Skip to main content

A debugging and profiling tool that can trace and visualize python code execution

Project description

VizTracer

build pypi

VizTracer is a deterministic debugging/profiling tool that can trace and visualize python code. The major data VizTracer displays is FEE(function entry/exit), or equivalently, the call stack.

Unlike traditional flame graph, which is normally generated by sampling profiler, VizTracer can display every function executed and the corresponding entry/exit time from the beginning of the program to the end, which is helpful for programmers to catch sporatic performance issues.

With VizTracer, the programmer can intuitively understand what their code is doing and how long each function takes.

You can take a look at the demo result of an example program running recursive merge and quick sort algorithm.

trace viewer is used to display the stand alone html data.

VizTracer also supports json output that complies with Chrome trace event format, which can be loaded using perfetto

Requirements

VizTracer requires python 3.5+. No other package is needed. For now, VizTracer binary on pip only supports CPython + Linux. However, in theory the source code can build on Windows/MacOS.

Install

The prefered way to install VizTracer is via pip

pip install viztracer

You can also download the source code and build it yourself.

Usage

There are a couple ways to use VizTracer

Command Line

The easiest way to use VizTracer it through command line. Assume you have a python script to profile and the normal way to run it is:

python3 my_script.py

You can simply use VizTracer as

python3 -m viztracer my_script.py

which will generate a result.html file in the directory you run this command. Open it in browser and there's your result.

If your script needs arguments like

python3 my_script.py arg1 arg2

Just feed it as it is to VizTracer

python3 -m viztracer my_script.py arg1 arg2

You can also specify the tracer to be used in command line by passing --tracer argument. c tracer is the default value, you can use python tracer instead

python3 -m viztracer --tracer c my_script.py
python3 -m viztracer --tracer python my_script.py

You can specify the output file using -o or --output_file argument. The default output file is result.html. Two types of files are supported, html and json.

python3 -m viztracer -o other_name.html my_script.py
python3 -m viztracer -o other_name.json my_script.py

Inline

Sometimes the command line may not work as you expected, or you do not want to profile the whole script. You can manually start/stop the profiling in your script as well.

First of all, you need to import VizTracer class from the package, and make an object of it.

from viztracer import VizTracer

tracer = VizTracer()

If your code is executable by exec function, you can simply call tracer.run()

tracer.run("import random;random.randrange(10)")

This will as well generate a result.html file in your current directory. You can pass other file path to the function if you do not like the name result.html

tracer.run("import random; random.randrange(10)", output_file = "better_name.html")

When you need a more delicate profiler, you can manually enable/disable the profile using start() and stop() function.

tracer.start()
# Something happens here
tracer.stop()
tracer.save() # also takes output_file as an optional argument

With this method, you can only record the part that you are interested in

# Some code that I don't care
tracer.start()
# Some code I do care
tracer.stop()
# Some code that I want to skip
tracer.start()
# Important code again
tracer.stop()
tracer.save()

It is higly recommended that start() and stop() function should be in the same frame(same level on call stack). Problem might happen if the condition is not met

Display Result

By default, VizTracer will generate a stand alone HTML file which you can simply open with Chrome(maybe Firefox?). The front-end uses trace-viewer to show all the data.

However, you can generate json file as well, which complies to the chrome trace event format. You can load the json file on perfetto, which will replace the deprecated trace viewer in the future.

At the moment, perfetto did not support locally stand alone HTML file generation, so I'm not able to switch completely to it. The good news is that once you load the perfetto page, you can use it even when you are offline.

Trace Filter

Sometimes your code is really complicated or you need to run you program for a long time, which means the parsing time would be too long and the HTML/JSON file would be too large. There are ways in viztrace to filter out the data you don't need.

The filter mechanism only works in C tracer, and it works at tracing time, not parsing time. That means, using filters will introduce some extra overhead while your tracing, but will save significant memory, parsing time and disk space.

Currently we support two kinds of filters:

max_stack_depth

max_stack_depth is a straight forward way to filter your data. It limits the stack depth viztracer will trace, which cuts out deep call stacks, including some nasty recursive calls.

You can specify max_stack_depth in command line:

python3 -m viztracer --max_stack_depth 10 my_script.py

Or you can pass it as an argument to the VizTracer object:

from viztracer import VizTracer

tracer = VizTracer(max_stack_depth=10)

include_files and exclude_files

There are cases when you are only interested in functions in certain files. You can use include_files and exclude_files feature to filter out data you are not insterested in.

When you are using include_files, only the files and directories you specify are recorded. Similarly, when you are using exclude_files, files and directories you specify will not be recorded.

IMPORTANT: include_files and exclude_files can't be both spcified. You can only use one of them.

If a function is not recorded based on include_files or exclude_files rules, none of its descendent functions will be recorded, even if they match the rules

You can specify include_files and exclude_files in command line, but they can take more than one argument, which will make the following command ambiguous:

# Ambiguous command which should NOT be used
python3 -m viztracer --include_files ./src my_script.py

Instead, when you are using --include_files or --exclude_files, --run should be passed for the command that you actually want to execute:

# --run is used to solve ambiguity
python3 -m viztracer --include_files ./src --run my_script.py

However, if you have some other commands that can separate them and solve ambiguity, that works as well:

# This will work too
python3 -m viztracer --include_files ./src --max_stack_depth 5 my_script.py

You can also pass a list as an argument to VizTracer:

from viztracer import VizTracer

tracer = VizTracer(include_files=["./src", "./test/test1.py"])

Choose Tracer

The default tracer for current version is c tracer, which introduce a relatively small overhead(worst case 2-3x) but only works for CPython on Linux. However, if there's other reason that you would prefer a pure-python tracer, you can use python tracer using tracer argument when you initialize VizTracer object.

tracer = VizTracer(tracer="python")

python tracer will be deprecated because of the performance issue in the future

Cleanup of c Tracer

The interface for c trace is almost exactly the same as python tracer, except for the fact that c tracer does not support command line run now. However, to achieve lower overhead, some optimization is applied to c tracer so it will withhold the memory it allocates for future use to reduce the time it calls malloc(). If you want the c trace to free all the memory it allocates while collecting trace, use

tracer.cleanup()

Performance

Overhead is a big consideration when people choose profilers. VizTracer now has a similar overhead as native cProfiler. It works slightly worse in the worst case(Pure FEE) and better in easier case because even though it collects some extra information than cProfiler, the structure is lighter.

Admittedly, VizTracer is only focusing on FEE now, so cProfiler also gets other information that VizTracer does not acquire.

An example run for test_performance with Python 3.8 / Ubuntu 18.04.4 on Github VM

fib       (10336, 10336): 0.000852800 vs 0.013735200(16.11)[py] vs 0.001585900(1.86)[c] vs 0.001628400(1.91)[cProfile]
hanoi     (8192, 8192): 0.000621400 vs 0.012924899(20.80)[py] vs 0.001801800(2.90)[c] vs 0.001292900(2.08)[cProfile]
qsort     (10586, 10676): 0.003457500 vs 0.042572898(12.31)[py] vs 0.005594100(1.62)[c] vs 0.007573200(2.19)[cProfile]
slow_fib  (1508, 1508): 0.033606299 vs 0.038840998(1.16)[py] vs 0.033270399(0.99)[c] vs 0.032577599(0.97)[cProfile]

Limitations

VizTracer uses sys.setprofile() for its profiler capabilities, so it will conflict with other profiling tools which also use this function. Be aware of it when using VizTracer.

Bugs/Requirements

Please send bug reports and feature requirements through github issue tracker. VizTracer is currently under development now and it's open to any constructive suggestions.

License

Copyright Tian Gao, 2020.

Distributed under the terms of the Apache 2.0 license.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

viztracer-0.1.1-cp38-cp38-manylinux2010_x86_64.whl (674.4 kB view details)

Uploaded CPython 3.8 manylinux: glibc 2.12+ x86-64

viztracer-0.1.1-cp38-cp38-manylinux1_x86_64.whl (674.4 kB view details)

Uploaded CPython 3.8

viztracer-0.1.1-cp37-cp37m-manylinux2010_x86_64.whl (674.0 kB view details)

Uploaded CPython 3.7m manylinux: glibc 2.12+ x86-64

viztracer-0.1.1-cp37-cp37m-manylinux1_x86_64.whl (674.0 kB view details)

Uploaded CPython 3.7m

viztracer-0.1.1-cp36-cp36m-manylinux2010_x86_64.whl (673.0 kB view details)

Uploaded CPython 3.6m manylinux: glibc 2.12+ x86-64

viztracer-0.1.1-cp36-cp36m-manylinux1_x86_64.whl (673.0 kB view details)

Uploaded CPython 3.6m

viztracer-0.1.1-cp35-cp35m-manylinux2010_x86_64.whl (672.8 kB view details)

Uploaded CPython 3.5m manylinux: glibc 2.12+ x86-64

viztracer-0.1.1-cp35-cp35m-manylinux1_x86_64.whl (672.8 kB view details)

Uploaded CPython 3.5m

File details

Details for the file viztracer-0.1.1-cp38-cp38-manylinux2010_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp38-cp38-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 674.4 kB
  • Tags: CPython 3.8, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp38-cp38-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 c3d7316da852f782264e084b1c779ac3b16faf2a89dddab2f2bda4d93a05fbfc
MD5 e1222180c171f3af9ba18efcdede1109
BLAKE2b-256 990f652137510d46f5919a5817e3aad707fbff7d6f6136de74279e816281c65d

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 674.4 kB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 e0ab30da0e93d261be9ba211288f58baa98b74128fc51dc6dcb22da8e9dc00fa
MD5 48537a4e6a0052a06b883b6ab061c8de
BLAKE2b-256 a164a19ee27258107d2fdf05b3ab00dc7ac624a2e2db5f2ac07d4ac6fc6a7000

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp37-cp37m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp37-cp37m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 674.0 kB
  • Tags: CPython 3.7m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp37-cp37m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 527bb3609d2496d0c0857adc4e61db56ece54cda4e9d28fe62712027cbef2f9e
MD5 2e8d2c3f7bb64a8ff93b3b3a4ae738e2
BLAKE2b-256 74745270971c3e3417bb026304729ddd3d63369ffc9cecfb24c0a1efba6ba4c0

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 674.0 kB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 32eb3325642da1eb92a46ebd5f93cb960b4114717c212021e6ea16689207a9c0
MD5 f60c18869c7255efe78d58c9dc32986c
BLAKE2b-256 97f8771d7427e7691c936dbfa8aa46ed97fe4d28e452405fb1b79138225e7f78

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp36-cp36m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp36-cp36m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 673.0 kB
  • Tags: CPython 3.6m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp36-cp36m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 f15b7a6ead118fefe8ec1de821ee9539129fe6fe274b75896d035724dadee0b7
MD5 a28372d1c1532421fa8acdfab8a898b4
BLAKE2b-256 75d1636b380af433cef05229a2de2ea7d62ebcea06c41e5174fa4f81616f0dd0

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 673.0 kB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 9ff46b15ba44c89680240872541dfddbc14afb964e1c8c6045d0609ded9b5706
MD5 bb7ec92889d1f81af08c27b69ab2a1bb
BLAKE2b-256 435029d8d9bf2d01b9da8fd45eb05cc1e889917b9a39bc7a09d647b706fa34c0

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp35-cp35m-manylinux2010_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp35-cp35m-manylinux2010_x86_64.whl
  • Upload date:
  • Size: 672.8 kB
  • Tags: CPython 3.5m, manylinux: glibc 2.12+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp35-cp35m-manylinux2010_x86_64.whl
Algorithm Hash digest
SHA256 1c670d675c0828d3f7f55c5701916c29943d43a659bbf902bd1a9780d75e6692
MD5 15a65b48def9d89e3ef0c416220f8f0d
BLAKE2b-256 370262b6a72a35656e653e2005962ae4db1f3809bba5ed3000a51cd03a021186

See more details on using hashes here.

File details

Details for the file viztracer-0.1.1-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: viztracer-0.1.1-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 672.8 kB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/47.1.0 requests-toolbelt/0.9.1 tqdm/4.48.2 CPython/3.8.5

File hashes

Hashes for viztracer-0.1.1-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 c76719576fce7235e2a7b74c54813a5c985f52ee4255edd7bc1590f9bec8eb51
MD5 318e0761ad3aa10bb36e4aceb2715529
BLAKE2b-256 c086f6dfd571e020527bc4c9b829c449099e5b2929269f54afd35f4667910f9f

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page