Package: pysparkler
PySparklerPySparkler is a tool that upgrades your PySpark scripts to latest Spark version. It is a command line tool that takes a
PySpark script as input and outputs latest Spark version compatible script. It is written in Python and uses theLibCSTmodule to parse the input script and generate the output script.InstallationWe recommend installing PySparkler from PyPI usingpipxwhich allows us to install and
run Python Applications in Isolated Environments. To install pipx on your system, follow the instructionshere. Once pipx is installed, you can install PySparkler using:pipxinstallpysparklerThat's it! You are now ready to use PySparkler.pysparkler--helpGetting StartedProvide the path to the script you want to upgrade:pysparklerupgrade--input-file/path/to/script.pyPySparkler parses the code and can perform either of the following actions:Code Transformations- These are modifications that are performed on the code to make it compatible with the
latest Spark version. For example, if you are upgrading from Spark 2.4 to 3.0, PySparkler will alphabetically sort the
keyword arguments in theRowconstructor to preserve backwards compatible behavior. This action will also add a
comment to the end of statement line being modified to indicate that the code was modified by PySparkler, explaining
why.Code Hints- Python is a dynamically-typed language, so there are situations wherein PySparkler cannot, with
100% accuracy, determine if the code is eligible for a transformation. In such situations, PySparkler adds code hints to
guide the end-user to make an appropriate change if needed. Code hints are comments that are added to the end of a
statement line to suggest changes that may be needed it to make it compatible with the latest Spark version.
For example, if you are upgrading from Spark 2.4 to 3.0 and PySparkler detectsspark.sql.execution.arrow.enabledis
set toTruein your code, it will add a code hint to the end of the line to suggest settingspark.sql.execution.pandas.convertToArrowArraySafelytoTruein case you want to raise errors in case of Integer
overflow or Floating point truncation, instead of silent allows. As you can see the suggestion is pretty contextual and
may not be applicable in all cases. In cases where not applicable, the end-user can choose to ignore the code hint.NOTE: PySparkler tries to keep the code formatting intact as much as possible. However, it is possible that the
statement lines it takes actions on may fail the linting checks post changes. In such situations, the end-user will have
to fix the linting errors manually.PySpark Upgrades SupportedThis tool follows theApache Spark Migration guide for PySparkto upgrade your PySpark scripts. In the latest stable version it supports the following upgrades from the migration guide:MigrationSupportedDetailsUpgrading from PySpark 3.3 to 3.4❌LinkUpgrading from PySpark 3.2 to 3.3✅LinkUpgrading from PySpark 3.1 to 3.2✅LinkUpgrading from PySpark 2.4 to 3.0✅LinkUpgrading from PySpark 2.3 to 2.4✅LinkUpgrading from PySpark 2.3.0 to 2.3.1 and above✅LinkUpgrading from PySpark 2.2 to 2.3✅LinkUpgrading from PySpark 2.1 to 2.2✅NAUpgrading from PySpark 1.4 to 1.5❌LinkUpgrading from PySpark 1.0-1.2 to 1.3❌LinkFeatures SupportedThe tool supports the following features:FeatureSupportedUpgrade PySpark Python script✅Upgrade PySpark Jupyter Notebook✅Upgrade SQL✅Dry-run Mode✅Verbose Mode✅Customize code transformers using YAML config✅Upgrade PySpark Python scriptThe tool can upgrade a PySpark Python script. It takes the path to the script as input and upgrades it in place:pysparklerupgrade--input-file/path/to/script.pyIf you want to output the upgraded script to a different directory, you can use the--output-fileflag:pysparklerupgrade--input-file/path/to/script.py--output-file/path/to/output.pyUpgrade PySpark Jupyter NotebookThe tool can upgrade a PySpark Jupyter Notebook to latest Spark version. It takes the path to the notebook as input and
upgrades it in place:pysparklerupgrade--input-file/path/to/notebook.ipynbSimilar to upgrading python scripts, if you want to output the upgraded notebook to a different directory, you can use
the--output-fileflag:pysparklerupgrade--input-file/path/to/notebook.ipynb--output-file/path/to/output.ipynbTo change the output kernel name in the output Jupyter notebook, you can use the--output-kernelflag:pysparklerupgrade--input-file/path/to/notebook.ipynb--output-kernelspark33-python3Upgrade SQLPySparkler when encounters a SQL statement in the input script makes an attempt to upgrade them. However, it is not
always possible to upgrade certain formatted string SQL statements that have complex expressions within. In such
cases the tool does leave code hints to let users know that they need to upgrade the SQL themselves.To facilitate this, it exposes a commandupgrade-sqlfor users to perform this DIY. The steps for that include:De-template the SQL.Upgrade the de-templated SQL usingpysparkler upgrade-sql. See below for details.Re-template the upgraded SQL.Replace the old SQL with the upgraded SQL in the input script.In order to perform step #2 i.e. you can either echo the SQL statement and pipe it to the tool:echo"SELECT * FROM table"|pysparklerupgrade-sqlor you can use thecatcommand to pipe the SQL statement to the tool:cat/path/to/sql.sql|pysparklerupgrade-sqlDry-Run ModeFor both the above upgrade options, to run in dry mode, you can use the--dry-runflag. This will not write the
upgraded script but will print a unified diff of the input and output scripts for you to inspect the changes:pysparklerupgrade--input-file/path/to/script.py--dry-runVerbose ModeFor both the above upgrade options, to run in verbose mode, you can use the--verboseflag. This will print tool's
input variables, the input file content, the output content, and a unified diff of the input and output content:pysparkler--verboseupgrade--input-file/path/to/script.pyCustomize code transformers using YAML configThe tool uses a YAML config file to customize the code transformers. The config file can be passed using the--config-yamlflag:pysparkler--config-yaml/path/to/config.yamlupgrade--input-file/path/to/script.pyThe config file is a YAML file with the following structure:pysparkler:dry_run:false# Whether to run in dry-run modePY24-30-001:# The code transformer IDcomment:A new comment# The overriden code hint comment to be used by the code transformerPY24-30-002:enabled:false# Disable the code transformerContributingFor the development, Poetry is used for packing and dependency management. You can install this using:pipinstallpoetryIf you have an older version of pip and virtualenv you need to update these:pipinstall--upgradevirtualenvpipInstallationTo get started, you can runmake install, which installs Poetry and all the dependencies of the PySparkler library.
This also installs the development dependencies.makeinstallIf you don't want to install the development dependencies, you need to install usingpoetry install --only main.If you want to install the library on the host, you can simply runpip3 install -e .. If you wish to use a virtual
environment, you can runpoetry shell. Poetry will open up a virtual environment with all the dependencies set.IDE SetupTo set up IDEA with Poetry:Open up the Python project in IntelliJMake sure that you're on latest master (that includes Poetry)Go to File -> Project Structure (⌘;)Go to Platform Settings -> SDKsClick the + sign -> Add Python SDKSelect Poetry Environment from the left hand side bar and hit OKIt can take some time to download all the dependencies based on your internetGo to Project Settings -> ProjectSelect the Poetry SDK from the SDK dropdown, and click OKFor IDEA ≤2021 you need to install thePoetry 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.Lintingpre-commitis used for autoformatting and linting:makelintPre-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 runningpre-commit install.You can bump the integrations to the latest version usingpre-commit autoupdate. This will check if there is a newer
version of{black,mypy,isort,...}and update the yaml.TestingFor Python,pytestis used a testing framework in combination withcoverageto enforce 90%+ code coverage.maketestTo pass additional arguments to pytest, you can usePYTEST_ARGS. For example, to run pytest in verbose mode:maketestPYTEST_ARGS="-v"ArchitectureWhy 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 foundhere.