Availability and Reliability Engineering

Availability measures how much of the time a system is usable; reliability engineering is the discipline of deliberately designing, measuring and improving that number rather than treating it as an accident of how the system happened to be built. It applies engineering rigor — measurement, targets, trade-off analysis — to what used to be treated as purely operational firefighting.

SLIs, SLOs and Error Budgets

A Service Level Indicator (SLI) is a specific measured metric — for example, the percentage of requests served successfully within 300ms. A Service Level Objective (SLO) is a target for that indicator, such as 99.9% over a rolling 30 days. The gap between 100% and the SLO is the error budget: the amount of unreliability the system is allowed before it's considered out of compliance. Error budgets are useful because they turn "should we prioritize reliability work or new features this sprint" into a data-informed decision instead of a debate.

Capacity Planning

Capacity planning means understanding how load is trending and provisioning ahead of it — enough headroom to handle growth and traffic spikes, without paying for permanently idle capacity. This depends on having accurate historical usage data and a reasonable growth forecast, not just reacting once a system starts approaching its limits.

Failure Handling and Resilience

Resilient systems assume components will fail and are designed to degrade gracefully rather than fail completely. Common patterns include timeouts and retries with backoff for calls to dependencies, circuit breakers that stop calling a failing dependency instead of piling up retries, and bulkheads that isolate failures in one part of a system from cascading into others.

Incident Response

When something does fail, a defined incident response process — clear ownership of who's leading the response, a communication channel, and a documented escalation path — reduces the time between detection and resolution. Post-incident reviews focused on what happened and what can be improved (not on blame) are what turn incidents into lasting improvements rather than repeated occurrences.

Disaster Recovery

Disaster recovery planning defines how a system recovers from a large-scale failure — a full region outage, for example — including the recovery time and recovery point objectives discussed in infrastructure planning, and, critically, a tested procedure for actually executing that recovery rather than a document that has never been rehearsed.

Performance and Monitoring

Reliability and performance are closely linked: a system that's technically "up" but too slow to use is not meeting its users' actual needs. Monitoring needs to track both availability and performance against their respective targets, since either one degrading independently can represent a real reliability problem.

Operational Readiness

Operational readiness pulls these practices together into a single question worth asking before any system goes into production: if this fails at 3 a.m., does the team have the monitoring, the runbooks, the ownership and the tested recovery procedure to handle it — or would they be improvising for the first time under pressure?