Catalog
concept#Reliability#Architecture#Observability#Platform

High Availability (HA)

High Availability (HA) refers to architectural and operational principles that minimize downtime and ensure continuous service availability.

High availability (HA) denotes architectural and operational practices aimed at minimizing downtime and keeping services continuously accessible.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Advanced

Technical context

Load balancers and DNS servicesMonitoring and alerting platforms (e.g., Prometheus)Orchestration systems (e.g., Kubernetes)

Principles & goals

Redundancy over single point of failureAutomated failover and recoveryContinuous monitoring and health checks
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Incorrect assumptions about failure modes lead to incomplete protection
  • Untested failover processes may cause data loss or inconsistencies
  • Operational overhead for replication and configuration
  • Conduct regular failover and recovery tests
  • Isolate failure domains and define clear boundaries
  • Automated monitoring with clear SLOs

I/O & resources

  • Available infrastructure (datacenters, cloud regions)
  • Specific SLA and recovery requirements
  • Monitoring and observability tooling
  • Redundant system architecture
  • Documented operational and failover procedures
  • Measurable availability metrics

Description

High availability (HA) denotes architectural and operational practices aimed at minimizing downtime and keeping services continuously accessible. It includes redundancy, failover, replication, monitoring and recovery procedures. Implementing HA requires careful design, automated testing and operational runbooks to handle failures and maintain service levels for critical applications.

✔

  • Reduced downtime for end users
  • Improved operational stability and SLA attainment
  • Better fault tolerance and resilience

✖

  • Increased architecture and operations complexity
  • Higher infrastructure and operational costs
  • Limits for strictly consistent distributed data storage

  • Availability percentile (uptime %)

    Measures the percentage of time a service is reachable.

  • MTTR (Mean Time To Recovery)

    Average time to fix a failure and restore services.

  • Error rate after failover

    Share of failed transactions or requests after failover events.

Kubernetes control plane HA

Multiple API servers, etcd replication and a load balancer provide control plane redundancy.

Primary/replica database setup

Synchronized replicas and automated failover ensure transactional availability.

Multi-region web deployment

Load distribution across regions with geo-redundant storage reduces outage risk.

1

Requirements analysis and SLA definition

2

Design redundancy and failover mechanisms

3

Implementation, testing (chaos tests) and automation

4

Create runbooks and operations training

⚠️ Technical debt & bottlenecks

  • Legacy components without replication support
  • Insufficient automation for recovery steps
  • Missing documentation for failover flows
Single point of failureNetwork latencyData replication limitations
  • Implementing redundancy without monitoring
  • Costly multi-region strategy for non-critical services
  • Ignoring consistency requirements and misconfiguring replication
  • Assuming replication automatically prevents data loss
  • Lack of tests for rare failure scenarios
  • Unclear responsibilities during failover
System architecture and distributed systemsOperational experience with failover and recovery processesMonitoring, alerting and incident response
Expected availability (SLA requirements)Maximum recovery time (RTO/RPO)Fault tolerance and isolation of failure domains
  • • Budget constraints for redundancy
  • • Regulatory requirements for data locality
  • • Legacy systems with limited replication support