On 1/18/07, Brian Hurt <bhurt@xxxxxxxxxxxxxxxxx> wrote:
Is there any experience with Postgresql and really huge tables? I'm talking about terabytes (plural) here in a single table. Obviously the table will be partitioned, and probably spread among several different file systems. Any other tricks I should know about?
A pretty effective partitioning strategy that works in some cases is to identify a criteria in your dataset that isolates your data on a session basis. For example, if you have a company_id that divides up your company data and a session only needs to deal with company_id, you can separate out all your tables based on company_id into different schemas and have the session set the search_path variable when it logs in. Data that does not partition on your criteria sits in public schemas that all the companies can see. This takes advantage of a special trick regarding stored procedures that they do not attach to tables until the first time they are executed in a session -- keeping you from having to make a function for each schema. (note: views do not have this property). You can still cross query using views and the like or hand rolled sql. I would call this type of partitioning logical partitioning since you are leveraging logical divisions in your data. It obviously doesn't work in all cases but when it does it works great.
We have a problem of that form here. When I asked why postgres wasn't being used, the opinion that postgres would "just <explicitive> die" was given. Personally, I'd bet money postgres could handle the problem (and better than the ad-hoc solution we're currently using). But I'd like a couple of replies of the form "yeah, we do that here- no problem" to wave around.
pg will of course not die as when your dataset hits a certain threshold. It will become slower based on well know mathematical patterns that grow with your working set size. One of the few things that gets to be a pain with large tables is vacuum -- since you can't vacuum a piece of table and there are certain annoyances with having a long running vacuum this is something to think about. Speaking broadly about table partitioning, it optimizes one case at the expense of another. Your focus (IMO) should be on reducing your working set size under certain conditions -- not the physical file size. If you have a properly laid out and logical dataset and can identify special cases where you need some information and not other information, the partitioning strategy should fall into place, whether it is to do nothing, isolate data into separate schemas/tables/files, or use the built in table partitioning feature (which to be honest I am not crazy about). merlin
