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----
-title: Profiling Python web applications with visual tools
-permalink: /profiling-python-web-applications-with-visual-tools.html
-date: 2017-04-21T12:00:00+02:00
-layout: post
-type: post
-draft: false
----
-
-I have been profiling my software with KCachegrind for a long time now and I was
-missing this option when I am developing API's or other web services. I always
-knew that this is possible but never really took the time and dive into it.
-
-Before we begin there are some requirements. We will need to:
-
-- implement [cProfile](https://docs.python.org/2/library/profile.html#module-cProfile) into our web app,
-- convert output to [callgrind](http://valgrind.org/docs/manual/cl-manual.html) format with [pyprof2calltree](https://pypi.python.org/pypi/pyprof2calltree/),
-- visualize data with [KCachegrind](http://kcachegrind.sourceforge.net/html/Home.html) or [Profiling Viewer](http://www.profilingviewer.com/).
-
-
-If you are using MacOS you should check out [Profiling
-Viewer](http://www.profilingviewer.com/) or
-[MacCallGrind](http://www.maccallgrind.com/).
-
-![KCachegrind](/assets/posts/python-profiling/kcachegrind.png){:loading="lazy"}
-
-We will be dividing this post into two main categories:
-
-- writing simple web-service,
-- visualize profile of this web-service.
-
-## Simple web-service
-
-Let's use virtualenv so we won't pollute our base system. If you don't have
-virtualenv installed on your system you can install it with pip command.
-
-```bash
-# let's install virtualenv globally
-$ sudo pip install virtualenv
-
-# let's also install pyprof2calltree globally
-$ sudo pip install pyprof2calltree
-
-# now we create project
-$ mkdir demo-project
-$ cd demo-project/
-
-# now let's create folder where we will store profiles
-$ mkdir prof
-
-# now we create empty virtualenv in venv/ folder
-$ virtualenv --no-site-packages venv
-
-# we now need to activate virtualenv
-$ source venv/bin/activate
-
-# you can check if virtualenv was correctly initialized by
-# checking where your python interpreter is located
-# if command bellow points to your created directory and not some
-# system dir like /usr/bin/python then everything is fine
-$ which python
-
-# we can check now if all is good ➜ if ok couple of
-# lines will be displayed
-$ pip freeze
-# appdirs==1.4.3
-# packaging==16.8
-# pyparsing==2.2.0
-# six==1.10.0
-
-# now we are ready to install bottlepy ➜ web micro-framework
-$ pip install bottle
-
-# you can deactivate virtualenv but you will then go
-# under system domain ➜ for now don't deactivate
-$ deactivate
-```
-
-We are now ready to write simple web service. Let's create file app.py and paste
-code bellow in this newly created file.
-
-```python
-# -*- coding: utf-8 -*-
-
-import bottle
-import random
-import cProfile
-
-app = bottle.Bottle()
-
-# this function is a decorator and encapsulates function
-# and performs profiling and then saves it to subfolder
-# prof/function-name.prof
-# in our example only awesome_random_number function will
-# be profiled because it has do_cprofile defined
-def do_cprofile(func):
-  def profiled_func(*args, **kwargs):
-    profile = cProfile.Profile()
-    try:
-      profile.enable()
-      result = func(*args, **kwargs)
-      profile.disable()
-      return result
-    finally:
-      profile.dump_stats("prof/" + str(func.__name__) + ".prof")
-  return profiled_func
-
-
-# we use profiling over specific function with including
-# @do_cprofile above function declaration
-@app.route("/")
-@do_cprofile
-def awesome_random_number():
-  awesome_random_number = random.randint(0, 100)
-  return "awesome random number is " + str(awesome_random_number)
-
-@app.route("/test")
-def test():
-  return "dummy test"
-
-if __name__ == '__main__':
-  bottle.run(
-    app = app,
-    host = "0.0.0.0",
-    port = 4000
-  )
-
-# run with 'python app.py'
-# open browser 'http://0.0.0.0:4000'
-```
-
-When browser hits awesome\_random\_number() function profile is created in prof/
-subfolder.
-
-## Visualize profile
-
-Now let's create callgrind format from this cProfile output.
-
-```bash
-$ cd prof/
-$ pyprof2calltree -i awesome_random_number.prof
-# this creates 'awesome_random_number.prof.log' file in the same folder
-```
-
-This file can be opened with visualizing tools listed above. In this case we
-will be using Profilling Viewer under MacOS. You can open image in new tab.  As
-you can see from this example there is hierarchy of execution order of your
-code.
-
-![Profilling Viewer](/assets/posts/python-profiling/profiling-viewer.png){:loading="lazy"}
-
-> Make sure you convert output of the cProfile output every time you want to
-refresh and take a look at your possible optimizations because cProfile updates
-.prof file every time browser hits the function.
-
-This is just a simple example but when you are developing real-life applications
-this can be very illuminating, especially to see which parts of your code are
-bottlenecks and need to be optimized.
-
-## Update 2017-04-22
-
-Reddit user [mvt](https://www.reddit.com/user/mvt) also recommended this awesome
-web based profile visualizer [SnakeViz](https://jiffyclub.github.io/snakeviz/)
-that directly takes output from
-[cProfile](https://docs.python.org/2/library/profile.html#module-cProfile)
-module.
-
-<div class="reddit-embed" data-embed-media="www.redditmedia.com" data-embed-parent="false" data-embed-live="false" data-embed-uuid="583880c1-002e-41ed-a373-020a0ef2cff9" data-embed-created="2017-04-22T19:46:54.810Z"><a href="https://www.reddit.com/r/Python/comments/66v373/profiling_python_web_applications_with_visual/dgljhsb/">Comment</a> from discussion <a href="https://www.reddit.com/r/Python/comments/66v373/profiling_python_web_applications_with_visual/">Profiling Python web applications with visual tools</a>.</div><script async src="https://www.redditstatic.com/comment-embed.js"></script>
-
-```bash
-# let's install it globally as well
-$ sudo pip install snakeviz
-
-# now let's visualize
-$ cd prof/
-$ snakeviz awesome_random_number.prof
-# this automatically opens browser window and
-# shows visualized profile
-```
-
-![SnakeViz](/assets/posts/python-profiling/snakeviz.png){:loading="lazy"}
-
-Reddit user [ccharles](https://www.reddit.com/user/ccharles) suggested a better
-way for installing pip software by targeting user level instead of using sudo.
-
-<div class="reddit-embed" data-embed-media="www.redditmedia.com" data-embed-parent="false" data-embed-live="false" data-embed-uuid="f4f0459e-684d-441e-bebe-eb49b2f0a31d" data-embed-created="2017-04-22T19:46:10.874Z"><a href="https://www.reddit.com/r/Python/comments/66v373/profiling_python_web_applications_with_visual/dglpzkx/">Comment</a> from discussion <a href="https://www.reddit.com/r/Python/comments/66v373/profiling_python_web_applications_with_visual/">Profiling Python web applications with visual tools</a>.</div><script async src="https://www.redditstatic.com/comment-embed.js"></script>
-
-```bash
-# now we need to add this path to our $PATH variable
-# we do this my adding this line at the end of your
-# ~/.bashrc file
-PATH=$PATH:$HOME/.local/bin/
-
-# in order to use this new configuration you can close
-# and reopen terminal or reload .bashrc file
-$ source ~/.bashrc
-
-# now let's test if new directory is present in $PATH
-$ echo $PATH
-
-# now we can install on user level by adding --user
-# without use of sudo
-$ pip install snakeviz --user
-```
-
-Or as suggested by [mvt](https://www.reddit.com/user/mvt) you can 
-use [pipsi](https://github.com/mitsuhiko/pipsi).