ox-profile 0.2.14
Tools for statistical profiling.
License: MIT
Keywords: statistical profiling

# 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](https://github.com/emin63/ox_profile/blob/master/docs/overview_slides.pdf) 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

```sh
    $ 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.