Optimize MapReduce performance
Overview
This question evaluates understanding of the MapReduce programming model, distributed batch-processing concepts, and performance optimization for parallel CPU and network utilization within the system design domain.
Anthropic
Aug 4, 2025, 10:55 AM
Machine Learning Engineer
Technical Screen
System Design
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MapReduce Model and Optimization for Parallel Efficiency and Network Utilization
Context
You are designing a large-scale batch processing job (e.g., feature extraction, log aggregation, joins) over a distributed file system. The job must scale across many machines while keeping both CPU and network well utilized.
Tasks
- Explain the MapReduce programming model, including key stages (map, shuffle/sort, reduce), data partitioning, combiners, and fault tolerance.
- Describe how you would optimize a MapReduce job for parallel-computation efficiency (task sizing, skew handling, locality, memory/IO).
- Identify techniques to minimize network overhead and improve throughput when running large-scale parallel computations.
Solution
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