On 09/24/2015 07:42 PM, Robert Haas wrote:
On Thu, Sep 24, 2015 at 12:40 PM, Tomas Vondra
<tomas.von...@2ndquadrant.com> wrote:
There are two machines - one with 32GB of RAM and work_mem=2GB, the other
one with 256GB of RAM and work_mem=16GB. The machines are hosting about the
same data, just scaled accordingly (~8x more data on the large machine).

Let's assume there's a significant over-estimate - we expect to get about
10x the actual number of tuples, and the hash table is expected to almost
exactly fill work_mem. Using the 1:3 ratio (as in the query at the beginning
of this thread) we'll use ~512MB and ~4GB for the buckets, and the rest is
for entries.

Thanks to the 10x over-estimate, ~64MB and 512MB would be enough for the
buckets, so we're wasting ~448MB (13% of RAM) on the small machine and
~3.5GB (~1.3%) on the large machine.

How does it make any sense to address the 1.3% and not the 13%?

One of us is confused, because from here it seems like 448MB is 1.3%
of 32GB, not 13%.

Meh, you're right - I got the math wrong. It's 1.3% in both cases.

However the question still stands - why should we handle the over-estimate in one case and not the other? We're wasting the same fraction of memory in both cases.


Tomas Vondra                  http://www.2ndQuadrant.com
PostgreSQL Development, 24x7 Support, Remote DBA, Training & Services

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