Popular Reliability Articles

Distributed systems are designed from components that fail independently. Application instances restart, databases fail over, messages are delivered more than once, networks disconnect services, deployments interrupt requests, and long-running workflows can stop halfway through execution.

Reliable systems are not systems that never fail. Hardware fails, networks become slow, databases reach capacity, dependencies become unavailable, deployments introduce defects, and traffic exceeds expectations. Reliability comes from designing the system so individual failures do not automatically

Kubernetes provides primitives for scheduling, service discovery, health checks, scaling, configuration, and workload recovery, but production reliability depends on how those primitives are combined. A cluster can be technically healthy while applications suffer from poor scheduling, weak failure i

Production systems fail in many ways: dependencies become slow, application instances crash, queues accumulate work, databases reach capacity, networks become unreliable, deployments introduce defects, and sudden traffic spikes overload otherwise healthy services.

A running process is not necessarily a healthy service. An application can be alive while its connection pool is exhausted, still initializing, unable to accept traffic, stuck in a deadlock, or waiting for a dependency that will never recover without intervention.

Distributed systems communicate across networks where requests can become slow, connections can reset, instances can restart, and dependencies can temporarily reject traffic. A remote operation can succeed quickly, fail immediately, or remain uncertain long enough to exhaust resources in the calling