Spark Best Practices

Apache Spark Best Practices

Performance Optimization

Broadcast Joins (CRITICAL)

• Use broadcast(small_df) for small-large table joins
• Default broadcast threshold: 10MB (spark.sql.autoBroadcastJoinThreshold)
• Avoid broadcast for tables > 100MB

Shuffles (CRITICAL)

• Minimize shuffles: expensive operations
• Use coalesce() to reduce partitions without shuffle
• Use repartition() only when necessary (causes shuffle)
• Predicate pushdown: filter before joins

Caching

• Cache DataFrames used multiple times: df.cache() or df.persist()
• Choose storage level: MEMORY_ONLY, MEMORY_AND_DISK, DISK_ONLY
• Unpersist when done: df.unpersist()

Resource Management

Executor Configuration

• Executor memory: 80% of available memory per executor
• Executor cores: 4-5 cores per executor (optimal)
• Dynamic allocation: enable for varying workloads

Partitioning

• Optimal partition size: 100-200MB
• Too few partitions: underutilized cluster
• Too many partitions: task overhead

Data Processing

UDFs

• Prefer built-in functions over UDFs
• Use Pandas UDF for vectorized operations
• Avoid Python UDFs (serialization overhead)

Storage Formats

• Parquet: default for analytics (columnar, compression)
• ORC: alternative to Parquet
• Delta/Iceberg: ACID transactions, time travel

References

• Spark Performance Tuning

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