Optimize MapReduce performance

Q: Optimize MapReduce performance

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.

Q: How do I approach System Design interview questions?

System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master system design interviews.

Question

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.

Optimize MapReduce performance

MapReduce Model and Optimization for Parallel Efficiency and Network Utilization

Context

Tasks

Solution

Comments (0)