py-spy VS python-uncompyle6

For CPU cycles, py-spy[0] is getting more and more used. For RAM, I would like to known too...

[0] -- https://github.com/benfred/py-spy

Theres also Py Spy, a profiling tool that can generate flame charts containing a mix of python and C (or C++) calls.

https://github.com/benfred/py-spy

It's worked really well for my needs

You should profile your consumer to check the bottlenecks. You can use the excellent py-spy(written in Rust). IMO a few usage of Numba there and there should solve your performance issues.

So as a first step you'll want to profile your program to figure out where it's slow, and hopefully that'll also tell you why it's slow. I'm the (biased) author of the Sciagraph profiler which is designed for this sort of application (https://sciagraph.com) but you can also try py-spy, which isn't as well designed for data processing/analysis applications (e.g. it won't visualize parallelism at all) but can still be informative (https://github.com/benfred/py-spy). Both are written in Rust ;)

Any advantages/disadvantages compared to py-spy [1]?

[1]: https://github.com/benfred/py-spy

Python profiling is enabled primarily through cprofile, and can be visualized with help of tools like snakeviz (output flame graph can look like this). There are also memory profilers like memray which does in-depth traces, or sampling profilers like py-spy.

A good profiler would be https://github.com/benfred/py-spy . If you run your app/benchmark with it, it should be able to draw a flamegraph telling you where the majority of time is spent. The info here is quite fine grained so it would already tell you where the bottleneck is. Without a full-fledged profiler you can also measure the timings in various parts of the code to understand where the bottleneck is.

Might be worth raising an issue on py-spy (a python profiler written in rust which "supports profiling native python extensions written in languages like C/C++ or Cython" to see if that can close the loop.

Q: What is RAPID_API_KEY = os.environ.get('RAPID_API_KEY')? A: You should store configuration in environment variables; never in code. See 12 factors app. Python .pyc files can easily be "decompiled" to .py and reveal all secrets in code.

I was able to figure out that this is a python program which was compiled to an .exe. Using uncompyle6 and pyc2bytecode, I was able to decompile the .exe into the python bytecode...but I'm no expert at reading python bytecode. If you want to do this yourself, note that you will need to use the same version of python as the version used to make the exe (python 3.9). I did easily by changing the python_version in my Pipfile to 3.9 and using pipenv shell.

It's a Pyinstaller binary.(I have used it once before, so I just knew it by checking the file.) Use https://github.com/extremecoders-re/pyinstxtractor to extract its source code archive in binary (by just running python pyinstxtractor.py ./backdoor or something), now many .pyc files are extracted. Find src.pyc and it's malformed as Python3.9, so https://github.com/rocky/python-uncompyle6/ denies to decompile. But challenge information says it's Python3.8, so I write helloworld python script and execute it with Python3.8. It yields Python3.8 .pyc file. Analyze it and find signature is \x55. Change src.pyc's signature from \x61 to \x55 and decompile by running uncompyle6 backdoor-src.38.pyc > backdoor-src.py

If not, you’ll either need to install and use 3.8 to run the program, or you’ll need to help the author continue support beyond 3.8.