Optimizing tables to compare bilions of rows. How?

Dear all,

Currently I am working on a project that has to do with logging
visitors traffic.
Let's say, every time a visitor has visited the website, one row will
be inserted into a table.

Thing is, just like OneStat, Nedstat or whatever, this project
retrieves it's input from a large number of website. This may result
in let's say 1000 rows per minute from the start but probably a hell
of a lot more.

First question is: How many rows can be processed per minute, or
second?


Now my second question is more difficult. I will also need to compare
the results in a guide page, that will compare all the data collected
and for example show, which site has had the most visitors. For the
last day, week, year... whatever.

My problem is that if I would store all data in one table, this table
will very soon be very very large. I don't know the maximum number of
rows that are allowed, but this will sure influence the speed. The
more rows, the longer it will take of course to compare and show
results in the Guide Page. And finaly I will hit the maximum number of
rows anyhow.

What I am thinking of is to automatically generate a table for every
month but I am not sure if this is wise. Will I be able to compare
fast enough after a year? Let's say I have 12 tables and I want to
compare all rows, let's say there are one bilion rows in each
table...

What I actually like to know I think, is how the database of NedStat
is more or less structure. They probably must have over a milion rows
to store every minute... Can anyone tell me how the manage to store
and compare all this data?

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