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Tools for statistical profiling.

Project description

Introduction

The ox_profile package provides a python framework for statistical profiling. If you are using Flask, then ox_profile provides a flask blueprint so that you can start/stop/analyze profiling from within your application. You can also run the profiler stand-alone without Flask as well.

To learn more, you can browse the overview slides in PDF form or read more details below.

Why statistical profiling (and why ox_profile)?

Python contains many profilers which instrument your code and give you exact results. A main benefit here is you know exactly what your program is doing. The disadvantage is that there can be significant overhead. With a statistical profiler such as ox_profile, we sample a running program periodically to get a sense of what the program is doing with an overhead that can be tuned as desired.

One main use case for ox_profile specifically (and statistical profiling in general) is that you can apply it to a production server to see how things work “in the wild”.

There are other statistical profilers out there for python (such as statprof), which are pretty good and may be better for your needs than ox_profile. So why would you consider ox_profile? Some possible reasons include:

  1. Works on non-UNIX systems (e.g., works on Windows).
    • Many other statistical profilers use various excellent features of LINUX or UNIX while ox_profile only really relies on the python sys._current_frames method.
  2. Simple to understand.
    • The code for ox_profile is fairly simple. The main work is really inside ox_profile.core.sampling.Sampler so it is easy to reason about or modify if you need slightly different profiling.
  3. Flask Blueprint provided.
    • If you are using Flask, then you can register the ox_profile blueprint and easily get statistical profiling in your flask app.

Usage

Stand alone

You can install ox_profile using pip via something like

$ pip install ox_profile

The simplest way to run the profiler is by starting the launcher, calling some functions, and the printing the profiled data via something like:

>>> from ox_profile.core.launchers import SimpleLauncher
>>> profiler = SimpleLauncher.launch()      # Create and start a profiler.
>>> # call some functions
>>> print(profiler.show())                  # Print current results in preformated way.
>>> profiler.cancel()                       # Turns off the profiler for good.

Often you may want a slightly more sophisticated use case where you can pause and unpause the profiler and get more details about its status as shown below:

>>> from ox_profile.core import launchers
>>> profiler = launchers.SimpleLauncher()    # Create an instance of launcher to be your profiler
>>> profiler.start()                         # The profiler is a thread so we need to call start
>>> profiler.unpause()                       # The profiler starts out paused so we unpause it
>>> # call functions or start main program
>>> profiler.pause()                         # You can pause if done profiling or leave running
>>> query, total_records = profiler.query()  # Query for what the profiler has found
>>> info = ['%s: %s' % (i.name, i.hits) for i in query]
>>> print('Items in query:\n  - %s' % (('\n  - '.join(info))))
>>> profiler.cancel()                        # This turns off the profiler for good

With Flask

If you are using the python flask framework and have installed ox_profile (e.g., with pip install ox_profile) then you can simply do the following in the appropriate place after initializing your app:

from ox_profile.ui.flask.views import OX_PROF_BP
app.register_blueprint(OX_PROF_BP)
app.config['OX_PROF_USERS'] = {<admin_user_1>, <admin_user_2>, ...}

where <admin_user_>, etc. are strings referring to users who are allowed to access ox_profile. By default, we check current_user.name, but you can set the OX_PROF_USERNAME_FIELD in app.config to something else (e.g., 'email' to choose which field of current_user is checked against the sequence of strings in OX_PROF_USERS.

Pointing your browser to the route /ox_profile/status will then show you the profiling status. By default, ox_profile starts out paused so that it will not incur any overhead for your app. Go to the /ox_profile/unpause route to unpause and begin profiling so that /ox_profile/status shows something interesting.

Output

Currently ox_profile is in alpha mode and so the output is fairly bare bones. When you look at the results of calling the query method of an instance of SimpleLauncher in stand alone mode or at the /ox_profile/status route when running with flask, what you get is a raw list of each function your program has called along with how many times that function was called in our sampling.

Design

High Level Design

Python offers a number of ways to get profiling information. In addition to high-level profiling tools such as in the profile package, there are specialized functions like sys.settrace and sys.setprofile. These are used for deterministic profiling and relatively robust but have some overhead as they are invoked on each function call.

At a high level, we want a way to get a sample of what the python interpreter is doing at any give instance. The sampling approach has the advantage that by turning the sampling interval low enough, we can add arbitrarily low overhead and make profiling feasible in a production system. By taking a long enough sample, however, we should be able to get arbitrarily accurate profiling information.

Low Level Design

At a low level, we do this sampling using sys._current_frames. As suggested by the leading underscore, this system function may be a bit less robust. Indeed, the documentation says “This function should be used for specialized purposes only.” Hopefully the core python developers will not make major changes to such a useful function.

In any case, the most interesting class is the Sampler class in the ox_profile.core.sampling module. This class has a run method which does the following:

  1. Uses sys.setswitchinterval to try and prevent a thread context switch.
  2. Calls sys._current_frames to sample what the python interpreter is doing.
  3. Updates a simple in-memory database of what functions are running.

In principle, you could just use the Sampler via something like

>>> from ox_profile.core import sampling, recording
>>> sampler = sampling.Sampler(recording.CountingRecorder())
>>> def foo():
...     sampler.run()
...     return 'done'
...
>>> foo()

The above would have the sampler take a snapshot of the stack frames when the foo function is run. Of course, this isn’t very useful by itself because it just tells you that foo is being run. It could be useful if there were other threads which were running because the sampler would tell you what stack frame those threads were in.

In principle, you could just call the Sampler.run method to track other threads but that still isn’t very convenient. To make things easy to use, we provide the SimpleLauncher class in the ox_profile.core.launchers module as shown in the Usage section. The SimpleLauncher basically does the following:

  1. Creates an instance of the Sampler class with reasonable defaults.
  2. Initializes itself as a daemon thread and starts.
  3. Pauses itself so the thread does nothing so as to not load the system.
  4. Provides an unpause method you can use when you want to turn on profiling.
  5. Provides a pause method if you want to turn off profiling.

In principle, you don’t need much beyond the Sampler but the SimpleLauncher makes it easier to launch a Sampler in a separate thread.

Known Issues

Granularity

With statistical profiling, we need to ask the thread to sleep for some small amount so that it does not overuse CPU resources. Sadly, the minimum sleep time (using either time.sleep or wait on a thread event) is on the order of 1–10 milliseconds on most operating systems. This means that you can not efficiently do statistical profiling at a granularity finer than about 1 millisecond.

Thus you should consider statistical profiling as a tool to find the relatively slow issues in production and not a tool for optimizing issues faster than about a millisecond.

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