Unobtrusive benchmark and debug of Python applications

There are many tools available for Python to perform benchmarks and debugging. For example:

  • Hotshot is bundled in the standard library and provide useful data. Maybe you have to install an extra package on some linux distribution if I recall it correctly, because it's not GPL;
  • iPython provides a nice interface to perform live debugging, like automatic invocation of pdb on exceptions;
  • the standard module test provides pystone, that let you benchmark the computer in use before the timed tests. This is helpfull to bench the code on several computers: the measurements can be expressed in in pystones. In other words, you are able to have a reproducable measure of a piece of code and work on the code complexity to make it faster. In reality, any interference can change the results, but this is true as well for time measures.
  • all big python frameworks are using the logging module, so it's easy to hook in it if extra logging is needed.

But when trying to equip an application in order to find out why some functionalities are slow, or why something goes wrong, it's not always easy to set up precisely what you want to log if something is slow or what your want to hook if a bug appears. The simplest way is to call out all the mentioned tool from the code, but it too obtrusive. Another way is to use decorators.

To perform it, you'll have to:
- get and install iw.quality - create the benchmarking or debugging module

Get and install iw.quality

iw.quality gathers helpers for QA. It has an implementation of the Levenshtein distance discussed earlier, and now a decorator used for benchmarking and debugging purpose. Since it's available in PyPi, you should be able to install it like this:

$ easy_install iw.quality

See setuptools informations if you need to install easy_install itself.

Preparing the benchmark or the debugging

Whether you are about to benchmark or debug your program, you need to list all the places in your code where you need to hook a log or a pdb. Then you can create a specialized python module that can be used when needed. This module will simply decorate the functions you want to work with.

Benchmarking

Here's an example, let's equip sqlalchemy for benchmarking.

benchmarking.py file:

#

# benchmarking queries

#

from iw.quality.decorators import log_time

import sqlalchemy



def logger(msg):

    print msg



simple_logger = log_time(logger=logger)



sqlalchemy.create_engine = simple_logger(sqlalchemy.create_engine)

sqlalchemy.engine.Engine.execute = simple_logger(sqlalchemy.engine.Engine.execute)

The log_time decorator comes with a few parameters, like logger wich is called with the log message. By default it uses logging.info, but you can use your own like in the example. The chosen functions are then decorated.

Let's use it:

>>> import benchmarking      # applies the decorators

>>>from sqlalchemy import *



>>> db = create_engine('sqlite:///:memory:')

log_time::2007-10-18T21:50:52.352037::0.013::function 'create_engine',args: ('sqlite:///:memory:',), kw: {}

>>> db.execute('create table TEST(id int)')log_time::2007-10-18T21:52:13.761085::0.104::function 'execute', args:

(<sqlalchemy.engine.base.Engine object at 0x12e6e90>, 'create tableTEST(id int)'), kw: {}

<sqlalchemy.engine.base.ResultProxy object at 0x12e6ff0>

>>> db.execute('insert into TEST (id) values (1)')

log_time::2007-10-18T21:52:50.265860::0.000::function 'execute', args:(<sqlalchemy.engine.base.Engine object at 0x12e6e90>, 'insert into TEST

(id) values (1)'), kw: {}<sqlalchemy.engine.base.ResultProxy object at 0x12f50d0>

If you need to display more infos on the call, you can use your own formatter instead of the provoded one. Let's extend the benchmark file:

def formatter(execution_time, function, args, kw):

    return '%s = %.3f ms' % (function, execution_time)



simple_logger = log_time(logger=logger, formatter=formatter)

And rerun some code:

>>> from sqlalchemy import *

>>> db = create_engine('sqlite:///:memory:')

<function create_engine at 0x12328b0> = 0.014 ms

You can add a treshold on the function timing, to log only functions that are up to this treshold. This is useful to filter a bit.

Debugging

For debugging purpose, you can use the debug parameter:

def debug(e):

    import pdb

    pdb.set_trace()



simple_logger = log_time(logger=logger, formatter=formatter,

                         debugger=debug)

It will be called in case of an Exception:

from sqlalchemy import *



db = create_engine('gcckc')

--Return--

/Users/tziade/tests/benchmarking.py(14)debug()

(Pdb) c

/Users/tziade/tests/banchmarking.py(10)logger()

(Pdb) c

function create_engine at 0x12328f0> = 3.544 ms



Traceback (most recent call last):...



raise esqlalchemy.exceptions.ArgumentError: Could not parse rfc1738 URL from string 'gcckc'

Conclusion

By using this simple decorator, it's easy to group benchmarking and debugging in a specialized module, and generate custom reports. The best practice is to create a module per each use case. I didn't hook it on Hotshot or other utilities to let people use the tools they like.

Comments !