VizTracer is a low-overhead logging/debugging/profiling tool that can trace and visualize your python code execution.
The front-end UI is powered by Perfetto. Use "AWSD" to zoom/navigate. More help can be found in "Support - Controls".
- Detailed function entry/exit information on timeline with source code
- Super easy to use, no source code change for most features, no package dependency
- Supports threading, multiprocessing, subprocess and async
- Logs arbitrary function/variable using RegEx without code change
- Powerful front-end, able to render GB-level trace smoothly
- Works on Linux/MacOS/Windows
The preferred way to install VizTracer is via pip
pip install viztracer
Assume you have a python script to run:
python3 my_script.py arg1 arg2
You can simply use VizTracer by
viztracer my_script.py arg1 arg2
result.json file will be generated, which you can open with
vizviewer will host an HTTP server on
http://localhost:9001. You can also open your browser and use that address.
If you do not want vizviewer to open the webbrowser automatically, you can use
vizviewer --server_only result.json
If you just need to bring up the trace report once, and do not want the persistent server, use
vizviewer --once result.json
vizviewer result.json # You can display all the files in a directory and open them in browser too vizviewer ./ # For very large trace files, try external trace processor vizviewer --use_external_processor result.json
You can also generate standalone
viztracer -o result.html my_script.py arg1 arg2
The standalone HTML file is powered by catapult trace viewer which is an old tool Google made and is being replaced by Perfetto gradually.
Catapult trace viewer is sluggish with larger traces and is not actively maintained. It is recommended to use Perfetto instead.
However, if you really need a standalone HTML file, this is the only option. Perfetto does not support standalone files.
You can use vizviewer to open the html file as well, just to make the interface consistent
--open to open the reports right after tracing
viztracer --open my_script.py arg1 arg2 viztracer -o result.html --open my_script.py arg1 arg2
modules and console scripts(like
flask) are supported as well
viztracer -m your_module
viztracer flask run
You can also manually start/stop VizTracer in your script as well.
from viztracer import VizTracer tracer = VizTracer() tracer.start() # Something happens here tracer.stop() tracer.save() # also takes output_file as an optional argument
Or, you can do it with
with VizTracer(output_file="optional.json") as tracer: # Something happens here
If you are using Jupyter, you can use viztracer cell magics.
# You need to load the extension first %load_ext viztracer
%%viztracer # Your code after
VizTracer Report button will appear after the cell and you can click it to view the results
VizTracer can filter out the data you don't want to reduce overhead and keep info of a longer time period before you dump the log.
Extra Logs without Code Change
VizTracer can log extra information without changing your source code
- Any Variable/Attribute with RegEx
- Function Entry
- Variables in Specified Function
- Garbage Collector Operation
- Function Input Arguments
- Function Return Value
- Audit Events
- Raised Exceptions
Add Custom Event
VizTracer supports inserting custom events while the program is running. This works like a print debug, but you can know when this print happens while looking at trace data.
Multi Thread Support
VizTracer supports python native
threading module without the need to do any modification to your code. Just start
VizTracer before you create threads and it will just work.
For other multi-thread scenarios, you can use
enable_thread_tracing() to let VizTracer know about the thread to trace it.
Refer to multi thread docs for details
Multi Process Support
loky out of the box.
For more general multi-process cases, VizTracer can support with some extra steps.
Refer to multi process docs for details
asyncio natively, but could enhance the report by using
Refer to async docs for details
VizTracer can show flamegraph of traced data.
vizviewer --flamegraph result.json
VizTracer supports remote attach to an arbitrary Python process to trace it, as long as viztracer is importable
Refer to remote attach docs
VizTracer needs to dump the internal data to json format. It is recommended for the users to install
orjson, which is much faster than the builtin
json library. VizTracer will try to import
orjson and fall back to the builtin
json library if
orjson does not exist.
You can virtually debug your program with you saved json report. The interface is very similar to
pdb. Even better, you can go back in time
because VizTracer has all the info recorded for you.
Refer to the docs for detailed commands
VizTracer will introduce 2x to 3x overhead in the worst case. The overhead is much better if there are less function calls or if filters are applied correctly.
An example run for test_performance with Python 3.8 / Ubuntu 18.04.4 on Github VM
fib: 0.000678067(1.00)[origin] 0.019880272(29.32)[py] 0.011103901(16.38)[parse] 0.021165599(31.21)[json] 0.001344933(1.98)[c] 0.008181911(12.07)[parse] 0.015789866(23.29)[json] 0.001472846(2.17)[cProfile] hanoi (6148, 4100): 0.000550255(1.00)[origin] 0.016343521(29.70)[py] 0.007299123(13.26)[parse] 0.016779364(30.49)[json] 0.001062505(1.93)[c] 0.006416136(11.66)[parse] 0.011463236(20.83)[json] 0.001144914(2.08)[cProfile] qsort (8289, 5377): 0.002817679(1.00)[origin] 0.052747431(18.72)[py] 0.011339725(4.02)[parse] 0.023644345(8.39)[json] 0.004767673(1.69)[c] 0.008735166(3.10)[parse] 0.017173703(6.09)[json] 0.007248019(2.57)[cProfile] slow_fib (1135, 758): 0.028759652(1.00)[origin] 0.033994071(1.18)[py] 0.001630461(0.06)[parse] 0.003386635(0.12)[json] 0.029481623(1.03)[c] 0.001152415(0.04)[parse] 0.002191417(0.08)[json] 0.028289305(0.98)[cProfile]
For full documentation, please see https://viztracer.readthedocs.io/en/stable
Please send bug reports and feature requests through github issue tracker. VizTracer is currently under development now and it's open to any constructive suggestions.
Copyright Tian Gao, 2020.
Distributed under the terms of the Apache 2.0 license.