On 26 Dec 2006 at 2:55p -0500, Tom Lane wrote:
Kevin Hunter <hunteke@xxxxxxxxxxx> 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
