designing-distributed-systemslisted

# Designing Distributed Systems Design scalable, reliable, and fault-tolerant distributed systems using proven patterns and consistency models. ## Purpose Distributed systems are the foundation of modern cloud-native applications. Understanding fundamental trade-offs (CAP theorem, PACELC), consistency models, replication patterns, and resilience strategies is essential for building systems that scale globally while maintaining correctness and availability. ## When to Use This Skill Apply when: - Designing microservices architectures with multiple services - Building systems that must scale across multiple datacenters or regions - Choosing between consistency vs availability during network partitions - Selecting replication strategies (single-leader, multi-leader, leaderless) - Implementing distributed transactions (saga pattern, event sourcing, CQRS) - Designing partition-tolerant systems with proper consistency guarantees - Building resilient services with circuit breakers, bulkheads, retries - Implementing service discovery and inter-service communication ## Core Concepts ### CAP Theorem Fundamentals **CAP Theorem:** In a distributed system experiencing a network partition, choose between Consistency (C) or Availability (A). Partition tolerance (P) is mandatory. ``` Network partitions WILL occur → Always design for P During partition: ├─ CP (Consistency + Partition Tolerance) │ Use when: Financial transactions, inventory, seat booking │ Trade-off: System unavail