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🗺️
Map-Reduce
使用 map 和 reduce 操作将计算分布到多个节点上
复杂度: high并行化
In 30 seconds
- What
- 将大规模数据集拆分给并行的工作节点，各自独立处理一个子集，再把结果合并成最终答案。
- When to use
- 处理海量数据集时，单机执行太慢，且中间结果可以独立计算。
- Watch out
- 在数据集较小或 reduce 操作开销高时，协调成本和网络延迟可能抵消并行化带来的收益。
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使用 map 和 reduce 操作将计算分布到多个节点上
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模式： 将数据切分为分块，并行处理后聚合结果
原因： 最大化吞吐量，利用多核或多服务，可横向扩展
关键洞察： Chunk[1..N] → Map(f) → [Result1..N] → Reduce → Final_Output
Discover how Map-Reduce relates to other patterns
Prerequisites, next steps, and learning progression
Linear processing foundation that Map-Reduce parallelizes
💡 Understanding linear processing helps design effective parallel decomposition
More flexible parallel distribution with heterogeneous processing
💡 Natural evolution when you need different operations on different data types
Recursive parallel decomposition with work stealing
💡 Advanced parallelization with dynamic load balancing
Complex parallel workflows with state management
💡 Enterprise-grade parallel processing with sophisticated orchestration
Promise-based concurrency without explicit chunking
💡 Simpler approach when data doesn't need explicit partitioning
More flexible distribution for heterogeneous tasks
💡 Better when operations vary significantly across data items
Large-scale parallel analysis for risk assessment and fraud detection
Parallel processing of large document collections and knowledge bases
Parallel code analysis and testing across large codebases
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