Message-ID: <4591A266.4050107@earlham.edu>
Date: Tue, 26 Dec 2006 17:29:58 -0500
From: Kevin Hunter <hunteke@earlham.edu>
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To: PostrgreSQL Performance List <pgsql-performance@postgresql.org>
Subject: Re: [NOVICE] Partitioning
References: <45916BBB.5070602@earlham.edu> <28034.1167162937@sss.pgh.pa.us>
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On 26 Dec 2006 at  2:55p -0500, Tom Lane wrote:
> Kevin Hunter <hunteke@earlham.edu> writes:
>> A friend has asked me about creating a unique table for individual users 
>> that sign up for his site.  (In essence, each user who signs up would 
>> essentially get a set of CREATE TABLE {users,friends,movies,eats}_<id> ( 
>> ... ); statements executed, the idea being to reduce the number of rows 
>> that the DB needs to search or index/update in regards to a particular 
>> user id.)  The just seems ludicrous to me, because the database still 
>> needs to find those tables from its internal structure, not to mention 
>> that it just seems silly to me from a design perspective.  Something 
>> about unable to optimize any queries because not only is the WHERE 
>> clause in flux, but so is the FROM clause.
>
>> Question: Could someone explain to me why this would be bad idea, 
>> because I can't put into words why it is.
>
> I thought you did a fine job right there ;-).  In essence this would be 
> replacing one level of indexing with two, which is unlikely to be a win. 
> If you have exactly M rows in each of N tables then theoretically your 
> lookup costs would be about O(log(N) + log(M)), which is nominally the 
> same as O(log(M*N)) which is the cost to index into one big table --- so 
> at best you break even, and that's ignoring the fact that index search 
> has a nonzero startup cost that'll be paid twice in the first case. 
> But the real problem is that if the N tables contain different numbers 
> of rows then you have an unevenly filled search tree, which is a net 
> loss.

Hurm.  If I remember my Algorithms/Data Structures course, that implies
that table lookup is implemented with a B-Tree . . . right?  Since at
SQL preparation time the tables in the query are known, why couldn't you
use a hash lookup?  In the above case, that would make it effectively
O(1 + log(M)) or O(log(M)).  Granted, it's /still/ a bad idea because of
the next paragraph . . .

> Most DBMSes aren't really designed to scale to many thousands of tables 
> anyway.  In Postgres this would result in many thousands of files in 
> the same database directory, which probably creates some filesystem 
> lookup inefficiencies in addition to whatever might be inherent to 
> Postgres.

So, still a bad idea, but I couldn't immediately think of why.  Thank you.

> Partitioning is indeed something that is commonly done, but on a very 
> coarse grain --- you might have a dozen or two active partitions, not 
> thousands.  The point of partitioning is either to spread a huge table 
> across multiple filesystems (and how many filesystems have you got?) 
> or else to make predictable removals of segments of the data cheap (for 
> instance, dropping the oldest month's worth of data once a month, in a 
> table where you only keep the last year or so's worth of data on-line).

Ah!  I was missing where to hang/put partitioning in my head.  Thank you 
again!

> I can't see doing it on a per-user basis.

Perhaps not on a per-user basis, but he could certainly improve access
times by partitioning even coursely.  I'll point him in that direction 
Are there other, perhaps better ways to improve the access times?  (Now 
I'm curious just for my sake.)  The best that I keep reading is just to 
do as much in parallel as possible (i.e. multiple systems) and to use 
Postgres ( :) ).

Thanks,

Kevin