What are the different Spark optimization techniques?

Spark offers several optimization techniques that improve performance and resource usage:

1. Partitioning – Choosing an appropriate number of partitions and using custom partitioners to reduce data shuffling.
2. Caching / Persistence – Storing intermediate RDDs/DataFrames in memory or on disk to avoid recomputation.
3. Broadcast Joins – Broadcasting the smaller dataset to all executors to eliminate shuffle.
4. Predicate Pushdown – Filtering data as early as possible, especially when reading from Parquet or ORC files.
5. Whole‑Stage Code Generation – Spark’s Catalyst optimizer generates bytecode for entire stages, reducing interpreter overhead.
6. Shuffle Partition Tuning – Setting spark.sql.shuffle.partitions to match cluster size and workload.
7. Column Pruning – Selecting only needed columns to reduce I/O.
8. Skew Handling – Using salting or skewed join strategies to mitigate data skew.
9. Adaptive Query Execution (AQE) – Dynamically adjusting join strategies and partition counts based on runtime statistics.
10. Resource Configuration – Tuning executor memory, cores, and GC settings for optimal throughput.

Applying these techniques in combination often yields the best performance gains.