Horton

Re: Default setting for enable_hashagg_disk

Peter Geoghegan <pg@bowt.ie>

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From: Peter Geoghegan <pg@bowt.ie>
To: Tomas Vondra <tomas.vondra@2ndquadrant.com>
Cc: Robert Haas <robertmhaas@gmail.com>, Tom Lane <tgl@sss.pgh.pa.us>, Jeff Davis <pgsql@j-davis.com>, Alvaro Herrera <alvherre@2ndquadrant.com>, David Rowley <dgrowleyml@gmail.com>, Stephen Frost <sfrost@snowman.net>, Andres Freund <andres@anarazel.de>, Bruce Momjian <bruce@momjian.us>, Justin Pryzby <pryzby@telsasoft.com>, Melanie Plageman <melanieplageman@gmail.com>, "pgsql-hackers@postgresql.org" <pgsql-hackers@postgresql.org>
Date: 2020-07-25T16:39:50Z
Lists: pgsql-hackers, pgsql-docs

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On Fri, Jul 24, 2020 at 12:55 PM Peter Geoghegan <pg@bowt.ie> wrote:
> Could that be caused by clustering in the data?
>
> If the input data is in totally random order then we have a good
> chance of never having to spill skewed "common" values. That is, we're
> bound to encounter common values before entering spill mode, and so
> those common values will continue to be usefully aggregated until
> we're done with the initial groups (i.e. until the in-memory hash
> table is cleared in order to process spilled input tuples). This is
> great because the common values get aggregated without ever spilling,
> and most of the work is done before we even begin with spilled tuples.
>
> If, on the other hand, the common values are concentrated together in
> the input...

I still don't know if that was a factor in your example, but I can
clearly demonstrate that the clustering of data can matter a lot to
hash aggs in Postgres 13. I attach a contrived example where it makes
a *huge* difference.

I find that the sorted version of the aggregate query takes
significantly longer to finish, and has the following spill
characteristics:

"Peak Memory Usage: 205086kB  Disk Usage: 2353920kB  HashAgg Batches: 2424"

Note that the planner doesn't expect any partitions here, but we still
get 2424 batches -- so the planner seems to get it totally wrong.
OTOH, the same query against a randomized version of the same data (no
longer in sorted order, no clustering) works perfectly with the same
work_mem (200MB):

"Peak Memory Usage: 1605334kB"

Hash agg avoids spilling entirely (so the planner gets it right this
time around). It even uses notably less memory.

-- 
Peter Geoghegan

Commits

  1. Add hash_mem_multiplier GUC.

  2. HashAgg: use better cardinality estimate for recursive spilling.

  3. Remove hashagg_avoid_disk_plan GUC.

  4. Doc fixup for hashagg_avoid_disk_plan GUC.

  5. Rework HashAgg GUCs.

  6. Disk-based Hash Aggregation.

  7. Implement partition-wise grouping/aggregation.

  8. Defer creation of partially-grouped relation until it's needed.