Re: Details about pg_stat_bgwriter - Mailing list pgsql-admin

Greg Smith wrote:
> You don't much with a single snapshot of pg_stat_bgwriter data.  Try
> saving this instead:
> select *,now() from pg_stat_bgwriter;
> And then take another snapshot at least a few hours later, preferably
> the next day.  With two snapshots and timestamps on them, then it's
> possible to make some sense of the numbers.

I probably should have explained the next part.  I've now shared what I
do with this information at
http://www.pgcon.org/2010/schedule/events/218.en.html

Basically, if you put the data from the two snapshots into one of the
Statistics Spreadsheet versions, you'll get several derived numbers that
pop out:

-Average checkpoint frequency
-Average size of each checkpoint
-Average rate at which new buffers are allocated
-Average rate of writes out of the buffer cache
-Percentage of writes done by checkpoints, the background writer LRU
cleaner, and client backends

These are the sort of things you can actually think about in useful
real-world terms.  And if you tune the database by doing things like
increasing checkpoint_segments/checkpoint_timeout or changing the size
of shared_buffers, you can see what impact that has on the overall
system response, by noting how it changes these numeric measures.  It's
a bit more exact of an approach for tuning such things than what people
have traditionally done here.  Increasing shared_buffers and watching
the total writes/second number drop significantly is more exact than
just nothing whether the server waiting for I/O percentage dropped or not.

I also highly recommend installing pg_buffercache and looking at what's
inside your buffer cache too--the data actually being cached by the
server never fails to surprise people, relative to what they expected
was being cached.  Source for how to analyze those things is in the
pg_buffercache samples shell script, the "Buffer contents summary, with
percentages" is the one that gives useful output.  One of the thing I
did in the presentation that does not come through on the slides is note
how the distribution of high usage count data in your buffer cache
determines whether you will benefit from making it large or not.  I've
reduced this to a single figure of merit now:  "cumulative % of buffers
that have a usage count of >=2".  On a system that benefits from having
a big buffer cache, that number will be quite large (86% in the first of
the samples in the spreadsheet).  On one that doesn't, it will be small
(35% on the last system listed there).  You really have to tune those
two types of workload differently.

--
Greg Smith  2ndQuadrant US  Baltimore, MD
PostgreSQL Training, Services and Support
greg@2ndQuadrant.com   www.2ndQuadrant.us