Lately I worked quite a bit with Python and Linux, writing monitoring and automation utilities. I am in a transition period, so I thought I ought to write some Python stuff interfacing with MySQL for fun, and start positioning myself for expanded career horizon, I hope.

To get started, I thought it would be fun to rewrite a Perl utility I wrote before with Python. That script converts MySQL character sets to utf8, a very common task for wikis and blogs during an upgrade. This time, I did everything from scratch: firing up an Amazon EC2 Linux instance, hand install and configuring MySQL 5.1.50 (creating mysql user, group, wget tarball, setting directory ownership and permissions, creating symbolic to MySQL binaries, editing my.cnf, /etc/init.d/ and chkconfig automatic startup, environmental variables, the works), compiled and …

This is my first attempt at creating a plugin for MySQL Workbench.  As a first step, I’ve created a plugin that summarizes the slow query log if it’s output to the slow_log table, which is an option available in MySQL version 5.1 or newer.  It’s similar to the mysqldumpslow perl script, except that it doesn’t require perl, which should be more convenient on Windows.  In my next update, the plugin will provide the same summary statistics for the slow query log file on disk.

While the slow query log reports query time, lock time, rows sent and rows examined for each query; it’s often useful to group and aggregate similar queries for analysis.  For example, here’s a sample of the plugin output, which is sorted by count, after just a few clicks on a drupal6 site:

The plugin …

We continue to see more evidence of the themes we discuss in our latest CAOS special report, Seeding the Clouds, which examines the open source software used in cloud computing, the vendors backing open source, the cloud providers using it and the impact on the industry.

First, as usual, we are seeing consistencies between our own research — which indicates open source is a huge part of today’s cloud computing offerings from major providers like Amazon, Google, Rackspace, Terremark and VMware — and that of code analysis and management vendor Black Duck. In its analysis of code that runs the cloud, Black Duck also found a preponderance of open source pieces, in many cases the same projects we profile in our report.

Indeed, open source software is an important part of the infrastructure, …

In this latest release I’ve added a basic MySQL connection pool to the benchmarking script which improves the method in which connections to MySQL are handled and reused. In addition, there have been some optimizations made to the thread handler functions for better debug reporting. Download the latest release now and see how your MySQL server performs against the rest of the community! Download here: download page.

Here are some quick commands for installing the proper packages and requirements for the MySQL dbbenchmark program.

export PKG_PATH="ftp://openbsd.mirrors.tds.net/pub/OpenBSD/4.7/packages/amd64/"
pkg_add -i -v wget
wget http://dbbenchmark.googlecode.com/files/dbbenchmark-version-0.1.beta_rev26.tar.gz
pkg_add -i -v python
Ambiguous: choose package for python
 a       0: 
         1: python-2.4.6p2
         2: python-2.5.4p3
         3: python-2.6.3p1
Your choice: 2

pkg_add -i -v py-mysql
pkg_add -i -v mysql
pkg_add -i -v mysql-server
ln -s /usr/local/bin/python2.5 /usr/bin/python
gzip -d dbbenchmark-version-0.1.beta_rev26.tar.gz
tar -xvf dbbenchmark-version-0.1.beta_rev26.tar
cd dbbenchmark-version-0.1.beta_rev26
./dbbenchmark.py --print-sql
 - login to mysql and execute sql commands
./dbbenchmark.py

Quick solution to an issue that the affected Debian Lenny release: the process used to collect the MEMORY_ACTIVE_BYTES variable has been modified to correct a situation where some systems report an array of memory information instead of the expected single integer value. The bug has been fixed in revision 21 and the current download (revision 22) is available for download or svn update. As usual, you can download the MySQL dbbenchmark script here: [downloads].

Thanks goes to Brian Vowell at Evernote.com for bringing this bug to my attention. The original bug report can be found here: [link]

There are many times when writing an application that single threaded database operations are simply too slow. In these cases it’s a matter of course that you’ll use multi-threading or forking to spawn secondary processes to handle the database actions. In this simple example for Python multi-threading you’ll see the how simple it is to improve the performance of your python app.

#!/usr/bin/python
## DATE: 2010-08-30
## AUTHOR: Matt Reid
## WEBSITE: http://themattreid.com
## LICENSE: BSD http://www.opensource.org/licenses/bsd-license.php
## Copyright 2010-present Matt Reid

from __future__ import division
from socket import gethostname;
import threading
import sys
import os
import MySQLdb

class threader(threading.Thread):
    def __init__(self,method):
        threading.Thread.__init__(self)
        self.tx =
        self.method = method
    def run(self):
        run_insert()

def run_insert():
    sql = "INSERT INTO table (`id`,`A`,`B`,`C`) VALUES …

We had a very successful weekend of Planet.mysql users submitting their database statistics so I’ve pushed some code into a new release today so that everyone can benefit from some new features. The biggest change is to the threading logic. Previously the benchmarking script was serializing MySQL operations and only making use of a secondary thread (not the invoking thread) to query the database. Now you have the option of running with “–threads=x” to make use of your multi-core server. A good example of this improvement was on my Macbook Pro; before the threading change it was inserting ~700/sec, after the code change I tried –threads=4 and saw an improvement to ~900/sec. Rather significant.

Download the new script now and see how your server compares to the ones in the …

MySQL Connector/Python is (or should be) compliant with the Python DB-API 2.0 specification. This means that you can use DBUtils' PooledDB module to implement database connection pooling.

Here below you'll find an example which will output the connection ID of each connection requested through the pooling mechanism.

from DBUtils.PooledDB import PooledDB
import mysql.connector

def main():
    pool_size = 3
    pool = PooledDB(mysql.connector, pool_size,
        database='test', user='root', host='127.0.0.1')
    
    cnx = [None,] * pool_size
    for i in xrange(0,pool_size):
        cnx[i] = pool.connection()
        cur = cnx[i].cursor()
        cur.execute("SELECT CONNECTION_ID()")
        print …

MySQL Connector/Python is (or should be) compliant with the Python DB-API 2.0 specification. This means that you can use DBUtils’ PooledDB module to implement database connection pooling.

Here below you’ll find an example which will output the connection ID of each connection requested through the pooling mechanism.

from DBUtils.PooledDB import PooledDB
import mysql.connector

def main():
    pool_size = 3
    pool = PooledDB(mysql.connector, pool_size,
        database='test', user='root', host='127.0.0.1')

    cnx = [None,] * pool_size
    for i in xrange(0,pool_size):
        cnx[i] = pool.connection()
        cur = cnx[i].cursor()
        cur.execute("SELECT CONNECTION_ID()")
        print …