Have you optimized Spark jobs for performance?

Optimizing Spark jobs involves several layers. First, ensure data is partitioned appropriately; use coalesce or repartition to match the cluster size and avoid small files. Second, cache or persist intermediate RDDs/DataFrames that are reused, but only when memory allows. Third, prefer built‑in functions over UDFs to keep execution in the JVM and benefit from Catalyst optimizations. Fourth, use broadcast joins for small tables to eliminate shuffles. Fifth, tune shuffle partitions (spark.sql.shuffle.partitions) and memory fractions (spark.memory.fraction, spark.memory.storageFraction). Sixth, enable Tungsten and whole‑stage code generation. Finally, profile jobs with the UI or Spark History Server, inspect stages, and adjust executor memory/cores. These steps reduce shuffle, memory spills, and CPU overhead, leading to faster runtimes.