Latest Reliability & Resilience Articles

The Bulkhead Pattern is a reliability pattern that isolates resources so a failure or overload in one part of a system cannot consume all available capacity and bring down unrelated functionality.

A circuit breaker is a reliability pattern that temporarily stops calls to a failing dependency instead of repeatedly sending requests that are likely to fail.

Availability is the ability of a software system to remain accessible and usable when users need it.

Reliability is the ability of a software system to keep doing the correct thing over time, even when parts of the system fail.

Multi-region architecture addresses a failure boundary that multi-zone systems cannot: the loss or severe degradation of an entire cloud region . Regional outages are uncommon, but when they occur they can affect compute, networking, managed databases, queues, control planes, and other services simu

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.

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.

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 rarely fail completely at once. More often, one dependency becomes unavailable, a database becomes slow, a cache misses excessively, an external provider reaches its rate limit, or the system receives more traffic than it can process normally.

Distributed systems fail in different ways. A downstream service can become unavailable, one dependency can consume all shared resources, or incoming traffic can exceed the capacity of an otherwise healthy application. Treating these situations with the same reliability mechanism usually produces po

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

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

A distributed system rarely fails as one complete unit. More often, one dependency becomes slow, one availability zone loses connectivity, one database replica falls behind, or one request times out while the operation still completes in the background.

Availability patterns are system design techniques that keep applications operational during failures, overload, dependency issues, or partial outages.

Availability is one of the core goals in system design. It answers a simple but critical question: can users access the system when they need it? A highly available system continues to work despite server failures, database issues, traffic spikes, deployments, network problems, or even regional outa

Production systems must handle three different forms of pressure: growth, failure, and overload . Scalability addresses growth, availability keeps critical functionality reachable during failures, and stability prevents degraded components or excessive load from causing uncontrolled system-wide fail