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Comment by WALoeIII | Hacker News Reader

5.1 scales better, and I would bet they're looking into XtraDB (if not already using it) which will go even further on multi-core machines.

edit - 5.1 (recent releases) scales better mostly due to InnoDB 1.0.4 which incorporates a number of patches from Google (Mark Callagan), Percona and more.

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but still, it doesn't scale as linearly as other databases like Oracle or PostgreSQL. Even compared against MySQL 5.1, PostgreSQL scales linearly all the way up to 128 cores -- so it seems like for big databases, you'd want to use something that can actually utilize all 128 cores.

I'm pretty sure they're using MySQL because it's popular, not because it scales. I figure when people hit 10 million rows in a MySQL table, they start looking at sharding/partitioning instead of buying a bigger machine.

MySQL scales for reads pretty well since it has a well understood and mature replication system. In contrast, the PostgreSQL replication landscape is somewhat muddy. Scaling writes is a challenge of course, and PostgreSQL might be the better choice there for getting the most out of big hardware. Scaling up can really cost though, as the price-to-performance ratio gets outside of the sweet spot when the hardware you're buying gets more exotic.

I'm not debating which database scales better. I'm questioning why anyone would use a "big" server for mysql. If you're going to do replication with mysql, you don't need 10 servers with 68 gigs of memory -- you'd probably be better off with 100 servers with 4 gigs of memory.

However, if you're going to go with "big" database servers, one should use Oracle or PostgreSQL since they can actually make use of the memory & cpu cores.

Any 64-bit MySQL will do fine with big memory. It is with > 16 cores that modern MySQL suffers from concurrency problems with mutex contention. These AMZN big memory instances have 4/8 cores, well within the area where MySQL performs well.

Is 10 million rows really the number? I have log files on my disk that are much bigger than that, and I manage them fine with "grep". Inside a database, I would consider this amount of data "trivial".

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