pyspark-antipattern 0.1.5

PySpark antipattern linter for CI/CD pipelines

pyspark-antipattern

A fast, opinionated PySpark linter that challenges your code against antipattern rules — written in Rust, installable as a Python package, and designed to run in CI/CD pipelines.

This linter is intentionally strict. It will flag patterns that are technically valid Python but known to cause performance, scalability, or maintainability problems in PySpark. Every violation is a conversation starter, not necessarily a hard blocker — it is up to you to decide whether to fix it, downgrade it to a warning, or suppress it for a specific line. The goal is to make the trade-offs visible before they become production incidents.

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Why this exists

PySpark is easy to misuse. .collect() on a 10 GB DataFrame, .withColumn() called in a loop, UDFs where built-in functions exist — these patterns work fine locally and silently destroy performance at scale. This tool catches them early, at commit time, before they reach your cluster.

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Installation

pip install pyspark-antipattern

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Usage

Check a single file:

pyspark-antipattern check pipeline.py

Check an entire directory recursively:

pyspark-antipattern check src/

Use a custom config location:

pyspark-antipattern check src/ --config path/to/pyproject.toml

Exit codes

• 0 — no errors (warnings are allowed)
• 1 — one or more error-level violations found

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CLI output

Default output — violations only:

With show_information = true — inline explanation for each rule:

With show_best_practice = true — best-practice guidance for each rule:

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Rules

Rules are organized by category in the docs/rules/ folder. Each rule has its own markdown file with a full explanation and best-practice guidance.

Category	Folder	Focus
ARR — Array	docs/rules/arr/	Array function antipatterns
D — Driver	docs/rules/driver/	Actions that pull data to the driver node
F — Format	docs/rules/format/	Code style and DataFrame API misuse
L — Looping	docs/rules/looping/	DataFrame operations inside loops
P — Pandas	docs/rules/pandas/	Pandas interop pitfalls
PERF — Performance	docs/rules/performance/	Runtime performance antipatterns
S — Shuffle	docs/rules/shuffle/	Joins, partitioning, and data movement
U — UDF	docs/rules/udf/	User-defined functions and their alternatives

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Configuration

Add a [tool.pyspark-antipattern] section to your project's pyproject.toml:

[tool.pyspark-antipattern]

# Rules listed here cause exit code 1 (default: all rules are failing)
# failing_rules = []

# Downgrade these rules from error to warning (exit code stays 0)
warning_rules = ["F008", "F011"]

# Completely silence these rules — no output, no exit code impact
# Accepts exact rule IDs or single-letter group prefixes
ignore_rules = ["S004"]                # silence one rule
# ignore_rules = ["F"]                 # silence all F rules
# ignore_rules = ["S", "L", "D001"]    # silence all S and L rules

# Show inline explanation for each rule that fired (default: false)
show_information = false

# Show best-practice guidance for each rule that fired (default: false)
show_best_practice = false

# S004: flag when the weighted count of .distinct() calls exceeds this (default: 5)
distinct_threshold = 5

# S008: flag when the weighted count of explode() calls exceeds this (default: 3)
explode_threshold = 3

# L001/L002/L003: flag for-loops where range(N) > threshold;
#                 while-loops always assume 99 iterations (default: 10)
loop_threshold = 10

# Directories to skip during recursive scanning (default: common build/venv dirs)
# exclude_dirs = ["my_generated_code", "vendor"]

Suppressing a specific line

Add a # noqa: pap: RULE_ID comment to suppress one or more rules on that line:

result = df.collect()  # noqa: pap: D001
bad_join = df.crossJoin(other)  # noqa: pap: S010, S002

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CI/CD integration

GitHub Actions

- name: Lint PySpark code
  run: |
    pip install pyspark-antipattern
    pyspark-antipattern check src/

The job fails automatically if any error-level rule fires. Warnings are reported but do not block the pipeline.

Pre-commit hook

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: pyspark-antipattern
        name: PySpark antipattern linter
        entry: pyspark-antipattern check
        language: system
        types: [python]
        pass_filenames: false
        args: ["src/"]

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A word on strictness

This linter will challenge code that your team may have written deliberately and knowingly. That is by design.

Each violation is not a verdict — it is a question: "Did you mean to do this, and do you understand the trade-off?" If the answer is yes, suppress the rule on that line or downgrade it to a warning in your config. If the answer is no, you just avoided a production issue.

The strictest setup is the default: every rule is a hard error. Relax only what you have a documented reason to relax.

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Author

Skander Boudawara — [e-mail]