Short analytical pieces on the mechanics of failure in distributed systems: what the received wisdom gets wrong, and the conditions under which it stops being true.
Failure Modes are written from published postmortems, incident write-ups and engineering documentation, which are listed at the foot of every piece. Nothing in this section has been run, measured or operated here — where a number, a threshold or a result appears, it belongs to the source it is credited to.
The standard architectural argument for multi-region deployment rests on a simple premise: if one location fails, the other remains functional. This logic treats geographic distance as a sufficient condition for fault isolation. However, redundancy only…
The blameless postmortem is widely celebrated as a cultural achievement. It removes the fear of punishment, allowing engineers to describe exactly what they did without self-censorship. This openness is genuinely valuable; it surfaces the messy reality of…
The standard advice for building resilient clients is straightforward: always set a timeout on every remote call. The gRPC documentation explicitly states that without an explicit deadline, a client may wait for a response effectively forever. This guidance…
The standard justification for placing a cache in front of a database is straightforward: it absorbs repeated reads, reducing the number of queries that reach the primary storage layer. On a normal day, this works as advertised. The application layer checks…
The common assumption is that when a node fails, the Kubernetes control plane automatically moves the affected workload to a healthy node, rendering the incident self-healing. This view conflates the creation of a replacement object with the successful…
A liveness probe is a binary question: is this process alive and responsive? It is not a question about correctness, nor about the state of the database, nor about the availability of the message queue. The Kubernetes documentation defines the liveness probe…
The common belief is that a retry policy acts as a safety net, smoothing over transient glitches in a distributed system. This view treats each service call as an isolated event, where a failed attempt is simply a momentary hiccup that a second try can…
