Observability for Beginners: Logs, Metrics, Traces, and Everything Around Them

2026-06-18 · Source: ByteByteGo Newsletter · Field: Technology & Digital — Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Novice, quick

Summary

The article introduces observability as the practice of understanding a running service's internal state by analyzing the events it generates. These events, such as requests, function executions, and errors, each possess a specific time, context, and outcome. Observability primarily relies on three data types: logs, metrics, and traces. Logs capture individual events as text lines, providing detailed snapshots. Metrics aggregate or count numerous events, offering quantitative insights into system behavior. Traces link related events as they traverse multiple services, illustrating end-to-end request flows. Fundamental observability concepts like cardinality, sampling, and correlation stem directly from how these event streams are structured and processed.

Key takeaway

For a Software Engineer or DevOps Engineer building or maintaining distributed systems, understanding the distinct roles of logs, metrics, and traces is crucial. You should design your services to emit these three types of data appropriately from the outset. This foundational knowledge ensures you can effectively diagnose issues, monitor performance, and gain comprehensive visibility into complex service interactions, preventing reactive firefighting and enabling proactive system health management.

Key insights

Observability relies on logs, metrics, and traces to understand service events and their interconnections.

Principles

• Service events have time, context, and outcome.
• Logs, metrics, traces are distinct views of events.
• Cardinality, sampling, correlation derive from event structure.

Topics

• Observability
• Logs
• Metrics
• Traces
• Distributed Systems
• Event Monitoring

Best for: Software Engineer, DevOps Engineer

Related on AIssential

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• Agent Tracing and Observability: Log & Debug Complex AI Systems — Multi-agent systems require specialized observability beyond traditional logging to debug coordination and self-modification failures.
• LLM Observability with LangSmith -Part 1: Tracing Everything & Building Audit-Grade Callbacks — LLM apps require specialized observability and audit trails to mitigate "quiet failures" and ensure compliance.
• How Netflix Maps Thousands of Microservices in Real-Time — Unifying diverse data sources into a real-time, queryable service topology graph enhances incident resolution.
• How the Datadog MCP server can help improve IT operational insight and observability — The Datadog MCP Server translates natural language into actionable observability insights across diverse IT data.

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Editorial summary, takeaway, and curation by AIssential. Original article published by ByteByteGo Newsletter.