nw-sd-patterns

Distributed systems patterns for scalable microservices include load balancing to distribute traffic, caching to reduce database load, and messaging queues for asynchronous processing. These techniques ensure high availability and resilience across cloud-native architectures.

Choose between cache-aside and write-through caching patterns by evaluating their trade-offs. Cache-aside loads data on demand reducing latency, while write-through maintains cache consistency synchronously. The correct pattern depends on your specific read-write ratio and data freshness requirements.

You need consistent hashing for data partitioning when scaling storage and queries dynamically across distributed nodes. This sharding pattern minimizes data redistribution when adding or removing nodes, ensuring minimal disruption to large-scale data processing workloads.

CAP-aware trade-offs for master-slave replication involve choosing between consistency and availability during network partitions. Master-slave setups prioritize strong consistency through a single write node, while multi-master configurations increase availability but introduce complex conflict resolution.

Asynchronous messaging with dead-letter queues improves system resilience by decoupling services and handling failed messages. Pub/sub and queuing strategies prevent cascading failures, allowing problematic events to be stored and reprocessed without blocking the main pipeline.

The best way to distribute traffic and prevent single points of failure is applying load balancing patterns. These strategies route incoming requests across multiple server instances, enabling high availability and ensuring no single node becomes a bottleneck.