Performance & Cost
Large-Scale Spark Performance Tuning on Amazon EMR
Amazon · Marketing Measurement
Diagnosed and tuned large-scale Apache Spark jobs running on Amazon EMR that were slow and expensive at scale. By profiling the workloads and fixing the real bottlenecks — data skew, excessive shuffles, suboptimal partitioning, and executor/memory configuration — and right-sizing the EMR clusters, I dramatically cut job runtime and compute cost, saving millions of dollars in annual savings while reducing software delivery latency.
$ MillionsSaved per year
Sparkon Amazon EMR
↓ Cost& runtime
Profile Spark jobs
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Find bottlenecks (skew · shuffle · partitions)
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Tune (partitioning · caching · executors)
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Right-size EMR
→
Lower cost & runtime
Highlights
- Profiled large-scale Spark jobs to pinpoint bottlenecks — data skew, excessive shuffles, suboptimal partitioning, and memory pressure.
- Tuned partitioning, caching/persistence, join strategies, and executor/memory configuration for major throughput gains.
- Right-sized Amazon EMR clusters (instance types, autoscaling, spot) to cut compute cost.
- Reduced job runtime and cluster cost — delivering millions of dollars in annual savings.
- Improved pipeline reliability and reduced software delivery latency.