Re: Qual push down to table AM - Mailing list pgsql-hackers

On Thu, 28 Aug 2025 at 01:27, Julien Tachoires <julien@tachoires.me> wrote:
>
> Hi,
>
> Please find attached a patch set proposal intended to implement WHERE
> clauses (qual) push down to the underlying table AM during
> table/sequential scan execution.
>
> The primary goal of this project is to convert quals to ScanKeys and
> pass them to the table AMs. Table AMs are then allowed to apply early
> tuple filtering during table (sequential) scans. Applying filtering at
> the table storage level is something necessary for non row-oriented
> table storage like columnar storage. Index organized table is another
> table storage that would need quals push down.
>
> AFAIK, CustomScan is the one and only way to go for having table scan
> using quals pushed down, but each table AM must implement its own
> mechanism. IMHO, having this feature available in core would help the
> development of new table AMs. About Heap, some performance testing
> (detailed at the end of this message) shows between 45% and 60%
> improvement in seq scan execution time when only one tuple is returned
> from the table.
>
> Only a few expressions are supported: OpExpr (<key> <operator> <value>),
> ScalarArrayOpExpr (<key> <operator> ANY|ALL(ARRAY[...]), and NullTest.
> Row comparison is not yet supported as this part is still not clear to
> me. On the right part of the expression, we support: constant, variable,
> function call, and subquery (InitPlan only).
>
> In terms of security, we check if the function related to the operator
> is not user defined: only functions from the catalog are supported. We
> also check that the function is "leakproof".
>
> Pushing down quals does not guaranty to the executor that the tuples
> returned during table scan satisfy a qual, as we don't know if the table
> AM (potentially implemented via an extension) has applied tuple
> filtering. In order to ensure to produce the right response to the where
> clause, pushed down quals are executed twice per tuple returned: once by
> the table AM, and once by the executor. This produces a performance
> regression (15-17%) where almost the entire table is returned (see perf.
> test results at the end of the message). This could be optimized by
> flagging the tuples filtered by the table AM, this way we could avoid
> the re-execution of the pushed down quals.
>
>
> Details about the patch files
>
> v1-0001-Pass-the-number-of-ScanKeys-to-scan_rescan.patch: This patch
> adds the number of ScanKeys passed via scan_rescan() as a new argument.
> The number of ScanKeys was only passed to the table AM via begin_scan(),
> but not in scan_rescan().
>
> v1-0002-Simple-quals-push-down-to-table-AMs.patch: Core of the feature,
> this patch adds qual push down support for OpExpr expressions.
>
> v1-0003-Add-the-table-reloption-quals_push_down.patch: Adds a new
> reloption: quals_push_down used to enable/disable qual push down for a
> table. Disabled by default.
>
> v1-0004-Add-tests-for-quals-push-down-to-table-AM.patch: Regression
> tests.
>
> v1-0005-Push-down-IN-NOT-IN-array-quals-to-table-AMs.patch:
> ScalarArrayOpExpr support.
>
> v1-0006-Push-down-IS-IS-NOT-NULL-quals-to-table-AMs.patch: NullTest
> support.
>
>
> Performance testing
>
> Head:
> CREATE TABLE t (i INTEGER);
>
> Patch:
> CREATE TABLE t (i INTEGER) WITH (quals_push_down = on);
>
> n=1M:
> INSERT INTO t SELECT generate_series(1, 1000000);
> VACUUM t;
>
> n=10M:
> TRUNCATE t;
> INSERT INTO t SELECT generate_series(1, 10000000);
> VACUUM t;
>
> n=100M:
> TRUNCATE t;
> INSERT INTO t SELECT generate_series(1, 100000000);
> VACUUM t;
>
>
> Case #1: SELECT COUNT(*) FROM t WHERE i = 50000;
>
>         |       n=1M      |        n=10M      |         n=100M
>         +--------+--------+---------+---------+----------+---------
>         |  Head  |  Patch |  Head   |  Patch  |  Head    |  Patch
> --------+--------+--------+---------+---------+----------+---------
> Test #1 | 38.903 | 21.308 | 365.707 | 155.429 | 3939.937 | 1564.182
> Test #2 | 39.239 | 21.271 | 364.206 | 153.127 | 3872.370 | 1527.988
> Test #3 | 39.015 | 21.958 | 365.434 | 154.498 | 3812.382 | 1525.535
> --------+--------+--------+---------+---------+----------+---------
>
> --------+--------+--------+---------+---------+----------+---------
> Average | 39.052 | 21.512 | 365.116 | 154.351 | 3874.896 | 1539.235
> Std dev |  0.171 |  0.386 |   0.800 |   1.158 |   63.815 |   21.640
> --------+--------+--------+---------+---------+----------+---------
> Gain    |          44.91% |            57.73% |             60.28%
>
>
> Case #2: SELECT COUNT(*) FROM t WHERE i >= 2;
>
>         |       n=1M      |        n=10M      |         n=100M
>         +--------+--------+---------+---------+----------+---------
>         |  Head  |  Patch |  Head   |  Patch  |  Head    |  Patch
> --------+--------+--------+---------+---------+----------+---------
> Test #1 | 68.422 | 81.233 | 674.397 | 778.427 | 6845.165 | 8071.627
> Test #2 | 69.237 | 80.868 | 682.976 | 774.417 | 6533.091 | 7668.477
> Test #3 | 69.579 | 80.418 | 676.072 | 791.465 | 6917.964 | 7916.182
> --------+--------+--------+---------+---------+----------+---------
>
> --------+--------+--------+---------+---------+----------+---------
> Average | 69.079 | 80.840 | 677.815 | 781.436 | 6765.407 | 7885.429
> Std dev |  0.594 |  0.408 |   4.547 |   8.914 |  204.457 |  203.327
> --------+--------+--------+---------+---------+----------+---------
> Gain    |         -17.02% |           -15.29% |            -16.56%
>
>
> Thoughts?
>
> Best regards,
>
> --
> Julien Tachoires

Hi!
I was also always wondering if something like quals pushing can be
implemented in Postgres. It is indeed very beneficial for Column-based
processing in MPP databases, Greenplum and Cloudberry to name a few. I
did my own micro-research a while ago (while working on some
Cloudberry features), so here are my thoughts on the subject.

What this patchset is doing, is passing ScanKeys directly to tableam
somewhat blindly. In speedups processing execution-phase. While I do
not have strong objections against this approach, I suspect this
breaks some layers of abstractions and *silent* (or maybe documented)
agreements of what are responsibilities of TableAM functions. So,
passing ScanKeys directly to TAM is used on HEAD for catalog-access
only. Correct me if I'm wrong. For all other types of relation each
query is planned, which includes

(1) building data access patch thought various data access methods (indexes)
(2) Decide for each Qual which indexes can be used to satisfy this qual
(3) Using Cost Model for filtering best options

All of this can not be done with your approach?

Cost model can give hints to the optimizer that this TAM will process
some qual much faster than any by-index access. Smart cost
model/optimizer can realise that selecting only few of all attributes
from column-orietired relation + filter when using SIMD etc can be
really cheap.

So maybe the good shape of this patch would be something that could
choose between seqscan and indexscan in planner time?

-- 
Best regards,
Kirill Reshke