---name: spark-optimizationdescription: 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.---

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()