Working with petabyte-scale data in Spark has taught me that small inefficiencies get really expensive at scale. A few strategies that have helped me optimize both performance and resource usage:

1. Partitioning smartly: One of my biggest lessons was that default partitioning rarely works well. I always try to repartition or coalesce based on the downstream workload. For wide transformations, I repartition by a key that distributes evenly (like user ID). Also, I avoid tiny files by tuning the number of output partitions before writing.

2. Watch out for data skew: Skewed joins used to kill my jobs. Now I actively analyze key distributions and use techniques like:

• Salting the join key.

• Broadcast joins when one side is small enough.

• Skew hints in Spark 3+ (e.g., skew=true) to let the optimizer handle it.

3. Cache only when needed: It’s tempting to cache() everything, but I’ve learned to cache only when reuse justifies the memory cost. Otherwise, it slows things down and eats up executors.

4. Code-level tips:

• Avoid wide transformations in early stages—filter early and push computations as close to the source as possible.

• Prefer mapPartitions over map when doing expensive operations.

• Use reduceByKey instead of groupByKey—that alone cut some of my jobs’ time in half.

5. Config tuning: Tweaking configs like spark.sql.shuffle.partitions, executor memory, and enabling dynamic allocation helped improve cluster utilization. Also, setting a sensible spark.sql.autoBroadcastJoinThreshold has been a game changer.

6. Monitor and iterate: Always keep an eye on the Spark UI. If a stage is taking forever, it’s usually a skew issue, spilled shuffle, or bad join strategy. I’ve built a habit of looking at stage-level metrics after every big run.