Failure Detection in Distributed Systems

Issue #66: System Design Interview Roadmap

When Netflix's chaos engineering team intentionally kills production servers during peak hours, they're not just testing resilience—they're validating the sophisticated failure detection mechanisms that keep 230 million subscribers streaming without interruption. This seemingly reckless practice reveals a profound truth: the difference between a system that merely handles failures and one that thrives amid chaos lies in how quickly and accurately it can detect when things go wrong.

Today, we'll explore the intricate world of failure detection in distributed systems, uncovering the algorithms, patterns, and production insights that separate amateur implementations from battle-tested enterprise architectures. Plus, you'll build a complete hands-on demo to see these concepts in action.

The Hidden Complexity of "Is It Working?"

At first glance, failure detection seems trivial—just ping a service and see if it responds. Yet this oversimplification has led to countless production incidents. The reality is far more nuanced. Consider this: when a service takes 15 seconds to respond instead of its usual 200ms, is it failing? What about when it returns correct data but consumes 10x more CPU? Or when it processes requests correctly but stops updating its health check endpoint?

These scenarios illustrate the fundamental challenge: failure is not binary. Modern distributed systems exist in a spectrum of degraded states, each requiring different detection strategies and response mechanisms.