Distributed Systems Series — Part 5.2: Scalability & Performance Why Latency at Scale Is a Different Problem Post 5.1 established what scalability means and identified Amdahl’s Law as the mathematical ceiling on parallelism. This post addresses the latency dimension of scalability — specifically why latency behaviour at scale is fundamentally different from latency at low … Read more
Engineering guidelines for replication, consistency, and consensus in distributed systems — with a complete design review checklist covering failure design, consistency model selection, replication configuration, consensus placement, performance, and observability.
Performance trade-offs in distributed replication and consensus — write latency, tail latency, write vs read scalability, consensus throughput limits, batching and pipelining, geographic distribution costs, and backpressure design.
The network is unreliable by nature. Latency, message loss, and partitions make distributed systems behave in unexpected and often confusing ways.