Apache Spark Optimization: Partitioning and Bucketing Guide
Optimizing Spark jobs is crucial for performance. Here’s how to use partitioning and bucketing effectively. Partitioning from pyspark.sql import SparkSession spark = SparkSession.builder.appName("Optimization").getOrCreate() # Repartition df = df.repartition(10, "column_name") # Coalesce df = df.coalesce(5) Bucketing df.write \ .bucketBy(10, "bucket_column") \ .sortBy("sort_column") \ .saveAsTable("bucketed_table") Broadcast Joins from pyspark.sql.functions import broadcast result = large_df.join(broadcast(small_df), "key") Caching df.cache() df.persist() Best Practices Partition appropriately Use bucketing for joins Broadcast small tables Cache frequently used data Monitor performance Conclusion Optimize Spark jobs for better performance! ⚡