What other optimization techniques have you used in PySpark?

In my experience with PySpark, I have applied a range of optimization techniques to reduce runtime and resource consumption. First, I carefully design partitioning schemes—using hash or range partitioning—to ensure data is evenly distributed and to avoid skew, which can cause certain tasks to become bottlenecks. Second, I persist or cache intermediate DataFrames that are reused across multiple stages; this prevents recomputation and speeds up iterative jobs. Third, I use broadcast joins for small lookup tables, which eliminates the shuffle phase and dramatically reduces network traffic. Fourth, I prefer the DataFrame API over low‑level RDDs whenever possible, because Spark’s Catalyst optimizer and Tungsten execution engine can apply whole‑stage code generation and efficient memory management. I also prune columns and apply filters early in the query plan to reduce the amount of data shuffled. Additionally, I tune the number of shuffle partitions (spark.sql.shuffle.partitions) to match the cluster size and adjust executor memory settings to avoid garbage‑collection pauses. Finally, I monitor job stages via the Spark UI, identify slow stages, and iteratively refine the code. These practices collectively improve performance and make my PySpark jobs more scalable.