Re: Hardware/OS recommendations for large databases ( 5TB)
От | James Mello |
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Тема | Re: Hardware/OS recommendations for large databases ( 5TB) |
Дата | |
Msg-id | 775F825B46B551499DC75DC4E92F80AB0E3623@jupiter.ad.haydrian.com обсуждение исходный текст |
Ответы |
Re: Hardware/OS recommendations for large databases (
Re: Hardware/OS recommendations for large databases ( 5TB) |
Список | pgsql-performance |
Unless there was a way to guarantee consistency, it would be hard at best to make this work. Convergence on large data sets across boxes is non-trivial, and diffing databases is difficult at best. Unless there was some form of automated way to ensure consistency, going 8 ways into separate boxes is *very* hard. I do suppose that if you have fancy storage (EMC, Hitachi) you could do BCV or Shadow copies. But in terms of commodity stuff, I'd have to agree with Merlin. -----Original Message----- From: pgsql-performance-owner@postgresql.org [mailto:pgsql-performance-owner@postgresql.org] On Behalf Of William Yu Sent: Tuesday, November 15, 2005 10:57 AM To: pgsql-performance@postgresql.org Subject: Re: [PERFORM] Hardware/OS recommendations for large databases ( 5TB) Merlin Moncure wrote: >>You could instead buy 8 machines that total 16 cores, 128GB RAM and > > It's hard to say what would be better. My gut says the 5u box would > be a lot better at handling high cpu/high concurrency problems...like > your typical business erp backend. This is pure speculation of > course...I'll defer to the experts here. In this specific case (data warehouse app), multiple machines is the better bet. Load data on 1 machine, copy to other servers and then use a middleman to spread out SQL statements to each machine. I was going to suggest pgpool as the middleman but I believe it's limited to 2 machines max at this time. I suppose you could daisy chain pgpools running on every machine. ---------------------------(end of broadcast)--------------------------- TIP 9: In versions below 8.0, the planner will ignore your desire to choose an index scan if your joining column's datatypes do not match
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