Resilience & Failure Handling

About

Resilience in system design refers to the ability of a system to recover from failures and continue operating with minimal disruption. Failure handling involves identifying, mitigating, and recovering from different types of failures.

A resilient system ensures high availability, reliability, and fault tolerance by employing various techniques like redundancy, failover mechanisms, circuit breakers, retries, and graceful degradation.

Characteristics of Resilient Systems

• Fault Tolerance – The ability to continue operating despite hardware/software failures.

• Self-Healing – Automatically detects and recovers from failures.

• Elasticity – Adapts to changing workloads without failure.

• Graceful Degradation – Continues partial functionality even under failure conditions.

• Redundancy – Uses backup resources to maintain service availability.

Types of Failures in Distributed Systems

Failures can occur at different levels in a system:

A. Hardware Failures

• Disk Failures – Hard drive crashes, data corruption.

• Network Failures – Packet loss, high latency, network partitioning.

• Power Failures – Data center outages, insufficient backup power.

B. Software Failures

• Application Crashes – Unhandled exceptions, memory leaks, out-of-memory errors.

• Deadlocks & Race Conditions – Threads competing for shared resources.

• Configuration Issues – Incorrect database credentials, invalid settings.

C. Human Errors

• Deployment Mistakes – Pushing buggy code to production.

• Misconfigurations – Incorrect firewall settings, wrong database schema updates.

• Accidental Data Deletion – Human-caused data loss or corruption.

D. External Dependencies Failures

• Third-Party API Failures – External service outages.

• Cloud Service Downtime – AWS, Azure, or Google Cloud region failures.

Failure Handling Strategies

To build resilience, systems use different strategies to detect, recover from, and prevent failures.

A. Fault Detection

1. Health Checks – Periodically test components (e.g., API health endpoints).

2. Logging & Monitoring – Track system behavior using logs and alerts (e.g., Prometheus, ELK Stack).

3. Heartbeats & Watchdogs – Periodic "I am alive" signals from services.

4. Latency & Error Rate Tracking – Detect slow responses and failures.

B. Fault Recovery Mechanisms

1. Retries & Exponential Backoff

   • Retries failed operations with increasing delay.

   • Prevents excessive load on a failing system.

2. Circuit Breakers

   • Stops making requests to a failing service and attempts recovery after some time.

   • Example: Netflix’s Hystrix circuit breaker pattern.

3. Failover & Redundancy

   • Uses backup systems when the primary fails.

   • Example: Master-slave database replication.

4. Graceful Degradation

   • System provides partial functionality when under failure conditions.

   • Example: A search engine showing cached results when the database is unavailable.

5. Load Balancing

   • Distributes traffic across multiple instances to avoid overloading one node.

   • Example: Nginx, HAProxy, AWS Elastic Load Balancer (ELB).

6. Data Replication & Backups

   • Stores copies of data to recover from failures.

   • Example: Database replication in PostgreSQL, MySQL.

Patterns for Resilience

To enhance resilience, modern system architectures use various design patterns.

A. Leader Election

• Used in distributed systems to designate a primary (leader) node.

• If the leader fails, another node takes over.

• Example: Zookeeper, Raft Algorithm, Paxos Protocol.

B. Bulkhead Pattern

• Isolates components so that failures in one do not bring down the entire system.

• Example: Separating services into different clusters (e.g., database pool partitioning).

C. Event Sourcing & CQRS

• Logs every system event, allowing easy rollback in case of failures.

• Example: Apache Kafka event-driven architecture.

D. Multi-Region Deployments

• Runs services in multiple regions to survive regional failures.

• Example: AWS services using Route 53 global traffic routing.

Netflix Resilience Engineering

Netflix is known for its highly resilient system design. Some of its resilience strategies include:

1. Chaos Engineering

   • Uses Chaos Monkey to randomly terminate instances to test resilience.

   • Helps identify weaknesses before real failures occur.

2. Circuit Breakers & Bulkheads

   • Uses Hystrix to handle failures in microservices.

   • Prevents cascading failures across the system.

3. Auto Recovery & Self-Healing

   • Services automatically restart on failure.

   • Uses Eureka Service Discovery for failover handling.