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Map-Reduce
Distributes computation across multiple nodes using map and reduce operations
🎯 30-Second Overview
Pattern: Split data into chunks, process in parallel, then aggregate results
Why: Maximizes throughput, utilizes multiple cores/services, scales horizontally
Key Insight: Chunk[1..N] → Map(f) → [Result1..N] → Reduce → Final_Output
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Large datasets to process
- • Independent, repeatable operations
- • CPU/IO bound tasks
- • Need horizontal scaling
Avoid When
- • Small datasets (overhead exceeds benefit)
- • Sequential dependencies
- • Memory-intensive aggregation
- • Real-time streaming needs
📊 Key Metrics
💡 Top Use Cases
Pattern Relationships
Discover how Map-Reduce relates to other patterns
Prerequisites, next steps, and learning progression
Prerequisites
(1)Sequential Chaining
lowprompt chainingLinear processing foundation that Map-Reduce parallelizes
💡 Understanding linear processing helps design effective parallel decomposition
Next Steps
(3)Scatter-Gather
mediumparallelizationMore flexible parallel distribution with heterogeneous processing
💡 Natural evolution when you need different operations on different data types
Fork-Join
mediumparallelizationRecursive parallel decomposition with work stealing
💡 Advanced parallelization with dynamic load balancing
Stateful Graph Workflows
very-highplanning executionComplex parallel workflows with state management
💡 Enterprise-grade parallel processing with sophisticated orchestration
Alternatives
(2)Async-Await
lowparallelizationPromise-based concurrency without explicit chunking
💡 Simpler approach when data doesn't need explicit partitioning
Scatter-Gather
mediumparallelizationMore flexible distribution for heterogeneous tasks
💡 Better when operations vary significantly across data items
Industry Applications
Financial Services
Large-scale parallel analysis for risk assessment and fraud detection
Content & Knowledge
Parallel processing of large document collections and knowledge bases
Software Development
Parallel code analysis and testing across large codebases
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.
Map-Reduce
Distributes computation across multiple nodes using map and reduce operations
🎯 30-Second Overview
Pattern: Split data into chunks, process in parallel, then aggregate results
Why: Maximizes throughput, utilizes multiple cores/services, scales horizontally
Key Insight: Chunk[1..N] → Map(f) → [Result1..N] → Reduce → Final_Output
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Large datasets to process
- • Independent, repeatable operations
- • CPU/IO bound tasks
- • Need horizontal scaling
Avoid When
- • Small datasets (overhead exceeds benefit)
- • Sequential dependencies
- • Memory-intensive aggregation
- • Real-time streaming needs
📊 Key Metrics
💡 Top Use Cases
Pattern Relationships
Discover how Map-Reduce relates to other patterns
Prerequisites, next steps, and learning progression
Prerequisites
(1)Sequential Chaining
lowprompt chainingLinear processing foundation that Map-Reduce parallelizes
💡 Understanding linear processing helps design effective parallel decomposition
Next Steps
(3)Scatter-Gather
mediumparallelizationMore flexible parallel distribution with heterogeneous processing
💡 Natural evolution when you need different operations on different data types
Fork-Join
mediumparallelizationRecursive parallel decomposition with work stealing
💡 Advanced parallelization with dynamic load balancing
Stateful Graph Workflows
very-highplanning executionComplex parallel workflows with state management
💡 Enterprise-grade parallel processing with sophisticated orchestration
Alternatives
(2)Async-Await
lowparallelizationPromise-based concurrency without explicit chunking
💡 Simpler approach when data doesn't need explicit partitioning
Scatter-Gather
mediumparallelizationMore flexible distribution for heterogeneous tasks
💡 Better when operations vary significantly across data items
Industry Applications
Financial Services
Large-scale parallel analysis for risk assessment and fraud detection
Content & Knowledge
Parallel processing of large document collections and knowledge bases
Software Development
Parallel code analysis and testing across large codebases
References & Further Reading
Deepen your understanding with these curated resources
Contribute to this collection
Know a great resource? Submit a pull request to add it.