spark-optimization

SKILL.md

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()

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