When building or tuning a Spark job, what was the most significant performance bottleneck you encountered, and how did you address it?

The most significant bottleneck I encountered was data skew during a join operation. One key value appeared in a large proportion of the data, causing a single reducer to process the majority of the workload while others finished quickly. To mitigate this, I applied a salting technique: I added a random prefix to the skewed key before the join, effectively distributing the heavy key across multiple partitions. After the join, I removed the salt. I also increased the number of shuffle partitions to match the cluster size, used broadcast joins for small lookup tables, and enabled Tungsten optimizations by turning on whole-stage code generation. Additionally, I cached intermediate results that were reused across stages to avoid recomputation. These changes reduced the job runtime from 45 minutes to 12 minutes and eliminated the straggler tasks, resulting in a more balanced and efficient execution.