Re: Optimizing bulk update performance

On 27/04/13 12:14, Yang Zhang wrote:
> It currently takes up to 24h for us to run a large set of UPDATE
> statements on a database, which are of the form:
>
> UPDATE table SET field1 = constant1, field2 = constant2, ... WHERE
> id = constid
>
> (We're just overwriting fields of objects identified by ID.)
>
> The tables have handfuls of indices each and no foreign key constraints.
> No COMMIT is made till the end.
>
> It takes 2h to import a `pg_dump` of the entire DB. This seems like a
> baseline we should reasonably target.
>
> Short of producing a custom program that somehow reconstructs a dataset
> for Postgresql to re-import, is there anything we can do to bring the
> bulk UPDATE performance closer to that of the import? (This is an area
> that we believe log-structured merge trees handle well, but we're
> wondering if there's anything we can do within Postgresql.)
>
> Some ideas:
>
> - dropping all non-ID indices and rebuilding afterward?
> - increasing checkpoint_segments, but does this actually help sustained
> long-term throughput?
> - using the techniques mentioned here? (Load new data as table, then
> "merge in" old data where ID is not found in new data)
> <http://www.postgresql.org/message-id/3a0028490809301807j59498370m1442d8f5867e9668@mail.gmail.com>
>
> Basically there's a bunch of things to try and we're not sure what the
> most effective are or if we're overlooking other things. We'll be
> spending the next few days experimenting, but we thought we'd ask here
> as well.
>
> Thanks.
>
>
People will need to know your version of Postgres & which Operating
System etc. plus details of CPU RAM, and Disks... AS well as what
changes you have made to postgresql.conf...

I would be inclined to DROP all indexes and reCREATE them later.

Updating a row might lead to new row being added in a new disk page, so
I suspect that updates will hit every index associated with the table
with the (possible exception of partial indexes).

Running too many updates in one transaction, maymean that Postgres may
need to use disk work files.

Depending on RAM etc, it may pay to increase some variables tat affect
how Postgres uses RAM, some of theseare per session.

Cheers,
Gavin