designing-distributed-systems

Choosing consistency models for distributed systems depends on business requirements for availability and correctness. Eventual consistency improves availability, while strong consistency ensures correctness. The CAP and PACELC theorems guide these trade-offs.

The best way to architect fault-tolerant microservices is by applying resilience patterns like circuit breakers and retries. Designing distributed systems with these strategies prevents cascading failures and ensures reliable operation during partial network failures.

Selecting a replication pattern for a scalable distributed architecture involves comparing leader-follower and leaderless strategies. These replication strategies distribute data across nodes, directly impacting system scalability, reliability, and fault tolerance.

The CAP theorem affects distributed system design by proving that a network partition forces a choice between consistency and availability. Understanding these core concepts dictates whether your architecture prioritizes correctness or continuous uptime.

You should use partitioning strategies in distributed databases when scalability requires distributing data across multiple nodes. Partitioning divides datasets to improve query performance and fault tolerance, preventing single-node bottlenecks.

Patterns that handle distributed transactions in microservices include sagas and event sourcing. These approaches manage data consistency across services by sequencing local operations and capturing state changes as reliable events.