The MySQL Cluster data node log files can become very big. The best solution is to actually fix the underlying problem. But if you know what you are doing, you can work around it and filter out these annoying log entries.

An example of ‘annoying’ entries is when you run MySQL Cluster on virtual machines (not good!) and disks and OS can’t follow any more; a few lines from the ndb_X_out.log:

2011-04-03 10:52:31 [ndbd] WARNING  -- Ndb kernel thread 0 is stuck in: Scanning Timers elapsed=100
2011-04-03 10:52:31 [ndbd] INFO     -- timerHandlingLab now: 1301820751642 sent: 1301820751395 diff: 247
2011-04-03 10:52:31 [ndbd] INFO     -- Watchdog: User time: 296  System time: 536
2011-04-03 10:52:31 [ndbd] INFO     -- Watchdog: User time: 296  System time: 536
2011-04-03 10:52:31

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

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 from an existing

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 know

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'},

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

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 "Cnx

This post explains how to disable Arbitration when using MySQL Cluster (http://www.mysql.com/products/database/cluster/). It gives a case where this could be useful.

First, a piece of advice: you do not want to run MySQL Cluster with arbitration disabled. But if you must, e.g. because of an oversight in your implementation, you can.
Arbitration is very important in MySQL Cluster. It makes sure you don't end up with a Split Brain situation: 2 halves working independently, continuing changing data, making it impossible for them to work together later on.

Here are some tips that have been recently published on Java EE 6 & GlassFish:

• Migrating JSF 1.2 + RichFaces 3.x to Java EE 6 / GlassFish v3
• Mercurial and OpenSolaris and GlassFish
• How do I setup a DataSource in Embedded GlassFish when using Arquillian?
•

You are using MySQL Cluster and crazy enough to digest NDB API? Sick of SQL? Here's a treat: a function to make C/C++ strings ready for inserting into a VARCHAR field. The special thing about them is that the length is prefixed in the first 2 bytes.

void make_ndb_varchar(char *buffer, char *str)
{
  int len = strlen(str);
  int hlen = (len > 255) ? 2 : 1;
  buffer[0] = len & 0xff;
  if( len > 255 )
    buffer[1] = (len / 256);
  strcpy(buffer+hlen, str);
}

Yes, you can use memcpy. Whatever floats your boat.

Lets use this function for a table t1, defined as follows (note: latin1!):

CREATE TABLE t1 (
  id INT UNSIGNED NOT NULL,
  vc VARCHAR(128),
  vclong VARCHAR(1280),
  PRIMARY KEY (id)
  ) ENGINE=NDB DEFAULT CHARSET=latin1

Here is part of the code, simplified

This post explains how to compile oursql and install it on MacOS 10.6. oursql is a Python database interface for MySQL, an alternative to MySQL for Python (i.e. MySQLdb) and MySQL Connector/Python.

First, find out which MySQL you installed. This can be either the 32-bit or the 64-bit version. To make sure, find the mysqld (e.g. in /usr/local/mysql/bin) and do the following in a Terminal window:

shell> file /usr/local/mysql/bin/mysqld
.../mysqld: Mach-O 64-bit executable x86_64

If you see x86_64, you got 64-bit, otherwise 32-bit. If you see both, then you have a universal build.

This post explains how to compile oursql and install it on MacOS 10.6. oursql is a Python database interface for MySQL, an alternative to MySQL for Python (i.e. MySQLdb) and MySQL Connector/Python.

First, find out which MySQL you installed. This can be either the 32-bit or the 64-bit version. To make sure, find the mysqld (e.g. in /usr/local/mysql/bin) and do the following in a Terminal window:

shell> file /usr/local/mysql/bin/mysqld
.../mysqld: Mach-O 64-bit executable x86_64

If you see x86_64, you got 64-bit, otherwise 32-bit. If you see both, then you have