#Silk

Silk is a live profiling and inspection tool for the Django framework. It primarily consists of:

• Middleware for intercepting Requests/Responses
• A wrapper around SQL execution for profiling of database queries
• A context manager/decorator for profiling blocks of code and functions either manually or dynamically.
• A user interface for inspection and visualisation of the above.

Documentation is below and a live demo is available at http://mtford.co.uk/silk/, where the tool is actively profiling my website and blog!

Contents

• Requirements
• Features
  • Request Inspection
  • SQL Inspection
  • Profiling
    • Decorator
    • Context Manager
• Experimental Features
  • Dynamic Profiling
  • Code Generation
• Installation
  • Existing Release
    • Pip
    • [Github Tag](#Github Tag)
  • Master
• Roadmap

Requirements

Silk has been tested with:

• Django: 1.5, 1.6
• Python: 2.7, 3.3, 3.4

I left out Django <1.5 due to the change in {% url %} syntax. Python 2.6 is missing collections.Counter. Python 3.0 and 3.1 are not available via Travis and also are missing the u'xyz' syntax for unicode. Workarounds can likely be found for all these if there is any demand. Django 1.7 is currently untested.

Features

Request Inspection

The Silk middleware intercepts and stores requests and responses and stores them in the configured database. These requests can then be filtered and inspecting using Silk's UI through the request overview:

It records things like:

• Time taken
• Num. queries
• Time spent on queries
• Request/Response headers
• Request/Response bodies

and so on.

Further details on each request are also available by clicking the relevant request:

SQL Inspection

Silk also intercepts SQL queries that are generated by each request. We can get a summary on things like the tables involved, number of joins and execution time:

Before diving into the stack trace to figure out where this request is coming from:

Profiling

Silk can also be used to profile random blocks of code/functions. It provides a decorator and a context manager for this purpose.

For example:

@silk_profile(name='View Blog Post')
def post(request, post_id):
    p = Post.objects.get(pk=post_id)
    return render_to_response('post.html', {
        'post': p
    })

Whenever a blog post is viewed we get an entry within the Silk UI:

Silk profiling not only provides execution time, but also collects SQL queries executed within the block in the same fashion as with requests:

Decorator

The silk decorator can be applied to both functions and methods

@silk_profile(name='View Blog Post')
def post(request, post_id):
    p = Post.objects.get(pk=post_id)
    return render_to_response('post.html', {
        'post': p
    })

class MyView(View):    
	@silk_profile(name='View Blog Post')
	def get(self, request):
		p = Post.objects.get(pk=post_id)
    	return render_to_response('post.html', {
        	'post': p
    	})

Context Manager

Using a context manager means we can add additional context to the name which can be useful for narrowing down slowness to particular database records.

def post(request, post_id):
    with silk_profile(name='View Blog Post #%d' % self.pk):
        p = Post.objects.get(pk=post_id)
    	return render_to_response('post.html', {
        	'post': p
    	})

Experimental Features

The below features are still in need of thorough testing and should be considered experimental.

Dynamic Profiling

One of Silk's more interesting features is dynamic profiling. If for example we wanted to profile a function in a dependency to which we only have read-only access (e.g. system python libraries owned by root) we can add the following to settings.py to apply a decorator at runtime:

SILKY_DYNAMIC_PROFILING = [{
    'module': 'path.to.module',
    'function': 'MyClass.bar'
}]

which is roughly equivalent to:

class MyClass(object):
    @silk_profile()
    def bar(self):
        pass

The below summarizes the possibilities:

"""
Dynamic function decorator
"""

SILKY_DYNAMIC_PROFILING = [{
    'module': 'path.to.module',
    'function': 'foo'
}]

# ... is roughly equivalent to
@silk_profile()
def foo():
    pass

"""
Dynamic method decorator
"""

SILKY_DYNAMIC_PROFILING = [{
    'module': 'path.to.module',
    'function': 'MyClass.bar'
}]

# ... is roughly equivalent to
class MyClass(object):

    @silk_profile()
    def bar(self):
        pass

"""
Dynamic code block profiling
"""

SILKY_DYNAMIC_PROFILING = [{
    'module': 'path.to.module',
    'function': 'foo',
    # Line numbers are relative to the function as opposed to the file in which it resides
    'start_line': 1,
    'end_line': 2,
    'name': 'Slow Foo'
}]

# ... is roughly equivalent to
def foo():
    with silk_profile(name='Slow Foo'):
        print (1)
        print (2)
    print(3)
    print(4)

Note that dynamic profiling behaves in a similar fashion to that of the python mock framework in that we modify the function in-place e.g:

""" my.module """
from another.module import foo

# ...do some stuff
foo()
# ...do some other stuff

,we would profile foo by dynamically decorating my.module.foo as opposed to another.module.foo:

SILKY_DYNAMIC_PROFILING = [{
    'module': 'my.module',
    'function': 'foo'
}]

If we were to apply the dynamic profile to the functions source module another.module.foo after it has already been imported, no profiling would be triggered.

Code Generation

Silk currently generates two bits of code per request:

Both are intended for use in replaying the request. The curl command can be used to replay via command-line and the python code can be used within a Django unit test or simply as a standalone script.

Installation

Existing Release

Pip

Silk is on PyPi. Install via pip (into your virtualenv) as follows:

pip install django-silk

Github Tag

Releases of Silk are available on github.

Once downloaded, run:

pip install dist/django-silk-<version>.tar.gz

Then configure Silk in settings.py:

MIDDLEWARE_CLASSES = (
    ...
    'silk.middleware.SilkyMiddleware',
)

INSTALLED_APPS = (
    ...
    'silk'
)

and to your urls.py:

urlpatterns += patterns('', url(r'^silk', include('silk.urls', namespace='silk')))

before running syncdb:

python manage.py syncdb

Silk will automatically begin interception of requests and you can proceed to add profiling if required. The UI can be reached at /silk/

Master

First download the source, unzip and navigate via the terminal to the source directory. Then run:

python package.py mas

You can either install via pip:

pip install dist/django-silk-mas.tar.gz

or run setup.py:

tar -xvf dist/django-silk-mas.tar.gz
python dist/django-silk-mas/setup.py

You can then follow the steps in 'Existing Release' to include Silk in your Django project.

Roadmap

I would eventually like to use this in a production environment. There are a number of things preventing that right now:

• Effect on performance.
  • For every SQL query executed, Silk executes another.
• Questionable stability.
• Space concerns.
  • Silk would quickly generate a huge number of database records.
  • Silk saves down both the request body and response body for each and every request handled by Django.
• Security risks involved in making the Silk UI available.
  • e.g. POST of password forms
  • exposure of session cookies