How to Catch Spark Performance Regressions Across Runs

Spark job regressions can remain hidden when normal runtime variation is large, making historical baselines more reliable than fixed thresholds or memory-based judgments. The recommended approach is to collect completed-run metrics from the Spark History Server REST API, establish each job’s P50 and P95 runtime range, flag outliers and sustained median shifts, and distinguish isolated spikes from step-change regressions and gradual drift. Performance changes may result from Spark or Databricks Runtime upgrades that alter Adaptive Query Execution behavior, join strategies, optimizer rules, and physical plans, as well as data growth, skew, degraded file layouts, cluster contention, executor churn, and autoscaling behavior. Comparing a known-good run with a slow run requires examining stage durations, task counts, shuffle activity, spill, input size, and especially physical-plan differences such as changed joins or added exchanges. While the History Server provides the underlying event-log data, scripting longitudinal comparisons helps overcome its lack of built-in cross-run analysis; Quanton’s agent is presented as a tool for diagnosing flagged runs at the stage level, and Spark Analyzer can generate before-and-after reports to document regressions or improvements.

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