distributed-systems-fundamentals

When selecting consensus algorithms for distributed systems, Paxos offers foundational theoretical correctness while Raft provides a more understandable leader-based design. This Skill guides you through selecting the appropriate protocol based on your specific failure detection, network partition handling, and consistency requirements.

The CAP theorem addresses distributed database behavior during network partitions, forcing a choice between consistency and availability. PACELC extends this by also dictating latency versus consistency tradeoffs during normal operation, helping you navigate partial failure scenarios without unexpected production outages.

Designing idempotent retry logic for distributed transactions requires understanding partial failure and clock skew. This Skill explains how to implement reliable payment processing and state synchronization across microservices while avoiding common distributed architecture anti-patterns.

Use Conflict-free Replicated Data Types (CRDTs) instead of traditional replication strategies when your distributed system prioritizes high availability and eventual consistency over strong linearizability. This Skill helps you evaluate consistency models to match your specific multi-datacenter requirements.

Vector clocks and Lamport timestamps solve distributed systems timekeeping challenges by establishing causal ordering of events without relying on synchronized physical clocks. This Skill explains how to implement these logical clock mechanisms to detect concurrent updates and resolve conflicts.

The 8 Fallacies of Distributed Computing assume network reliability, zero latency, and infinite bandwidth, directly causing microservices architecture failures. This Skill provides a quick reference checklist to identify these fallacies and mitigate partial failures during architecture reviews.