skanderboudawara

skanderboudawara / pyspark-antipattern

Public

Pyspark linter for antipattern

skanderboudawara.github.iopyspark-antipattern
20
0
100% credibility
Found Apr 06, 2026 at 20 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Rust
AI Summary

A tool that reviews PySpark code files to find and explain performance problems like data pulls to the main computer or wasteful loops.

How It Works

1
🔍 Discover the helper

You hear about a smart checker that spots hidden slowdowns in your data processing scripts before they cause big problems.

2
📥 Add it to your toolkit

Grab the tool and set it up alongside your usual data work environment with a simple download.

3
🔎 Scan your files

Point it at your code folders or specific scripts to review for common trouble spots.

4
⚠️ Spot the issues

It highlights risky patterns like grabbing too much data or repeating slow steps, with friendly explanations and fixes.

5
✏️ Clean up your code

Follow the simple suggestions to rewrite the tricky parts and make everything smoother.

6
⚙️ Tailor the checks

Adjust what it focuses on, like ignoring minor things or matching your setup.

🚀 Run faster pipelines

Your data jobs now avoid crashes and delays, saving time and headaches every day.

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Star Growth

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AI-Generated Review

What is pyspark-antipattern?

Pyspark-antipattern is a fast Rust-powered linter that scans Python code for PySpark antipatterns like driver data pulls, shuffle explosions, UDF overuse, and looping transformations. It catches over 60 rules across categories such as driver actions, arrays, and performance pitfalls before they hit your cluster, preventing OOMs and slow jobs. Install via pip, run `pyspark-antipattern check file.py` or a directory, and integrate into pyspark github actions or pre-commit for automated checks on pyspark github code.

Why is it gaining traction?

Unlike generic Python linters, it targets PySpark-specific issues with precise rules, inline explanations, and best-practice fixes—perfect for pyspark notebook github files or pyspark sql github scripts. Configurable via pyproject.toml for ignoring rules, severity thresholds, or your cluster's PySpark version, plus per-line noqa suppressions. Developers love catching "works locally, explodes in prod" bugs early in CI, with full docs for every rule.

Who should use this?

Data engineers maintaining pyspark github projects or pipelines prone to collect() leaks and inefficient joins. Teams reviewing pyspark example github repos, pyspark github tutorial code, or pyspark github issues from slow queries. Suited for pyspark github repo owners enforcing quality in shared pyspark notebook github assets.

Verdict

Add it to your CI for any serious PySpark workload—solid rules and GitHub Actions integration outweigh the early-stage 20 stars and 1.0% credibility score. Docs are comprehensive; test on a pyspark github tutorial first to build confidence.

(198 words)

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