Definitions
Reliability
The probability that a system performs correctly during a specific time duration.
Focus: Preventing failures from happening in the first place.
Availability
The percentage of time a system is operational and accessible.
Focus: Minimizing downtime when failures do occur.
Key insight: A system can be available but unreliable (returns wrong data), or reliable but not highly available (goes down often but works correctly when up).
Availability Levels & SLAs
Reliability Patterns
Common patterns for building reliable systems that can handle failures gracefully.
Implementation: Load balancers, database replicas, multi-AZ deployment
Implementation: Netflix Hystrix, fail-fast when dependency unavailable
Implementation: Separate thread pools, connection pools per service
Implementation: Exponential backoff, jitter, max retry limits
MTBF vs MTTR
MTBF (Mean Time Between Failures)
Average time between system failures. Focus on preventing failures.
- • Better testing and quality assurance
- • Redundant components and systems
- • Regular maintenance and updates
MTTR (Mean Time To Recovery)
Average time to restore service after a failure. Focus on fast recovery.
- • Automated monitoring and alerting
- • Quick rollback mechanisms
- • Clear incident response procedures
Formula: Availability = MTBF / (MTBF + MTTR)
Strategy: Increase MTBF and decrease MTTR for higher availability.
Reducing MTTR Strategies
Real-world Examples
Netflix: 99.99% Availability
Strategy: Chaos engineering, microservices, multi-region deployment
Can lose an entire AWS region without service impact. Intentionally breaks things in production to test resilience.
Google: 99.99% SLA
Strategy: Global load balancing, automatic failover, SRE practices
Site Reliability Engineering (SRE) teams focus on automation and reducing toil. Error budgets balance reliability vs feature velocity.
AWS: 99.99% SLA
Strategy: Multi-AZ deployment, auto-scaling, health checks
Each service has specific SLA commitments. Availability Zones are isolated from each other to prevent cascading failures.
Common Failure Modes
Hardware Failures
Disk crashes, network outages, power failures. Solution: Redundancy and quick replacement.
Software Bugs
Memory leaks, infinite loops, null pointer exceptions. Solution: Testing, monitoring, quick rollback.
Human Errors
Misconfigurations, accidental deletions, deployment mistakes. Solution: Automation, safeguards, training.
Cascading Failures
One component failure triggers others. Solution: Circuit breakers, bulkhead pattern, graceful degradation.
Load Spikes
Traffic exceeds capacity. Solution: Auto-scaling, load balancing, rate limiting.
Dependencies
External service failures. Solution: Timeouts, retries, fallback mechanisms, caching.
Building Reliable Systems: Checklist
Design Phase
- ✓ Identify single points of failure
- ✓ Plan for redundancy at each layer
- ✓ Design for graceful degradation
- ✓ Define SLA requirements early
- ✓ Plan monitoring and alerting strategy
Implementation Phase
- ✓ Implement circuit breakers
- ✓ Add comprehensive logging
- ✓ Set up health checks
- ✓ Create deployment automation
- ✓ Test failure scenarios
🧮 SLA Calculator
Calculate actual downtime and costs based on availability targets
Inputs
Result
99.9% availability means 8.76 hours downtime per year
🎯 Failure Response Scenarios
Learn from real outage responses and their business impact
Scenarios
Context
Main database goes down, automatic failover to replica
Metrics
Outcome
Service restored quickly with minimal data loss. 99.995% monthly availability maintained.
Key Lessons
- •Automated failover is crucial for database reliability
- •Read replicas can serve traffic during primary failures
- •Monitoring with sub-minute detection prevents longer outages
- •Regular failover testing ensures systems work under pressure