Jul

22

Serving Django Static Files through Apache

Filed Under Django, Python, Quick Tips | 11 Comments

Django’s development server is capable of serving static (media) files thanks to the view django.views.static.serve. Popular web servers like Apache, Lighttpd or NGINX are much faster though, and as such should be used in production mode. Our goal is to bypass Django and let Apache (or other valid alternatives) directly serve static files like images, videos, CSS, JavaScript files, and so on, for us.

Generally speaking, for performance reasons, it’s advised that you have two different webservers serving your dynamic requests and static files. In practice, for smaller sites, people often opt to simply use one webserver. In this article, I’ll discuss how to serve the static files within your Django project, through Apache.

The first thing we need to do is distinguish between development and production mode. We can do so by simply specifying DEBUG = True (development), or DEBUG = False (production) within our settings.py file.

settings.py may include (among others) the following declarations:

# Absolute path to the project directory
BASE_PATH = os.path.dirname(os.path.abspath(__file__))

# Main URL for the project
BASE_URL = 'http://example.org'

DEBUG = False

# Absolute path to the directory that holds media
MEDIA_ROOT = '%s/media/' % BASE_PATH

# URL that handles the media served from MEDIA_ROOT
MEDIA_URL = '%s/site_media/' % BASE_URL

# URL prefix for admin media -- CSS, JavaScript and images.
ADMIN_MEDIA_PREFIX = "%sadmin/" % MEDIA_URL

*PATH constants indicate paths on your filesystem (e.g., /home/myuser/projects/myproject), while *URL constants indicate the actual URL needed to reach a given page or file.

Notice that it’s not unusual to have a /site_media URL that corresponds to a /media folder. In the example above, I opted to separate regular media files for the project from the standard ones that ship with Django for the admin section. To do this, all we have to do is create a symbolic link as follows:

ln -s /usr/lib/python2.5/site-packages/django/contrib/admin/media /path/to/myproject/media/admin

When you’re in development mode, and DEBUG = True, you want to let Django serve your static files. This can be done by adding the following snippet (or similar) to your urls.py:

if settings.DEBUG:
    urlpatterns += patterns('',
        (r'^site_media/(?P<path>.*)$', 'django.views.static.serve', {'document_root': settings.MEDIA_ROOT}),
    )

In production mode, the code contained within the if clause will not be executed as we’ve set DEBUG to False within settings.py.

From the Django side of things, we are good. We now need to instruct Apache. Within your virtual host file, you can specify something along the lines of:

<VirtualHost *:80>

  #...

  SetHandler python-program
  PythonHandler django.core.handlers.modpython
  SetEnv DJANGO_SETTINGS_MODULE myproject.settings
  PythonDebug On
  PythonAutoReload Off
  PythonPath "['/usr/lib/python2.5/site-packages/django', '/path/to/myproject'] + sys.path"

  #...

  Alias /site_media "/path/to/myproject/media"
  <Location "/site_media">
    SetHandler None
  </Location>
</VirtualHost>

The first group of declarations essentially tells Apache to use mod_python to handle any incoming requests. However, we don’t want Django to deal with static files, so the second group of declarations, aliases/maps the /site_media URL with the actual media directory on the server, and tells Apache to threat it as static content (with SetHandler None) bypassing de facto Django.

May

18

Memoization in Ruby and Python

Filed Under Python, Quick Tips, Ruby | 15 Comments

Wikipedia defines memoization as “an optimization technique used primarily to speed up computer programs by having function calls avoid repeating the calculation of results for previously-processed inputs.”. This typically means caching the returning value of a function in a dictionary of sorts using the parameters passed to the function as a key. This is done in order to reuse that returning value immediately without calculating it again, when the function is invoked with the same arguments. Even though we are trading space for time, it is often invaluable for speeding up certain recursive functions and when dealing with dynamic programming where intermediate calls are often repeated many times.

Using memoization in Ruby is very easy thanks to the memoize gem. The first step to getting started is therefore to install it:

$ sudo gem install memoize
Successfully installed memoize-1.2.3
1 gem installed
Installing ri documentation for memoize-1.2.3...
Installing RDoc documentation for memoize-1.2.3...

Now we can use the memoize method as illustrated in the example below:

require 'rubygems'
require 'memoize'
require 'benchmark'
include Memoize

def fib(n)
  return n if n < 2
  fib(n-1) + fib(n-2)
end

Benchmark.bm(15) do |b|
  b.report("Regular fib:") { fib(35) }
  b.report("Memoized fib:") { memoize(:fib); fib(35)}
end

In the first block we simply invoke fib(35), while in the second one we first invoke the method memoize(:fib) to memoize the method fib. Running this code on my machine prints the following:

                     user     system      total        real
Regular fib:    55.230000   0.160000  55.390000 ( 55.819205)
Memoized fib:    0.000000   0.000000   0.000000 (  0.001305)

We went from almost a minute of run time to an instantaneous execution. Optionally we could even pass a file location to the function memoize and this would use marshaling to dump and load the cached values on/from disk.

For Python we can write a simple decorator that behaves in a similar manner. In its simplest form it can be implemented as follows:

# memoize.py

def memoize(function):
    cache = {}
    def decorated_function(*args):
        try:
            return cache[args]
        except KeyError:
            val = function(*args)
            cache[args] = val
            return val
    return decorated_function

Or more efficiently:

# memoize.py

def memoize(function):
    cache = {}
    def decorated_function(*args):
        if args in cache:
            return cache[args]
        else:
            val = function(*args)
            cache[args] = val
            return val
    return decorated_function

When the memoized function has been invoked, we look in the cache to see if an entry for the given arguments already exist. If it does, we immediately return that value. If not, we call the function, cache the results and return its returning value.

Truth be told, the limit of this approach lies in the fact that since we are using a dictionary, only immutable objects can be used as keys. For example, we can use a tuple but are not allowed to have a list as a parameter. For the example within this article, this approach will suffice, but to take advantage of memoization when using arguments that are mutable, you may want to consider the approach described in this recipe.

We can now rewrite the Ruby example above in Python as follows:

import timeit
from memoize import memoize

def fib1(n):
    if n < 2:
        return n
    else:
        return fib1(n-1) + fib1(n-2)

@memoize
def fib2(n):
    if n < 2:
        return n
    else:
        return fib2(n-1) + fib2(n-2)	

t1 = timeit.Timer("fib1(35)", "from __main__ import fib1")
print t1.timeit(1)
t2 = timeit.Timer("fib2(35)", "from __main__ import fib2")
print t2.timeit(1)

Running this code on my machine prints the following:

9.32223105431
0.000314950942993

In Python 2.5’s case by employing memoization we went from more than nine seconds of run time to an instantaneous result.

Granted we don’t write Fibonacci applications for a living, but the benefits and principles behind these examples still stand and can be applied to everyday programming whenever the opportunity, and above all the need, arises.

Nov

27

Resolving the gray window when running db2setup

Filed Under DB2, Quick Tips | 6 Comments

You drank the Kool-Aid and downloaded the awesomeness which is DB2 Express-C. Good job! Next you proceed to install it on Linux with sudo ./db2setup and boom, instead of a launchpad all you see is a gray window. Now what?

This problem is a known Java bug (resolved in Java 6) that shows up on Linux distros where Compiz effects are enabled. For example, this problem manifests itself in recent Ubuntu releases, including 8.10, where Compiz is enabled by default.

There are a couple of easy ways to solve this problem though. The first is to temporary disable these effects during the installation and turn them back on when you’ve finished installing. In Ubuntu, you can do this by clicking on the Appearance menu, Visual Effects tab and then selecting None. The second method is to run export AWT_TOOLKIT=MToolkit, before running sudo ./db2setup.

A new setup is in the works to solve this issue, but for the time being, you can use the workarounds above to install DB2 Express-C on Linux.

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