map 操作と reduce 操作を用いて、計算を複数のノードに分散します
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パターン: データをチャンクに分割し、並列に処理してから結果を集約する
理由: スループットを最大化し、複数のコアやサービスを活用し、水平にスケールする
重要なポイント: 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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