On 7th of April 2018, Alvaro Herrera committed patch:
Support partition pruning at execution time
Existing partition pruning is only able to work at plan time, for query
quals that appear in the parsed query. This is good but limiting, as
there can be parameters that appear later that can be usefully used to
further prune partitions.
This commit adds support for pruning subnodes of Append which cannot
possibly contain any matching tuples, during execution, by evaluating
Params to determine the minimum set of subnodes that can possibly match.
We support more than just simple Params in WHERE clauses. Support
1. Parameterized Nested Loop Joins: The parameter from the outer side of the
join can be used to determine the minimum set of inner side partitions to
2. Initplans: Once an initplan has been executed we can then determine which
partitions match the value from the initplan.
Partition pruning is performed in two ways. When Params external to the plan
are found to match the partition key we attempt to prune away unneeded Append
subplans during the initialization of the executor. This allows us to bypass
the initialization of non-matching subplans meaning they won't appear in the
EXPLAIN or EXPLAIN ANALYZE output.
For parameters whose value is only known during the actual execution
then the pruning of these subplans must wait. Subplans which are
eliminated during this stage of pruning are still visible in the EXPLAIN
output. In order to determine if pruning has actually taken place, the
EXPLAIN ANALYZE must be viewed. If a certain Append subplan was never
executed due to the elimination of the partition then the execution
timing area will state "(never executed)". Whereas, if, for example in
the case of parameterized nested loops, the number of loops stated in
the EXPLAIN ANALYZE output for certain subplans may appear lower than
others due to the subplan having been scanned fewer times. This is due
to the list of matching subnodes having to be evaluated whenever a
parameter which was found to match the partition key changes.
This commit required some additional infrastructure that permits the
building of a data structure which is able to perform the translation of
the matching partition IDs, as returned by get_matching_partitions, into
the list index of a subpaths list, as exist in node types such as
Append, MergeAppend and ModifyTable. This allows us to translate a list
of clauses into a Bitmapset of all the subpath indexes which must be
included to satisfy the clause list.
Author: David Rowley, based on an earlier effort by Beena Emerson
Reviewers: Amit Langote, Robert Haas, Amul Sul, Rajkumar Raghuwanshi,
Continue reading Waiting for PostgreSQL 11 – Support partition pruning at execution time
On 28th of March 2018, Andrew Dunstan committed patch:
Fast ALTER TABLE ADD COLUMN with a non-NULL default
Currently adding a column to a table with a non-NULL default results in
a rewrite of the table. For large tables this can be both expensive and
disruptive. This patch removes the need for the rewrite as long as the
default value is not volatile. The default expression is evaluated at
the time of the ALTER TABLE and the result stored in a new column
(attmissingval) in pg_attribute, and a new column (atthasmissing) is set
to true. Any existing row when fetched will be supplied with the
attmissingval. New rows will have the supplied value or the default and
so will never need the attmissingval.
Any time the table is rewritten all the atthasmissing and attmissingval
settings for the attributes are cleared, as they are no longer needed.
The most visible code change from this is in heap_attisnull, which
acquires a third TupleDesc argument, allowing it to detect a missing
value if there is one. In many cases where it is known that there will
not be any (e.g. catalog relations) NULL can be passed for this
Andrew Dunstan, heavily modified from an original patch from Serge
Reviewed by Tom Lane, Andres Freund, Tomas Vondra and David Rowley.
Continue reading Waiting for PostgreSQL 11 – Fast ALTER TABLE ADD COLUMN with a non-NULL default
Support parallel btree index builds.
To make this work, tuplesort.c and logtape.c must also support
parallelism, so this patch adds that infrastructure and then applies
it to the particular case of parallel btree index builds. Testing
to date shows that this can often be 2-3x faster than a serial
The model for deciding how many workers to use is fairly primitive
at present, but it's better than not having the feature. We can
refine it as we get more experience.
Peter Geoghegan with some help from Rushabh Lathia. While Heikki
Linnakangas is not an author of this patch, he wrote other patches
without which this feature would not have been possible, and
therefore the release notes should possibly credit him as an author
of this feature. Reviewed by Claudio Freire, Heikki Linnakangas,
Thomas Munro, Tels, Amit Kapila, me.
Continue reading Waiting for PostgreSQL 11 – Support parallel btree index builds.
I missed it completely, but on 24th of March 2017, Alvaro Herrera committed patch:
Implement multivariate n-distinct coefficients
Add support for explicitly declared statistic objects (CREATE
STATISTICS), allowing collection of statistics on more complex
combinations that individual table columns. Companion commands DROP
STATISTICS and ALTER STATISTICS ... OWNER TO / SET SCHEMA / RENAME are
added too. All this DDL has been designed so that more statistic types
can be added later on, such as multivariate most-common-values and
multivariate histograms between columns of a single table, leaving room
for permitting columns on multiple tables, too, as well as expressions.
This commit only adds support for collection of n-distinct coefficient
on user-specified sets of columns in a single table. This is useful to
estimate number of distinct groups in GROUP BY and DISTINCT clauses;
estimation errors there can cause over-allocation of memory in hashed
aggregates, for instance, so it's a worthwhile problem to solve. A new
special pseudo-type pg_ndistinct is used.
(num-distinct estimation was deemed sufficiently useful by itself that
this is worthwhile even if no further statistic types are added
immediately; so much so that another version of essentially the same
functionality was submitted by Kyotaro Horiguchi:
though this commit does not use that code.)
Author: Tomas Vondra. Some code rework by Álvaro.
Afterwards, there were couple more commits related to it:
- On 5th of April 2017, patch committed by Simon Riggs
- On 17th of April 2017, patch committed by Alvaro Herrera
- On 12nd of May 2017, patch committed by Alvaro Herrera
Continue reading Waiting for PostgreSQL 10 – Implement multivariate n-distinct coefficients
Lately at least two people on irc asked questions similar to “how do I know how many queries there are in database, per second?“.
So, let's see what we can find out.
Continue reading What's happening in my database?
On 7th of April, Teodor Sigaev committed patch:
Phrase full text search.
Patch introduces new text search operator (<-> or <DISTANCE>) into tsquery.
On-disk and binary in/out format of tsquery are backward compatible.
It has two side effect:
- change order for tsquery, so, users, who has a btree index over tsquery,
should reindex it
- less number of parenthesis in tsquery output, and tsquery becomes more
Authors: Teodor Sigaev, Oleg Bartunov, Dmitry Ivanov
Reviewers: Alexander Korotkov, Artur Zakirov
Continue reading Waiting for 9.6 – Phrase full text search.
On 1st of April, Teodor Sigaev committed patch:
Bloom index contrib module
Module provides new access method. It is actually a simple Bloom filter
implemented as pgsql's index. It could give some benefits on search
with large number of columns.
Module is a single way to test generic WAL interface committed earlier.
Author: Teodor Sigaev, Alexander Korotkov
Reviewers: Aleksander Alekseev, Michael Paquier, Jim Nasby
Continue reading Waiting for 9.6 – Bloom index contrib module
On 21st of March, Robert Haas committed patch:
Support parallel aggregation.
Parallel workers can now partially aggregate the data and pass the
transition values back to the leader, which can combine the partial
results to produce the final answer.
David Rowley, based on earlier work by Haribabu Kommi. Reviewed by
Álvaro Herrera, Tomas Vondra, Amit Kapila, James Sewell, and me.
Continue reading Waiting for 9.6 – Support parallel aggregation.
On 11th of February, Tom Lane committed patch:
Remove GROUP BY columns that are functionally dependent on other columns.
If a GROUP BY clause includes all columns of a non-deferred primary key,
as well as other columns of the same relation, those other columns are
redundant and can be dropped from the grouping; the pkey is enough to
ensure that each row of the table corresponds to a separate group.
Getting rid of the excess columns will reduce the cost of the sorting or
hashing needed to implement GROUP BY, and can indeed remove the need for
a sort step altogether.
This seems worth testing for since many query authors are not aware of
the GROUP-BY-primary-key exception to the rule about queries not being
allowed to reference non-grouped-by columns in their targetlists or
HAVING clauses. Thus, redundant GROUP BY items are not uncommon. Also,
we can make the test pretty cheap in most queries where it won't help
by not looking up a rel's primary key until we've found that at least
two of its columns are in GROUP BY.
David Rowley, reviewed by Julien Rouhaud
Continue reading Waiting for 9.6 – Remove GROUP BY columns that are functionally dependent on other columns.
On 20th of January, Robert Haas committed patch:
The core innovation of this patch is the introduction of the concept
of a partial path; that is, a path which if executed in parallel will
generate a subset of the output rows in each process. Gathering a
partial path produces an ordinary (complete) path. This allows us to
generate paths for parallel joins by joining a partial path for one
side (which at the baserel level is currently always a Partial Seq
Scan) to an ordinary path on the other side. This is subject to
various restrictions at present, especially that this strategy seems
unlikely to be sensible for merge joins, so only nested loops and
hash joins paths are generated.
This also allows an Append node to be pushed below a Gather node in
the case of a partitioned table.
Testing revealed that early versions of this patch made poor decisions
in some cases, which turned out to be caused by the fact that the
original cost model for Parallel Seq Scan wasn't very good. So this
patch tries to make some modest improvements in that area.
There is much more to be done in the area of generating good parallel
plans in all cases, but this seems like a useful step forward.
Patch by me, reviewed by Dilip Kumar and Amit Kapila.
Continue reading Waiting for 9.6 – Support parallel joins, and make related improvements.