Design for Resilience & Fault Tolerance

This standard segment focuses on designing systems that are inherently resilient, capable of gracefully handling failures and maintaining operational stability under adverse conditions.

1. Design for Resilience & Fault Tolerance:

Systems must be designed with the understanding that failures are inevitable. The goal is to build systems that can withstand disruptions and maintain critical functionality, ensuring a seamless user experience.

• 1.1 Fault Handling Mechanisms:
  • 1.1.1 Circuit Breakers:
    • Implement circuit breaker patterns to prevent cascading failures and protect downstream services.
    • Use libraries like Hystrix or Polly to automate circuit breaker behaviour.
  • 1.1.2 Retries & Timeouts:
    • Implement retry mechanisms with exponential backoff to handle transient failures.
    • Set appropriate timeouts to prevent indefinite waiting and resource exhaustion.
  • 1.1.3 Fallbacks & Caching:
    • Provide fallback mechanisms to return cached data or default responses during failures.
    • Utilise client-side and server-side caching to reduce dependencies on external services.
• 1.2 Graceful Degradation Strategies:
  • 1.2.1 Feature Toggles & Degradation Modes:
    • Implement feature toggles to dynamically disable non-essential functionality during failures.
    • Define degradation modes to maintain core functionality while sacrificing less critical features.
  • 1.2.2 Service Prioritisation:
    • Prioritise critical services and allocate resources accordingly during failures.
    • Implement queuing mechanisms to handle requests for non-critical services during periods of high load.
• 1.3 Chaos Engineering & Resilience Testing:
  • 1.3.1 Controlled Experiments:
    • Conduct controlled chaos engineering experiments to simulate real-world failure scenarios.
    • Use tools like Chaos Monkey or Gremlin to inject faults and test system resilience.
  • 1.3.2 Resilience Metrics & Monitoring:
    • Define key resilience metrics (e.g., fault injection rate, recovery time) and monitor them continuously.
    • Use monitoring tools to track system behaviour during chaos engineering exercises.
  • 1.3.3 Post-Mortem Analysis:
    • Conduct thorough post-mortem analyses of incidents and chaos engineering experiments.
    • Identify root causes and implement corrective actions to improve system resilience.
• 1.4 Redundancy & High Availability:
  • 1.4.1 Multi-Region Deployment:
    • Deploy applications across multiple geographical regions to mitigate the impact of regional outages.
    • Implement data replication and failover mechanisms.
  • 1.4.2 Load Balancing & Auto-Scaling:
    • Utilise load balancing and auto-scaling to distribute traffic and dynamically adjust resources.
    • Implement health checks and automated failover procedures.
  • 1.4.3 Data Replication & Consistency:
    • Implement data replication strategies to ensure data availability and consistency.
    • Utilise appropriate consistency models based on application requirements.

By implementing these principles, we can build systems that are not only functional but also robust and capable of withstanding the inevitable challenges of distributed systems.