/ Data Analysis

Performance analysis for Python

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.

tuna only displays the part of the timed call tree that can be deduced from the profile:

Installation

tuna is available from the Python Package Index, so
simply type

pip3 install tuna --user --upgrade

to install or upgrade.

Testing

To run the tuna unit tests, check out this repository and type

pytest

GitHub