Profiling and performance analysis

Performance analysis for Pylint

To analyse the performance of Pylint we recommend to use the cProfile module from stdlib. Together with the pstats module this should give you all the tools you need to profile a Pylint run and see which functions take how long to run.

The documentation for both modules can be found at cProfile.

To profile a run of Pylint over itself you can use the following code and run it from the base directory. Note that cProfile will create a document called stats that is then read by pstats. The human-readable output will be stored by pstats in ./profiler_stats. It will be sorted by cumulative time:

import cProfile
import pstats
import sys

sys.argv = ["pylint", "pylint"]
cProfile.run("from pylint import __main__", "stats")

with open("profiler_stats", "w", encoding="utf-8") as file:
    stats = pstats.Stats("stats", stream=file)
    stats.sort_stats("cumtime")
    stats.print_stats()

You can also interact with the stats object by sorting or restricting the output. For example, to only print functions from the pylint module and sort by cumulative time you could use:

import cProfile
import pstats
import sys

sys.argv = ["pylint", "pylint"]
cProfile.run("from pylint import __main__", "stats")

with open("profiler_stats", "w", encoding="utf-8") as file:
    stats = pstats.Stats("stats", stream=file)
    stats.sort_stats("cumtime")
    stats.print_stats("pylint/pylint")

Lastly, to profile a run over your own module or code you can use:

import cProfile
import pstats
import sys

sys.argv = ["pylint", "your_dir/your_file"]
cProfile.run("from pylint import __main__", "stats")

with open("profiler_stats", "w", encoding="utf-8") as file:
    stats = pstats.Stats("stats", stream=file)
    stats.sort_stats("cumtime")
    stats.print_stats()

The documentation of the pstats module discusses other possibilities to interact with the profiling output.

Performance analysis of a specific checker

To analyse the performance of specific checker within Pylint we can use the human-readable output created by pstats.

If you search in the profiler_stats file for the file name of the checker you will find all functional calls from functions within the checker. Let's say we want to check the visit_importfrom method of the variables checker:

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
622    0.006    0.000    8.039    0.013 /MY_PROGRAMMING_DIR/pylint/pylint/checkers/variables.py:1445(visit_importfrom)

The previous line tells us that this method was called 622 times during the profile and we were inside the function itself for 6 ms in total. The time per call is less than a millisecond (0.006 / 622) and thus is displayed as being 0.

Often you are more interested in the cumulative time (per call). This refers to the time spent within the function and any of the functions it called or the functions they called (etc.). In our example, the visit_importfrom method and all of its child-functions took a little over 8 seconds to execute, with an execution time of 0.013 ms per call.

You can also search the profiler_stats for an individual function you want to check. For example _analyse_fallback_blocks, a function called by visit_importfrom in the variables checker. This allows more detailed analysis of specific functions:

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
1    0.000    0.000    0.000    0.000 /MY_PROGRAMMING_DIR/pylint/pylint/checkers/variables.py:1511(_analyse_fallback_blocks)

Parsing the profiler stats with other tools

Often you might want to create a visual representation of your profiling stats. A good tool to do this is gprof2dot. This tool can create a .dot file from the profiling stats created by cProfile and pstats. You can then convert the .dot file to a .png file with one of the many converters found online.

You can read the gprof2dot documentation for installation instructions for your specific environment.

Another option would be snakeviz.