On May 2, 2008, at 2:02 PM, Craig James wrote:
On Fri, May 2, 2008 at 2:26 PM, Alexy Khrabrov
<deliverable@xxxxxxxxx> wrote:
I naively thought that if I have a 100,000,000 row table, of the form
(integer,integer,smallint,date), and add a real coumn to it, it
will scroll
through the memory reasonably fast.
In Postgres, an update is the same as a delete/insert. That means
that changing the data in one column rewrites ALL of the columns for
that row, and you end up with a table that's 50% dead space, which
you then have to vacuum.
Sometimes if you have a "volatile" column that goes with several
"static" columns, you're far better off to create a second table for
the volatile data, duplicating the primary key in both tables. In
your case, it would mean the difference between 10^8 inserts of
(int, float), very fast, compared to what you're doing now, which is
10^8 insert and 10^8 deletes of (int, int, smallint, date, float),
followed by a big vacuum/analyze (also slow).
The down side of this design is that later on, it requires a join to
fetch all the data for each key.
You do have a primary key on your data, right? Or some sort of index?
I created several indices for the primary table, yes. Sure I can do a
table for a volatile column, but then I'll have to create a new such
table for each derived column -- that's why I tried to add a column to
the existing table. Yet seeing this is really slow, and I need to to
many derived analyses like this -- which are later scanned in other
computations, so should persist -- I indeed see no other way but to
procreate derived tables with the same key, one column per each...
Cheers,
Alexy
