The ability of a system to continue operating correctly even when components fail.
Fault tolerance means systems continue working when parts fail. Hardware crashes, networks disconnect, software bugs occur - fault tolerant systems handle these gracefully instead of completely failing.
Users barely notice problems. The system either recovers automatically or degrades gracefully while maintaining core functionality.
Hardware Fails: Servers crash, disks die, networks have outages. These are inevitable, not hypothetical.
Software Has Bugs: Even well-tested code encounters edge cases in production.
Human Errors: Misconfigurations, accidental deletions, bad deployments happen.
Fault tolerance assumes failure is normal and designs systems to handle it.
No Single Point of Failure: Every critical component has backup.
Multiple Servers: Load balancers distribute traffic. One fails, others continue.
Multiple Data Centers: Entire data center fails, traffic routes to another.
Multiple Availability Zones: Cloud regions have zones. Zone failure does not affect other zones.
Redundancy costs money but prevents catastrophic failures.
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Health Checks: Periodically verify services are responsive.
Heartbeats: Services send "I am alive" signals. Missing heartbeats indicate failure.
Monitoring: Track metrics (CPU, memory, error rates). Anomalies suggest problems.
Alerting: Notify teams when failures detected.
Fast detection enables fast recovery. Cannot fix problems you do not know exist.
Automatic Failover: Backup takes over when primary fails. Minimal downtime.
Retry Logic: Transient failures often resolve themselves. Retry requests with exponential backoff.
Circuit Breakers: Stop calling failing services. Prevents cascade failures.
Graceful Degradation: Disable non-essential features when resources limited. Core functionality continues.
Data Replication: Store data copies on multiple servers. One server dies, data survives.
Database Failover: Replica promoted to primary when primary fails.
Geographic Replication: Copies in different regions survive regional disasters.
Replication protects against data loss and provides availability during failures.
One service fails, overwhelming others, causing them to fail, triggering more failures.
Circuit Breakers: Stop requests to failing services. Prevents overload.
Rate Limiting: Limit requests to prevent overload during recovery.
Bulkheads: Isolate resources. One pool of resources failing does not drain others.
Timeouts: Do not wait forever for responses. Fail fast and move on.
Design systems to contain failures, not propagate them.
Chaos Engineering: Intentionally break things in production to test resilience.
Netflix Chaos Monkey: Randomly terminates servers. Forces engineers to build resilient systems.
Failure Injection: Simulate network failures, service crashes, high latency in testing.
Disaster Recovery Drills: Practice recovery procedures regularly. Untested recovery plans fail during real disasters.
Test failure scenarios before they happen for real.
Netflix: Streams to millions despite constant server failures. Fault tolerance built into architecture.
Amazon: Entire AWS availability zone fails. Applications in other zones continue running.
Google: Services like Gmail rarely go down because of massive redundancy and automatic failover.
Cost: Redundancy requires more servers, storage, and complexity.
Complexity: Fault tolerant systems are harder to build and maintain.
Performance: Health checks, retries, and redundancy add overhead.
Consistency: Distributed systems with fault tolerance face consistency challenges.
Balance these against business requirements. Not everything needs five nines of uptime.
No Tolerance: Single server. It fails, everything fails.
Basic: Backup server. Manual failover. Minutes to hours of downtime.
High: Automatic failover. Read replicas. Seconds to minutes of downtime.
Extreme: Multi-region active-active. Zero downtime. Expensive and complex.
Choose appropriate level for your application. E-commerce checkout needs more tolerance than internal admin tools.
Stateless Services: No stored state. Easy to make fault tolerant. Any server handles any request.
Stateful Services: Store state (sessions, connections). Harder to make fault tolerant. State must replicate or persist.
Design services stateless when possible. Store state externally in databases or caches.
Fault tolerance is not optional for production systems. Plan for failure from day one.
Build redundancy, implement health checks, design automatic recovery. Test failure scenarios regularly.
The question is not if components will fail, but when. Fault tolerant systems survive those failures and keep serving users.
Start simple - multiple servers behind load balancer, database with replica. Add complexity as requirements grow.
Remember: perfect reliability is impossible. Focus on acceptable reliability at reasonable cost.
