Tests MongoDB/PostgreSQL performance under different concurrency settings

README

For PostgreSQL you'd need to increase your kernel shared memory quotas. For Mac OS X to set it to 40MBs do:

sudo sysctl -w kern.sysv.shmmax=41943040
sudo sysctl -w kern.sysv.shmall=10240

Then in mydb/postgresql.conf change:

max_connections = 128

More info here:

http://www.postgresql.org/docs/9.2/static/kernel-resources.html

speedtest.py

import os
import sys
import time
import random
import gevent
import gevent.monkey
gevent.monkey.patch_all()

import pymongo

import psycopg2
import gevent_psycopg2
gevent_psycopg2.monkey_patch()

class Runner(object):
    def __init__(self, maxconnections=1, maxtime=10.0):
        self.maxconnections = maxconnections
        self.maxtime = maxtime

    def run(self, kind):
        counts = []
        starts = []
        target = getattr(self, 'worker_%s' % (kind,))
        workers = [gevent.spawn(target, counts, starts, self.maxtime) for _ in xrange(self.maxconnections)]
        gevent.joinall(workers)
        mintime = None
        maxtime = None
        for t0 in starts:
            if mintime is None or mintime > t0:
                mintime = t0
            if maxtime is None or maxtime < t0:
                maxtime = t0
        return sum(counts, 0.0), maxtime - mintime

    def worker_mongodb(self, counts, starts, maxtime):
        table = pymongo.Connection()['mydb']['table']
        n = 0
        t0 = time.time()
        starts.append(t0)
        while True:
            table.insert({'t': int(time.time()), 'v': random.randint(0, 1000)}, safe=True)
            n += 1
            dt = time.time() - t0
            if dt >= maxtime:
                counts.append(float(n) / float(dt))
                break

    def worker_postgres(self, counts, starts, maxtime):
        conn = psycopg2.connect("host=localhost dbname=mydb")
        cursor = conn.cursor()
        cursor.execute("commit")
        n = 0
        t0 = time.time()
        starts.append(t0)
        while True:
            cursor.execute("insert into bigtable (timestamp, value) values (%s, %s)", (int(time.time()), random.randint(0, 1000)))
            n += 1
            dt = time.time() - t0
            if dt >= maxtime:
                counts.append(float(n) / float(dt))
                break

for i in xrange(8):
    print 2 ** i, 'connections'
    for kind in ('mongodb', 'postgres'):
        print '...%s:' % kind,
        ops, dev = Runner(2 ** i).run(kind)
        print '%.3f inserts/s, %dms startup deviation' % (ops, dev * 1000)