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I have PostgreSQL 9.0 64-bit on Windows Server 2003 64-bit. The system has 8 x 3GHz CPUs and 8GB of memory.

How can/should I configure the following settings?:

  • shared_buffers
  • work_mem
  • maintenance_work_mem

The database is used for anayltics. Only two or three users are connected at any given time running queries. Data sets can number between 1M and 15M rows, I think.

The underlying storage is an EMC CX storage array which is fibre-channel attached. The performance here is pretty good.

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You'll find detailed answers to these three at Tuning Your PostgreSQL Server, along with suggestions about a few other parameters you may want to tweak. You won't be able to use large settings for shared_buffers on Windows, there's a consistent fall-off where it stops helping around 512MB. Turn on log_temp_files and see what shows up to figure out if you really need to raise work_mem. Based on what you're saying about your data set, which doesn't sound like it issues large individual queries, you may not even need to worry about that. A moderate boost to maintenance_work_mem might be helpful for the background autovacuum work, but unless that becomes a problem for you it's not critical to adjust very high.

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The right values depend on the usage pattern. However here are some guidelines

shared_buffers: 25% of memory size for a dedicated postgresql server. work_mem: is used for operations like sorting. A single connection can use this amount multiple times so be careful with this one if you have lot of queries running concurrently. This one requires a lot of testing to see if it improves performance but doesn't make the system use to much memory. So if you increase this make sure your system doesn't start to use too much memory. Personally I often start with something like 4MB.

maintenance_work_mem: this one is for certain maintenance operations like vacuum and indexing setting it fairly high is in general save. 64M or 128M is normally plenty.

Also set effective cache size. This is a hint to the planner and should be set to the amount of memory used as disk cache by the os + the shared buffer size.

If you want to do extensive tuning I recommend reading a good book about it like: PostgreSQL 9.0 High Performance.

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For very large datasets you may find that a SAN is not optimal. SANs excel at lots and lots of small ios really fast. They are often just ok at sequential throughput, unless you've got a very fast interconnect to them, and even then they often just aren't optimized for sequential throughput. I have tested sequential and random read write performance on my machines with Areca and LSI RAID Cards with battery backed cache, native SAS interface with linux software RAID, and with a SAN on the backend. Fastest for random access was close to a tie with the SAN and the RAID cards, but for sequential throughput, the linux software RAID stomped them into the ground. Where the HW RAID can get 350M/s and the SAN was in the 100M/s range (it's connected on gig e) the native SAS with SW RAID gets around 1G/s reads and about 80% that on writes. All sequential of course. Don't assume your SAN is super fast for what you're doing, it might be, it might not be. Test it with bonnie++ or dd or something to get some idea of how fast it really is. If you're getting ~100MB/s sequential then it'll be painfully slow next to a cheaper machine with 4 or 8 7200RPM SATA drives running RAID-10 for analytics.

When you say 8x 3GHz CPUs do you mean 8x sockets each with 4 or 8 cores? Or 8 cores? Or 4 cores with hyperthreading? for your kind of work anything past 4 cores total is probably gonna be a waste. Anything past 8 cores is definitely a waste. With OLAP / Analytics you want fewer faster CPUs if you can get them.

On to your settings. shared_mem doesn't need to be real big. On windows the shared memory implementation is sub optimal for large values, and making it bigger seldom helps improve performance. that said, I'd test various values to see, but a few hundred megs is likely to be as fast as it gets. maintenance work mem can be in the gig range, but the big gain is cranking it up over 100M or so. work_mem is the postgresql foot gun. If you are gonna crank it up, and I recommend going to at least 16 or 32M on your machine, make sure you ARE limiting postgresql's max_connections parameter to a few dozen connections at most. If, somehow, someone launches a bunch of queries at once, you can quickly run out of RAM. Not good. OTOH, some testing will likely show that anything over a hundred or so doesn't really help a lot.

The danger with cranking up work_mem too high is that it will wind up pushing data cached by the OS out of cache, only to need to be reloaded again. The cost of hitting the disks to get that data is usually higher than the gain of really cranking it up.

A good rule of thumb is to keep work_mem*max_connections*2 < 1/4 of memory. So, on a machine with 64G ram and 100 connections, you'd want work_mem*200 < 16G or about 80 Megs max. This ensures that any pathological behaviour where all the connections run lots of sorts don't kill the machine too readily.

If you find that work_mem of 1G works much better than 100M etc then you can compromise by leaving regular work_mem lower for safety and having the single thread that runs big queries set its own work_mem on connection.

I agree with the previous poster that windows is suboptimal for pgsql, with an emphasis that it's much worse for OLAP where being able to allocate more shared_memory can be an advantage for pg / linux.

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OK. You say "IO performance is pretty good". That doesn't mean a lot but I suppose this kind of hardware has good sequential IO thgoughput...

Your queries seem of the style "churn through a lot of data to return some aggregate results" with low parallelism.

Advice on that will depend on how big the data is.

If your database (or at least the frequently accessed part) is small enough to be well cached in RAM, then your IO performance will not matter a lot (except for writes) ; however if your database is huge and you want to churn through it quickly, then sequential IO performance is going to matter.

Anyway first the easiest ones :

work_mem

When you do a query with a few sorts and hashes (for joins and aggregates) or materialized tuplestores, each one can use up to work_mem. Sorts can spill to disk, but not hashes. Note that if your query has N sorts it will use N times work_mem. With lots of users this is important. In your case, few users, you can set it pretty high, maybe 128MB. This way hashes will still be allowed even for largish datasets which can be a lot faster than sorting. You can change it before executing a query if you need, too.

maintenance_work_mem

Same thing, for index creation and the like. Creating a btree index is a big sort, so setting maintenance_work_mem to something large like 1-2GB will require less sorting passes (ie, temp files) if you create an index on a huge table. Just remember not to start 10 index creations concurrently when you restore that backup...

More details -> see documentation

As for shared_buffers, on Windows I don't know. You should ask the mailing list.

Also remember pg can use only one core per query, so disable hyperthreading. Several queries scanning the same table in parallel will synchronize to only read data once, though.

By the way, is there any reason you're not running Linux on that box ? PG is more "native" on Linux.

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Thanks. Very useful info. I am trying to get Linux installed. It is a box in our lab and we have one app that requires windows from which we get data for loading. I am trying to get them to cough up a Windows VM so we can swap the server to Linux. Fingers crossed. –  NJ May 3 '11 at 3:45
    
For your usage you may consider Linux Software RAID with SATA drives, too. Certainly cheaper and probably faster in raw throughput than your fibre channel for the same price... –  peufeu May 3 '11 at 9:47

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