Re: Thousands of tables versus on table?

david(at)lang(dot)hm wrote:
> On Mon, 4 Jun 2007, Scott Marlowe wrote:
>
>> Gregory Stark wrote:
>>> "Thomas Andrews" <tandrews(at)soliantconsulting(dot)com> writes:
>>>
>>>
>>> > I guess my real question is, does it ever make sense to create
>>> thousands > of
>>> > tables like this?
>>> >
>>> Sometimes. But usually it's not a good idea.
>>> What you're proposing is basically partitioning, though you may not
>>> actually
>>> need to put all the partitions together for your purposes.
>>> Partitioning's
>>> main
>>> benefit is in the management of the data. You can drop and load
>>> partitions
>>> in
>>> chunks rather than have to perform large operations on millions of
>>> records.
>>>
>>> Postgres doesn't really get any faster by breaking the tables up like
>>> that. In
>>> fact it probably gets slower as it has to look up which of the
>>> thousands
>>> of
>>> tables you want to work with.
>>>
>>
>> That's not entirely true. PostgreSQL can be markedly faster using
>> partitioning as long as you always access it by referencing the
>> partitioning key in the where clause. So, if you partition the table
>> by date, and always reference it with a date in the where clause, it
>> will usually be noticeably faster. OTOH, if you access it without
>> using a where clause that lets it pick partitions, then it will be
>> slower than one big table.
>>
>> So, while this poster might originally think to have one table for
>> each user, resulting in thousands of tables, maybe a compromise where
>> you partition on userid ranges would work out well, and keep each
>> partition table down to some 50-100 thousand rows, with smaller
>> indexes to match.
>>
>
> what if he doesn't use the postgres internal partitioning, but instead
> makes his code access the tables named responsesNNNNN where NNNNN is
> the id of the customer?
>
> this is what it sounded like he was asking initially.

Sorry, I think I initially read your response as "Postgres doesn't
really get any faster by breaking the tables up" without the "like that"
part.

I've found that as long as the number of tables is > 10,000 or so,
having a lot of tables doesn't seem to really slow pgsql down a lot.
I'm sure that the tipping point is dependent on your db machine. I
would bet that if he's referring to individual tables directly, and each
one has hundreds instead of millions of rows, the performance would be
better. But the only way to be sure is to test it.