Thursday, October 11, 2012

Round Robin Replication using GTID

In a previous post I showed how to implement multi-source round-robin replication in pure SQL using the tables that are needed for crash-safe replication. I also outlined a revised version of this approach in the Replication Tips & Tricks presentation I gave at MySQL Connect. This was, however, before the GTID (Global Transaction ID) implementation was done. Now that they are introduced, multi-source replication is even easier since you no longer have to keep track of the positions.

Figure 1. Tables for storing information about masters
CREATE TABLE my_masters (
    host CHAR(50) NOT NULL,
    port INT NOT NULL DEFAULT 3306,
    PRIMARY KEY (idx),
    UNIQUE INDEX (host,port)

CREATE TABLE current_master (
    idx INT

CREATE TABLE replication_user(
    name CHAR(40),
    passwd CHAR(40)

One caveat is that this only works if you are replicating from servers that have GTID enabled, so if you are trying to replicate from a pre-5.6 server, you can use the original implementation. I have added a re-factored version of the code last in this post, and you can also find some utility procedures that I use in the version described here.

For the version that uses GTID, we still keep the two tables we were using in the original implementation around, but we remove the file and position from the tables, giving the definitions seen in Figure 1. Also, the user and password is stored in a separate table and is assumed to be identical for all machines.

To fetch the new master, I created a fetch_next_master procedure that fetches the next master in turn and then advance current_master to the next master. The second select in the code below is used to handle the case that you have a table with masters defined as in Table 1.

delimiter $$
CREATE PROCEDURE fetch_next_master(
    OUT p_host CHAR(50), OUT p_port INT UNSIGNED,
    OUT p_file CHAR(50), OUT p_pos BIGINT)
   DECLARE l_next_idx INT DEFAULT 1;

   SELECT idx INTO l_next_idx FROM my_masters
    WHERE idx > (SELECT idx FROM current_master)
    ORDER BY idx LIMIT 1;

   SELECT idx INTO l_next_idx FROM my_masters
    WHERE idx >= l_next_idx
    ORDER BY idx LIMIT 1;

   UPDATE current_master SET idx = l_next_idx;

   SELECT host, port INTO p_host, p_port
     FROM my_masters WHERE idx = l_next_idx;
END $$
delimiter ;

Since we no longer need to save the position, the code for multi_source event is significantly simpler. All that is necessary is to change master to the next master in turn: the server remembers what transactions are missing automatically and will start replicating from the correct position.

delimiter $$
CREATE EVENT multi_source
   DECLARE l_host CHAR(50);
   DECLARE l_user CHAR(40);
   DECLARE l_passwd CHAR(40);
   DECLARE l_file CHAR(50);


   CALL stop_slave_gracefully();
   CALL fetch_next_master(l_host, l_port);
   SELECT name, passwd INFO l_user, l_passwd FROM replication_user;
   CALL change_master(l_host, l_port, l_user, l_passwd);
END $$
delimiter ;

Full code for original implementation

Here is the code for replicating from pre-5.6 to 5.6 using the replication tables added for implementing crash-safe slaves.

Compared to the version described in the earlier post, I have added a few utility procedures such as a procedure to stop the slave gracefully. The procedure will first stop the I/O thread, and then empty the relay log before stopping the SQL thread. This is mainly to avoid having to re-transfer a lot of events from the master. Compared to the version provided in the previous post, I factored out some separate procedures. You can see the re-factored version last in the post.

delimiter $$
CREATE PROCEDURE change_master(
    p_host CHAR(40), p_port INT,
    p_user CHAR(40), p_passwd CHAR(40),
    p_file CHAR(40), p_pos LONG)
                     CONCAT('MASTER_HOST = "', p_host, '", '),
                     CONCAT('MASTER_PORT = ', p_port, ', '),
                     CONCAT('MASTER_USER = "', p_user, '", '),
                     CONCAT('MASTER_PASSWORD = "', p_passwd, '"'));

     SET @cmd = CONCAT(@cmd,
                       CONCAT(', MASTER_LOG_FILE = "', p_file, '"'),
                       CONCAT(', MASTER_LOG_POS = ', p_pos));
   END IF;
   PREPARE change_master FROM @cmd;
   EXECUTE change_master;
   DEALLOCATE PREPARE change_master;
END $$
delimiter ;

delimiter $$
CREATE PROCEDURE save_position()
   DECLARE l_msg CHAR(60);

   UPDATE my_masters AS m,
          mysql.slave_relay_log_info AS rli
      SET m.log_pos = rli.master_log_pos,
          m.log_file = rli.master_log_name
    WHERE idx = (SELECT idx FROM current_master);
END $$
delimiter ;

delimiter $$
CREATE PROCEDURE fetch_next_master(
    OUT p_host CHAR(40), OUT p_port INT UNSIGNED,
    OUT p_file CHAR(40), OUT p_pos BIGINT)
   DECLARE l_next_idx INT DEFAULT 1;

   SELECT idx INTO l_next_idx FROM my_masters
    WHERE idx > (SELECT idx FROM current_master)
    ORDER BY idx LIMIT 1;

   SELECT idx INTO l_next_idx FROM my_masters
    WHERE idx >= l_next_idx
    ORDER BY idx LIMIT 1;

   UPDATE current_master SET idx = l_next_idx;

   SELECT host, port, log_pos, log_file
     INTO p_host, p_port, p_pos, p_file
     FROM my_masters
    WHERE idx = l_next_idx;
END $$
delimiter ;

delimiter $$
CREATE EVENT multi_source
   DECLARE l_host CHAR(40);
   DECLARE l_user CHAR(40);
   DECLARE l_passwd CHAR(40);
   DECLARE l_file CHAR(40);


   CALL save_position();
   CALL fetch_next_master(l_host, l_port, l_file, l_pos);
   SELECT name, passwd INTO l_user, l_passwd FROM replication_user;
   CALL change_master(l_host, l_port, l_user, l_passwd, l_file, l_pos);
END $$
delimiter ;

Tuesday, June 05, 2012

Binary Log Group Commit in MySQL 5.6

With the release of MySQL 5.6 binary log group commit is included, which is a feature focused on improving performance of a server when the binary log is enabled. In short, binary log group commit improve performance by grouping several writes to the binary log instead of writing them one by one, but let me digress a little on how transactions are logged to the binary log before going into the details. Before going into details about the problem and the implementation, let look at what you do to turn it on.


Well... we actually have a few options to tweak it, but nothing required to turn it on. It even works for existing engines since we did not have to extend the handlerton interface to implement the binary log group commit. However, InnoDB has some optimizations to take advantage of the binary log group commit implementation.

This is a global variable that can be set without stopping the server.

If this is off (0), transactions may be committed in parallel. In some circumstances, this might offer some performance boost. For the measurements we did, there were no significant improvement in throughput, but we decided to keep the option anyway since there are special cases were it can offer improvements.

This variable controls when to stop skimming the flush queue (more about that below) and move on as soon as possible. Note that this is not a timeout on how often the binary log should be written to disk since grabbing the queue and writing it to disk takes time.

Transactions galore...

As the server executes transactions the server will collect the changes done by the transaction in a per-connection transaction cache. If statement-based replication is used, the statements will be written to the transaction cache, and if row-based replication is used, the actual rows changed will be written to the transaction cache. Once the transaction commits, the transaction cache is written to the binary log as one single block. This allow each session to execute independently of each others and only need to take a lock on the binary log when writing the transaction data to it. Since transactions are isolated from each others it is enough to serialize the transactions on commits. (Of course, this is in an ideal world. Transactions can see other transactions changes if you set a different transaction isolation level. You would never do that unless you knew exactly what you're doing... right?)
Figure 1. Two-Phase Commit Protocol (2PC)
In order to keep the storage engine and the binary log in sync, the server employs a two-phase commit protocol (or just 2PC) that you can see in Figure 1. As you can see, there is a call to write() and one call to fsync() in the diagram: I'll get to that is just a moment, so stay tuned.

The entire point of using a two-phase commit protocol is to be able to guarantee that the transaction is either both in the engine and the binary log (or in neither) even in the event that the server crashes after the prepare, and subsequently recovers. That is, it should not be possible that the transaction is in the engine but not in the binary log, or vice verse. Two-phase commit solves this by requiring that once a transaction is prepared in the engine, it can be either fully committed or fully rolled back even if the server crashes and recover. So, on recovery, the storage engine will then provide the server with all the transactions that are prepared but not yet committed, and the server will then commit the transaction if it can be found in the binary log, and roll it back otherwise.

This is, however, only possible if the transaction can be guaranteed to be persistent in the binary log before committing the transaction in the engine. Since disks are slow and memory fast, the operating system tries to improve performance by keeping part of the file in memory instead of writing directly to disk. Once enough changes have been written, or the memory is needed for something else, the changes are written to disk. This is good for the operating system (and also for anybody using the computer), but causes a problem for the database server since if the server crashes, it is possible that the transaction is committed in the storage engine, but there is no trace of it in the binary log.

For recovery to work properly, it is therefore necessary to ensure that the file is really on disk, which is why there is a call to fsync() in Figure 1, which makes the in-memory part of the file to be written to disk.

The Infamous prepare_commit_mutex

Figure 2. Missing un-committed transactions
When the server recovers, it has access to the binary log and can therefore decide what to commit and what to rollback, but what if there is no binary log?

In the general case, a recovery can just roll back all prepared transactions and start again. After all, the transactions that were just prepared but not committed are safe to roll back. They just move the database to the state it had just before starting those transactions. Any clients being connected has not got an indication that the transaction is committed, so they will realize that the transactions have to be re-executed.

There is another case where recovery is being used in this way and that is when using on-line backup methods such as InnoDB Hot Backup (which is used in the MySQL Enterprise Backup). These tools take a copy of the database files and InnoDB transaction logs directly—which is an easy way to take a backup—but it means that the transaction logs contain transactions that have just been prepared. On recovery, they roll back all the transactions and have a database in a consistent state.

Since these on-line backup methods are often used to bootstrap new slaves, the binary log position of the last committed transaction is written in the header of the InnoDB redo log. On recovery, the recovery program print the binary log position of the last committed transaction and you can use this information with the CHANGE MASTER command to start replicating from the correct position. For this to work correctly, it is necessary that all the transactions are committed in the same order as they are written to the binary log. If they are not, there can be "holes" where some transactions are written to the binary log, but not yet committed, which cause the slave to miss transactions that were not committed. The problematic case that can arise is what you see in Figure 3 below.

Figure 3. Committing in parallel
You can see an example of this in Figure 2, where replication will start from the last committed position, but there is a transaction that were just prepared and hence was rolled back when the backup was restored on the slave.

To solve this, InnoDB added a mutex called the prepare_commit_mutex that was taken when preparing a transaction and released when committing the transaction. This is a simple solution to the problem, but causes some problems that we will get to in a minute. Basically, the prepare_commit_mutex solve the problem by forcing the call sequence to be as in Figure 4.

Figure 4. Sequencing transactions

Steady as she goes... NOT!

Since disk writes are slow, writing every transaction to disk will affect performance quite a lot... well, actually very much...

To try to handle that, there is a server option sync_binlog that can be set to how often the binary log should be written to disk. If it is set to 0, the operating system will decide on when the file pages should be written to disk, if it is set to 1, then fsync() will be called after every transaction being written to the binary log. In general, if you set sync_binlog to N, you can at most lose N-1 transactions, so in practice there are just two useful settings: sync_binlog=0 means that you accept that some transactions can be lost and handle it some other way, and sync_binlog=1 means that you do not accept to lose any transactions at all. You could of course set it to some other value to get something in between, but in reality you can either handle transaction loss or not.

To improve performance, the common case is to bundle many writes with each sync: this is what the operating system does, and that is what we should be able to do. However, if you look at Figure 4 you see that there is no way to place a fsync() call in that sequence so that several transactions are written to disk at the same time. Why? Because at any point in that sequence there is at most one prepared and written transaction. However, if you go back to Figure 3, you can see that it would be possible to place an fsync() as shown and write several transactions to disk at the same time. If it was possible, then all transactions written to the binary log before the fsync() call would be written to disk at once. But this means that it is necessary to order the commits in the same order as the writes without using the prepare_commit_mutex.

So, how does all this work then...

The binary log group commit implementation used split the commit procedure into several stages as you can see in Figure 5. The stages are entirely internal to the binary log commit procedure and does not affect anything else. In theory, it would be possible to have another replication implementation with another policy for ordering commits. Since the commit procedure is separated into stages, there can be several threads processing transactions at the same time, which also improves throughput.
Figure 5. Commit Procedure Stages
For each stage, there is an input queue where sessions queue up for processing. If a thread registers in an empty queue, it is considered the stage leader otherwise, the session is a follower. Stage leaders will bring all the threads in the queue through the stage and then register the leader and all followers for the next stage. Followers will move off to the side and wait for a leader to signal that the entire commit is done. Since it is possible that a leader registers to a non-empty queue, a leader can decide to become a follower and go off waiting as well, but a follower can never become a leader.

When a leader enters a stage, it will grab the entire queue in one go and process it in order according to the stage. After the queue is grabbed, other sessions can register for the stage while the leader processes the old queue.

In the flush stage, all the threads that registered will have their caches written to the binary log. Since all the transactions are written to an internal I/O cache, the last part of the stage is writing the memory cache to disk (which means it is written to the file pages, also in memory).

In the sync stage, the binary log is synced to disk according to the settings of sync_binlog. If sync_binlog=1 all sessions that were flushed in the flush stage will be synced to disk each time.

In the commit stage, the sessions will that registered for the stage will be committed in the engine in the order they registered, all work is here done by the stage leader. Since order is preserved in each stage of the commit procedure, the writes and the commits will be made in the same order.

After the commit stage has finished executing, all threads that were in the queue for the commit stage will be marked as done and will be signaled that they can continue. Each session will then return from the commit procedure and continue executing.

Thanks to the fact that the leader registers for the next queue and is ready to become a follower, the stage that is slowest will accumulate the most work. This is typically the sync stage, at least for normal hard disks. However, it is critical to fill the flush stage with as many transactions as possible, so the flush stage is treated a little special.

In the flush stage, the leader will skim the the sessions one by one from the flush queue (the input queue for the flush stage). As long as the last session was not remove the from the queue, or the first session was unqueued more than binlog_max_flush_queue_time microseconds ago, this process will continue. There are two different conditions that can stop the process:

  • If the queue is empty, the leader immediately advanced to the next stage and registers all sessions processed to the sync stage queue.
  • If the timeout was reached, the entire queue is grabbed and the sessions transaction caches are flushed (as before). The leader then advance to the sync stage.
Figure 6. Comparing 5.6.5 and 5.6 June labs release

Performance, performance, performance...

I'm sure you all wonder what the improvements are, so without further delay, let's have a look at the results of some benchmarks we have done on the labs tree. There has been several improvements that is not related to the binary log, so I will just focus on the results involving the binary log. In Figure 6 you see a benchmark comparing the 5.6.5 release with the 5.6 June labs release using the binary log. These benchmarks were executed on an 2.00 GHz 48-core Intel® Xeon® 7540 with 512 GiB memory and using SSD disks.

As you can see, the throughput has increased tremendously, with increases ranging from a little less than 2 and approaching 4 times that of 5.6.5. To a large extent, the improvements are in the server itself, but what is interesting is that with binary log group commit, the server is able to keep the pace. Even with sync_binlog=0 on 5.6.5 and sync_binlog=1 on the 5.6 labs release, the 5.6 labs release outperforms 5.6.5 by a big margin.

Another interesting aspect is that even with sync_binlog=1 the server performs nearly as well when using sync_binlog=0. On higher number of connections (roughly more than 50), the difference in throughput is varying between 0% [sic] and with a more typical throughput between 5% and 10%. However, there is a drop of roughly 20% in the lower range. This looks very strange, especially in the light that the performance is almost equal in the higher range, so what is causing that drop and is there anything that can be done about it?

Figure 7. Benchmark of Binary Log Group Commit
The answer comes from some internal benchmarks done while developing the feature. For these tests we were using Sysbench on a 64-core Intel® Xeon® X7560 running at 2.27GHz with 126 GB memory and a HDD.

In the benchmarks that you can see in Figure 7 the enhanced version of 5.6 with and without the binary log group commit is compared. The enhanced version of 5.6 include some optimizations to improve performance that are not available in the latest 5.6 DMR, but most are available in the labs tree. However, these are benchmarks done while developing, so it is not really possible to compare them with Figure 6 above, but it will help understand why we have a 20% drop at the lower number of connections.

The bottom line in Figure 7 is the enhanced 5.6 branch without binary log group commit and using sync_binlog=1, which does not scale very well. (This is nothing new, and is why implementing binary log group commit is a good idea.) Note that even at sync_binlog=0 the staged commit architecture scale better than the old implementation. If you look at the other lines in the figure, you can see that even when the enhanced 5.6 server is running with sync_binlog=0, the binary log group commit implementation outperforms the enhanced 5.6 branch at roughly 105 simultaneous threads with sync_binlog=1.

Also note that the difference between sync_binlog=1 and sync_binlog=0 is diminishing as the number of simultaneous connections is increased, to vanish completely at roughly 160 simultaneous connections. We haven't made a deep analysis of this, but while using the performance schema to analyze the performance of each individual stage, we noted that the sync time was completely dominating the performance (no surprise there, just giving the background), and that all available transactions "piled up" in the sync stage queue. Since each connection can at most have one ongoing transaction, it means that at 32 connections, there can never be more than 32 transactions in the queue. As a matter of fact, one can expect that over a long run, roughly half of the connections are in the queue and half of the connections are inside the sync stage (this was also confirmed in the measurements mentioned above), so at lower number of connections it is just not possible to fill the queue enough to utilize the system efficiently.

The conclusion is that reducing the sync time would probably make the difference between sync_binlog=0 and sync_binlog=1 smaller even on low number of connections. We didn't do any benchmarks using disks with battery-backed caches (which should reduce the sync time significantly, if not entirely eliminates it), but it would be really interesting to see the effect of that on performance.

Summary and closing remarks

  • The binary logging code has been simplified and optimized, leading to improved performance even when using sync_binlog=0.
  • The prepare_commit_mutex is removed from the code and instead the server orders transactions correctly.
  • Transactions can be written and committed as groups without losing any transactions, giving around 3 times improvement in performance on both sync_binlog=1 and sync_binlog=0.
  • The difference between sync_binlog=0 and sync_binlog=1 is small and reduces as the load increases on the system.
  • Existing storage engines benefit from binary log group commit since there are no changes to the handlerton interface.
Binary log group commit is one of a range of important new enhancements to replication in MySQL 5.6, including global transaction IDs (GTIDs), multi-threaded slave, crash safe slave and binary log, replication event checksums, and some more. You can learn more about all of these from our DevZone article:

You can also try out binary log group commit today by downloading the latest MySQL 5.6 build that is available on

Related links

  • It all started with this post where Mark points out the problem and show some results of their implementation.
  • The next year, Kristian Nielsen implemented binary log group commit for MariaDB and has a lot of good posts on the technical challenges in implementing it. This implementation is using an atomic queue and does flushing and syncing of the transactions as a batch, after which the sessions are signalled in order and commit their transactions.

Monday, February 20, 2012

Pythonic Database API: Now with Launchpad

In a previous post, I demonstrated a simple Python database API with a syntax similar to jQuery. The goal was to provide a simple API that would allow Python programmers to use a database without having to resort to SQL, nor having to use any of the good, but quite heavy, ORM implementations that exist. The code was just an experimental implementation, and I was considering putting it up on Launchpad.
I did some basic cleaning of the code, turned it into a Python package, and pushed it to Launchpad. I also added some minor changes, such as introducing a define function to define new tables instead of automatically creating one when an insert was executed. Automatically constructing a table from values seems neat, but in reality it is quite difficult to ensure that it has the right types for the application. Here is a small code example demonstrating how to use the define function together with some other operations.
import mysql.api.simple as api

server = api.Server(host="")

    { 'name': 'more', 'type': int },
    { 'name': 'magic', 'type': str },

items = [
    {'more': 3, 'magic': 'just a test'},
    {'more': 3, 'magic': 'just another test'},
    {'more': 4, 'magic': 'quadrant'},
    {'more': 5, 'magic': 'even more magic'},

for item in items:
The table is defined by providing a dictionary for each row that you want in the table. The two most important fields in the dictionary is name and type. The name field is used to supply a name for the field, and the type field is used to provide a type of the column. The type is denoted using a basic Python type constructor, which then maps internally to a SQL type. So, for example, int map to the SQL INT type, and bool map to the SQL type BIT(1). This choice of deciding to use Python types are simply because it is more natural for a Python programmer to define the tables from the data that the programmer want to store in the database. I this case, I would be less concerned with how the types are mapped, just assuming that it is mapped in a way that works. It is currently not possible to register your own mappings, but that is easy to add.So, why provide the type object and not just a string with the type name? The idea I had here is that since Python has introspection (it is a dynamic language after all), it would be possible to add code that read the provided type objects and do things with them, such as figuring out what fields there are in the type. It's not that I plan to implement this, but even though this is intended to be a simple database interface, there is no reason to tie ones hands from start, so this simple approach will provide some flexibility if needed in the future.


Some additional links that you might find useful:
You need to have Connector/Python installed to be able to use this package.
This is a JavaScript library that provide a similar interface to a database. It claims to be an ORM layer, but is not really. It is more similar to what I have written above.
Roland's MQL to SQL and Presentation on SlideShare is also some inspiration for alternatives.

Monday, January 23, 2012

MySQL: Python, Meta-Programming, and Interceptors

I recently found Todd's posts on interceptors which allow callbacks (called interceptors) to be registered with the connector so that you can intercept a statement execution, commit, or any of the many extension points supported by Connector/Java. This is a language feature that allow you to implement a number of new features without having to change the application code such as load-balancing policies, profiling queries or transactions, or debugging an application.

Since Python is a dynamic language, it is easy to add interceptors to any method in Connector/Python, without having to extend the connector with specific code. This is something that is possible in dynamic languages such as Python, Perl, JavaScript, and even some lesser known languages such as Lua and Self. In this post, I will describe how and also give an introduction to some of the (in my view) more powerful features of Python.

In order to create an interceptor, you need to be able to do these things:

  • Catch an existing method in a class and replace it with a new one.
  • Call the original function, if necessary.
  • For extra points: catch an existing method in an object and replace a new one.
You will in this post see how all three of these problems are solved in Python. You will see and use decorators to be able to define methods in existing classes and object, and closures to be able to call the original version of the methods. By picking this approach, it will not be necessary to change the implementation: in fact, you can use this code to replace any method in any class, not only in Connector/Python.

Table 1. Attributes for methods
Method Instance
Name Unbound Bound
__name__ Name of Method
im_func "Inner" function of the method
im_self None Class instance for the method
im_class Class that the method belongs to
In addition to being able to replace methods in the class, we would also like to be able to replace methods in instances of a class ("objects" in the traditional sense). This is useful to create specialized objects, for example for tracking particular cases where a method is used.

In order to understand how the replacement works, you should understand that in Python (and the dynamic languages mentioned above), all objects can have attributes, including classes, functions, and a bunch of other esoteric constructions. Each type of object has a set of pre-defined attributes with well-defined meaning. For classes (and class instances), methods are stored as attributes of the class (or class instance) and can therefore be replaced with other methods that you build dynamically. However, it requires some tinkering to take an existing "normal" function definition and "imbue" it with whatever "tincture" that makes it behave as a method of the class or class instance.

Depending on where the method comes from, it can be either unbound and bound. Unbound methods are roughly equivalent to member function pointers in C++: they reference a function, but not the instance. In contrast, bound methods have an instance tied to it, so when you call them, they already know what instance they belong to and will use it. Methods have a set of attributes, of which the four in Table 1 interests us. If a method is fetched from a class (to be precise, from a class object), it will be unbound and im_self will be None. If the method is fetched from a class instance, it will be bound and im_self will be set to the instance it belongs to. These attributes are all the "tincture" you need make our own instance methods. The code for doing the replacement described above is simply:

import functools, types

def replace_method(orig, func):
    functools.update_wrapper(func, orig.im_func)
    new = types.MethodType(func, orig.im_self, orig.im_class)
    obj = orig.im_self or orig.im_class
    setattr(obj, orig.__name__, new)
The function uses two standard modules to make the job simpler, but the steps are:
  1. Copy the meta-information from the original method function to the new function using update_wrapper. This copies the name, module information, and documentation from the original method function to make it look like the original method.
  2. Create a new method instance from the method information of the original method using the constructor MethodType, but replace the "inner" function with the new function.
  3. Install the new instance method in the class or instance by replacing the attribute denoting the original method with the new method. Depending on whether the function is given a bound or unbound instance, either the method in the class or in the instance is replaced.
Using this function you can now replace a method in a class like this:
from mysql.connector import MySQLCursor

def my_execute(self, operation, params=None):

replace_method(MySQLCursor.execute, my_execute)
This is already pretty useful, but note that you can also replace only a specific instance as well by using replace_method(cursor.execute, my_execute). It was not necessary to change anything inside Connector/Python to intercept a method there, so you can actually apply this to any method in any of the classes in Connector/Python that you already have available. In order to make it even easier to use you'll see how to define a decorator that will install the function in the correct place at the same time as it is defined. The code for defining a decorator and an example usage is:
import functools, types
from mysql.connector import MySQLCursor

def intercept(orig):
    def wrap(func):
        functools.update_wrapper(func, orig.im_func)
        meth = types.MethodType(func, orig.im_self, orig.im_class)
        obj = orig.im_self or orig.im_class
        setattr(obj, orig.__name__, meth)
        return func
    return wrap

# Define a function using the decorator
def my_execute(self, operation, params=None):
The @intercept line before the definition of my_execute is where the new descriptor is used. The syntax is a shorthand that can be used to do some things with the function when defining it. It behaves as if the following code had been executed:
def _temporary(self, operation, params=None):
my_execute = intercept(MySQLCursor.execute)(_temporary)
As you can see here, whatever is given after the @ is used as a function and called with the function-being-defined as argument. This explains why the wrap function is returned from the decorator (it will be called with a reference to the function that is being defined), and also why the original function is returned from the wrap function (the result will be assigned to the function name).

Using a statement interceptor, you can catch the execution of statements and do some special magic on them. In our case, let's define an interceptor to catch the execution of a statement and log the result using the standard logging module. If you read the wrap function carefully, you probably noted that it uses a closure to access the value of orig when the decorator was called, not the value it happen to have when the wrap function is executed. This feature is very useful since a closure can also be used to get access to the original execute function and call it from within the new function. So, to intercept an execute call and log information about the statement using the logging module, you could use code like this:

from mysql.connector import MySQLCursor
original_execute = MySQLCursor.execute
def my_execute(self, operation, params=None):
    if params is not None:
        stmt = operation % self._process_params(params)
        stmt = operation
    result = original_execute(self, operation, params)
    logging.debug("Executed '%s', rowcount: %d", stmt, self.rowcount)
    logging.debug("Columns: %s", ', '. join(c[0] for c in self.description))
    return result
Now with this, you could implement your own caching layer to, for example, do a memcached lookup before sending the statement to the server for execution. I leave this as an exercises to the reader, or maybe I'll show you in a later post. &smiley; Implementing a lifecycle interceptor is similar, only that you replace, for example, the commit or rollback calls. However, implementing an exception interceptor is not obvious. Catching the exception is straightforward and can be done using the intercept decorator:
original_init = ProgrammingError.__init__
def catch_error(self, msg, errno):
    logging.debug("This statement didn't work: '%s', errno: %d", msg, errno)
    original_init(self, msg, errno=errno)
However, in order to do something more interesting, such as asking for some additional information from the database, it is necessary to either get hold of the cursor that was used to execute the query, or at least the connection. It is possible to dig through the interpreter stack, or try to override one of the internal methods that Connector/Python uses, but since that is very dependent on the implementation, I will not present that in this post. It would be good if the cursor is passed down to the exception constructor, but this requires some changes to the connector code.

Even though I have been programming in dynamic languages for decades (literally) it always amaze me how easy it is to accomplish things in these languages. If you are interested in playing around with this code, you can always fetch Connector/Python on Launchpad and try out the examples above. Some links and other assorted references related to this post are: