MapReduce Infrastructure
Overview A distributed, fault-tolerant MapReduce implementation built from scratch in C++ with gRPC and multithreading capabilities. Architecture Master-Worker Architecture: Centralized job scheduling and coordination Fault Tolerance: Automatic task reassignment and recovery mechanisms Load Balancing: Dynamic task distribution across worker nodes Network Communication: gRPC-based inter-node communication Key Features Distributed Processing: Handles large-scale data processing across multiple nodes Fault Recovery: Automatic detection and recovery from node failures Scalability: Supports dynamic addition/removal of worker nodes Performance Monitoring: Real-time metrics and progress tracking Technical Implementation Language: C++ for high performance Communication: gRPC for efficient network communication Concurrency: Multithreading for parallel task execution Storage: Efficient data serialization and storage management Use Cases Large-scale data processing Distributed computing research Educational purposes for understanding MapReduce concepts View on GitHub →