Spark Optimization

When to use this skill

Use Spark Optimization when an AI agent needs a reusable SKILL.md workflow for this job: 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.

When not to use it

Skip Spark Optimization when the task is outside the productivity category, or when a more specific skill in this directory already covers the same workflow with clearer triggers.

How to install

1. Personal install: create ~/.claude/skills/spark-optimization/SKILL.md (and any bundled scripts) so Claude Code, Claude Desktop, and compatible agents can load it in every project.
2. Project install: commit the same folder at .claude/skills/spark-optimization/ so teammates get the skill with the repo.
3. Restart the agent session after copying files so it re-scans the skills directory, then ask for the task in words that match the skill description.

Full install guide for Claude, Cursor, and Codex

What this skill does

# Apache Spark Optimization

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

## When to Use This Skill

- 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

### 1. Spark Execution Model

``` Driver Program ↓ Job (triggered by action) ↓ Stages (separated by shuffles) ↓ Tasks (one per partition) ```

### 2. Key Performance Factors

| Factor | Impact | Solution | | ----------------- | --------------------- | ----------------------------- | | **Shuffle** | Network I/O, disk I/O | Minimize wide transformations | | **Data Skew** | Uneven task duration | Salting, broadcast joins | | **Serialization** | CPU overhead | Use Kryo, columnar formats | | **Memory** | GC pressure, spills | Tune executor memory | | **Partitions** | Parallelism | Right-size partitions |

## Quick Start

```python from pyspark.sql import SparkSession from pyspark.sql import functions as F

# Create optimized Spark session spark = (SparkSession.builder .appName("OptimizedJob") .config("spark.sql.adaptive.enabled", "true") .config("spark.sql.adaptive.coalescePartitions.enabled", "true") .config("spark.sql.adaptive.skewJoin.enabled", "true") .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer") .config("spark.sql.shuffle.partitions", "200") .getOrCreate())

# Read with optimized settings df = (spark.read .format("parquet") .option("mergeSchema", "false") .load("s3://bucket/data/"))

# Efficient transformations result = (df .filter(F.col("date") >= "2024-01-01") .select("id", "amount", "category") .groupBy("category") .agg(F.sum("amount").alias("total")))

result.write.mode("overwrite").parquet("s3://bucket/output/") ```

## Detailed patterns and worked examples

Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient.

## Best Practices

### Do's

- **Enable AQE** - Adaptive query execution handles many issues - **Use Parquet/Delta** - Columnar formats with compression - **Broadcast small tables** - Avoid shuffle for small joins - **Monitor Spark UI** - Check for skew, spills, GC - **Right-size partitions** - 128MB - 256MB per partition

### Don'ts

- **Don't collect large data** - Keep data distributed - **Don't use UDFs unnecessarily** - Use built-in functions - **Don't over-cache** - Memory is limited - **Don't ignore data skew** - It dominates job time - **Don't use `.count()` for existence** - Use `.take(1)` or `.isEmpty()`

Intended uses

• 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