Overview
Direct Answer
Metrics are quantitative measurements collected and recorded over time to monitor system performance, infrastructure health, and business outcomes. They form the empirical foundation for observability, enabling organisations to detect anomalies, optimise resource allocation, and validate operational decisions.
How It Works
Systems and applications emit raw data—CPU utilisation, response latency, error rates, and transaction throughput—which collection agents scrape or receive via instrumentation. These values are aggregated, stored in time-series databases, and queried through dashboards or alerting rules to reveal patterns and deviations from baseline behaviour.
Why It Matters
Metrics enable rapid incident response by exposing degradation before user impact occurs. They justify infrastructure investment by quantifying bottlenecks, reduce mean-time-to-recovery through targeted troubleshooting, and provide objective evidence for capacity planning and cost optimisation decisions.
Common Applications
Monitoring CPU and memory across server clusters, tracking API response times and error rates in microservices architectures, measuring database query performance in production environments, and correlating application latency with business transaction success rates.
Key Considerations
Cardinality explosion—excessive label combinations—can overwhelm storage systems and query performance. Choosing appropriate sampling rates and retention policies requires balancing observability depth against operational cost and compliance requirements.
Cited Across coldai.org12 pages mention Metrics
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