High Availability
Redundancy, failover, multi-AZ, RTO/RPO.
Theory
High availability minimizes downtime through redundancy and automatic failover. Expressed as "nines": 99.9% allows ~8.7 hours downtime per year; 99.99% allows ~52 minutes.
RTO (Recovery Time Objective) is max acceptable downtime; RPO (Recovery Point Objective) is max acceptable data loss. Active-passive pairs standby capacity; active-active serves from multiple nodes simultaneously with conflict handling.
Deploy across multiple availability zones within a region for infrastructure redundancy. Multi-region adds disaster recovery at the cost of replication lag and cross-region consistency complexity. Automate failover with health-checked load balancers or leader election.
Production rollouts require idempotent automation, peer review, staged apply, and documented rollback — treat changes as production code.
Interviewers want STAR stories linking High Availability to measurable outcomes: fewer outages, faster deploys, lower cost, or reduced toil.
Architecture Diagram
Users / clients
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High Availability
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Core services
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Data + observabilityExamples
# High Availability
# Redundancy, failover, multi-AZ, RTO/RPO.
# Validate in staging before production rollout.Interview Questions
What problem does High Availability solve?
It addresses the core use case described in production architecture — map features to reliability, scale, or velocity outcomes.
Key components of High Availability?
Identify inputs, outputs, control plane, data plane, and failure domains — interviewers want structured decomposition.
Common production pitfalls?
Misconfiguration, missing observability, no rollback path, and scaling bottlenecks under peak load.
How do you test changes safely?
Staging parity, canary/gradual rollout, automated health checks, and documented rollback.
Metrics to prove success?
Error rate, latency percentiles, throughput, cost, and toil reduction — pick one primary SLO.
Beginner vs advanced concern?
Beginners focus on setup; advanced teams focus on blast radius, security boundaries, and operability at 10× scale.
Best Practices
- Treat High Availability config as code with review and CI validation.
- Define SLOs and dashboards before production cutover.
- Document rollback and ownership for on-call.
- Use least privilege for credentials.
Common Mistakes
- Adopting High Availability without measurable success criteria.
- No staging environment mirroring production constraints.
- Missing rollback path during incidents.
- Undocumented on-call expectations.
Trade-off Analysis
High Availability improves redundancy, failover, multi-az, rto/rpo. but adds operational and cognitive complexity — justify with load and team size.
Favor simplicity until metrics (p99 latency, error rate, cost) prove the pattern necessary.
Every redundancy layer trades capital/operational cost for availability — align with explicit SLO targets.
Document accepted inconsistency windows and recovery behavior before production cutover.
Cheat Sheet
Practical Exercises
Stand up High Availability locally or in free tier; document commands and failure recovery.
Introduce misconfiguration; practice detection and rollback under time limit.