On 2018-10-10 17:19:50 -0400, Ravi Krishna wrote:
> > On Oct 10, 2018, at 17:18 , Andres Freund <andres@anarazel.de> wrote:
> > On October 10, 2018 2:15:19 PM PDT, Ravi Krishna <srkrishna1@aol.com> wrote:
> >> If I have a large file with say 400 million rows, can I first split it
> >> into 10 files of 40 million rows each and then fire up 10 different
> >> COPY sessions , each reading from a split file, but copying into the
> >> same table. I thought not. It will be great if we can do this.
> >
> > Yes, you can.
> >
> Thank you. Let me test it and see the benefit. We have a use case for this.
You should of course test this on your own hardware with your own data,
but here are the results of a simple benchmark (import 1 million rows
into a table without indexes via different methods) I ran a few weeks
ago on one of our servers:
https://github.com/hjp/dbbench/blob/master/import_pg_comparison/results/claudrin.2018-09-22/results.png
y axis is rows per second. x axis are different runs, sorted from
slowest to fastest (so 2 is the median).
As you can see it doesn't parallelize perfectly: 2 copy processes are
only about 50 % faster than 1, and 4 are about 33 % faster than 2. But
there is a still quite a respectable performance boost.
hp
PS: The script is of course in the same repo, but I didn't include the
test data because I don't think I'm allowed to include that.
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