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