Spark optimization

Skill wshobson/agents/plugins/data-engineering/skills/spark-optimization

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Install

npx -y skills add wshobson/agents --skill spark-optimization

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What its author says it does

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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.

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.

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

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()