Spark Best Practices

Writing efficient Spark jobs starts with choosing the DataFrame or Dataset API over raw RDDs whenever possible, since the Catalyst optimizer can rewrite DataFrame queries into more efficient physical plans, while RDD transformations execute exactly as written with no such optimization. Minimizing wide transformations that trigger shuffles - such as groupByKey, repartition, or joins on unfiltered data - keeps network I/O low and jobs fast.

Caching should be applied deliberately: call .cache() or .persist() only on DataFrames that will be reused across multiple actions, such as a filtered dataset queried by three downstream aggregations, and always call .unpersist() once the data is no longer needed to free executor memory. Choosing the right storage level - MEMORY_ONLY for datasets that comfortably fit in RAM versus MEMORY_AND_DISK_SER for larger ones - prevents costly recomputation without exhausting cluster memory.

Data skew - where one partition holds far more rows than others, often from a popular join key like a single retailer ID - causes a handful of tasks to run far longer than the rest and stalls the whole stage. Techniques like salting the skewed key with a random suffix, broadcasting small dimension tables under spark.sql.autoBroadcastJoinThreshold, and enabling Adaptive Query Execution (AQE) to dynamically split skewed partitions at runtime all reduce this straggler effect.

Right-sizing executors matters as much as tuning code: too few cores per executor underutilizes parallelism, while too many can cause excessive garbage collection pauses and contention for shared memory, so a common starting point is 4-5 cores and 16-32GB per executor, adjusted after inspecting the Spark UI's stage and task timelines. Enabling dynamic allocation lets the cluster scale executors up during a heavy shuffle stage and scale back down during light stages, avoiding both resource starvation and idle billing.