Slow loading of large in-memory tables

Philipp K. Janert, Ph.D. janert at
Tue Sep 7 07:14:50 CEST 2004

Dear All!

I am trying to load a relatively large table (about 1 Million
rows) into an sqlite table, which is kept in memory. The
load process is very slow - on the order of 15 minutes or

I am accessing sqlite from Python, using the pysqlite driver.
I am loading all records first using cx.execute( "insert ..." ).
Only once I have run cx.execute() for all records, I commit all 
the preceding inserts with conn.commit()

I have tried using cx.executemany(), but if anything, this
makes the process slower.

I have not tried mucking manually with transactions.
I have sufficiently convinced myself that the slow part
is in fact the cx.execute() - not reading the data from file
or anything else.

My system specs and versions:
SuSE 9.1
Python 2.3.3
SQLite 2.8.12
pysqlite 0.5.1
1 GB memory (I am not swapping, this is not the problem).

Are there ways to make this process faster? 

Also, I want to keep the DB in memory, since I use it later
to run a very DB-intensive simulation against it. However,
this implies that I need to load the DB from the same python
program which will later run the simulation - I think.

Any hints appreciated!

(Please cc me when replying to the list in regards to this 

Best regards,


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