How do you optimize Spark jobs?

Optimizing Spark jobs involves several layers. First, choose the right partitioning: partition data by key to reduce shuffles and enable co‑location of related records. Second, cache or persist intermediate RDDs/DataFrames that are reused, but avoid over‑caching. Third, use broadcast joins for small tables to eliminate shuffle. Fourth, enable Tungsten and whole‑stage code generation by using DataFrame/Dataset APIs instead of low‑level RDDs. Fifth, tune configuration parameters such as spark.sql.shuffle.partitions, spark.executor.memory, and spark.driver.memory based on cluster size. Sixth, avoid wide transformations (groupBy, reduceByKey) on skewed data by salting keys or using skew‑aware joins. Finally, monitor job metrics via Spark UI, identify long stages, and adjust partition sizes or use dynamic allocation. These practices collectively reduce execution time and resource consumption.