seniorHadoop
What is Hadoop shuffle optimization techniques?
Updated May 16, 2026
Short answer
Shuffle optimization reduces network and disk overhead during MapReduce execution.
Deep explanation
Shuffle is the most expensive phase in MapReduce. Optimization techniques include combiners, compression, custom partitioners, map-side aggregation, and minimizing intermediate data size. Efficient serialization also reduces overhead.
Real-world example
Reducing network traffic in large-scale log aggregation systems.
Common mistakes
- Ignoring shuffle cost when designing MapReduce jobs.
Follow-up questions
- Why shuffle is expensive?
- What is map-side combine?