A tool that upgrades your PySpark scripts to Spark 3.3 as per Spark migration Guideline
Project description
PySparkler
About
PySparkler is a tool that upgrades your PySpark scripts to Spark 3.3. It is a command line tool that takes a PySpark script as input and outputs a Spark 3.3 compatible script. It is written in Python and uses the LibCST module to parse the input script and generate the output script.
Basic Usage
Install from PyPI:
pip install pysparkler
Provide the path to the script you want to upgrade:
pysparkler upgrade --input-file /path/to/script.py
Contributing
For the development, Poetry is used for packing and dependency management. You can install this using:
pip install poetry
If you have an older version of pip and virtualenv you need to update these:
pip install --upgrade virtualenv pip
Installation
To get started, you can run make install, which installs Poetry and all the dependencies of the PySparkler library.
This also installs the development dependencies.
make install
If you don't want to install the development dependencies, you need to install using poetry install --only main.
If you want to install the library on the host, you can simply run pip3 install -e .. If you wish to use a virtual
environment, you can run poetry shell. Poetry will open up a virtual environment with all the dependencies set.
IDE Setup
To set up IDEA with Poetry:
- Open up the Python project in IntelliJ
- Make sure that you're on latest master (that includes Poetry)
- Go to File -> Project Structure (⌘;)
- Go to Platform Settings -> SDKs
- Click the + sign -> Add Python SDK
- Select Poetry Environment from the left hand side bar and hit OK
- It can take some time to download all the dependencies based on your internet
- Go to Project Settings -> Project
- Select the Poetry SDK from the SDK dropdown, and click OK
For IDEA ≤2021 you need to install the Poetry integration as a plugin.
Now you're set using Poetry, and all the tests will run in Poetry, and you'll have syntax highlighting in the pyproject.toml to indicate stale dependencies.
Linting
pre-commit is used for autoformatting and linting:
make lint
Pre-commit will automatically fix the violations such as import orders, formatting etc. Pylint errors you need to fix yourself.
In contrast to the name suggest, it doesn't run the checks on the commit. If this is something that you like, you can
set this up by running pre-commit install.
You can bump the integrations to the latest version using pre-commit autoupdate. This will check if there is a newer
version of {black,mypy,isort,...} and update the yaml.
Testing
For Python, pytest is used a testing framework in combination with coverage to enforce 90%+ code coverage.
make test
To pass additional arguments to pytest, you can use PYTEST_ARGS. For example, to run pytest in verbose mode:
make test PYTEST_ARGS="-v"
Architecture
Why LibCST?
LibCST is a Python library that provides a concrete syntax tree (CST) for Python code. CST preserves even the whitespaces of the source code which is very important since we only want to modify the code and not the formatting.
How does it work?
Using the codemod module of LibCST can simplify the process of writing a PySpark migration script, as it allows us to write small, reusable transformers and chain them together to perform a sequence of transformations.
Why Transformer Codemod? Why not Visitor?
The main advantage of using a Transformer is that it allows for more fine-grained control over the transformation process. Transformer classes can be defined to apply specific transformations to specific parts of the codebase, and multiple Transformer classes can be combined to form a chain of transformations. This can be useful when dealing with complex codebases where different parts of the code require different transformations.
More on this can be found here.
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