High-Performance Distributed Caching Layer
Flow Cache is a high-performance distributed caching layer built with C++, designed to significantly reduce database read latency in microservices architectures. By implementing gRPC for efficient inter-service communication and Redis as the underlying cache store, Flow Cache provides a robust, scalable solution for modern distributed systems.
The system is fully containerized using Docker, enabling seamless deployment across various environments and smooth integration with existing microservices infrastructure. This project demonstrates advanced systems programming concepts including distributed systems design, network communication protocols, and container orchestration.
Built with C++ for maximum performance, achieving microsecond-level response times for cache operations and significantly reducing database load.
Leverages Redis for distributed caching capabilities, enabling horizontal scaling and ensuring cache consistency across multiple service instances.
Implements gRPC for efficient, type-safe communication between services with built-in load balancing and bidirectional streaming support.
Fully containerized application ensuring consistent deployment across development, staging, and production environments with minimal configuration overhead.
Achieves significant reduction in database read latency by serving frequently accessed data from in-memory cache, improving overall system responsiveness.
Designed specifically for microservices architectures with easy integration patterns and minimal service disruption during deployment.
Flow Cache implements a three-tier architecture:
Handles cache key generation, TTL management, and eviction policies. Implements intelligent caching strategies to maximize hit rates.
Exposes cache operations through well-defined gRPC interfaces, supporting both unary and streaming RPCs for different use cases.
Provides thread-safe connection pooling to Redis, handling connection lifecycle and automatic reconnection in case of failures.
Challenge: Maintaining cache consistency across distributed services
Solution: Implemented cache invalidation patterns with pub/sub notifications and optimistic locking for concurrent updates
Challenge: Minimizing network overhead in distributed caching
Solution: Utilized gRPC's HTTP/2 multiplexing and protobuf serialization for efficient data transfer
Challenge: Ensuring horizontal scalability without performance degradation
Solution: Designed stateless cache service instances with Redis cluster support for distributed data storage
Modern C++ features, memory management, and performance optimization techniques
CAP theorem, consistency models, and distributed caching patterns
RPC framework implementation, service definition, and efficient serialization
Docker containerization, multi-container applications, and deployment strategies
In-memory data structures, persistence mechanisms, and performance tuning
Service communication patterns, API design, and system integration