Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Situation**: Daily aggregation pipeline ran 4+ hours; blocking downstream. **Task**: Reduce to under 1 hour. **Action**: (1) Profiled with Spark UI—full table scans, large shuffles. (2) Partitioned source by date; partition pruning. (3) Replaced reduceByKey with aggregateByKey...
Situation: Daily aggregation pipeline ran 4+ hours; blocking downstream. Task: Reduce to under 1 hour. Action: (1) Profiled with Spark UI—full table scans, large shuffles. (2) Partitioned source by date; partition pruning. (3) Replaced reduceByKey with aggregateByKey to reduce shuffle. (4) Broadcast small dimensions. (5) Tuned executor memory and parallelism. (6) Documented rationale; added monitoring. Result: Runtime under 35 minutes. Leadership: Drove optimization; mentored on profiling. Data-Driven: Measured before optimizing; focused on bottlenecks.
Red Flag: Optimizing without profiling—shotgun approach. Pro-Move: Mention Spark UI, specific bottleneck (e.g., shuffle); quantify improvement (4hr→35min); reference monitoring for regressions.
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