Catalog
concept#Reliability#Observability#Architecture#DevOps

System Reliability

Concept describing a system's ability to reliably deliver services, tolerate faults, and maintain expected availability and service levels over time.

System reliability describes a system's ability to deliver services consistently over time, tolerate faults, and maintain expected availability.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Advanced

Technical context

APM and monitoring tools (e.g., Prometheus, Grafana)Incident management systems (e.g., PagerDuty)CI/CD pipelines for automated tests and deploys

Principles & goals

Design for failure: systems must assume and tolerate failures.Measurable targets: use SLOs and SLIs as decision basis.Observability before debugging: telemetry is the primary indicator of health.
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Incorrect SLOs lead to unnecessary costs or poor UX.
  • Too much complexity can introduce new failure modes.
  • Insufficient testing causes false assumptions about safety.
  • SLO-driven decisions instead of pure uptime targets
  • Implement automated recovery paths
  • Transparent postmortems with concrete actions

I/O & resources

  • Architecture diagrams and dependencies
  • Historic incident and performance data
  • Business requirements and availability targets
  • SLO/SLI definitions and monitoring dashboards
  • Recovery and failover plans
  • Action lists from postmortems

Description

System reliability describes a system's ability to deliver services consistently over time, tolerate faults, and maintain expected availability. It covers design principles, redundancy, observability, and operational processes for failure handling and recovery, evaluating trade-offs between cost, performance, and complexity. The goal is measurable dependability throughout the lifecycle.

✔

  • Higher availability and reduced downtime.
  • Improved customer trust through measurable service levels.
  • Faster fault detection and remediation via observability.

✖

  • Increased costs due to redundancy and monitoring.
  • Increased complexity in architecture and operational processes.
  • Not all failures are technically controllable (e.g., third-party services).

  • Availability (Uptime)

    Percentage of time a service is available.

  • Mean Time to Recovery (MTTR)

    Average time to remediate a failure and restore service.

  • Error rate

    Proportion of failed requests relative to total requests.

Banking platform with 99.99% availability

A bank implemented redundancy, strict SLOs and automated failover to reliably process financial transactions.

E-commerce during peak loads

Scalable architectures and circuit breakers prevented cascading failures during peak loads.

SaaS provider using chaos engineering

Regular chaos tests improved resilience and uncovered unknown dependencies.

1

Analyze current availability and dependencies

2

Define SLOs/SLIs and instrument metrics

3

Plan and implement redundancy, failover and test strategies

4

Introduce regular chaos and recovery tests

⚠️ Technical debt & bottlenecks

  • Legacy systems without observability integration
  • Manual failover processes
  • Unclear ownership of critical paths
Single Point of FailureNetwork latencyStateful services replication
  • Setting SLOs so strict that releases are blocked
  • Relying solely on scaling to solve latency issues
  • Monitoring data too aggregated to be actionable
  • Focusing on single metrics instead of user experience
  • Too many alerts without prioritization
  • Missing automation for frequent recovery steps
System architecture and distributed systemsObservability and monitoring expertiseIncident management and root-cause analysis
Availability and uptime requirementsFailover and recovery times (RTO/RPO)Observability and telemetry needs
  • • Budget limits for redundancy and testing
  • • Third-party dependencies
  • • Regulatory requirements for data residency