> good normalization skills are really important for large databases,
> along with materialization strategies for 'denormalized sets'.
Good points- thanks. I'm especially curious what others have done for
the materialization. The matview project on gborg appears dead, and I've
only found a smattering of references on google. My guess is, you roll
your own for optimal performance...
> regarding the number of rows, there is no limit to how much pg can
> handle per se, just some practical limitations, especially vacuum and
> reindex times. these are important because they are required to keep
> a handle on mvcc bloat and its very nice to be able to vaccum bits of
> your database at a time.
I was hoping for some actual numbers on "practical". Hardware isn't too
much of an issue (within reason- we're not talking an amazon or google
here... the SunFire X4500 looks interesting... )- if a customer wants to
store that much data, and pay for it, we'll figure out how to do it. I'd
just rather not have to re-design the database. Say the requirement is
to keep 12 months of data accessible, each "scan" produces 100M records,
and I run one per month. What happens if the customer wants to run it
once a week? I was more trying to figure out at what point (ballpark)
I'm going to have to look into archive tables and things of that nature
(or at Bizgres/MPP). It's easier for us to add more/bigger hardware, but
not so easy to redesign/add history tables...
>
> just another fyi, if you have a really big database, you can forget
> about doing pg_dump for backups (unless you really don't care about
> being x day or days behind)...you simply have to due some type of
> replication/failover strategy. i would start with pitr.
>
> merlin
I was originally thinking replication, but I did notice some nice pitr
features in 8.x - I'll have to look into that some more.
Thanks for the pointers though...
- Bucky
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