Performance analysis for Python

tuna

tuna is a modern, lightweight Python profile viewer inspired by the amazing SnakeViz. It handles runtime and import profiles, has zero dependencies, uses d3 and bootstrap, and avoids certain errors present in SnakeViz.

Create a runtime profile with

python -mcProfile -o program.prof yourfile.py

or an import
profile
with

python -X importtime yourfile.py 2> import.log

and show it with

tuna program.prof

Why tuna doesn't show the whole call tree

The whole timed call tree cannot be retrieved from profile data. Python developers
made the decision to only store parent data in profiles because it can be computed
with little overhead. To illustrate, consider the following program.

import time


def a(t0, t1):
    c(t0)
    d(t1)
    return


def b():
    return a(1, 4)


def c(t):
    time.sleep(t)
    return


def d(t):
    time.sleep(t)
    return


if __name__ == "__main__":
    a(4, 1)
    b()

The root process (__main__) calls a() which spends 4 seconds in c() and 1 second
in d(). __main__ also calls b() which calls a(), this time spending 1 second in
c() and 4 seconds in d(). The profile, however, will only store that c() spent a
total of 5 seconds when called from a(), and likewise d(). The information that the
program spent more time in c() when called in root -> a() -> c() than when called in
root -> b() -> a() -> c() is not present in the profile.