuple(node->ss.ss_ScanTupleSlot);
outerPlan = outerPlanState(node);
ExecEndNode(outerPlan);
}
void
ExecReScanAgg(AggState *node)
{
ExprContext *econtext = node->ss.ps.ps_ExprContext;
2015-05-24 03:35:49 +02:00
PlanState *outerPlan = outerPlanState(node);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
Agg *aggnode = (Agg *) node->ss.ps.plan;
int transno;
2015-05-24 03:35:49 +02:00
int numGroupingSets = Max(node->maxsets, 1);
int setno;
node->agg_done = false;
if (node->aggstrategy == AGG_HASHED)
{
/*
2005-10-15 04:49:52 +02:00
* In the hashed case, if we haven't yet built the hash table then we
* can just return; nothing done yet, so nothing to undo. If subnode's
* chgParam is not NULL then it will be re-scanned by ExecProcNode,
* else no reason to re-scan it at all.
*/
if (!node->table_filled)
return;
/*
* If we do have the hash table, and the subplan does not have any
* parameter changes, and none of our own parameter changes affect
* input expressions of the aggregated functions, then we can just
* rescan the existing hash table; no need to build it again.
*/
if (outerPlan->chgParam == NULL &&
!bms_overlap(node->ss.ps.chgParam, aggnode->aggParams))
{
ResetTupleHashIterator(node->perhash[0].hashtable,
&node->perhash[0].hashiter);
select_current_set(node, 0, true);
return;
}
}
/* Make sure we have closed any open tuplesorts */
for (transno = 0; transno < node->numtrans; transno++)
{
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
for (setno = 0; setno < numGroupingSets; setno++)
{
AggStatePerTrans pertrans = &node->pertrans[transno];
if (pertrans->sortstates[setno])
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
tuplesort_end(pertrans->sortstates[setno]);
pertrans->sortstates[setno] = NULL;
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
}
}
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/*
* We don't need to ReScanExprContext the output tuple context here;
* ExecReScan already did it. But we do need to reset our per-grouping-set
* contexts, which may have transvalues stored in them. (We use rescan
* rather than just reset because transfns may have registered callbacks
* that need to be run now.) For the AGG_HASHED case, see below.
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*/
for (setno = 0; setno < numGroupingSets; setno++)
{
ReScanExprContext(node->aggcontexts[setno]);
}
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/* Release first tuple of group, if we have made a copy */
if (node->grp_firstTuple != NULL)
{
heap_freetuple(node->grp_firstTuple);
node->grp_firstTuple = NULL;
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
ExecClearTuple(node->ss.ss_ScanTupleSlot);
/* Forget current agg values */
MemSet(econtext->ecxt_aggvalues, 0, sizeof(Datum) * node->numaggs);
MemSet(econtext->ecxt_aggnulls, 0, sizeof(bool) * node->numaggs);
/*
* With AGG_HASHED/MIXED, the hash table is allocated in a sub-context of
* the hashcontext. This used to be an issue, but now, resetting a context
* automatically deletes sub-contexts too.
*/
if (node->aggstrategy == AGG_HASHED || node->aggstrategy == AGG_MIXED)
{
ReScanExprContext(node->hashcontext);
/* Rebuild an empty hash table */
build_hash_table(node);
node->table_filled = false;
/* iterator will be reset when the table is filled */
}
if (node->aggstrategy != AGG_HASHED)
{
2004-08-29 07:07:03 +02:00
/*
2005-10-15 04:49:52 +02:00
* Reset the per-group state (in particular, mark transvalues null)
2004-08-29 07:07:03 +02:00
*/
for (setno = 0; setno < numGroupingSets; setno++)
{
MemSet(node->pergroups[setno], 0,
sizeof(AggStatePerGroupData) * node->numaggs);
}
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
/* reset to phase 1 */
initialize_phase(node, 1);
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
node->input_done = false;
node->projected_set = -1;
}
if (outerPlan->chgParam == NULL)
ExecReScan(outerPlan);
}
/***********************************************************************
* API exposed to aggregate functions
***********************************************************************/
/*
* AggCheckCallContext - test if a SQL function is being called as an aggregate
*
* The transition and/or final functions of an aggregate may want to verify
* that they are being called as aggregates, rather than as plain SQL
* functions. They should use this function to do so. The return value
* is nonzero if being called as an aggregate, or zero if not. (Specific
* nonzero values are AGG_CONTEXT_AGGREGATE or AGG_CONTEXT_WINDOW, but more
* values could conceivably appear in future.)
*
* If aggcontext isn't NULL, the function also stores at *aggcontext the
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
* identity of the memory context that aggregate transition values are being
* stored in. Note that the same aggregate call site (flinfo) may be called
* interleaved on different transition values in different contexts, so it's
* not kosher to cache aggcontext under fn_extra. It is, however, kosher to
* cache it in the transvalue itself (for internal-type transvalues).
*/
int
AggCheckCallContext(FunctionCallInfo fcinfo, MemoryContext *aggcontext)
{
if (fcinfo->context && IsA(fcinfo->context, AggState))
{
if (aggcontext)
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
{
2015-05-24 03:35:49 +02:00
AggState *aggstate = ((AggState *) fcinfo->context);
ExprContext *cxt = aggstate->curaggcontext;
2015-05-24 03:35:49 +02:00
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
*aggcontext = cxt->ecxt_per_tuple_memory;
}
return AGG_CONTEXT_AGGREGATE;
}
if (fcinfo->context && IsA(fcinfo->context, WindowAggState))
{
if (aggcontext)
*aggcontext = ((WindowAggState *) fcinfo->context)->curaggcontext;
return AGG_CONTEXT_WINDOW;
}
/* this is just to prevent "uninitialized variable" warnings */
if (aggcontext)
*aggcontext = NULL;
return 0;
}
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
* AggGetAggref - allow an aggregate support function to get its Aggref
*
* If the function is being called as an aggregate support function,
* return the Aggref node for the aggregate call. Otherwise, return NULL.
*
* Aggregates sharing the same inputs and transition functions can get
* merged into a single transition calculation. If the transition function
* calls AggGetAggref, it will get some one of the Aggrefs for which it is
* executing. It must therefore not pay attention to the Aggref fields that
* relate to the final function, as those are indeterminate. But if a final
* function calls AggGetAggref, it will get a precise result.
*
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
* Note that if an aggregate is being used as a window function, this will
* return NULL. We could provide a similar function to return the relevant
* WindowFunc node in such cases, but it's not needed yet.
*/
Aggref *
AggGetAggref(FunctionCallInfo fcinfo)
{
if (fcinfo->context && IsA(fcinfo->context, AggState))
{
AggState *aggstate = (AggState *) fcinfo->context;
AggStatePerAgg curperagg;
AggStatePerTrans curpertrans;
/* check curperagg (valid when in a final function) */
curperagg = aggstate->curperagg;
if (curperagg)
return curperagg->aggref;
/* check curpertrans (valid when in a transition function) */
curpertrans = aggstate->curpertrans;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
if (curpertrans)
return curpertrans->aggref;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
}
return NULL;
}
/*
* AggGetTempMemoryContext - fetch short-term memory context for aggregates
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* This is useful in agg final functions; the context returned is one that
* the final function can safely reset as desired. This isn't useful for
* transition functions, since the context returned MAY (we don't promise)
* be the same as the context those are called in.
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* As above, this is currently not useful for aggs called as window functions.
*/
MemoryContext
AggGetTempMemoryContext(FunctionCallInfo fcinfo)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
{
if (fcinfo->context && IsA(fcinfo->context, AggState))
{
AggState *aggstate = (AggState *) fcinfo->context;
return aggstate->tmpcontext->ecxt_per_tuple_memory;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
}
return NULL;
}
/*
* AggStateIsShared - find out whether transition state is shared
*
* If the function is being called as an aggregate support function,
* return true if the aggregate's transition state is shared across
* multiple aggregates, false if it is not.
*
* Returns true if not called as an aggregate support function.
* This is intended as a conservative answer, ie "no you'd better not
* scribble on your input". In particular, will return true if the
* aggregate is being used as a window function, which is a scenario
* in which changing the transition state is a bad idea. We might
* want to refine the behavior for the window case in future.
*/
bool
AggStateIsShared(FunctionCallInfo fcinfo)
{
if (fcinfo->context && IsA(fcinfo->context, AggState))
{
AggState *aggstate = (AggState *) fcinfo->context;
AggStatePerAgg curperagg;
AggStatePerTrans curpertrans;
/* check curperagg (valid when in a final function) */
curperagg = aggstate->curperagg;
if (curperagg)
return aggstate->pertrans[curperagg->transno].aggshared;
/* check curpertrans (valid when in a transition function) */
curpertrans = aggstate->curpertrans;
if (curpertrans)
return curpertrans->aggshared;
}
return true;
}
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
/*
* AggRegisterCallback - register a cleanup callback for an aggregate
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* This is useful for aggs to register shutdown callbacks, which will ensure
* that non-memory resources are freed. The callback will occur just before
* the associated aggcontext (as returned by AggCheckCallContext) is reset,
* either between groups or as a result of rescanning the query. The callback
* will NOT be called on error paths. The typical use-case is for freeing of
* tuplestores or tuplesorts maintained in aggcontext, or pins held by slots
* created by the agg functions. (The callback will not be called until after
* the result of the finalfn is no longer needed, so it's safe for the finalfn
* to return data that will be freed by the callback.)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
*
* As above, this is currently not useful for aggs called as window functions.
*/
void
AggRegisterCallback(FunctionCallInfo fcinfo,
ExprContextCallbackFunction func,
Datum arg)
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
{
if (fcinfo->context && IsA(fcinfo->context, AggState))
{
AggState *aggstate = (AggState *) fcinfo->context;
ExprContext *cxt = aggstate->curaggcontext;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
Support GROUPING SETS, CUBE and ROLLUP. This SQL standard functionality allows to aggregate data by different GROUP BY clauses at once. Each grouping set returns rows with columns grouped by in other sets set to NULL. This could previously be achieved by doing each grouping as a separate query, conjoined by UNION ALLs. Besides being considerably more concise, grouping sets will in many cases be faster, requiring only one scan over the underlying data. The current implementation of grouping sets only supports using sorting for input. Individual sets that share a sort order are computed in one pass. If there are sets that don't share a sort order, additional sort & aggregation steps are performed. These additional passes are sourced by the previous sort step; thus avoiding repeated scans of the source data. The code is structured in a way that adding support for purely using hash aggregation or a mix of hashing and sorting is possible. Sorting was chosen to be supported first, as it is the most generic method of implementation. Instead of, as in an earlier versions of the patch, representing the chain of sort and aggregation steps as full blown planner and executor nodes, all but the first sort are performed inside the aggregation node itself. This avoids the need to do some unusual gymnastics to handle having to return aggregated and non-aggregated tuples from underlying nodes, as well as having to shut down underlying nodes early to limit memory usage. The optimizer still builds Sort/Agg node to describe each phase, but they're not part of the plan tree, but instead additional data for the aggregation node. They're a convenient and preexisting way to describe aggregation and sorting. The first (and possibly only) sort step is still performed as a separate execution step. That retains similarity with existing group by plans, makes rescans fairly simple, avoids very deep plans (leading to slow explains) and easily allows to avoid the sorting step if the underlying data is sorted by other means. A somewhat ugly side of this patch is having to deal with a grammar ambiguity between the new CUBE keyword and the cube extension/functions named cube (and rollup). To avoid breaking existing deployments of the cube extension it has not been renamed, neither has cube been made a reserved keyword. Instead precedence hacking is used to make GROUP BY cube(..) refer to the CUBE grouping sets feature, and not the function cube(). To actually group by a function cube(), unlikely as that might be, the function name has to be quoted. Needs a catversion bump because stored rules may change. Author: Andrew Gierth and Atri Sharma, with contributions from Andres Freund Reviewed-By: Andres Freund, Noah Misch, Tom Lane, Svenne Krap, Tomas Vondra, Erik Rijkers, Marti Raudsepp, Pavel Stehule Discussion: CAOeZVidmVRe2jU6aMk_5qkxnB7dfmPROzM7Ur8JPW5j8Y5X-Lw@mail.gmail.com
2015-05-16 03:40:59 +02:00
RegisterExprContextCallback(cxt, func, arg);
return;
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
}
elog(ERROR, "aggregate function cannot register a callback in this context");
Support ordered-set (WITHIN GROUP) aggregates. This patch introduces generic support for ordered-set and hypothetical-set aggregate functions, as well as implementations of the instances defined in SQL:2008 (percentile_cont(), percentile_disc(), rank(), dense_rank(), percent_rank(), cume_dist()). We also added mode() though it is not in the spec, as well as versions of percentile_cont() and percentile_disc() that can compute multiple percentile values in one pass over the data. Unlike the original submission, this patch puts full control of the sorting process in the hands of the aggregate's support functions. To allow the support functions to find out how they're supposed to sort, a new API function AggGetAggref() is added to nodeAgg.c. This allows retrieval of the aggregate call's Aggref node, which may have other uses beyond the immediate need. There is also support for ordered-set aggregates to install cleanup callback functions, so that they can be sure that infrastructure such as tuplesort objects gets cleaned up. In passing, make some fixes in the recently-added support for variadic aggregates, and make some editorial adjustments in the recent FILTER additions for aggregates. Also, simplify use of IsBinaryCoercible() by allowing it to succeed whenever the target type is ANY or ANYELEMENT. It was inconsistent that it dealt with other polymorphic target types but not these. Atri Sharma and Andrew Gierth; reviewed by Pavel Stehule and Vik Fearing, and rather heavily editorialized upon by Tom Lane
2013-12-23 22:11:35 +01:00
}
/*
* aggregate_dummy - dummy execution routine for aggregate functions
*
* This function is listed as the implementation (prosrc field) of pg_proc
* entries for aggregate functions. Its only purpose is to throw an error
* if someone mistakenly executes such a function in the normal way.
*
* Perhaps someday we could assign real meaning to the prosrc field of
* an aggregate?
*/
Datum
aggregate_dummy(PG_FUNCTION_ARGS)
{
elog(ERROR, "aggregate function %u called as normal function",
fcinfo->flinfo->fn_oid);
return (Datum) 0; /* keep compiler quiet */
}