On my machine (Laptop with Pentium-M 1.6 GHz and 512MB DDR333) I get the
following timings :
Big Joins Query will all the fields and no order by (I just put a SELECT
* in the first table) yielding about 6k rows :
=> 12136.338 ms
Replacing the SELECT * from the table with many fields by just a SELECT
of the foreign key columns :
=> 1874.612 ms
I felt like playing a bit so I implemented a hash join in python
(download the file, it works on Miroslav's data) :
All timings do not include time to fetch the data from the database.
Fetching all the tables takes about 1.1 secs.
* With something that looks like the current implementation (copying
tuples around) and fetching all the fields from the big table :
=> Fetching all the tables : 1.1 secs.
=> Joining : 4.3 secs
* Fetching only the integer fields
=> Fetching all the tables : 0.4 secs.
=> Joining : 1.7 secs
* A smarter join which copies nothing and updates the rows as they are
processed, adding fields :
=> Fetching all the tables : 1.1 secs.
=> Joining : 0.4 secs
With the just-in-time compiler activated, it goes down to about 0.25
seconds.
First thing, this confirms what Tom said.
It also means that doing this query in the application can be a lot
faster than doing it in postgres including fetching all of the tables.
There's a problem somewhere ! It should be the other way around ! The
python mappings (dictionaries : { key : value } ) are optimized like crazy
but they store column names for each row. And it's a dynamic script
language ! Argh.
Note : run the program like this :
python test.py |less -S
So that the time spent scrolling your terminal does not spoil the
measurements.
Download test program :
http://boutiquenumerique.com/pf/miroslav/test.py
