Observability vs Monitoring - Whats the difference?

Summary

This video explains the key differences between monitoring and observability in modern systems. Monitoring is a proactive tool for tracking system health using predefined thresholds, alerting you when something goes wrong. Observability, on the other hand, provides deeper insights into *why* issues occur, using logs, metrics, and distributed tracing to help debug complex systems. While monitoring acts as an early warning system, observability helps trace the root cause of problems. The video emphasizes that both tools are essential, and recommends starting with monitoring, then building observability based on real debugging needs.

Takeaways

• 😀 Monitoring is the practice of tracking system health by comparing key metrics against predefined thresholds.
• 😀 Common monitoring metrics include infrastructure data like CPU and memory usage and application performance like server response times.
• 😀 Monitoring alerts you to issues you’ve anticipated by defining specific thresholds, like CPU usage exceeding 80%.
• 😀 A limitation of monitoring is that it can only warn you about problems you've predefined, not the unexpected ones.
• 😀 Observability, on the other hand, helps you understand why an issue happened by providing detailed logs, metrics, and distributed tracing.
• 😀 Observability provides context, enabling you to know the why, how, when, and where of an issue, even if it wasn’t anticipated.
• 😀 The challenge with observability is balancing the data you collect. Collecting too much data can be impractical and costly, as seen with Coinbase’s $65 million spending on excessive data storage.
• 😀 Monitoring and observability complement each other—monitoring acts as the early warning system, while observability helps to dig deeper into the problem and find the root cause.
• 😀 Monitoring tells you what’s wrong and when, but observability reveals how and why the issue occurred.
• 😀 An analogy: monitoring is like a car's dashboard showing basic metrics (speed, fuel), while observability is like a car diagnostic tool that tells you exactly what's wrong under the hood.
• 😀 You need both monitoring and observability in modern cloud-based, microservices architectures to effectively track and resolve system issues. Monitoring alone isn’t enough to catch all issues.

Q & A

What is monitoring in the context of system management?

Monitoring refers to the practice of continuously tracking system health by measuring key performance metrics against predefined thresholds, such as CPU usage, memory consumption, or server response times. It alerts users when these metrics exceed the set thresholds, indicating potential issues.

What does monitoring help you detect?

Monitoring helps detect issues based on predefined conditions or thresholds. For example, if a server's CPU usage exceeds 80%, monitoring will alert the user, indicating that something might be wrong with the system.

How is observability different from monitoring?

While monitoring alerts you to issues and tells you when and what has gone wrong, observability goes further by providing deep insights into *why* and *how* issues occur. It uses tools like structured logs, metrics, and distributed tracing to help you understand the full context of system failures.

What are the three key tools of observability?

The three key tools of observability are: structured logs (for detailed event records), metrics (for system performance data), and distributed tracing (to follow requests across services from start to finish).

Why is observability important in modern systems?

Observability is crucial because modern systems, particularly those with microservices and cloud infrastructure, are complex. With observability, you can trace errors, understand system behavior, and pinpoint the exact cause of problems, even those you didn’t anticipate.

What does monitoring tell you versus observability?

Monitoring tells you *what* is wrong (e.g., CPU usage is high) and *when* the issue occurred. Observability, on the other hand, helps you understand *how* and *why* the issue happened, providing deeper context and allowing you to diagnose the root cause.

Can monitoring alone handle issues in complex systems?

No, monitoring alone is not enough in complex systems like microservices or cloud-based environments. While monitoring can alert you to issues, you also need observability to trace the problem and understand the underlying causes in detail.

Why can't a system record absolutely everything for observability?

Recording everything for observability would be impractical and expensive to scale. It would generate vast amounts of data, making it difficult to store and analyze effectively. Instead, a balance needs to be struck between capturing enough data for debugging and managing costs.

What analogy is used to explain the difference between monitoring and observability?

The analogy compares monitoring to a car's dashboard, which shows basic metrics like speed and fuel, and observability to a car diagnostic tool that reveals the exact problem under the hood when something goes wrong.

Which should you choose: monitoring or observability?

It’s not a matter of choosing one over the other. Both are essential. Monitoring is your first line of defense for catching issues early, while observability is needed to trace problems in complex systems and find the root cause. Both work together for optimal system health.