Have python-archives also take tar.gz

python-archives currently only takes zip.

In our use case, we want to package the whole conda environment into python-archives, similar to how the docs suggest about using venv (Python virtual environment). As we use PyFlink for ML, there are inevitably a few large dependencies (tensorflow, torch, pyarrow), as well as a lot of small dependencies.

This pattern is not friendly for zip. According to the post, zip compresses each file independently, and it is not performing good when dealing with a lot of small files. On the other hand, tar simply bundles all files into a tarball, then we can apply gzip to the whole tarball to achieve smaller size. This may explain why the official packaging tool - conda pack   conda pack  produces tar.gz by default, even though zip is an option if we really want to.

To further prove the idea, I use my laptop and conda env to run an experiment. My OS: macOS 10.15.7

1. Create an environment.yaml as well as a requirements.txt
2. Run `conda env create -f environment.yaml` to create the conda env
3. Run conda pack to produce a tar.gz
4. Run conda pack faetflow-ml-env.zip to produce a zip

More details:

environment.yaml

name: featflow-ml-env
channels: 
- pytorch
- conda-forge
- defaults
dependencies: 
- python=3.7
- pytorch=1.8.0
- scikit-learn=0.23.2
- pip
- pip: 
- -r file:requirements.txt

requirements.txt

apache-flink==1.12.0
deepctr-torch==0.2.6
black==20.8b1
confluent-kafka==1.6.0
pytest==6.2.2
testcontainers==3.4.0
kafka-python==2.0.2

 
End result: the tar.gz is 854M, the zip is 1.6G

So, long story short, python-archives only support zip, while zip is not a good choice for packaging ML libs. Let's change this by adding python-archives tar.gz support.

Change will happen in this way: In ProcessPythonEnvironmentManager.java, check the suffix. If tar.gz, unarchive it using gzip decompresser.