ox_profile 0.1.0

Tools for 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.

# Why statistical profiling?

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".

# Usage

## 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`.

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.

## Stand alone

You can run the profiler without flask simply by starting the launcher
and then running queries when convenient via something like:

```
>>> from ox_profile.core import launchers
>>> launcher = launchers.SimpleLauncher()
>>> launcher.start()
>>> launcher.unpause()
>>> <call some functions>
>>> query, total_records = launcher.sampler.my_db.query()
>>> info = ['%s: %s' % (i.name, i.hits) for i in query]
>>> print('Items in query:\n - %s' % (('\n - '.join(info))))
>>> launcher.cancel() # This turns off the profiler for good
```

# Output

Currently `ox_profile` is in alpha mode and so the output is fairly
bare bones. When you look at the results of
`launcher.sampler.my_db.query()` 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.

# 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.