Spark Performance Tuning: Why Is My Spark Job Slow?

Spark performance tuning is presented as a diagnostic process centered on identifying one of six common causes of slow jobs: data skew, excessive shuffling, disk spilling, garbage-collection pressure, small files, or inefficient query plans. The guidance explains how Spark UI metrics can distinguish these issues, such as unusually long straggler tasks for skew, large shuffle read/write volumes and fetch waits for network bottlenecks, spill columns for memory pressure, high executor GC time for heap stress, large numbers of tiny scan tasks for small files, and unexpected SortMergeJoin operations for stale statistics or poor join selection. Recommended remedies include Adaptive Query Execution, key salting, broadcasting small join tables, pre-aggregation, partition sizing, memory and executor adjustments, table compaction, clustering, refreshed statistics, and join hints. It also describes Quanton’s monitoring, diagnostic alerts, embedded AI agent, and Onehouse’s Spark Analyzer CLI as tools intended to automate analysis and compare performance across runs, while claiming Quanton can improve benchmark performance relative to open-source Spark without code changes.

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