Design a scalable MapReduce pipeline
Overview
This question evaluates proficiency in designing large-scale MapReduce-style batch processing systems, covering data schemas and partitioning, parallelization and sharding strategies, network optimization techniques, fault tolerance semantics, handling skew/stragglers, and performance/complexity estimation for ML feature aggregation.
Anthropic
Aug 1, 2025, 12:00 AM
Machine Learning Engineer
Technical Screen
System Design
0
0
Design a Large-Scale MapReduce-Style Data Processing System
Context
You are designing a batch pipeline, using a MapReduce-style architecture, to aggregate raw event logs into daily user-level features for downstream machine learning. The system must scale to tens of terabytes per day, run reliably, and minimize resource usage.
Requirements
- Define input and output schemas (types, partitioning/layout on storage).
- Describe the partitioning/sharding strategy and parallelization model.
-
Explain how to minimize network traffic via:
- Data locality
- Combiners
- Serialization choices
- Compression
- Request batching
- Handle data skew and stragglers.
- Implement fault tolerance and retries; justify at-least-once vs exactly-once semantics.
- Provide complexity analysis and rough throughput/latency estimates.
- Outline key metrics and experiments to validate efficiency and guide tuning.
Solution
Login required
Show