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spark-optimization

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Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/data/spark-optimization-juarezroncalli-ferramentas/SKILL.md

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Apache Spark Optimization

Production patterns for optimizing Apache Spark jobs including partitioning strategies, memory management, shuffle optimization, and performance tuning.

Do not use this skill when

  • The task is unrelated to apache spark optimization
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

  • Optimizing slow Spark jobs
  • Tuning memory and executor configuration
  • Implementing efficient partitioning strategies
  • Debugging Spark performance issues
  • Scaling Spark pipelines for large datasets
  • Reducing shuffle and data skew

Core Concepts

🧠 Knowledge Modules (Fractal Skills)

1. 1. Spark Execution Model

2. 2. Key Performance Factors

3. Pattern 1: Optimal Partitioning

4. Pattern 2: Join Optimization

5. Pattern 3: Caching and Persistence

6. Pattern 4: Memory Tuning

7. Pattern 5: Shuffle Optimization

8. Pattern 6: Data Format Optimization

9. Pattern 7: Monitoring and Debugging

10. Do's

11. Don'ts