In the previous chapters, we learned how to scale systems to handle load and how to keep them available when components fail. But there is a third pillar that completes the foundation: reliability.

A reliable system performs its intended function correctly and consistently, even in the face of faults. While availability asks "Is the system up?", reliability asks "Is the system doing what it should?"

The distinction matters. Consider a payment system that is always available but occasionally charges customers twice. Or a messaging app that delivers messages out of order.

These systems are available, but they are not reliable. And unreliable systems destroy user trust faster than unavailable ones.

Reliability is what builds user trust. A system that users can depend on to work correctly, every time, is a system worth building.

Reliability vs Availability

These terms are often confused but represent different qualities:

A system can be positioned anywhere on the availability-reliability matrix:

The second quadrant, high availability with low reliability, is particularly dangerous. Users trust systems that are always responsive. When that trust is misplaced, the consequences can be severe.

Defining Reliability

Reliability is typically measured by four key metrics. Understanding these helps you set targets and measure progress.

1. Mean Time Between Failures (MTBF)

How long the system operates correctly between failures.

MTBF = Total Operating Time / Number of Failures

Higher MTBF means more reliable. A system with 2,000 hour MTBF fails on average once every 83 days.

2. Mean Time To Recovery (MTTR)

How long it takes to restore the system after a failure.

MTTR = Total Downtime / Number of Failures

Lower MTTR means faster recovery. Even unreliable systems can achieve high availability if they recover quickly enough.

3. Error Rate

Percentage of requests that result in errors.

Error Rate = Failed Requests / Total Requests × 100%

4. Data Correctness

Percentage of responses that contain correct data.

Correctness = Correct Responses / Total Responses × 100%

This is the often-overlooked metric. A system can have 99.99% availability and 0.01% error rate, but if 1% of successful responses contain wrong data, you have a reliability problem. Users received a response, it just was not the right one.

Key Principles of Reliable Systems

To build reliable systems, engineers typically focus on several core principles:

Redundancy

Redundancy means having backup components ready to take over if one part fails. This could involve multiple servers, duplicate network paths, or backup databases.

Failover Mechanisms

Failover is the process by which a system automatically switches to a redundant or standby component when a failure is detected. This ensures continuous operation without noticeable disruption to users.

Load Balancing

Load balancing distributes incoming traffic across multiple servers. This not only improves performance but also prevents any single server from becoming a single point of failure.

Monitoring and Alerting

A reliable system is constantly monitored. Tools and dashboards track system health and performance, while alerting mechanisms notify engineers of issues before they escalate into major problems.

Graceful Degradation

Even when parts of the system fail, a well-designed system can still provide core functionality rather than going completely offline. This concept is known as graceful degradation.

Techniques to Enhance Reliability

Now that we understand the principles, let’s look at some practical techniques to implement reliability in your systems.

1. Redundant Architectures

Set up multiple instances of critical components.

For example, if you have a web server handling user requests, deploy several servers behind a load balancer:

If one server fails, the load balancer automatically routes traffic to the remaining servers.

2. Data Replication

Ensure your data is not stored in a single location. Use data replication strategies across multiple databases or data centers.

This way, if one database fails, the system can still access a copy from another location.

3. Health Checks and Auto-Restart

Implement health checks that continuously monitor system components. When a component fails, automated systems can restart or replace it.

Tools like Kubernetes use health checks to manage containerized applications effectively.

4. Circuit Breakers

In a microservices architecture, one service failing can cascade failures throughout the system. Circuit breakers detect when a service is failing and temporarily cut off requests to prevent overload, allowing the system to recover gracefully.

5. Caching

Caching reduces the load on your servers by temporarily storing frequently accessed data. Even if your primary data source is slow or temporarily unavailable, a cached copy can serve the request, improving reliability.