Having been running PostgreSQL 8.4 for a number of years in production (if it ain’t broke…) the time came to do an upgrade (8.4 on Centos 5 was getting a bit dated). We decided, after some discussion, to take up our hosting provider on a managed HA Windows cluster with shared storage and a redundant slave replica. This was mainly to reduce our own internal server management overhead, as they would be taking care of all parts of the servers’ maintenance.
This all sounded great. After some tests at moderate load, we migrated the databases one by one via a piped pg_dump/pg_restore, which was simplicity itself apart from the outage. But then, we started getting some reports of slowness, which was worrying. I updated the application to log slow queries (easier to do this at the application level so that they could go into Splunk) and I was seeing SOME instances (not all) of query execution previously taking ~3sec take anywhere from 9 seconds to 200(!) seconds. As you can imagine, this was very disconcerting.
Of course, beforehand the server was configured using Windows ‘best practice’ settings, which is mostly the same as Linux but to have a smaller shared_buffers and effective_cache_size settings per the PostgreSQL Wiki page.
The primary approach we started to take was to use
EXPLAIN ANALYSE extensively to work out if it was just a case of this new server not being ‘run in’ effectively and not having enough stats. Some solid gains were made, but the problem was not ‘average’ query time, it was the ‘worst case’ query time. Some queries would run fine one day but terribly the next, and of course once the PostgreSQL cache was primed it was as speedy as ever. Many indexes were changed and updated, the DB was VACUUM ANALYSEd, all the usual stuff. In a display of desperation, we even ‘defragged’ the disk (which would have felt very retro if it was not so unpleasant)
At no point was RAM, CPU or Disk IO even stretched. The servers were effectively over-specced, as we ramped them up to see if that would help, and it did, but not to the extent needed in those worst-case queries.
Another very annoying thing was that apparently the PostgreSQL server was not able to maintain connections properly. Despite having more than enough in the max_connections setting, we consistently saw these kinds of errors reported by the client (namespace from the ruby-pg client):
PGError: server closed the connection unexpectedly This probably means the server terminated abnormally before or while processing the request. PGError: no connection to the server PG::Error: could not receive data from server: Connection timed out
The last of these errors was particularly nasty, as usually the statement was executed successfully, but the client had no idea and no record, so while each DB was internally consistent, inter-db communication processes would get out of sync and needed to be manually reconciled.
So, how did we solve these problems?
We didn’t. We moved everything off the cluster to a new Linux PostgreSQL 9.2 master/slave streaming replication setup. Many many thanks to Bartek Ciszkowski for his excellent writeup, which laid a very solid foundation for our new setup. Since the new DB servers went into production, all of the work we’ve done with indexes has meant the sites are performing faster than ever, and we’ve not seen one of the errors above either (touch wood!).
Incidentally, we use (async) streaming replication with another PITR recovery setup, which uses old-style backup commands rather than pg_basebackup, so that we can rsync update the base regularly without having to store a new copy every time. Also note, because of the implementation of replication, you can’t run these backup commands on the slave, they have to be run on the master, which is a shame. Still, we rsync to the slave for the PITR backup so at least it’s not doing double disk IO.
Relink A PostgreSQL 9.2 Streaming Replication Primer
New Replication and Recovery Features in PostgreSQL 9.1
And of course, the PostgreSQL docs