Setting client flags with MySQL Connector/Python works a bit differently than the other MySQL Python drivers. This blog post describes how to set and unset flags, like the CLIENT_FOUND_ROWS.

The default client flags for the MySQL Client/Server protocol can be retrieved using the constants.ClientFlag class:

>>> from mysql.connector.constants import ClientFlag
>>> defaults = ClientFlag.get_default()
>>> print ClientFlag.get_bit_info(defaults)
['SECURE_CONNECTION', 'TRANSACTIONS', 'CONNECT_WITH_DB',
 'PROTOCOL_41', 'LONG_FLAG', 'MULTI_RESULTS',
 'MULTI_STATEMENTS', 'LONG_PASSWD']

To set an extra flag when connecting to MySQL you use the client_flags argument of connect()-method. For example, you’d like to have the …

MySQL v5.5 is GA, but is it working with Python? Yes, it does. Below you’ll find some quick, small tests I did with MySQLdb, oursql and our own MySQL Connector/Python.

My desktop is a Mac, but when it works on that, I’m sure it works elsewhere too. If not, just let us know!

MySQL for Python (aka MySQLdb)

Installing MySQL v5.5.8 64-bit from tar ball on MacOS X 10.6, it compiled fine and the module loaded giving me the …

MySQL Connector/Python 0.3.0, a development release, is available for download:
https://launchpad.net/myconnpy/+download

Since version 0.3.0 is still a development release, or ‘alpha’, it is not
recommended to run this in production.

MySQL Connector/Python 0.3.0 adds following features:

• Python v2.4 support is back.
• Support for compressed protocol.
• Support for SSL connections (when Python’s ssl module is available).
• Support for packets which are bigger than 16MB.
• Max allowed packetsize defaults to 1GB.
• Some performance improvements.

See the ChangeLog for extra details.

Please …

This blog post shows how to create a cursor class for MySQL Connector/Python which will allow you to cache queries. It will hold the query itself and the result in a global variable.

Note: this is a proof of concept and is only meant as a demonstration on how to extend MySQL Connector/Python.

Why query caching?

You are doing lots of queries that have the same result. It would be expensive to always run the same exact query. MySQL has already a query cache, and there is also memcached. But you like MySQL Connector/Python so much you’d like to do it yourself.

A cursor caching queries and their result

To demonstrate a simple implementation of a query cache, we inherit …

MySQL Connector/Python doesn’t buffer results by default. This means you have to fetch the rows when you issued a SELECT. This post describes how you can change this behavior.

Why buffering result sets?

Buffering or storing the result set on the client side is handy when you, for example, would like to use multiple cursors per connection and you’de like to traverse each one interleaved.

Keep in mind that with bigger result sets, the client side will use more memory. You just need to find out for yourself what’s best. When you know result sets are mostly small, you might opt to buffer.

MySQLdb by default buffers results and you need to use a different cursor to disable it. oursql does not buffer by default. This is good to …

Lately I’ve had to do some environment load testing so I wrote this quick script. It can be modified as needed but the basic idea is that it spawns $x threads (–threads) and then sends two connections (or however many you want with –per-connection=) per thread to the URL (–url=). You can have it wait a configurable time between connections as well (–wait=).

The url is appended with a 32 character randomized string so that any database/caching on the backend of the site isn’t serving data from a warm cache. You can hunt down the string length for 32 and change it to whatever you want. Feel free to change and use as needed, just keep my info at top.

#!/usr/bin/python
################################################################################
## DATE: 2010-10-26
## AUTHOR: Matt Reid
## MAIL: mreid@kontrollsoft.com
## SITE: http://kontrollsoft.com
## LICENSE: BSD http://www.opensource.org/licenses/bsd-license.php …

This post describes how to make a custom cursor returning rows as dictionaries using MySQL Connctor/Python v0.2 (or later).

Problem: you want to fetch rows from the database and return them as a dictionary with keys being the column names.

First, lets check how you would do it without any custom cursor.

cnx = mysql.connector.connect(host='localhost',database='test')
cur = cnx.cursor()
cur.execute("SELECT c1, c2 FROM t1")
result = []
columns = tuple( [d[0].decode('utf8') for d in cur.description] )
for row in cur:
  result.append(dict(zip(columns, row)))
pprint(result)
cur.close()
cnx.close()
[python]

The above results in an output like this:

[python light="true"]
[{u'c1': datetime.datetime(2010, 10, 13, 8, 55, 35), u'c2': u'ham'},
 {u'c1': datetime.datetime(2010, 10, …

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!