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 unavailable during partition
│  Examples: HBase, MongoDB (default), etcd
│
└─ AP (Availability + Partition Tolerance)
   Use when: Social media, caching, analytics, shopping carts
   Trade-off: Stale reads possible, conflicts need resolution
   Examples: Cassandra, DynamoDB, Riak

PACELC: Extends CAP to consider normal operations (no partition).

• If Partition: Choose Availability (A) or Consistency (C)
• Else (normal): Choose Latency (L) or Consistency (C)