William Yu <wyu@talisys.com> writes:
> 1 beefy server w/ 32GB RAM = $16K
>
> I know what I would choose. I'd get the mega server w/ a ton of RAM and skip
> all the trickyness of partitioning a DB over multiple servers. Yes your data
> will grow to a point where even the XXGB can't cache everything. On the
> otherhand, memory prices drop just as fast. By that time, you can ebay your
> original 16/32GB and get 64/128GB.
a) What do you do when your calculations show you need 256G of ram? [Yes such
machines exist but you're not longer in the realm of simply "add more RAM".
Administering such machines is nigh as complex as clustering]
b) What do you do when you find you need multiple machines anyways to divide
the CPU or I/O or network load up. Now you need n big beefy servers when n
servers 1/nth as large would really have sufficed. This is a big difference
when you're talking about the difference between colocating 16 1U boxen with
4G of ram vs 16 4U opterons with 64G of RAM...
All that said, yes, speaking as a user I think the path of least resistance is
to build n complete slaves using Slony and then just divide the workload.
That's how I'm picturing going when I get to that point.
Even if I just divide the workload randomly it's easier than building a
machine with n times the cpu and i/o. And if I divide the workload up in a way
that correlates with data in the database I can probably get close to the same
performance as clustering. The actual cost of replicating the unused data is
slight. And the simplicity of master-slave makes it much more appealing than
full on clustering.
--
greg
