Change one more place where ExecInitCheck/ExecPrepareCheck's insistence
on getting implicit-AND-format quals wasn't really helpful, because the
caller had to do make_ands_implicit() for no reason that it cared about.
Using ExecPrepareExpr directly simplifies the code and saves cycles.
The only remaining use of these functions is to process
resultRelInfo->ri_PartitionCheck quals. However, implicit-AND format
does seem to be what we want for that, so leave it alone.
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
Currently, the whole row is shown without column names. Instead,
adopt a style similar to _bt_check_unique() in ExecFindPartition()
and show the failing key: (key1, ...) = (val1, ...).
Amit Langote, per a complaint from Simon Riggs. Reviewed by me;
I also adjusted the grammar in one of the comments.
Discussion: http://postgr.es/m/9f9dc7ae-14f0-4a25-5485-964d9bfc19bd@lab.ntt.co.jp
According to the comments in tupconvert.c, it's necessary to perform
tuple conversion when either table has OIDs, and this was previously
checked by ensuring that the tdtypeid value matched between the tables
in question. However, that's overly stringent: we have access to
tdhasoid and can test directly whether OIDs are present, which lets us
avoid conversion in cases where the type OIDs are different but the
tuple descriptors are entirely the same (and neither has OIDs). This
is useful to the partitioning code, which can thereby avoid converting
tuples when inserting into a partition whose columns appear in the
same order as the parent columns, the normal case. It's possible
for the tuple routing code to avoid some additional overhead in this
case as well, so do that, too.
It's not clear whether it would be OK to skip this when both tables
have OIDs: do callers count on this to build a new tuple (losing the
previous OID) in such instances? Until we figure it out, leave the
behavior in that case alone.
Amit Langote, reviewed by me.
In 2ac3ef7a01, we changed things so that
it's possible for a different TupleTableSlot to be used for partitioned
tables at successively lower levels. If we do end up changing the slot
from the original, we must update ecxt_scantuple to point to the new one
for partition key of the tuple to be computed correctly.
Reported by Rajkumar Raghuwanshi. Patch by Amit Langote.
Discussion: http://postgr.es/m/CAKcux6%3Dm1qyqB2k6cjniuMMrYXb75O-MB4qGQMu8zg-iGGLjDw%40mail.gmail.com
We've accumulated quite a bit of stuff with which pgindent is not
quite happy in this code; clean it up to provide a less-annoying base
for future pgindent runs.
The code here previously tried to call the partitioning operator, but
really the right thing to do (and the safe thing to do) is use
datumIsEqual().
Amit Langote, but I expanded the comment and fixed a compiler warning.
If either bound is infinite, then we shouldn't even try to perform a
comparison of the values themselves. Rearrange the logic so that
we don't.
Per buildfarm member skink and Tom Lane.
Since 69f4b9c plain expression evaluation (and thus normal projection)
can't return sets of tuples anymore. Thus remove code dealing with
that possibility.
This will require adjustments in external code using
ExecEvalExpr()/ExecProject() - that should neither be hard nor very
common.
Author: Andres Freund and Tom Lane
Discussion: https://postgr.es/m/20160822214023.aaxz5l4igypowyri@alap3.anarazel.de
Account for the fact that the highest bound less than or equal to the
upper bound might be either the lower or the upper bound of the
overlapping partition, depending on whether the proposed partition
completely contains the existing partition or merely overlaps it.
Also, we need not continue searching for even greater bound in
partition_bound_bsearch() once we find the first bound that is *equal*
to the probe, because we don't have duplicate datums. That spends
cycles needlessly.
Amit Langote, per a report from Amul Sul. Cosmetic changes by me.
Discussion: http://postgr.es/m/CAAJ_b94XgbqVoXMyxxs63CaqWoMS1o2gpHiU0F7yGnJBnvDc_A%40mail.gmail.com
In ExecInsert(), do not switch back to the root partitioned table
ResultRelInfo until after we finish ExecProcessReturning(), so that
RETURNING projection is done using the partition's descriptor. For
the projection to work correctly, we must initialize the same for each
leaf partition during ModifyTableState initialization.
Amit Langote
Move the code for doing parent attnos to child attnos mapping for Vars
in partition constraint expressions to a separate function
map_partition_varattnos() and call it from the appropriate places.
Doing it in get_qual_from_partbound(), as is now, would produce wrong
result in certain multi-level partitioning cases, because it only
considers the current pair of parent-child relations. In certain
multi-level partitioning cases, attnums for the same key attribute(s)
might differ between various levels causing the same attribute to be
numbered differently in different instances of the Var corresponding
to a given attribute.
With this commit, in generate_partition_qual(), we first generate the
the whole partition constraint (considering all levels of partitioning)
and then do the mapping, so that Vars in the final expression are
numbered according the leaf relation (to which it is supposed to apply).
Amit Langote, reviewed by me.
RelationGetPartitionQual() and generate_partition_qual() are always
called with recurse = true, so we don't need an argument for that.
Extracted by me from a larger patch by Amit Langote.
The previous coding failed to work correctly when we have a
multi-level partitioned hierarchy where tables at successive levels
have different attribute numbers for the partition key attributes. To
fix, have each PartitionDispatch object store a standalone
TupleTableSlot initialized with the TupleDesc of the corresponding
partitioned table, along with a TupleConversionMap to map tuples from
the its parent's rowtype to own rowtype. After tuple routing chooses
a leaf partition, we must use the leaf partition's tuple descriptor,
not the root table's. To that end, a dedicated TupleTableSlot for
tuple routing is now allocated in EState.
Amit Langote
Table partitioning is like table inheritance and reuses much of the
existing infrastructure, but there are some important differences.
The parent is called a partitioned table and is always empty; it may
not have indexes or non-inherited constraints, since those make no
sense for a relation with no data of its own. The children are called
partitions and contain all of the actual data. Each partition has an
implicit partitioning constraint. Multiple inheritance is not
allowed, and partitioning and inheritance can't be mixed. Partitions
can't have extra columns and may not allow nulls unless the parent
does. Tuples inserted into the parent are automatically routed to the
correct partition, so tuple-routing ON INSERT triggers are not needed.
Tuple routing isn't yet supported for partitions which are foreign
tables, and it doesn't handle updates that cross partition boundaries.
Currently, tables can be range-partitioned or list-partitioned. List
partitioning is limited to a single column, but range partitioning can
involve multiple columns. A partitioning "column" can be an
expression.
Because table partitioning is less general than table inheritance, it
is hoped that it will be easier to reason about properties of
partitions, and therefore that this will serve as a better foundation
for a variety of possible optimizations, including query planner
optimizations. The tuple routing based which this patch does based on
the implicit partitioning constraints is an example of this, but it
seems likely that many other useful optimizations are also possible.
Amit Langote, reviewed and tested by Robert Haas, Ashutosh Bapat,
Amit Kapila, Rajkumar Raghuwanshi, Corey Huinker, Jaime Casanova,
Rushabh Lathia, Erik Rijkers, among others. Minor revisions by me.