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databrickslabs / pylint-plugin

Databricks Plugin for PyLint
https://pypi.org/project/databricks-labs-pylint/
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databricks pylint-plugin python

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PyLint Plugin for Databricks

python codecov lines of code

PyLint serves as a valuable tool for developers by performing various checks on code quality. It scrutinizes the length of lines, ensures conformity to coding standards regarding variable naming, validates the usage of imported modules, verifies the implementation of declared interfaces, identifies instances of duplicated code, and much more. This plugin extends PyLint with checks for common mistakes and issues in Python code specifically in Databricks Environment.

Installation as PyLint plugin

You can install this project via pip:

pip install databricks-labs-pylint

and then use it with pylint:

pylint --load-plugins=databricks.labs.pylint.all <your-python-file>.py

You can also add databricks.labs.pylint.all to load-plugins configuration in your pylintrc or pyproject.toml file.

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Integration with Databricks CLI

You can use this plugin with Databricks CLI to check individual notebooks or entire directories.

First, you need to install this plugin locally:

databricks labs install pylint-plugin

Then, you can call the nbcheck command without any arguments to lint all Python notebooks in you home folder:

databricks labs pylint-plugin nbcheck

Or you can specify a --path flag to lint a specific notebook or folder:

databricks labs pylint-plugin nbcheck --path /Users/me@example.com/PrepareData

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PyLint Ecosystem

More than 400k repositories use PyLint, and it is one of the most popular static code analysis tools in the Python ecosystem. This plugin allows you to work with PyLint in the same way you are used to, but with additional checks for Databricks-specific issues. It is also compatible with the following PyLint integrations:

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Why not (just) Ruff?

Even though Ruff is 10x+ faster than PyLint, it doesn't have a plugin system yet, nor does it have a feature parity with PyLint yet. Other projects use MyPy, Ruff, and PyLint together to achieve the most comprehensive code analysis. You can try using Ruff and just the checkers from this plugin in the same CI pipeline and pre-commit hook.

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Automated code analysis

Every check has a code, that follows an existing convention:

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databricks-airflow checker

To use this checker, add databricks.labs.pylint.airflow to load-plugins configuration in your pylintrc or pyproject.toml file.

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W8901: missing-data-security-mode

XXX cluster missing data_security_mode required for Unity Catalog compatibility. Before you enable Unity Catalog, you must set the data_security_mode to 'NONE', so that your existing jobs would keep the same behavior. Failure to do so may cause your jobs to fail with unexpected errors.

To disable this check on a specific line, add # pylint: disable=missing-data-security-mode at the end of it.

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W8902: unsupported-runtime

XXX cluster has unsupported runtime: XXX. The runtime version is not supported by Unity Catalog. Please upgrade to a runtime greater than or equal to 11.3.

To disable this check on a specific line, add # pylint: disable=unsupported-runtime at the end of it.

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databricks-dbutils checker

To use this checker, add databricks.labs.pylint.dbutils to load-plugins configuration in your pylintrc or pyproject.toml file.

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R8903: dbutils-fs-cp

Use Databricks SDK instead: w.dbfs.copy(XXX, XXX). Migrate all usage of dbutils to Databricks SDK. See the more detailed documentation at https://databricks-sdk-py.readthedocs.io/en/latest/workspace/files/dbfs.html

To disable this check on a specific line, add # pylint: disable=dbutils-fs-cp at the end of it.

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R8904: dbutils-fs-head

Use Databricks SDK instead: with w.dbfs.download(XXX) as f: f.read(). Migrate all usage of dbutils to Databricks SDK. See the more detailed documentation at https://databricks-sdk-py.readthedocs.io/en/latest/workspace/files/dbfs.html

To disable this check on a specific line, add # pylint: disable=dbutils-fs-head at the end of it.

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R8905: dbutils-fs-ls

Use Databricks SDK instead: w.dbfs.list(XXX). Migrate all usage of dbutils to Databricks SDK. See the more detailed documentation at https://databricks-sdk-py.readthedocs.io/en/latest/workspace/files/dbfs.html

To disable this check on a specific line, add # pylint: disable=dbutils-fs-ls at the end of it.

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R8906: dbutils-fs-mount

Mounts are not supported with Unity Catalog, switch to using Unity Catalog Volumes instead. Migrate all usage to Unity Catalog

To disable this check on a specific line, add # pylint: disable=dbutils-fs-mount at the end of it.

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R8907: dbutils-credentials

Credentials utility is not supported with Unity Catalog. Migrate all usage to Unity Catalog

To disable this check on a specific line, add # pylint: disable=dbutils-credentials at the end of it.

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R8908: dbutils-notebook-run

Use Databricks SDK instead: w.jobs.submit( tasks=[jobs.SubmitTask(existing_cluster_id=..., notebook_task=jobs.NotebookTask(notebook_path=XXX), task_key=...) ]).result(timeout=timedelta(minutes=XXX)). Migrate all usage of dbutils to Databricks SDK. See the more detailed documentation at https://databricks-sdk-py.readthedocs.io/en/latest/workspace/jobs/jobs.html

To disable this check on a specific line, add # pylint: disable=dbutils-notebook-run at the end of it.

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R8909: pat-token-leaked

Use Databricks SDK instead: from databricks.sdk import WorkspaceClient(); w = WorkspaceClient(). Do not hardcode secrets in code, use Databricks SDK instead, which natively authenticates in Databricks Notebooks. See more at https://databricks-sdk-py.readthedocs.io/en/latest/authentication.html

To disable this check on a specific line, add # pylint: disable=pat-token-leaked at the end of it.

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R8910: internal-api

Do not use internal APIs, rewrite using Databricks SDK: XXX. Do not use internal APIs. Use Databricks SDK for Python: https://databricks-sdk-py.readthedocs.io/en/latest/index.html

To disable this check on a specific line, add # pylint: disable=internal-api at the end of it.

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databricks-legacy checker

To use this checker, add databricks.labs.pylint.legacy to load-plugins configuration in your pylintrc or pyproject.toml file.

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R8911: legacy-cli

Don't use databricks_cli, use databricks.sdk instead: pip install databricks-sdk. Migrate all usage of Legacy CLI to Databricks SDK. See the more detailed documentation at https://databricks-sdk-py.readthedocs.io/en/latest/index.html

To disable this check on a specific line, add # pylint: disable=legacy-cli at the end of it.

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W8912: incompatible-with-uc

Incompatible with Unity Catalog: XXX. Migrate all usage to Databricks Unity Catalog. Use https://github.com/databrickslabs/ucx for more details

To disable this check on a specific line, add # pylint: disable=incompatible-with-uc at the end of it.

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databricks-notebooks checker

To use this checker, add databricks.labs.pylint.notebooks to load-plugins configuration in your pylintrc or pyproject.toml file.

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C8913: notebooks-too-many-cells

Notebooks should not have more than 75 cells. Otherwise, it's hard to maintain and understand the notebook for other people and the future you

To disable this check on a specific line, add # pylint: disable=notebooks-too-many-cells at the end of it.

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R8914: notebooks-percent-run

Using %run is not allowed. Use functions instead of %run to avoid side effects and make the code more testable. If you need to share code between notebooks, consider creating a library. If still need to call another code as a separate job, use Databricks SDK for Python: https://databricks-sdk-py.readthedocs.io/en/latest/index.html

To disable this check on a specific line, add # pylint: disable=notebooks-percent-run at the end of it.

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spark checker

To use this checker, add databricks.labs.pylint.spark to load-plugins configuration in your pylintrc or pyproject.toml file.

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C8915: spark-outside-function

Using spark outside the function is leading to untestable code. Do not use global spark object, pass it as an argument to the function instead, so that the function becomes testable in a CI/CD pipelines.

To disable this check on a specific line, add # pylint: disable=spark-outside-function at the end of it.

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C8917: use-display-instead-of-show

Rewrite to display in a notebook: display(XXX). Use display() instead of show() to visualize the data in a notebook.

To disable this check on a specific line, add # pylint: disable=use-display-instead-of-show at the end of it.

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W8916: no-spark-argument-in-function

Function XXX is missing a 'spark' argument. Function refers to a global spark variable, which may not always be available. Pass the spark object as an argument to the function instead, so that the function becomes testable in a CI/CD pipelines.

To disable this check on a specific line, add # pylint: disable=no-spark-argument-in-function at the end of it.

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readability checker

To use this checker, add databricks.labs.pylint.readability to load-plugins configuration in your pylintrc or pyproject.toml file.

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R8923: rewrite-as-for-loop

List comprehension spans multiple lines, rewrite as for loop. List comprehensions in Python are typically used to create new lists by iterating over an existing iterable in a concise, one-line syntax. However, when a list comprehension becomes too complex or spans multiple lines, it may lose its readability and clarity, which are key advantages of Python's syntax.

To disable this check on a specific line, add # pylint: disable=rewrite-as-for-loop at the end of it.

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mocking checker

To use this checker, add databricks.labs.pylint.mocking to load-plugins configuration in your pylintrc or pyproject.toml file.

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R8918: explicit-dependency-required

Obscure implicit test dependency with mock.patch(XXX). Rewrite to inject dependencies through constructor.. Using patch to mock dependencies in unit tests can introduce implicit dependencies within a class, making it unclear to other developers. Constructor arguments, on the other hand, explicitly declare dependencies, enhancing code readability and maintainability. However, reliance on patch for testing may lead to issues during refactoring, as updates to underlying implementations would necessitate changes across multiple unrelated unit tests. Moreover, the use of hard-coded strings in patch can obscure which unit tests require modification, as they lack strongly typed references. This coupling of the class under test to concrete classes signifies a code smell, and such code is not easily portable to statically typed languages where monkey patching isn't feasible without significant effort. In essence, extensive patching of external clients suggests a need for refactoring, with experienced engineers recognizing the potential for dependency inversion in such scenarios.

To address this issue, refactor the code to inject dependencies through the constructor. This approach explicitly declares dependencies, enhancing code readability and maintainability. Moreover, it allows for dependency inversion, enabling the use of interfaces to decouple the class under test from concrete classes. This decoupling facilitates unit testing, as it allows for the substitution of mock objects for concrete implementations, ensuring that the class under test behaves as expected. By following this approach, you can create more robust and maintainable unit tests, improving the overall quality of your codebase.

Use require-explicit-dependency option to specify the package names that contain code for your project.

To disable this check on a specific line, add # pylint: disable=explicit-dependency-required at the end of it.

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R8919: obscure-mock

Obscure implicit test dependency with MagicMock(). Rewrite with create_autospec(ConcreteType).. Using MagicMock to mock dependencies in unit tests can introduce implicit dependencies within a class, making it unclear to other developers. create_autospec(ConcreteType) is a better alternative, as it automatically creates a mock object with the same attributes and methods as the concrete class. This approach ensures that the mock object behaves like the concrete class, allowing for more robust and maintainable unit tests. Moreover, reliance on MagicMock for testing leads to issues during refactoring, as updates to underlying implementations would necessitate changes across multiple unrelated unit tests.

To disable this check on a specific line, add # pylint: disable=obscure-mock at the end of it.

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R8921: mock-no-assign

Mock not assigned to a variable: XXX. Every mocked object should be assigned to a variable to allow for assertions.

To disable this check on a specific line, add # pylint: disable=mock-no-assign at the end of it.

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R8922: mock-no-usage

Missing usage of mock for XXX. Usually this check means a hidden bug, where object is mocked, but we don't check if it was used correctly. Every mock should have at least one assertion, return value, or side effect specified.

To disable this check on a specific line, add # pylint: disable=mock-no-usage at the end of it.

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eradicate checker

To use this checker, add databricks.labs.pylint.eradicate to load-plugins configuration in your pylintrc or pyproject.toml file.

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C8920: dead-code

Remove commented out code: XXX. Version control helps with keeping track of code changes. There is no need to keep commented out code in the codebase. Remove it to keep the codebase clean.

To disable this check on a specific line, add # pylint: disable=dead-code at the end of it.

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Testing in isolation

To test this plugin in isolation, you can use the following command:

pylint --load-plugins=databricks.labs.pylint.all --disable=all --enable=missing-data-security-mode,unsupported-runtime,dbutils-fs-cp,dbutils-fs-head,dbutils-fs-ls,dbutils-fs-mount,dbutils-credentials,dbutils-notebook-run,pat-token-leaked,internal-api,legacy-cli,incompatible-with-uc,notebooks-too-many-cells,notebooks-percent-run,spark-outside-function,use-display-instead-of-show,no-spark-argument-in-function,rewrite-as-for-loop,explicit-dependency-required,obscure-mock,mock-no-assign,mock-no-usage,dead-code .

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Project Support

Please note that this project is provided for your exploration only and is not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS, and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of this project.

Any issues discovered through the use of this project should be filed as GitHub Issues on this repository. They will be reviewed as time permits, but no formal SLAs for support exist.