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From: Chanukya SDS <chanukyasds@gmail.com>
Date: Fri, 17 Oct 2025 11:23:47 +0530
Message-ID: <CAB4f4B5cXFEGr09MUgyFxeoa-QBEqMoNUp7HTpNey7G96Jt6qw@mail.gmail.com>
Subject: pg_running_stats - mergeable running statistics (Welford/Chan)
 extension for postgresql
To: pgsql-general@lists.postgresql.org
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Hi all,

I=E2=80=99d like to share a new PostgreSQL extension called pg_running_stat=
s.

It implements mergeable, numerically stable running statistics using the
Welford and Chan algorithms.
Unlike the built-in aggregates such as avg(), variance(), and stddev(),
which require scanning the entire dataset, pg_running_stats maintains a
compact internal state that can be updated or merged incrementally.

This makes it well-suited for:

   1. streaming or real-time analytics where data arrives continuously,
   2. incremental computation over large tables,
   3. parallel or distributed queries that need to merge partial aggregates
   efficiently.


The extension computes:
mean, variance, standard deviation, skewness, kurtosis, and min/max all in
a single pass.

It=E2=80=99s written entirely in C, depends only on PostgreSQL headers, and=
 builds
cleanly on macOS (Homebrew) and Linux using PGXS.

Source and documentation:
https://github.com/chanukyasds/pg_running_stats

Any feedback, testing, or suggestions for improvement would be very welcome=
.

Thanks,
Chanukya

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<div dir=3D"auto"><span style=3D"font-family:arial,sans-serif">Hi all,</spa=
n><br></div><div dir=3D"auto"><div class=3D"gmail_quote gmail_quote_contain=
er"><div dir=3D"ltr"><font face=3D"arial, sans-serif" style=3D"font-family:=
arial,sans-serif;color:rgb(0,0,0)"><br>I=E2=80=99d like to share a new Post=
greSQL extension called pg_running_stats.<br><br>It implements mergeable, n=
umerically stable running statistics using the Welford and Chan algorithms.=
 =C2=A0<br>Unlike the built-in aggregates such as avg(), variance(), and st=
ddev(), which require scanning the entire dataset, pg_running_stats maintai=
ns a compact internal state that can be updated or merged incrementally.<br=
><br>This makes it well-suited for:<br></font><ol><li><font face=3D"arial, =
sans-serif" style=3D"font-family:arial,sans-serif;color:rgb(0,0,0)">streami=
ng or real-time analytics where data arrives continuously,<br></font></li><=
li><font face=3D"arial, sans-serif" style=3D"font-family:arial,sans-serif;c=
olor:rgb(0,0,0)">incremental computation over large tables,</font></li><li>=
<font face=3D"arial, sans-serif" style=3D"font-family:arial,sans-serif;colo=
r:rgb(0,0,0)">parallel or distributed queries that need to merge partial ag=
gregates efficiently.</font></li></ol><font face=3D"arial, sans-serif" styl=
e=3D"font-family:arial,sans-serif;color:rgb(0,0,0)"><br>The extension compu=
tes:<br>mean, variance, standard deviation, skewness, kurtosis, and min/max=
 all in a single pass.<br><br>It=E2=80=99s written entirely in C, depends o=
nly on PostgreSQL headers, and builds cleanly on macOS (Homebrew) and Linux=
 using PGXS.<br><br>Source and documentation:<br></font><font face=3D"arial=
, sans-serif" style=3D"font-family:arial,sans-serif;color:rgb(0,0,0)"><a hr=
ef=3D"https://github.com/chanukyasds/pg_running_stats" target=3D"_blank" st=
yle=3D"font-family:arial,sans-serif">https://github.com/chanukyasds/pg_runn=
ing_stats</a><br><br>Any feedback, testing, or suggestions for improvement =
would be very welcome.<br><br>Thanks,<br>Chanukya</font><br></div>
</div></div>

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