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From: Sami Imseih <samimseih@gmail.com>
Date: Tue, 17 Feb 2026 17:59:12 -0600
Message-ID: <CAA5RZ0tiUbG5RNK-Es+=R9RGpUkCBeJ_WQA5GzpAD3QiDgS3MA@mail.gmail.com>
Subject: Re: [PATCH] Add last_executed timestamp to pg_stat_statements
To: Pavlo Golub <pavlo.golub@cybertec.at>
Cc: Christoph Berg <myon@debian.org>, Bertrand Drouvot <bertranddrouvot.pg@gmail.com>, 
	"pgsql-hackers@lists.postgresql.org" <pgsql-hackers@lists.postgresql.org>
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Precedence: bulk

> > I am not sure if this benchmark is enough to build confidence in this approach.
> > Workloads with nested queries and tack. 'all' are now paying an even heavier
> > cost.
>
> I would love to see and test sql that will reveal the bigger overhead.
> Do you have something specific on your mind? I'd love to share
> results, we can use them later considering further updates to pgss.

Here is something I was experimenting with today. I ran 2
benchmarks; one on HEAD and one with GetCurrentTimestamp()
added when we are accumulating stats.

"""
    /* Increment the counts, except when jstate is not NULL */
    if (!jstate)
    {
        Assert(kind == PGSS_PLAN || kind == PGSS_EXEC);

        GetCurrentTimestamp();
"""

The benchmak script is a series of "SELECT;"

# select_tx.sql
"""
begin;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
select;
end;
"""

The benchmark was on my Ubuntu on EC2 c5a.12xlarge,
with default pg_stat_statements settings ( no plan tracking
and top tracking only ).

pgbench command:
```
pgbench -c48 -j16 -P1 -f select_tx.sql -T120
```

Results for 3 runs

## HEAD
tps = 29351.794589 (without initial connection time)
tps = 29470.287111 (without initial connection time)
tps = 29902.245458 (without initial connection time)

## with GetCurrentTimestamp()
tps = 28569.471891 (without initial connection time)
tps = 28013.051778 (without initial connection time)
tps = 28518.468843 (without initial connection time)

I see around 4-5% performance degradation.


--
Sami Imseih
Amazon Web Services (AWS)