Allowing only on/off meant that all either all existing configuration
guides would become obsolete if we disabled it by default, or that we
would have to accept a performance loss in the default config if we
enabled it by default. By allowing 'auto' as a middle ground, the
performance cost is only paid by those who enable pg_stat_statements and
similar modules.
I only edited the release notes to comment-out a paragraph that is now
factually wrong; further edits are probably needed to describe the
related change in more detail.
Author: Julien Rouhaud <rjuju123@gmail.com>
Reviewed-by: Justin Pryzby <pryzby@telsasoft.com>
Discussion: https://postgr.es/m/20210513002623.eugftm4nk2lvvks3@nol
Also "make reformat-dat-files".
The only change worthy of note is that pgindent messed up the formatting
of launcher.c's struct LogicalRepWorkerId, which led me to notice that
that struct wasn't used at all anymore, so I just took it out.
Previously long was used as the data type for some counters in BufferUsage
and WalUsage. But long is only four byte, e.g., on Windows, and it's entirely
possible to wrap a four byte counter. For example, emitting more than
four billion WAL records in one transaction isn't actually particularly rare.
To avoid the overflows of those counters, this commit changes the data type
of them from long to int64.
Suggested-by: Andres Freund
Author: Masahiro Ikeda
Reviewed-by: Fujii Masao
Discussion: https://postgr.es/m/20201221211650.k7b53tcnadrciqjo@alap3.anarazel.de
Discussion: https://postgr.es/m/af0964ac-7080-1984-dc23-513754987716@oss.nttdata.com
Here we adjust the EXPLAIN ANALYZE output for Result Cache so that we
don't show any Result Cache stats for parallel workers who don't
contribute anything to Result Cache plan nodes.
I originally had ideas that workers who don't help could still have their
Result Cache stats displayed. The idea with that was so that I could
write some parallel Result Cache regression tests that show the EXPLAIN
ANALYZE output. However, I realized a little too late that such tests
would just not be possible to have run in a stable way on the buildfarm.
With that knowledge, before 9eacee2e6 went in, I had removed all of the
tests that were showing the EXPLAIN ANALYZE output of a parallel Result
Cache plan, however, I forgot to put back the code that adjusts the
EXPLAIN output to hide the Result Cache stats for parallel workers who
were not fast enough to help out before query execution was over. All
other nodes behave this way and so should Result Cache.
Additionally, with this change, it now seems safe enough to remove the SET
force_parallel_mode = off that I had added to the regression tests.
Also, perform some cleanup in the partition_prune tests. I had adjusted
the explain_parallel_append() function to sanitize the Result Cache
EXPLAIN ANALYZE output. However, since I didn't actually include any
parallel Result Cache tests that show their EXPLAIN ANALYZE output, that
code does nothing and can be removed.
In passing, move the setting of memPeakKb into the scope where it's used.
Reported-by: Amit Khandekar
Author: David Rowley, Amit Khandekar
Discussion: https://postgr.es/m/CAJ3gD9d8SkfY95GpM1zmsOtX2-Ogx5q-WLsf8f0ykEb0hCRK3w@mail.gmail.com
This is only a latent bug, since these calls are only reached for
non-text output formats, and currently none of those will print
the units. Still, we should get it right in case that ever changes.
Justin Pryzby
Discussion: https://postgr.es/m/20210415163846.GA3315@telsasoft.com
Use the in-core query id computation for pg_stat_activity,
log_line_prefix, and EXPLAIN VERBOSE.
Similar to other fields in pg_stat_activity, only the queryid from the
top level statements are exposed, and if the backends status isn't
active then the queryid from the last executed statements is displayed.
Add a %Q placeholder to include the queryid in log_line_prefix, which
will also only expose top level statements.
For EXPLAIN VERBOSE, if a query identifier has been computed, either by
enabling compute_query_id or using a third-party module, display it.
Bump catalog version.
Discussion: https://postgr.es/m/20210407125726.tkvjdbw76hxnpwfi@nol
Author: Julien Rouhaud
Reviewed-by: Alvaro Herrera, Nitin Jadhav, Zhihong Yu
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu, Hou Zhijie
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This removes "Add Result Cache executor node". It seems that something
weird is going on with the tracking of cache hits and misses as
highlighted by many buildfarm animals. It's not yet clear what the
problem is as other parts of the plan indicate that the cache did work
correctly, it's just the hits and misses that were being reported as 0.
This is especially a bad time to have the buildfarm so broken, so
reverting before too many more animals go red.
Discussion: https://postgr.es/m/CAApHDvq_hydhfovm4=izgWs+C5HqEeRScjMbOgbpC-jRAeK3Yw@mail.gmail.com
Here we add a new executor node type named "Result Cache". The planner
can include this node type in the plan to have the executor cache the
results from the inner side of parameterized nested loop joins. This
allows caching of tuples for sets of parameters so that in the event that
the node sees the same parameter values again, it can just return the
cached tuples instead of rescanning the inner side of the join all over
again. Internally, result cache uses a hash table in order to quickly
find tuples that have been previously cached.
For certain data sets, this can significantly improve the performance of
joins. The best cases for using this new node type are for join problems
where a large portion of the tuples from the inner side of the join have
no join partner on the outer side of the join. In such cases, hash join
would have to hash values that are never looked up, thus bloating the hash
table and possibly causing it to multi-batch. Merge joins would have to
skip over all of the unmatched rows. If we use a nested loop join with a
result cache, then we only cache tuples that have at least one join
partner on the outer side of the join. The benefits of using a
parameterized nested loop with a result cache increase when there are
fewer distinct values being looked up and the number of lookups of each
value is large. Also, hash probes to lookup the cache can be much faster
than the hash probe in a hash join as it's common that the result cache's
hash table is much smaller than the hash join's due to result cache only
caching useful tuples rather than all tuples from the inner side of the
join. This variation in hash probe performance is more significant when
the hash join's hash table no longer fits into the CPU's L3 cache, but the
result cache's hash table does. The apparent "random" access of hash
buckets with each hash probe can cause a poor L3 cache hit ratio for large
hash tables. Smaller hash tables generally perform better.
The hash table used for the cache limits itself to not exceeding work_mem
* hash_mem_multiplier in size. We maintain a dlist of keys for this cache
and when we're adding new tuples and realize we've exceeded the memory
budget, we evict cache entries starting with the least recently used ones
until we have enough memory to add the new tuples to the cache.
For parameterized nested loop joins, we now consider using one of these
result cache nodes in between the nested loop node and its inner node. We
determine when this might be useful based on cost, which is primarily
driven off of what the expected cache hit ratio will be. Estimating the
cache hit ratio relies on having good distinct estimates on the nested
loop's parameters.
For now, the planner will only consider using a result cache for
parameterized nested loop joins. This works for both normal joins and
also for LATERAL type joins to subqueries. It is possible to use this new
node for other uses in the future. For example, to cache results from
correlated subqueries. However, that's not done here due to some
difficulties obtaining a distinct estimation on the outer plan to
calculate the estimated cache hit ratio. Currently we plan the inner plan
before planning the outer plan so there is no good way to know if a result
cache would be useful or not since we can't estimate the number of times
the subplan will be called until the outer plan is generated.
The functionality being added here is newly introducing a dependency on
the return value of estimate_num_groups() during the join search.
Previously, during the join search, we only ever needed to perform
selectivity estimations. With this commit, we need to use
estimate_num_groups() in order to estimate what the hit ratio on the
result cache will be. In simple terms, if we expect 10 distinct values
and we expect 1000 outer rows, then we'll estimate the hit ratio to be
99%. Since cache hits are very cheap compared to scanning the underlying
nodes on the inner side of the nested loop join, then this will
significantly reduce the planner's cost for the join. However, it's
fairly easy to see here that things will go bad when estimate_num_groups()
incorrectly returns a value that's significantly lower than the actual
number of distinct values. If this happens then that may cause us to make
use of a nested loop join with a result cache instead of some other join
type, such as a merge or hash join. Our distinct estimations have been
known to be a source of trouble in the past, so the extra reliance on them
here could cause the planner to choose slower plans than it did previous
to having this feature. Distinct estimations are also fairly hard to
estimate accurately when several tables have been joined already or when a
WHERE clause filters out a set of values that are correlated to the
expressions we're estimating the number of distinct value for.
For now, the costing we perform during query planning for result caches
does put quite a bit of faith in the distinct estimations being accurate.
When these are accurate then we should generally see faster execution
times for plans containing a result cache. However, in the real world, we
may find that we need to either change the costings to put less trust in
the distinct estimations being accurate or perhaps even disable this
feature by default. There's always an element of risk when we teach the
query planner to do new tricks that it decides to use that new trick at
the wrong time and causes a regression. Users may opt to get the old
behavior by turning the feature off using the enable_resultcache GUC.
Currently, this is enabled by default. It remains to be seen if we'll
maintain that setting for the release.
Additionally, the name "Result Cache" is the best name I could think of
for this new node at the time I started writing the patch. Nobody seems
to strongly dislike the name. A few people did suggest other names but no
other name seemed to dominate in the brief discussion that there was about
names. Let's allow the beta period to see if the current name pleases
enough people. If there's some consensus on a better name, then we can
change it before the release. Please see the 2nd discussion link below
for the discussion on the "Result Cache" name.
Author: David Rowley
Reviewed-by: Andy Fan, Justin Pryzby, Zhihong Yu
Tested-By: Konstantin Knizhnik
Discussion: https://postgr.es/m/CAApHDvrPcQyQdWERGYWx8J%2B2DLUNgXu%2BfOSbQ1UscxrunyXyrQ%40mail.gmail.com
Discussion: https://postgr.es/m/CAApHDvq=yQXr5kqhRviT2RhNKwToaWr9JAN5t+5_PzhuRJ3wvg@mail.gmail.com
This patch makes two closely related sets of changes:
1. For UPDATE, the subplan of the ModifyTable node now only delivers
the new values of the changed columns (i.e., the expressions computed
in the query's SET clause) plus row identity information such as CTID.
ModifyTable must re-fetch the original tuple to merge in the old
values of any unchanged columns. The core advantage of this is that
the changed columns are uniform across all tables of an inherited or
partitioned target relation, whereas the other columns might not be.
A secondary advantage, when the UPDATE involves joins, is that less
data needs to pass through the plan tree. The disadvantage of course
is an extra fetch of each tuple to be updated. However, that seems to
be very nearly free in context; even worst-case tests don't show it to
add more than a couple percent to the total query cost. At some point
it might be interesting to combine the re-fetch with the tuple access
that ModifyTable must do anyway to mark the old tuple dead; but that
would require a good deal of refactoring and it seems it wouldn't buy
all that much, so this patch doesn't attempt it.
2. For inherited UPDATE/DELETE, instead of generating a separate
subplan for each target relation, we now generate a single subplan
that is just exactly like a SELECT's plan, then stick ModifyTable
on top of that. To let ModifyTable know which target relation a
given incoming row refers to, a tableoid junk column is added to
the row identity information. This gets rid of the horrid hack
that was inheritance_planner(), eliminating O(N^2) planning cost
and memory consumption in cases where there were many unprunable
target relations.
Point 2 of course requires point 1, so that there is a uniform
definition of the non-junk columns to be returned by the subplan.
We can't insist on uniform definition of the row identity junk
columns however, if we want to keep the ability to have both
plain and foreign tables in a partitioning hierarchy. Since
it wouldn't scale very far to have every child table have its
own row identity column, this patch includes provisions to merge
similar row identity columns into one column of the subplan result.
In particular, we can merge the whole-row Vars typically used as
row identity by FDWs into one column by pretending they are type
RECORD. (It's still okay for the actual composite Datums to be
labeled with the table's rowtype OID, though.)
There is more that can be done to file down residual inefficiencies
in this patch, but it seems to be committable now.
FDW authors should note several API changes:
* The argument list for AddForeignUpdateTargets() has changed, and so
has the method it must use for adding junk columns to the query. Call
add_row_identity_var() instead of manipulating the parse tree directly.
You might want to reconsider exactly what you're adding, too.
* PlanDirectModify() must now work a little harder to find the
ForeignScan plan node; if the foreign table is part of a partitioning
hierarchy then the ForeignScan might not be the direct child of
ModifyTable. See postgres_fdw for sample code.
* To check whether a relation is a target relation, it's no
longer sufficient to compare its relid to root->parse->resultRelation.
Instead, check it against all_result_relids or leaf_result_relids,
as appropriate.
Amit Langote and Tom Lane
Discussion: https://postgr.es/m/CA+HiwqHpHdqdDn48yCEhynnniahH78rwcrv1rEX65-fsZGBOLQ@mail.gmail.com
This implements asynchronous execution, which runs multiple parts of a
non-parallel-aware Append concurrently rather than serially to improve
performance when possible. Currently, the only node type that can be
run concurrently is a ForeignScan that is an immediate child of such an
Append. In the case where such ForeignScans access data on different
remote servers, this would run those ForeignScans concurrently, and
overlap the remote operations to be performed simultaneously, so it'll
improve the performance especially when the operations involve
time-consuming ones such as remote join and remote aggregation.
We may extend this to other node types such as joins or aggregates over
ForeignScans in the future.
This also adds the support for postgres_fdw, which is enabled by the
table-level/server-level option "async_capable". The default is false.
Robert Haas, Kyotaro Horiguchi, Thomas Munro, and myself. This commit
is mostly based on the patch proposed by Robert Haas, but also uses
stuff from the patch proposed by Kyotaro Horiguchi and from the patch
proposed by Thomas Munro. Reviewed by Kyotaro Horiguchi, Konstantin
Knizhnik, Andrey Lepikhov, Movead Li, Thomas Munro, Justin Pryzby, and
others.
Discussion: https://postgr.es/m/CA%2BTgmoaXQEt4tZ03FtQhnzeDEMzBck%2BLrni0UWHVVgOTnA6C1w%40mail.gmail.com
Discussion: https://postgr.es/m/CA%2BhUKGLBRyu0rHrDCMC4%3DRn3252gogyp1SjOgG8SEKKZv%3DFwfQ%40mail.gmail.com
Discussion: https://postgr.es/m/20200228.170650.667613673625155850.horikyota.ntt%40gmail.com
This adds a new executor node named TID Range Scan. The query planner
will generate paths for TID Range scans when quals are discovered on base
relations which search for ranges on the table's ctid column. These
ranges may be open at either end. For example, WHERE ctid >= '(10,0)';
will return all tuples on page 10 and over.
To support this, two new optional callback functions have been added to
table AM. scan_set_tidrange is used to set the scan range to just the
given range of TIDs. scan_getnextslot_tidrange fetches the next tuple
in the given range.
For AMs were scanning ranges of TIDs would not make sense, these functions
can be set to NULL in the TableAmRoutine. The query planner won't
generate TID Range Scan Paths in that case.
Author: Edmund Horner, David Rowley
Reviewed-by: David Rowley, Tomas Vondra, Tom Lane, Andres Freund, Zhihong Yu
Discussion: https://postgr.es/m/CAMyN-kB-nFTkF=VA_JPwFNo08S0d-Yk0F741S2B7LDmYAi8eyA@mail.gmail.com
If a cross-partition UPDATE violates a constraint on the target partition,
and the columns in the new partition are in different physical order than
in the parent, the error message can reveal columns that the user does not
have SELECT permission on. A similar bug was fixed earlier in commit
804b6b6db4.
The cause of the bug is that the callers of the
ExecBuildSlotValueDescription() function got confused when constructing
the list of modified columns. If the tuple was routed from a parent, we
converted the tuple to the parent's format, but the list of modified
columns was grabbed directly from the child's RTE entry.
ExecUpdateLockMode() had a similar issue. That lead to confusion on which
columns are key columns, leading to wrong tuple lock being taken on tables
referenced by foreign keys, when a row is updated with INSERT ON CONFLICT
UPDATE. A new isolation test is added for that corner case.
With this patch, the ri_RangeTableIndex field is no longer set for
partitions that don't have an entry in the range table. Previously, it was
set to the RTE entry of the parent relation, but that was confusing.
NOTE: This modifies the ResultRelInfo struct, replacing the
ri_PartitionRoot field with ri_RootResultRelInfo. That's a bit risky to
backpatch, because it breaks any extensions accessing the field. The
change that ri_RangeTableIndex is not set for partitions could potentially
break extensions, too. The ResultRelInfos are visible to FDWs at least,
and this patch required small changes to postgres_fdw. Nevertheless, this
seem like the least bad option. I don't think these fields widely used in
extensions; I don't think there are FDWs out there that uses the FDW
"direct update" API, other than postgres_fdw. If there is, you will get a
compilation error, so hopefully it is caught quickly.
Backpatch to 11, where support for both cross-partition UPDATEs, and unique
indexes on partitioned tables, were added.
Reviewed-by: Amit Langote
Security: CVE-2021-3393
IF NOT EXISTS was ignored when specified in an EXPLAIN query for CREATE
MATERIALIZED VIEW or CREATE TABLE AS. Hence, if this clause was
specified, the caller would get a failure if the relation already
exists instead of a success with a NOTICE message.
This commit makes the behavior of IF NOT EXISTS in EXPLAIN consistent
with the non-EXPLAIN'd DDL queries, preventing a failure with IF NOT
EXISTS if the relation to-be-created already exists. The skip is done
before the SELECT query used for the relation is planned or executed,
and a "dummy" plan is generated instead depending on the format used by
EXPLAIN.
Author: Bharath Rupireddy
Reviewed-by: Zhijie Hou, Michael Paquier
Discussion: https://postgr.es/m/CALj2ACVa3oJ9O_wcGd+FtHWZds04dEKcakxphGz5POVgD4wC7Q@mail.gmail.com
The ExplainCloseGroup arguments for incremental sort usage data
didn't match the corresponding ExplainOpenGroup. This only matters
for XML-format output, which is probably why we'd not noticed.
Daniel Gustafsson, per bug #16683 from Frits Jalvingh
Discussion: https://postgr.es/m/16683-8005033324ad34e9@postgresql.org
A number of places were using appendStringInfo() when they could have been
using appendStringInfoString() instead. While there's no functionality
change there, it's just more efficient to use appendStringInfoString()
when no formatting is required. Likewise for some
appendStringInfoString() calls which were just appending a single char.
We can just use appendStringInfoChar() for that.
Additionally, many places were using appendPQExpBuffer() when they could
have used appendPQExpBufferStr(). Change those too.
Patch by Zhijie Hou, but further searching by me found significantly more
places that deserved the same treatment.
Author: Zhijie Hou, David Rowley
Discussion: https://postgr.es/m/cb172cf4361e4c7ba7167429070979d4@G08CNEXMBPEKD05.g08.fujitsu.local
Instead of allocating all the ResultRelInfos upfront in one big array,
allocate them in ExecInitModifyTable(). es_result_relations is now an
array of ResultRelInfo pointers, rather than an array of structs, and it
is indexed by the RT index.
This simplifies things: we get rid of the separate concept of a "result
rel index", and don't need to set it in setrefs.c anymore. This also
allows follow-up optimizations (not included in this commit yet) to skip
initializing ResultRelInfos for target relations that were not needed at
runtime, and removal of the es_result_relation_info pointer.
The EState arrays of regular result rels and root result rels are merged
into one array. Similarly, the resultRelations and rootResultRelations
lists in PlannedStmt are merged into one. It's not actually clear to me
why they were kept separate in the first place, but now that the
es_result_relations array is indexed by RT index, it certainly seems
pointless.
The PlannedStmt->resultRelations list is now only needed for
ExecRelationIsTargetRelation(). One visible effect of this change is that
ExecRelationIsTargetRelation() will now return 'true' also for the
partition root, if a partitioned table is updated. That seems like a good
thing, although the function isn't used in core code, and I don't see any
reason for an FDW to call it on a partition root.
Author: Amit Langote
Discussion: https://www.postgresql.org/message-id/CA%2BHiwqGEmiib8FLiHMhKB%2BCH5dRgHSLc5N5wnvc4kym%2BZYpQEQ%40mail.gmail.com
Commit ce77abe63c allowed EXPLAIN (BUFFERS) to report the information
on buffer usage during planning phase. However three issues were
reported regarding this feature.
(1) Previously, EXPLAIN option BUFFERS required ANALYZE. So the query
had to be actually executed by specifying ANALYZE even when we
want to see only the planner's buffer usage. This was inconvenient
especially when the query was write one like DELETE.
(2) EXPLAIN included the planner's buffer usage in summary
information. So SUMMARY option had to be enabled to report that.
Also this format was confusing.
(3) The output structure for planning information was not consistent
between TEXT format and the others. For example, "Planning" tag
was output in JSON format, but not in TEXT format.
For (1), this commit allows us to perform EXPLAIN (BUFFERS) without
ANALYZE to report the planner's buffer usage.
For (2), this commit changed EXPLAIN output so that the planner's
buffer usage is reported before summary information.
For (3), this commit made the output structure for planning
information more consistent between the formats.
Back-patch to v13 where the planner's buffer usage was allowed to
be reported in EXPLAIN.
Reported-by: Pierre Giraud, David Rowley
Author: Fujii Masao
Reviewed-by: David Rowley, Julien Rouhaud, Pierre Giraud
Discussion: https://postgr.es/m/07b226e6-fa49-687f-b110-b7c37572f69e@dalibo.com
9bdb300de modified the EXPLAIN output for Hash Aggregate to show details
from parallel workers. However, it neglected to consider that a given
parallel worker may not have assisted with the given Hash Aggregate. This
can occur when workers fail to start or during Parallel Append with
enable_partitionwise_join enabled when only a single worker is working on
a non-parallel aware sub-plan. It could also happen if a worker simply
wasn't fast enough to get any work done before other processes went and
finished all the work.
The bogus output came from the fact that ExplainOpenWorker() skipped
showing any details for non-initialized workers but show_hashagg_info()
did show details from the worker. This meant that the worker properties
that were shown were not properly attributed to the worker that they
belong to.
In passing, we also now don't show Hash Aggregate properties for the
leader process when it did not contribute any work to the Hash Aggregate.
This can occur either during Parallel Append when only a parallel worker
worked on a given sub plan or with parallel_leader_participation set to
off. This aims to make the behavior of Hash Aggregate's EXPLAIN output
more similar to Sort's.
Reported-by: Justin Pryzby
Discussion: https://postgr.es/m/20200805012105.GZ28072%40telsasoft.com
Backpatch-through: 13, where the original breakage was introduced
There were various unnecessary differences between Hash Agg's EXPLAIN
ANALYZE output and Hash Join's. Here we modify the Hash Agg output so
that it's better aligned to Hash Join's.
The following changes have been made:
1. Start batches counter at 1 instead of 0.
2. Always display the "Batches" property, even when we didn't spill to
disk.
3. Use the text "Batches" instead of "HashAgg Batches" for text format.
4. Use the text "Memory Usage" instead of "Peak Memory Usage" for text
format.
5. Include "Batches" before "Memory Usage" in both text and non-text
formats.
In passing also modify the "Planned Partitions" property so that we show
it regardless of if the value is 0 or not for non-text EXPLAIN formats.
This was pointed out by Justin Pryzby and probably should have been part
of 40efbf870.
Reviewed-by: Justin Pryzby, Jeff Davis
Discussion: https://postgr.es/m/CAApHDvrshRnA6C0VFnu7Fb9TVvgGo80PUMm5+2DiaS1gEkPvtw@mail.gmail.com
Backpatch-through: 13, where HashAgg batching was introduced
The Sort node does not put a space between the number of kilobytes and
the "kB" of memory or disk space used, but HashAgg does. Here we align
HashAgg to do the same as Sort. Sort has been displaying this
information for longer than HashAgg, so it makes sense to align HashAgg
to Sort rather than the other way around.
Reported-by: Justin Pryzby
Discussion: https://postgr.es/m/20200708163021.GW4107@telsasoft.com
Backpatch-through: 13, where the hashagg started showing these details
The "Disk Usage" and "HashAgg Batches" properties in the EXPLAIN ANALYZE
output for HashAgg were previously only shown if the number of batches
was greater than 0. Here we change this so that these properties are
always shown for EXPLAIN ANALYZE formats other than "text". The idea here
is that since the HashAgg could have spilled to disk if there had been
more data or groups to aggregate, then it's relevant that we're clear in
the EXPLAIN ANALYZE output when no spilling occurred in this particular
execution of the given plan.
For the "text" EXPLAIN format, we still hide these properties when no
spilling occurs. This EXPLAIN format is designed to be easy for humans
to read. To maintain the readability we have a higher threshold for which
properties we display for this format.
Discussion: https://postgr.es/m/CAApHDvo_dmNozQQTmN-2jGp1vT%3Ddxx7Q0vd%2BMvD1cGpv2HU%3DSg%40mail.gmail.com
Backpatch-through: 13, where the hashagg spilling code was added.
explain_get_index_name() applied quote_identifier() to the index name.
This is fine for text output, but the non-text output formats all have
their own quoting conventions and would much rather start from the
actual index name. For example in JSON you'd get something like
"Index Name": "\"My Index\"",
which is surely not desirable, especially when the same does not
happen for table names. Hence, move the responsibility for applying
quoting out to the callers, where it can go into already-existing
special code paths for text format.
This changes the API spec for users of explain_get_index_name_hook:
before, they were supposed to apply quote_identifier() if necessary,
now they should not. Research suggests that the only publicly
available user of the hook is hypopg, and it actually forgot to
apply quoting anyway, so it's fine. (In any case, there's no
behavioral change for the output of a hook as seen in non-text
EXPLAIN formats, so this won't break any case that programs should
be relying on.)
Digging in the commit logs, it appears that quoting was included in
explain_get_index_name's duties when commit 604ffd280 invented it;
and that was fine at the time because we only had text output format.
This should have been rethought when non-text formats were invented,
but it wasn't.
This is a fairly clear bug for users of non-text EXPLAIN formats,
so back-patch to all supported branches.
Per bug #16502 from Maciek Sakrejda. Patch by me (based on
investigation by Euler Taveira); thanks to Julien Rouhaud for review.
Discussion: https://postgr.es/m/16502-57bd1c9f913ed1d1@postgresql.org
Since 1f39bce02, HashAgg nodes have had the ability to spill to disk when
memory consumption exceeds work_mem. That commit added new properties to
EXPLAIN ANALYZE to show the maximum memory usage and disk usage, however,
it didn't quite go as far as showing that information for parallel
workers. Since workers may have experienced something very different from
the main process, we should show this information per worker, as is done
in Sort.
Reviewed-by: Justin Pryzby
Reviewed-by: Jeff Davis
Discussion: https://postgr.es/m/CAApHDvpEKbfZa18mM1TD7qV6PG+w97pwCWq5tVD0dX7e11gRJw@mail.gmail.com
Backpatch-through: 13, where the hashagg spilling code was added.
Thomas Munro fixed a longstanding annoyance in pg_bsd_indent, that
it would misformat lines containing IsA() macros on the assumption
that the IsA() call should be treated like a cast. This improves
some other cases involving field/variable names that match typedefs,
too. The only places that get worse are a couple of uses of the
OpenSSL macro STACK_OF(); we'll gladly take that trade-off.
Discussion: https://postgr.es/m/20200114221814.GA19630@alvherre.pgsql
Includes some manual cleanup of places that pgindent messed up,
most of which weren't per project style anyway.
Notably, it seems some people didn't absorb the style rules of
commit c9d297751, because there were a bunch of new occurrences
of function calls with a newline just after the left paren, all
with faulty expectations about how the rest of the call would get
indented.
The explain format used by incremental sort was somewhat inconsistent
with other nodes, making it harder to parse and understand. This commit
addresses that by
- adding an extra space to better separate groups of values
- using colons instead of equal signs to separate key/value
- properly capitalizing first letter of a key
- using separate lines for full and pre-sorted groups
These changes were proposed by Justin Pryzby and mostly copy the final
explain format used to report WAL usage.
Author: Justin Pryzby
Reviewed-by: James Coleman
Discussion: https://postgr.es/m/20200419023625.GP26953@telsasoft.com
Incremental sort always processes at least one full group group before
switching to prefix groups, so it's enough to check just the number of
full groups. There was no risk of division by zero due to the extra
condition, but it made the code harder to understand.
Reported-by: Ranier Vilela
Discussion: https://postgr.es/m/CAEudQAp+7qoS92-4V1vLChpdY3vEkLCbf+gye6P-4cirE-0z0A@mail.gmail.com
In commit 33e05f89c5, we have added the option to display WAL usage
statistics in Explain and auto_explain. The display format used two spaces
between each field which is inconsistent with Buffer usage statistics which
is using one space between each field. Change the format to make WAL usage
statistics consistent with Buffer usage statistics.
This commit also changed the usage of "full page writes" to
"full page images" for WAL usage statistics to make it consistent with
other parts of code and docs.
Author: Julien Rouhaud, Amit Kapila
Reviewed-by: Justin Pryzby, Kyotaro Horiguchi and Amit Kapila
Discussion: https://postgr.es/m/CAB-hujrP8ZfUkvL5OYETipQwA=e3n7oqHFU=4ZLxWS_Cza3kQQ@mail.gmail.com
Before discarding the old hash table in ExecReScanHashJoin, capture
its statistics, ensuring that we report the maximum hashtable size
across repeated rescans of the hash input relation. We can repurpose
the existing code for reporting hashtable size in parallel workers
to help with this, making the patch pretty small. This also ensures
that if rescans happen within parallel workers, we get the correct
maximums across all instances.
Konstantin Knizhnik and Tom Lane, per diagnosis by Thomas Munro
of a trouble report from Alvaro Herrera.
Discussion: https://postgr.es/m/20200323165059.GA24950@alvherre.pgsql
It turns out that the code did indeed rely on a zeroed
TuplesortInstrumentation.sortMethod field to indicate
"this worker never did anything", although it seems the
issue only comes up during certain race-condition-y cases.
Hence, rearrange the TuplesortMethod enum to restore
SORT_TYPE_STILL_IN_PROGRESS to having the value zero,
and add some comments reinforcing that that isn't optional.
Also future-proof a loop over the possible values of the enum.
sizeof(bits32) happened to be the correct limit value,
but only by purest coincidence.
Per buildfarm and local investigation.
Discussion: https://postgr.es/m/12222.1586223974@sss.pgh.pa.us
Incremental Sort is an optimized variant of multikey sort for cases when
the input is already sorted by a prefix of the requested sort keys. For
example when the relation is already sorted by (key1, key2) and we need
to sort it by (key1, key2, key3) we can simply split the input rows into
groups having equal values in (key1, key2), and only sort/compare the
remaining column key3.
This has a number of benefits:
- Reduced memory consumption, because only a single group (determined by
values in the sorted prefix) needs to be kept in memory. This may also
eliminate the need to spill to disk.
- Lower startup cost, because Incremental Sort produce results after each
prefix group, which is beneficial for plans where startup cost matters
(like for example queries with LIMIT clause).
We consider both Sort and Incremental Sort, and decide based on costing.
The implemented algorithm operates in two different modes:
- Fetching a minimum number of tuples without check of equality on the
prefix keys, and sorting on all columns when safe.
- Fetching all tuples for a single prefix group and then sorting by
comparing only the remaining (non-prefix) keys.
We always start in the first mode, and employ a heuristic to switch into
the second mode if we believe it's beneficial - the goal is to minimize
the number of unnecessary comparions while keeping memory consumption
below work_mem.
This is a very old patch series. The idea was originally proposed by
Alexander Korotkov back in 2013, and then revived in 2017. In 2018 the
patch was taken over by James Coleman, who wrote and rewrote most of the
current code.
There were many reviewers/contributors since 2013 - I've done my best to
pick the most active ones, and listed them in this commit message.
Author: James Coleman, Alexander Korotkov
Reviewed-by: Tomas Vondra, Andreas Karlsson, Marti Raudsepp, Peter Geoghegan, Robert Haas, Thomas Munro, Antonin Houska, Andres Freund, Alexander Kuzmenkov
Discussion: https://postgr.es/m/CAPpHfdscOX5an71nHd8WSUH6GNOCf=V7wgDaTXdDd9=goN-gfA@mail.gmail.com
Discussion: https://postgr.es/m/CAPpHfds1waRZ=NOmueYq0sx1ZSCnt+5QJvizT8ndT2=etZEeAQ@mail.gmail.com
This commit adds a new option WAL similar to existing option BUFFERS in the
EXPLAIN command. This option allows to include information on WAL record
generation added by commit df3b181499 in EXPLAIN output.
This also allows the WAL usage information to be displayed via
the auto_explain module. A new parameter auto_explain.log_wal controls
whether WAL usage statistics are printed when an execution plan is logged.
This parameter has no effect unless auto_explain.log_analyze is enabled.
Author: Julien Rouhaud
Reviewed-by: Dilip Kumar and Amit Kapila
Discussion: https://postgr.es/m/CAB-hujrP8ZfUkvL5OYETipQwA=e3n7oqHFU=4ZLxWS_Cza3kQQ@mail.gmail.com
When BUFFERS option is enabled, EXPLAIN command includes the information
on buffer usage during each plan node, in its output. In addition to that,
this commit makes EXPLAIN command include also the information on
buffer usage during planning phase, in its output. This feature makes it
easier to discern the cases where lots of buffer access happen during
planning.
This commit revives the original commit ed7a509571 that was reverted by
commit 19db23bcbd. The original commit had to be reverted because
it caused the regression test failure on the buildfarm members prion and
dory. But since commit c0885c4c30 got rid of the caues of the test failure,
the original commit can be safely introduced again.
Author: Julien Rouhaud, slightly revised by Fujii Masao
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/16109-26a1a88651e90608@postgresql.org
When BUFFERS option is enabled, EXPLAIN command includes the information
on buffer usage during each plan node, in its output. In addition to that,
this commit makes EXPLAIN command include also the information on
buffer usage during planning phase, in its output. This feature makes it
easier to discern the cases where lots of buffer access happen during
planning.
Author: Julien Rouhaud, slightly revised by Fujii Masao
Reviewed-by: Justin Pryzby
Discussion: https://postgr.es/m/16109-26a1a88651e90608@postgresql.org
This commit adds query_string argument into the planner-related functions
and hook and allows us to pass the query string to them.
Currently there is no user of the query string passed. But the upcoming patch
for the planning counters will add the planning hook function into
pg_stat_statements and the function will need the query string. So this change
will be necessary for that patch.
Also this change is useful for some extensions that want to use the query
string in their planner hook function.
Author: Pascal Legrand, Julien Rouhaud
Reviewed-by: Yoshikazu Imai, Tom Lane, Fujii Masao
Discussion: https://postgr.es/m/CAOBaU_bU1m3_XF5qKYtSj1ua4dxd=FWDyh2SH4rSJAUUfsGmAQ@mail.gmail.com
Discussion: https://postgr.es/m/1583789487074-0.post@n3.nabble.com
While performing hash aggregation, track memory usage when adding new
groups to a hash table. If the memory usage exceeds work_mem, enter
"spill mode".
In spill mode, new groups are not created in the hash table(s), but
existing groups continue to be advanced if input tuples match. Tuples
that would cause a new group to be created are instead spilled to a
logical tape to be processed later.
The tuples are spilled in a partitioned fashion. When all tuples from
the outer plan are processed (either by advancing the group or
spilling the tuple), finalize and emit the groups from the hash
table. Then, create new batches of work from the spilled partitions,
and select one of the saved batches and process it (possibly spilling
recursively).
Author: Jeff Davis
Reviewed-by: Tomas Vondra, Adam Lee, Justin Pryzby, Taylor Vesely, Melanie Plageman
Discussion: https://postgr.es/m/507ac540ec7c20136364b5272acbcd4574aa76ef.camel@j-davis.com
Commit 499be013d added this field in a rather poorly-thought-through
manner, with the result being that rather than being a field of the
Append or MergeAppend plan node as intended (and as it seems to be,
in text format), it was actually an element of the "Plans" subgroup.
At least in JSON format, that's flat out invalid syntax, because
"Plans" is an array not an object.
While it's not hard to move the generation of the field so that it
appears where it's supposed to, this does result in a visible change
in field order in text format, in cases where a Append or MergeAppend
plan node has any InitPlans attached. That's slightly annoying to
do in stable branches; but the alternative of continuing to emit
broken non-text formats seems worse.
Also, since the set of fields emitted is not supposed to be
data-dependent in non-text formats, make sure that "Subplans Removed"
appears in Append and MergeAppend nodes even when it's zero, in those
formats. (The previous coding made it look like it could appear in
some other node types such as BitmapAnd, but we don't actually support
runtime pruning there, so don't emit it in those cases.)
Per bug #16171 from Mahadevan Ramachandran. Fix by Daniel Gustafsson
and Tom Lane, reviewed by Hamid Akhtar. Back-patch to v11 where this
code came in.
Discussion: https://postgr.es/m/16171-b72259ab75505fa2@postgresql.org
In non-TEXT output formats, the "Settings" field should appear when
requested, even if it would be empty.
Also, get rid of the premature optimization of counting all the
GUC_EXPLAIN variables at startup. Since there was no provision for
adjusting that count later, all it'd take would be some extension marking
a parameter as GUC_EXPLAIN to risk an assertion failure or memory stomp.
We could make get_explain_guc_options() count those variables on-the-fly,
or dynamically resize its array ... but TBH I do not think that making a
transient array of pointers a bit smaller is worth any extra complication,
especially when you consider all the other transient space EXPLAIN eats.
So just allocate that array at the max possible size.
In HEAD, also add some regression test coverage for this feature.
Because of the memory-stomp hazard, back-patch to v12 where this
feature was added.
Discussion: https://postgr.es/m/19416.1580069629@sss.pgh.pa.us
Previously, it was possible for EXPLAIN ANALYZE of a parallel query
to produce several different "Workers" fields for a single plan node,
because different portions of explain.c independently generated
per-worker data and wrapped that output in separate fields. This
is pretty bogus, especially for the structured output formats: even
if it's not technically illegal, most programs would have a hard time
dealing with such data.
To improve matters, add infrastructure that allows redirecting
per-worker values into a side data structure, and then collect that
data into a single "Workers" field after we've finished running all
the relevant code for a given plan node.
There are a few visible side-effects:
* In text format, instead of something like
Sort Method: external merge Disk: 4920kB
Worker 0: Sort Method: external merge Disk: 5880kB
Worker 1: Sort Method: external merge Disk: 5920kB
Buffers: shared hit=682 read=10188, temp read=1415 written=2101
Worker 0: actual time=130.058..130.324 rows=1324 loops=1
Buffers: shared hit=337 read=3489, temp read=505 written=739
Worker 1: actual time=130.273..130.512 rows=1297 loops=1
Buffers: shared hit=345 read=3507, temp read=505 written=744
you get
Sort Method: external merge Disk: 4920kB
Buffers: shared hit=682 read=10188, temp read=1415 written=2101
Worker 0: actual time=130.058..130.324 rows=1324 loops=1
Sort Method: external merge Disk: 5880kB
Buffers: shared hit=337 read=3489, temp read=505 written=739
Worker 1: actual time=130.273..130.512 rows=1297 loops=1
Sort Method: external merge Disk: 5920kB
Buffers: shared hit=345 read=3507, temp read=505 written=744
* When JIT is enabled, any relevant per-worker JIT stats are attached
to the child node of the Gather or Gather Merge node, which is where
the other per-worker output has always been. Previously, that info
was attached directly to a Gather node, or missed entirely for Gather
Merge.
* A query's summary JIT data no longer includes a bogus
"Worker Number: -1" field.
A notable code-level change is that indenting for lines of text-format
output should now be handled by calling "ExplainIndentText(es)",
instead of hard-wiring how much space to emit. This seems a good deal
cleaner anyway.
This patch also adds a new "explain.sql" regression test script that's
dedicated to testing EXPLAIN. There is more that can be done in that
line, certainly, but for now it just adds some coverage of the XML and
YAML output formats, which had been completely untested.
Although this is surely a bug fix, it's not clear that people would
be happy with rearranging EXPLAIN output in a minor release, so apply
to HEAD only.
Maciek Sakrejda and Tom Lane, based on an idea of Andres Freund's;
reviewed by Georgios Kokolatos
Discussion: https://postgr.es/m/CAOtHd0AvAA8CLB9Xz0wnxu1U=zJCKrr1r4QwwXi_kcQsHDVU=Q@mail.gmail.com
Alvaro Herrera
Tom Lane
Fujii Masao
David Rowley
Bruce Momjian
Etsuro Fujita
Heikki Linnakangas
Michael Paquier
Peter Eisentraut
Tomas Vondra
Amit Kapila
Jeff Davis