Distributed Systems Series — Part 5.12: Scalability & Performance The End of the Beginning This is the forty-third and final post in a series that began with a simple question: what does it mean to build distributed systems correctly? Not just systems that work in development, not just systems that pass their tests, but systems … Read more
Distributed Systems Series — Part 5.9: Scalability & Performance When Single-Region Is No Longer Sufficient Every scalability mechanism covered so far in Part 5 — partitioning, load balancing, caching, backpressure, indexing, autoscaling — operates within a single geographic region. These mechanisms collectively allow a system to handle enormous load within one region. But they cannot … Read more
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
Distributed Systems Series — Part 5.1: Scalability & Performance What Scalability Actually Means Parts 1 through 4 of this series established how distributed systems work correctly and survive failures. Part 5 addresses the final dimension: how do systems handle growth? Scalability is one of the most overused and least precisely defined terms in software engineering. … Read more
Distributed Systems Series — Part 4.9: Fault Tolerance & High Availability The Gap Between Designed Resilience and Actual Resilience Parts 4.1 through 4.8 have established the complete fault tolerance and high availability engineering stack. Post 4.1 defined the failure taxonomy. Posts 4.2 and 4.3 established the fault tolerance and redundancy foundations. Post 4.4 covered failure … Read more
Distributed Systems Series — Part 4.7: Fault Tolerance & High Availability Failures Are Inevitable — Outages Are Not Every large distributed system experiences component failures continuously. Nodes crash, networks degrade, downstream services slow, disks fill, processes run out of memory. The engineering discipline is not preventing these failures — that is impossible at scale — … Read more