sqlite3 import performance

Ben Lee midfield at gmail.com
Fri Sep 5 20:33:18 CEST 2008


hi folks --

a quick python and sqlite3 performance question.  i find that
inserting a million rows of in-memory data into an in-memory database
via a single executemany() is about 30% slower than using the sqlite3
CLI and the .import command (reading the same data from a disk file,
even.)  i find this surprising, executemany() i assume is using a
prepared statement and this is exactly what the .import command does
(based on my quick perusal of the source.)

is this discrepancy to be expected?  where is the overhead coming
from?

for full disclosure:  the python code is at the end; run it first to
generate the data file.  to test the CLI, i couldn't find a better way
than to create an init file "sqlcmds" containing

create table test (k int primary key, v int not null);
.import data test

and then run

time sqlite3 -init sqlcmds ':memory:' '.quit'

the python code is

#!/usr/bin/env python

import sqlite3, random, timeit

con = None
def prepare():
    global con, cur

    con=sqlite3.connect(':memory:')
    con.isolation_level="EXCLUSIVE"
    cur=con.cursor()

def ins():
    global con, data, cur

    try:
	cur.execute('drop table test')
    except:
	pass
    cur.execute('create table test (key int primary key, val int not
null);')
    con.commit()

    cur.executemany("INSERT into test (key, val) values (?, 10)",data)
    con.commit()

hs = {}

print 'generating data...'
size = 1000000
data = [[a] for a in random.sample(xrange(10000000), size)]
print 'done!'

# save the data for the sqlite3 CLI
f = file('data', 'w')
f.writelines([str(a[0])+"|10\n" for a in data])
f.close()

print 'testing ' + str(size) + ' inserts...'
# test 100K inserts
t = timeit.Timer(stmt='ins()',
		 setup="import sqlite3\nfrom __main__ import prepare, ins
\nprepare()")
print t.repeat(3,1)

thanks, ben



More information about the Python-list mailing list