Thread: Postgres and really huge tables
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? 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. Brian
Brian Hurt 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? > > 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. It entirely depends on the machine and how things are accessed. In theory you could have a multi-terabyte table but my question of course is why in the world would you do that? That is what partitioning is for. Regardless, appropriate use of things like partial indexes should make it possible. Joshua D. Drake > > Brian > > > > ---------------------------(end of broadcast)--------------------------- > TIP 7: You can help support the PostgreSQL project by donating at > > http://www.postgresql.org/about/donate > -- === The PostgreSQL Company: Command Prompt, Inc. === Sales/Support: +1.503.667.4564 || 24x7/Emergency: +1.800.492.2240 Providing the most comprehensive PostgreSQL solutions since 1997 http://www.commandprompt.com/ Donate to the PostgreSQL Project: http://www.postgresql.org/about/donate PostgreSQL Replication: http://www.commandprompt.com/products/
> 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? > > 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. I've done a project using 8.1 on solaris that had a table that was closed to 2TB. The funny thing is that it just worked fine even without partitioning. But, then again: the size of a single record was huge too: ~ 50K. So there were not insanly many records: "just" something in the order of 10ths of millions. The queries just were done on some int fields, so the index of the whole thing fit into RAM. A lot of data, but not a lot of records... I don't know if that's valid. I guess the people at Greenplum and/or Sun have more exciting stories ;) Bye, Chris.
Brian Hurt <bhurt@janestcapital.com> writes: > Is there any experience with Postgresql and really huge tables? I'm > talking about terabytes (plural) here in a single table. The 2MASS sky survey point-source catalog http://www.ipac.caltech.edu/2mass/releases/allsky/doc/sec2_2a.html is 470 million rows by 60 columns; I don't have it loaded up but a very conservative estimate would be a quarter terabyte. (I've got a copy of the data ... 5 double-sided DVDs, gzipped ...) I haven't heard from Rae Stiening recently but I know he's been using Postgres to whack that data around since about 2001 (PG 7.1 or so, which is positively medieval compared to current releases). So at least for static data, it's certainly possible to get useful results. What are your processing requirements? regards, tom lane
Chris, On 1/18/07 1:42 PM, "Chris Mair" <chris@1006.org> wrote: > A lot of data, but not a lot of records... I don't know if that's > valid. I guess the people at Greenplum and/or Sun have more exciting > stories ;) You guess correctly :-) Given that we're Postgres 8.2, etc compatible, that might answer Brian's coworker's question. Soon we will be able to see that Greenplum/Postgres are handling the world's largest databases both in record count and size. While the parallel scaling technology we employ is closed source, we are still contributing scaling technology to the community (partitioning, bitmap index, sort improvements, resource management, more to come), so Postgres as a "bet" is likely safer and better than a completely closed source commercial product. - Luke
On Thu, 18 Jan 2007, Tom Lane wrote: > Brian Hurt <bhurt@janestcapital.com> writes: >> Is there any experience with Postgresql and really huge tables? I'm >> talking about terabytes (plural) here in a single table. > > The 2MASS sky survey point-source catalog > http://www.ipac.caltech.edu/2mass/releases/allsky/doc/sec2_2a.html > is 470 million rows by 60 columns; I don't have it loaded up but > a very conservative estimate would be a quarter terabyte. (I've > got a copy of the data ... 5 double-sided DVDs, gzipped ...) > I haven't heard from Rae Stiening recently but I know he's been using > Postgres to whack that data around since about 2001 (PG 7.1 or so, > which is positively medieval compared to current releases). So at > least for static data, it's certainly possible to get useful results. > What are your processing requirements? We are working in production with 2MASS and other catalogues, and 2MASS is not the biggest. The nomad catalog has more than milliard records. You could query them online http://vo.astronet.ru/cas/conesearch.php Everything is in PostgreSQL 8.1.5 and at present migrate to the 8.2.1, which is very slow, since slow COPY. The hardware we use is HP rx1620, dual Itanium2, MSA 20, currently 4.5 Tb. > > regards, tom lane > > ---------------------------(end of broadcast)--------------------------- > TIP 6: explain analyze is your friend > Regards, Oleg _____________________________________________________________ Oleg Bartunov, Research Scientist, Head of AstroNet (www.astronet.ru), Sternberg Astronomical Institute, Moscow University, Russia Internet: oleg@sai.msu.su, http://www.sai.msu.su/~megera/ phone: +007(495)939-16-83, +007(495)939-23-83
> A lot of data, but not a lot of records... I don't know if that's > valid. I guess the people at Greenplum and/or Sun have more exciting > stories ;) Not really. Pretty much multi-terabyte tables are fine on vanilla PostgreSQL if you can stick to partitioned and/or indexed access. If you need to do unindexed fishing expeditions on 5tb of data, then talk to Greenplum. http://www.powerpostgresql.com/Downloads/terabytes_osc2005.pdf -- --Josh Josh Berkus PostgreSQL @ Sun San Francisco
On Thu, 2007-01-18 at 14:31, Brian Hurt 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? > > 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. It really depends on what you're doing. Are you updating every row by a single user every hour, or are you updating dozens of rows by hundreds of users at the same time? PostgreSQL probably wouldn't die, but it may well be that for certain batch processing operations it's a poorer choice than awk/sed or perl. If you do want to tackle it with PostgreSQL, you'll likely want to build a truly fast drive subsystem. Something like dozens to hundreds of drives in a RAID-10 setup with battery backed cache, and a main server with lots of memory on board. But, really, it depends on what you're doing to the data.
On 1/18/07, Brian Hurt <bhurt@janestcapital.com> 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