Metadata-Version: 1.1
Name: django-silk
Version: 3.0.1
Summary: Silky smooth profiling for the Django Framework
Home-page: http://www.mtford.co.uk/projects/silk/
Author: Michael Ford
Author-email: mtford@gmail.com
License: MIT License
Description: Silk
        ====
        
        *Silk has now moved to the [Jazzband](https://jazzband.co/) organization and is looking for contributors - if you think you can help out, please get in touch!*
        
        [![TravisCI Build](https://img.shields.io/travis/jazzband/django-silk/master.svg)](https://travis-ci.org/jazzband/django-silk)
        [![PyPI Download](https://img.shields.io/pypi/v/django-silk.svg)](https://pypi.python.org/pypi/django-silk)
        [![PyPI Python Versions](https://img.shields.io/pypi/pyversions/django-silk.svg)](https://pypi.python.org/pypi/django-silk)
        [![Jazzband](https://jazzband.co/static/img/badge.svg)](https://jazzband.co/)
        
        Silk is a live profiling and inspection tool for the Django framework. Silk intercepts and stores HTTP requests and database queries before presenting them in a user interface for further inspection:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/1.png" width="720px"/>
        
        ## Contents
        
        * [Requirements](#requirements)
        * [Installation](#installation)
        * [Features](#features)
        * [Configuration](#configuration)
            * [Authentication/Authorisation](#authenticationauthorisation)
            * [Request/Response bodies](#requestresponse-bodies)
            * [Meta-Profiling](#meta-profiling)
            * [Recording a fraction of requests](#recording-a-fraction-of-requests)
            * [Limiting request/response data](#limiting-requestresponse-data)
            * [Clearing logged data](#clearing-logged-data)
        * [Contributing](#contributing)
            * [Development Environment](#development-environment)
        
        ## Requirements
        
        Silk has been tested with:
        
        * Django: 1.11, 2.0
        * Python: 2.7, 3.4, 3.5, 3.6
        
        
        ## Installation
        
        Via pip into a virtualenv:
        
        ```bash
        pip install django-silk
        ```
        
        In `settings.py` add the following:
        
        ```python
        MIDDLEWARE = [
            ...
            'silk.middleware.SilkyMiddleware',
            ...
        ]
        
        INSTALLED_APPS = (
            ...
            'silk'
        )
        ```
        
        **Note:** The middleware placement is sensitive. If the middleware before `silk.middleware.SilkyMiddleware` returns from `process_request` then `SilkyMiddleware` will never get the chance to execute. Therefore you must ensure that any middleware placed before never returns anything from `process_request`. See the [django docs](https://docs.djangoproject.com/en/dev/topics/http/middleware/#process-request) for more information on this.
        
        **Note:** If you are using `django.middleware.gzip.GZipMiddleware`, place that **before** `silk.middleware.SilkyMiddleware`, otherwise you will get an encoding error.
        
        To enable access to the user interface add the following to your `urls.py`:
        
        ```python
        urlpatterns += [url(r'^silk/', include('silk.urls', namespace='silk'))]
        ```
        
        before running migrate:
        
        ```bash
        python manage.py makemigrations
        
        python manage.py migrate
        
        python manage.py collectstatic
        ```
        
        
        Silk will automatically begin interception of requests and you can proceed to add profiling
        if required. The UI can be reached at `/silk/`
        
        ### Alternative Installation
        
        Via [github tags](https://github.com/jazzband/django-silk/releases):
        
        ```bash
        pip install https://github.com/jazzband/silk/archive/<version>.tar.gz
        ```
        
        You can install from master using the following, but please be aware that the version in master
        may not be working for all versions specified in [requirements](#requirements)
        
        ```bash
        pip install -e git+https://github.com/jazzband/django-silk.git#egg=silk
        ```
        
        ## Features
        
        Silk 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.
        
        ### Request Inspection
        
        The Silk middleware intercepts and stores requests and responses in the configured database.
        These requests can then be filtered and inspecting using Silk's UI through the request overview:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/1.png" width="720px"/>
        
        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:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/2.png" width="720px"/>
        
        ### 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 (the table can be sorted by clicking on a column header):
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/3.png" width="720px"/>
        
        Before diving into the stack trace to figure out where this request is coming from:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/5.png" width="720px"/>
        
        ### Profiling
        
        Turn on the SILKY_PYTHON_PROFILER setting to use Python's built-in cProfile profiler. Each request will be separately profiled and the profiler's output will be available on the request's Profiling page in the Silk UI.
        
        ```python
        SILKY_PYTHON_PROFILER = True
        ```
        
        If you would like to also generate a binary `.prof` file set the following:
        
        ```python
        SILKY_PYTHON_PROFILER_BINARY = True
        ```
        
        When enabled, a graph visualisation generated using [gprof2dot](https://github.com/jrfonseca/gprof2dot) and [viz.js](https://github.com/almende/vis) is shown in the profile detail page:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/10.png" width="720px"/>
        
        
        A custom storage class can be used for the saved generated binary `.prof` files:
         
        ```python
        SILKY_STORAGE_CLASS = 'path.to.StorageClass'
        ```
        
        The default storage class is `silk.storage.ProfilerResultStorage`, and when using that you can specify a path of your choosing. You must ensure the specified directory exists.
        
        ```python
        # If this is not set, MEDIA_ROOT will be used.
        SILKY_PYTHON_PROFILER_RESULT_PATH = '/path/to/profiles/'
        ```
        
        A download button will become available with a binary `.prof` file for every request. This file can be used for further analysis using [snakeviz](https://github.com/jiffyclub/snakeviz) or other cProfile tools 
        
        
        Silk can also be used to profile specific blocks of code/functions. It provides a decorator and a context
        manager for this purpose.
        
        For example:
        
        ```python
        from silk.profiling.profiler import silk_profile
        
        
        @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:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/7.png" width="720px"/>
        
        Silk profiling not only provides execution time, but also collects SQL queries executed within the block in the same fashion as with requests:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/8.png" width="720px"/>
        
        #### Decorator
        
        The silk decorator can be applied to both functions and methods
        
        ```python
        from silk.profiling.profiler import silk_profile
        
        
        # Profile a view function
        @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
            })
        
        
        # Profile a method in a view class
        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.
        
        ```python
        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
                })
        ```
        
        #### 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:
        
        ```python
        SILKY_DYNAMIC_PROFILING = [{
            'module': 'path.to.module',
            'function': 'MyClass.bar'
        }]
        ```
        
        which is roughly equivalent to:
        
        ```python
        class MyClass(object):
            @silk_profile()
            def bar(self):
                pass
        ```
        
        The below summarizes the possibilities:
        
        ```python
        
        """
        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:
        
        ```python
        """ 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`:
        
        ```python
        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:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/9.png" width="720px"/>
        
        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.
        
        ## Configuration
        
        ### Authentication/Authorisation
        
        By default anybody can access the Silk user interface by heading to `/silk/`. To enable your Django
        auth backend place the following in `settings.py`:
        
        ```python
        SILKY_AUTHENTICATION = True  # User must login
        SILKY_AUTHORISATION = True  # User must have permissions
        ```
        
        If `SILKY_AUTHORISATION` is `True`, by default Silk will only authorise users with `is_staff` attribute set to `True`.
        
        You can customise this using the following in `settings.py`:
        
        ```python
        def my_custom_perms(user):
            return user.is_allowed_to_use_silk
        
        SILKY_PERMISSIONS = my_custom_perms
        ```
        
        You can also use a `lambda`.
        
        ```python
        SILKY_PERMISSIONS = lambda user: user.is_superuser
        ```
        
        ### Request/Response bodies
        
        By default, Silk will save down the request and response bodies for each request for future viewing
        no matter how large. If Silk is used in production under heavy volume with large bodies this can have
        a huge impact on space/time performance. This behaviour can be configured with the following options:
        
        ```python
        SILKY_MAX_REQUEST_BODY_SIZE = -1  # Silk takes anything <0 as no limit
        SILKY_MAX_RESPONSE_BODY_SIZE = 1024  # If response body>1024kb, ignore
        ```
        
        ### Meta-Profiling
        
        Sometimes it is useful to be able to see what effect Silk is having on the request/response time. To do this add
        the following to your `settings.py`:
        
        ```python
        SILKY_META = True
        ```
        
        Silk will then record how long it takes to save everything down to the database at the end of each
        request:
        
        <img src="https://raw.githubusercontent.com/jazzband/django-silk/master/screenshots/meta.png"/>
        
        Note that in the above screenshot, this means that the request took 29ms (22ms from Django and 7ms from Silk)
        
        ### Recording a Fraction of Requests
        
        On high-load sites it may be helpful to only record a fraction of the requests that are made.To do this add the following to your `settings.py`:
        
        Note: This setting is mutually exclusive with SILKY_INTERCEPT_FUNC.
        
        ```python
        SILKY_INTERCEPT_PERCENT = 50 # log only 50% of requests
        ```
        
        #### Custom Logic for Recording Requests
        
        On high-load sites it may also be helpful to write your own logic for when to intercept requests.To do this add the following to your `settings.py`:
        
        Note: This setting is mutually exclusive with SILKY_INTERCEPT_PERCENT.
        
        ```python
        def my_custom_logic(request):
            return 'record_requests' in request.session
        
        SILKY_INTERCEPT_FUNC = my_custom_logic # log only session has recording enabled.
        ```
        
        You can also use a `lambda`.
        
        ```python
        # log only session has recording enabled.
        SILKY_INTERCEPT_FUNC = lambda request: 'record_requests' in request.session
        ```
        
        ### Limiting request/response data
        
        To make sure silky garbage collects old request/response data, a config var can be set to limit the number of request/response rows it stores.
        
        ```python
        SILKY_MAX_RECORDED_REQUESTS = 10**4
        ```
        
        The garbage collection is only run on a percentage of requests to reduce overhead.  It can be adjusted with this config:
        
        ```python
        SILKY_MAX_RECORDED_REQUESTS_CHECK_PERCENT = 10
        ```
        
        ### Clearing logged data
        
        A management command will wipe out all logged data:
        
        ```bash
        python manage.py silk_clear_request_log
        ```
        
        ## Contributing
        
        [![Jazzband](https://jazzband.co/static/img/jazzband.svg)](https://jazzband.co/)
        
        This is a [Jazzband](https://jazzband.co/) project. By contributing you agree to abide by the [Contributor Code of Conduct](https://jazzband.co/about/conduct) and follow the [guidelines](https://jazzband.co/about/guidelines).
        
        ### Development Environment
        
        Silk features a project named `project` that can be used for `silk` development. It has the `silk` code symlinked so
        you can work on the sample `project` and on the `silk` package at the same time.
        
        In order to setup local development you should first install all the dependencies for the test `project`. From the
        root of the `project` directory:
        
        ```bash
        pip install -r test-requirements.txt
        ```
        
        You will also need to install `silk`'s dependencies. From the root of the git repository:
        
        ```bash
        pip install -r requirements.txt
        ```
        
        At this point your virtual environment should have everything it needs to run both the sample `project` and
        `silk` successfully.
        
        Before running, you must set the `DB` and `DB_NAME` environment variables:
        
        ```bash
        export DB=sqlite3
        export DB_NAME=db.sqlite3
        ```
        
        For other combinations, check [`.travis.yml`](./.travis.yml).
        
        Now from the root of the sample `project` directory start the django server
        
        ```bash
        python manage.py runserver
        ```
        
        #### Running the tests
        
        ```bash
        cd project
        ./tests/test_migrations.sh
        python manage.py test --noinput
        ```
        
        Happy profiling!
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment
Classifier: Framework :: Django
Classifier: Framework :: Django :: 1.11
Classifier: Framework :: Django :: 2.0
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Internet :: WWW/HTTP
Classifier: Topic :: Internet :: WWW/HTTP :: Dynamic Content
