Distributed SystemsRaft ConsensusFault ToleranceGo

Distributed Fault-Tolerant Chat System

A distributed chat system with leader election, message replication, and failure recovery mechanisms.

Overview

A distributed chat application implementing Raft consensus for leader election and log replication. The system maintains consistency across multiple nodes and handles node failures gracefully.

Built to understand distributed consensus algorithms and fault tolerance patterns in real-world systems. Demonstrates handling of network partitions, leader failures, and message ordering guarantees.

Your Role

What I Built

Raft consensus algorithm implementation for leader election and log replication
Message replication protocol ensuring consistency across nodes
Failure detection and recovery mechanisms
Client-server communication layer with retry logic

What I Owned End-to-End

End-to-end system architecture and design decisions
Consensus algorithm correctness and testing
Performance optimization and latency reduction
Documentation and deployment scripts

Technical Highlights

Architecture Decisions

Multi-node cluster with leader-follower pattern
Log-based replication for message ordering
Heartbeat mechanism for failure detection
Client request routing through leader node

Algorithms / Protocols / Constraints

Raft consensus algorithm for distributed agreement
Leader election with randomized timeouts
Log replication with majority quorum requirement
State machine replication for message delivery

Failure Handling

Automatic leader election on leader failure
Network partition detection and handling
Message deduplication for retry scenarios
Graceful degradation when quorum is lost

Optimization Strategies

Batched log entries for reduced network overhead
Pipelined replication for improved throughput
Client-side caching to reduce leader load

Tech Stack

GogRPCProtocol BuffersDockerKubernetes

Results / Learnings

What Worked

Achieved 99.9% message delivery guarantee under normal conditions
Handled up to 3 simultaneous node failures in 5-node cluster
Maintained sub-100ms latency for message replication

What I Learned

Understanding tradeoffs between consistency and availability
Importance of careful timeout tuning in distributed systems
Complexity of handling network partitions correctly

Tradeoffs Considered

Chose strong consistency over availability (CP in CAP theorem)
Prioritized correctness over performance in consensus layer
Accepted higher latency for guaranteed message ordering

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