Re: Random Page Cost and Planner
| От | Bryan Hinton |
|---|---|
| Тема | Re: Random Page Cost and Planner |
| Дата | |
| Msg-id | AANLkTilLahnsIGdmBYjs7Q7uuCspoRXRg6cEGA18PDPx@mail.gmail.com обсуждение исходный текст |
| Ответ на | Re: Random Page Cost and Planner (David Jarvis <thangalin@gmail.com>) |
| Ответы |
Re: Random Page Cost and Planner
(David Jarvis <thangalin@gmail.com>)
|
| Список | pgsql-performance |
Agree with Tom on his point about avoidance of cost param adjustments to fit specific test cases.
Physically ordering the data by station ids triggers index use every time.
Next week's hardware upgrade should halve those times -- unless anyone has further suggestions to squeeze more performance out of PG. ;-)
Dave
A few suggestions...as I assume you own this database...
- check out pg_statio_user_tables - optimize your cache hit ratio on blocks read...different time durations... pg_stat_bgwriter (read from a script or something and snapshot)
- pg_buffercache in contrib/
- /proc/meminfo on linux
- find out exactly what is going on with your kernel buffer cache (size, how it is buffering) and if your controller or drive is using a read ahead cache.
- might want to play around with partial indexes vs. and/or range partitioning with exclusion constraints, etc.
- define I/O characteristics of the dataset - taking into account index clustering and index order on in-memory pages (i.e. re-cluster?), why need for multiple index if clustering indexes on heap?
- solidify the referential integrity constraints between those tables, on paper....define the use cases before modifying the database tables...i assume this is a dev database
- linux fs mount options to explore - i.e. noatime, writeback, etc.
-maybe look at prepared statements if you are running alot of similar queries from a single session? assuming web front end for your db - with say frequently queried region/category/dates for large read-only dataset with multiple join conditions?
There are some good presentations on pgcon.org from PGCon 2010 that was held last week...
If you take everything into account and model it correctly (not too loose, not too tight), your solution will be reusable and will save time and hardware expenses.
Regards -
Bryan
On Thu, May 27, 2010 at 2:43 AM, David Jarvis <thangalin@gmail.com> wrote:
Hi, Bryan.
I was just about to reply to the thread, thanks for asking. Clustering was key. After rebooting the machine (just to make sure absolutely nothing was cached), I immediately ran a report on Toronto: 5.25 seconds!
Here's what I did:
- Created a new set of tables that matched the old set, with statistics of 1000 on the station and taken (date) columns.
- Inserted the data from the old hierarchy into the new set, ordered by station id then by date (same seven child tables as before: one per category).
- I wanted to ensure a strong correlation between primary key and station id.
- Added three indexes per table: (a) station id; (b) date taken; and (c) station-taken-category.
- Set the station-taken-category index as CLUSTER.
- Vacuumed the new tables.
- Dropped the old tables.
- Set the following configuration values:
- shared_buffers = 1GB
- temp_buffers = 32MB
- work_mem = 32MB
- maintenance_work_mem = 64MB
- seq_page_cost = 1.0
- random_page_cost = 2.0
- cpu_index_tuple_cost = 0.001
- effective_cache_size = 512MB
- Vancouver: 4.2s
- Yellowknife: 1.7s
- Montreal: 6.5s
- Trois-Riviers: 2.8s
Physically ordering the data by station ids triggers index use every time.
Next week's hardware upgrade should halve those times -- unless anyone has further suggestions to squeeze more performance out of PG. ;-)
Dave
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