Name: pyinstrument
Owner: edX
Description: Call stack profiler for Python. Inspired by Apple's Instruments.app
Created: 2015-01-26 13:34:50.0
Updated: 2015-01-26 13:34:51.0
Pushed: 2015-01-27 14:49:24.0
Size: 729
Language: Python
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A Python profiler that records the call stack of the executing code, instead of just the final function in it.
It uses a statistical profiler, meaning the code samples the stack
periodically (every 1 ms). This is lower overhead than event-
based profiling (as done by profile and cProfile).
This module is still very young, so I'd love any feedback/bug reports/pull requests!
pip install -e git+https://github.com/joerick/pyinstrument.git#egg=pyinstrument
pyinstrument supports Python 2.7 and 3.3+.
You can call pyinstrument directly from the command line.
python -m pyinstrument [options] myscript.py [args...]
Options:
-h, --help show this help message and exit
--setprofile run in setprofile mode, instead of signal mode
--html output HTML instead of text
-o OUTFILE, --outfile=OUTFILE
save report to <outfile>
--unicode force unicode text output
--no-unicode force ascii text output
--color force ansi color text output
--no-color force no color text output
This will run myscript.py to completion or until you interrupt it, and
then output the call tree.
Add pyinstrument.middleware.ProfilerMiddleware to MIDDLEWARE_CLASSES.
If you want to profile your middleware as well as your view (you probably
do) then put it at the start of the list.
Add ?profile to the end of the request URL to activate the profiler.
Instead of seeing the output of your view, pyinstrument renders an HTML
call tree for the view (as in the screenshot above).
PYINSTRUMENT_PROFILE_DIRIf you're writing an API, it's not easy to change the URL when you want
to profile something. In this case, add
PYINSTRUMENT_PROFILE_DIR = 'profiles' to your settings.py.
pyinstrument will profile every request and save the HTML output to the
folder profiles in your working directory.
pyinstrument import Profiler
iler = Profiler() # or Profiler(use_signal=False), see below
iler.start()
de you want to profile
iler.stop()
t(profiler.output_text(unicode=True, color=True))
You can omit the unicode and color flags if your output/terminal does
not support them.
On Mac/Linux/Unix, pyinstrument can run in 'signal' mode. This uses
OS-provided signals to interrupt the process every 1ms and record the stack.
It gives much lower overhead (and thus accurate) readings than the standard
Python sys.setprofile style profilers. However, this can
only profile the main thread.
On Windows and on multi-threaded applications, a setprofile mode is
available by passing use_signal=False to the Profiler constructor. It works
exactly the same as the signal mode, but has higher overhead. See the below
table for an example of the amount of overhead.
This overhead is important because code that makes a lot of Python function calls will appear to take longer than code that does not.
| Django template render × 4000 | Overhead
—————————|——————————:|———: Base | 1.46s |
| |
pyinstrument (signal) | 1.84s | 26% cProfile | 2.18s | 49% pyinstrument (setprofile) | 5.33s | 365% profile | 25.39s | 1739%
To run in setprofile mode:
--setprofile if using the command-line interfacePYINSTRUMENT_USE_SIGNAL = False in Djangouse_signal=False in the constructor for the Python APIWhen profiling Django, I'd recommend disabling django-debug-toolbar, django-devserver etc., as their instrumentation distort timings.
In signal mode, any calls to time.sleep will return
immediately. This is because of an implementation detail of time.sleep,
but matches the behaviour of the C function sleep.
Some system calls can fail with IOError when being profiled in signal
mode. If this happens to you, your only option is to run in setprofile
mode.
pyinstrument command. You can now profile python scripts from the shell
by running $ pyinstrument script.py. This is now equivalent to
python -m pyinstrument. Thanks @asmeurer!Application code is highlighted in HTML traces to make it easier to spot
Added PYINSTRUMENT_PROFILE_DIR option to the Django interface, which
will log profiles of all requests to a file the specified folder. Useful
for profiling API calls.
Added PYINSTRUMENT_USE_SIGNAL option to the Django interface, for use
when signal mode presents problems.
The standard Python profilers profile and cProfile produce
output where time is totalled according to the time spent in each function.
This is great, but it falls down when you profile code where most time is
spent in framework code that you're not familiar with.
Here's an example of profile output when using Django.
151940 function calls (147672 primitive calls) in 1.696 seconds
Ordered by: cumulative time
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.696 1.696 profile:0(<code object <module> at 0x1053d6a30, file "./manage.py", line 2>)
1 0.001 0.001 1.693 1.693 manage.py:2(<module>)
1 0.000 0.000 1.586 1.586 __init__.py:394(execute_from_command_line)
1 0.000 0.000 1.586 1.586 __init__.py:350(execute)
1 0.000 0.000 1.142 1.142 __init__.py:254(fetch_command)
43 0.013 0.000 1.124 0.026 __init__.py:1(<module>)
388 0.008 0.000 1.062 0.003 re.py:226(_compile)
158 0.005 0.000 1.048 0.007 sre_compile.py:496(compile)
1 0.001 0.001 1.042 1.042 __init__.py:78(get_commands)
153 0.001 0.000 1.036 0.007 re.py:188(compile)
106/102 0.001 0.000 1.030 0.010 __init__.py:52(__getattr__)
1 0.000 0.000 1.029 1.029 __init__.py:31(_setup)
1 0.000 0.000 1.021 1.021 __init__.py:57(_configure_logging)
2 0.002 0.001 1.011 0.505 log.py:1(<module>)
When you're using big frameworks like Django, it's very hard to understand how your own code relates to these traces.
Pyinstrument records the entire stack, so tracking expensive calls is much easier.