Metrics
Explore how metrics differ from logs and why they are essential for real-time monitoring and debugging of distributed systems. Understand metric components like names, values, and labels, and discover how various metric types help track system performance. Learn to use tools like Prometheus and OpenTelemetry for collecting, querying, and visualizing metrics to detect anomalies and optimize software systems effectively.
Introduction
We had taken a detailed look at logging previously. Logs capture detailed textual records of events, errors, and transactions over time. Logs are a way for the system to communicate with its user or maintainer about what it is doing. They are invaluable for post-incident analysis, debugging, compliance, and auditing. In distributed systems analysis, root cause identification and remediation are supposed to be as and when the issue is seen. In such situations, where a real-time overview of a system’s health and performance are needed, logs alone won’t help. This is where metrics can help.
Metrics, in contrast to logs, offer a different perspective: They provide real-time, quantitative measurements of critical system parameters such as CPU utilization, memory usage, response times, and error rates. They excel in delivering immediate insights into the current state of a distributed system, enabling the detection of anomalies and performance issues as they occur.
In short, imagine logs as a plane’s black box that records every action and decision made during a flight. Metrics are like its dashboard, providing real-time quantitative measures that matter at the moment. Metrics are a numeric representation of data measured over intervals of time. We saw examples of how metrics can be useful in the Memory Leak lesson of this course. We'll explore more in this lesson.