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Map-Reduce
Distributes computation across multiple nodes using map and reduce operations
In 30 seconds
- What
- Splits a large dataset across parallel workers, each processing a subset independently, then combines results into a final answer.
- When to use
- Processing massive datasets where single-machine execution is too slow and intermediate results can be computed independently.
- Watch out
- Coordination overhead and network latency can outweigh parallelization gains on smaller datasets or with expensive reduce operations.
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Map-Reduce: Overview
Distributes computation across multiple nodes using map and reduce operations
- Parallel data processing
- Fault-tolerant execution
- Scalable architecture
- Result aggregation
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References
The papers, specifications, and repositories this pattern is based on.
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