What are some important Spark configuration settings you need to tune for performance?

Key Spark configuration settings that influence performance include: spark.executor.memory – memory per executor; spark.executor.cores – number of cores per executor; spark.executor.instances – number of executors; spark.driver.memory – memory for the driver; spark.sql.shuffle.partitions – number of partitions for shuffle operations; spark.default.parallelism – default parallelism level; spark.memory.fraction – fraction of executor memory used for execution and storage; spark.memory.storageFraction – fraction of memory reserved for storage; spark.sql.autoBroadcastJoinThreshold – size threshold for automatic broadcast joins; spark.dynamicAllocation.enabled – enable dynamic allocation of executors; spark.serializer – use Kryo for faster serialization. Tuning these settings involves balancing memory, CPU, and I/O based on workload characteristics. For example, increasing spark.sql.shuffle.partitions can reduce shuffle spill but may increase task overhead. Setting spark.memory.fraction to 0.6 and storageFraction to 0.2 is a common starting point for many workloads.