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Observability: What are Metrics?

A metric is not reality. It's a lossy measurement with assumptions baked in. When we talk about observability, most people think a metric is just "any number." But that's wrong. In observability, a metric is a specific data type with specific strengths and weaknesses. Understanding these trade-offs is what separates effective observability from expensive noise. In this video, I'll break down what metrics really are, how Time Series Databases store them, and where they break down. More importantly, I'll show you how metrics build models of complex systems and why that matters. WHAT YOU'LL LEARN What metrics really are: pre-computed summaries, sampled over time, lossy by design How Time Series Databases store metrics as arrays of timestamp-value pairs Why metrics are incredibly efficient: 1.5 bytes per sample on average How metrics build models (Newton vs Einstein analogy) Where metrics break down When to use metrics vs logs vs traces TIMESTAMPS 00:00 A Metric Is Not Reality 00:21 About Cardinality Cloud 00:37 What Is a Metric? 01:05 Metrics as a Data Type 01:42 The Four Golden Signals 02:21 How TSDBs Store Metrics 03:15 Why Metrics Are Efficient 03:44 Metrics Build Models (Newton & Einstein) 04:20 Where Metrics Break Down 05:11 Cardinality Explosion Explained 05:37 Wrapping Up ABOUT CARDINALITY CLOUD I'm Jack Neely, Independent Observability Architect and owner of Cardinality Cloud, LLC - where observability is leverage, not a tax. With 25 years of SRE and DevOps experience and over a decade as an observability subject matter expert, I help teams turn observability into a competitive advantage. Need help designing an observability strategy that fits your system, not just vendor best practices? Let's talk. Website: https://cardinality.cloud/ LinkedIn:   / jack-neely-47316575   Contact: jjneely@cardinality.cloud #observability #metrics #timeseries #prometheus #tsdb #devops #sre #monitoring #instrumentation #fourgoldensignals