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help / color / mirror / Atom feedFrom: Yugo Nagata <[email protected]>
To: Yugo Nagata <[email protected]>
Cc: Andres Freund <[email protected]>
Cc: [email protected]
Subject: Re: Allow to collect statistics on virtual generated columns
Date: Fri, 1 Aug 2025 00:28:30 +0900
Message-ID: <[email protected]> (raw)
In-Reply-To: <[email protected]>
References: <[email protected]>
<kp43uuykq64fyqxnlihq2rzntarextvzibuzturajfucl6bayo@6fcnflboemtt>
<[email protected]>
Hi,
On Tue, 24 Jun 2025 17:05:33 +0900
Yugo Nagata <[email protected]> wrote:
> Instead, I'm thinking of an alternative approach: expanding the expression
> at the time statistics are collected.
I've attached a new patch in this approache.
This allows to collect statistics on virtual generated columns.
During ANALYZE, generation expressions are expanded, and statistics are computed
using compute_expr_stats(). To support this, both compute_expr_stats() and AnlExprData
are now exported from extended_stats.c. However, since they are no longer specific
to extended statistics, it might be better to move them to analyze.c and vacuum.h.
To enable the optimizer to make use of these statistics, a new field named
virtual_gencols is added to RelOptInfo. This field holds the expressions of
virtual generated columns in the table. In examine_variable(), if an expression
in a WHERE clause matches a virtual generated column, the corresponding statistics
are used for that expression.
Example:
- Before applying the patch, the cardinality estimate is erroneous.
test=# create table t (i int, j int generated always as (i*10) virtual);
CREATE TABLE
test=# insert into t select generate_series(1,1000);
INSERT 0 1000
test=# insert into t select 1 from generate_series(1,1000);
INSERT 0 1000
test=# analyze t;
ANALYZE
test=# explain analyze select * from t where j = 10;
QUERY PLAN
-------------------------------------------------------------------------------------------------
Seq Scan on t (cost=0.00..0.02 rows=1 width=8) (actual time=0.031..0.806 rows=1001.00 loops=1)
Filter: ((i * 10) = 10)
Rows Removed by Filter: 999
Buffers: shared hit=9
Planning:
Buffers: shared hit=10
Planning Time: 0.299 ms
Execution Time: 0.948 ms
(8 rows)
- After applying the patch, the cardinality estimate is correct.
test=# analyze t;
ANALYZE
test=# explain analyze select * from t where j = 10;
QUERY PLAN
-----------------------------------------------------------------------------------------------------
Seq Scan on t (cost=0.00..41.50 rows=1001 width=8) (actual time=0.034..0.871 rows=1001.00 loops=1)
Filter: ((i * 10) = 10)
Rows Removed by Filter: 999
Buffers: shared hit=9
Planning:
Buffers: shared hit=6
Planning Time: 0.374 ms
Execution Time: 1.028 ms
(8 rows)
Note that the patch is still a work in progress, so documentation and tests are not included.
Regards,
Yugo Nagata
--
Yugo Nagata <[email protected]>
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