Next development release v0.2.0 of MySQL Connector/Python is available for download and testing. We still don’t recommend to use it in production: it is not beta or GA yet, but we are getting there.

Bug reports and feature requests are welcome through the Launchpad bug tracking tool.

Highlights:

• .executemany() now optimizes INSERT statements using the MySQL
  multiple row syntax.
• Setting sql_mode and time_zone when connecting as well as collation.
• Raw Cursors can be used when you want to do the conversion yourself.
• Unittests now bootstrap own MySQL server instance.
• Tidying the source tree.

Full list of …

As an update to my prior post, I’ve added a form to the workbench plugin.  Now, the user can select a slow query log file and generate statistics from it.  The plugin provides information to answer the following questions:

Figure 1. Sample plugin form

• What type of queries run most often?
• What type of queries are the slowest?
• Which queries access the most rows?
• Which queries send the most data?

The plugin scans the slow query log, aggregates similar queries, and provides summary statistics for each group.  It’s very similar to the mysqldumpslow perl utility, which is included in the mysql bin folder. …

There’s a new version of the dbbenchmark tool available. Now we’re creating the MySQL connection pool thread count based on automatic reporting of core quantity. There is still the same method to set the thread count manually if you are interested in finding your system’s thread limits. Download the MySQL benchmarking script now and add your server performance to the community database of results!

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 …