Hi
I have now tested through insert and the updated and it works extremely good
with out doing any partitioning on big table (428.812.8392 rows) this case,
when we used a common index as Tom Suggested.
We are able to insert 172.000 rows pr. second.
The number of rows are competed based total time from when we start to read the
csv files and until the last file is done. We use GNU parallel and run 5
threads. The number of inserts are actually 172.000 * 2 because first we copy
the rows into a temp table and there we prepare the date and then insert them
into the common big main table. There is no errors in the log.
We are able update 98.000 rows pr, second.
Since each update also means one insert we are close 200.000 inserts and
updates pr. second. For update we give a column column that is null a value.
Thats is done for all the 4.3 billions rows. We run 5 threads in parallel here
also, and there is no error and no dead locks.
To get around the problem with duplication of indexes it's solvable in this
project because first we add date and then we do analyses, this means that we
can have different indexes when adding data and we are using them.
In this project we going add about 25 billions geo located observations which
which will be used for doing analyses. I suppose that we at some level have to
do partitioning but so far Postgres has worked extremely well even if it's
based on MVCC.
Postgres/Postgis software and communities are sure for sure really fun to work
with Postgres/Postgis open source software hold a very high quality.
Thanks.
Lars
________________________________________
Fra: pgsql-performance-ow...@postgresql.org
<pgsql-performance-ow...@postgresql.org> på vegne av Tom Lane
<t...@sss.pgh.pa.us>
Sendt: 24. oktober 2016 14:52
Til: Lars Aksel Opsahl
Kopi: pgsql-performance@postgresql.org
Emne: Re: [PERFORM] Fast insert, but slow join and updates for table with 4
billion rows
Lars Aksel Opsahl <lars.ops...@nibio.no> writes:
> In this example I have two tables one with 4 billion rows and another with
> 50000 rows and then I try to do a standard simple join between this two
> tables and this takes 397391 ms. with this SQL (the query plan is added is
> further down)
This particular query would work a lot better if you had an index on
nora_bc25_observation (point_uid_ref, epoch), ie both join columns
in one index. I get the impression that that ought to be the primary
key of the table, which would be an even stronger reason to have a
unique index on it.
regards, tom lane
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