What specific techniques do you use while optimizing a price pump job?

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Optimizing a price pump job in PySpark involves several best practices:
1. **Data Caching & Persistence** – Cache intermediate DataFrames that are reused across stages to avoid recomputation. Use `persist(StorageLevel.MEMORY_AND_DISK)` for large datasets.
2. **Partitioning & Repartitioning** – Ensure data is partitioned on the join key to reduce shuffle. Use `repartition` or `coalesce` to adjust partition count based on cluster size.
3. **Broadcast Joins** – For small lookup tables, broadcast them to avoid costly shuffle joins.
4. **Avoid UDFs** – Prefer built‑in Spark SQL functions which are optimized and vectorized. If UDFs are necessary, use Pandas UDFs for better performance.
5. **Predicate Pushdown & Column Pruning** – Filter rows and select only needed columns early to reduce data movement.
6. **Efficient File Formats** – Store intermediate data in Parquet or ORC to leverage compression and schema evolution.
7. **Shuffle Configuration** – Tune `spark.sql.shuffle.partitions`, `spark.default.parallelism`, and `spark.sql.autoBroadcastJoinThreshold` to match cluster resources.
8. **Monitoring & Metrics** – Use Spark UI, Ganglia, or Prometheus to spot bottlenecks, skew, and GC overhead.
9. **Resource Allocation** – Allocate sufficient executors, memory, and cores; enable dynamic allocation if workloads vary.
10. **Code Review & Refactoring** – Keep transformations simple, avoid nested loops, and use `cache` sparingly.
By combining these techniques, you can significantly reduce execution time, memory usage, and overall cost of a price pump job.

What specific techniques do you use while optimizing a price pump job?

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