Re: multivariate statistics v14

Simon Riggs <simon@2ndquadrant.com>

From: Simon Riggs <simon@2ndQuadrant.com>
To: Tatsuo Ishii <ishii@postgresql.org>
Cc: Tomas Vondra <tomas.vondra@2ndquadrant.com>, Robert Haas <robertmhaas@gmail.com>, David Steele <david@pgmasters.net>, Tom Lane <tgl@sss.pgh.pa.us>, Álvaro Herrera <alvherre@2ndquadrant.com>, Petr Jelinek <petr@2ndquadrant.com>, Jeff Janes <jeff.janes@gmail.com>, PostgreSQL-development <pgsql-hackers@postgresql.org>
Date: 2016-04-10T08:25:48Z
Lists: pgsql-hackers
On 9 April 2016 at 18:37, Tatsuo Ishii <ishii@postgresql.org> wrote:

> > But I still think it wouldn't move the patch any closer to committable
> > state, because what it really needs is review whether the catalog
> > definition makes sense, whether it should be more like pg_statistic,
> > and so on. Only then it makes sense to describe the catalog structure
> > in the SGML docs, I think. That's why I added some basic SGML docs for
> > CREATE/DROP/ALTER STATISTICS, which I expect to be rather stable, and
> > not the catalog and other low-level stuff (which is commented heavily
> > in the code anyway).
>
> Without "user-level docs" (now I understand that the term means all
> SGML docs for you), it is very hard to find a visible
> characteristics/behavior of the patch. CREATE/DROP/ALTER STATISTICS
> just defines a user interface, and does not help how it affects to the
> planning. The READMEs do not help either.
>
> In this case reviewing your code is something like reviewing a program
> which has no specification.
>
> That's the reason why I said before below, but it was never seriously
> considered.
>

I would likely have said this myself but didn't even get that far.

Your contribution was useful and went further than anybody else's review,
so thank you.

-- 
Simon Riggs                http://www.2ndQuadrant.com/
<http://www.2ndquadrant.com/>
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

Commits

  1. Collect and use multi-column dependency stats

  2. Implement SortSupport for macaddr data type

  3. Implement multivariate n-distinct coefficients

  4. Generate fmgr prototypes automatically