Unified Observability with OpenObserve

Stop switching tabs. Start finding out what's going on.

Whenever there's a major outage, it always starts the very same way. Five tools are open, four teams are talking past each other, and one question nobody can settle quickly. The idea behind this book is to give engineers the tools they need to keep an eye on things without breaking the bottom line.This book brings together logs, metrics, traces, frontend sessions and AI telemetry into one place, so you can figure out what's going on when things go wrong. It's all about setting up a production-ready OpenObserve cluster, hooking up every signal through OpenTelemetry, and tweaking the data until long-term storage becomes budget-friendly.

There are several chapters that look at what can go wrong in a business, like infrastructure, networks, apps, data layers and user experience. They even talk about the AI service that went up by three times overnight. In the AI era, the way we think about failure has changed. Things like models drifting, prompts inflating and cost moving faster than latency. If you've got observability built for three signals, you won't be able to see any of them. This book shows what a unified platform can do.

Key Learnings

• Build a signal inventory tying every business driver to a measurable objective.
• Deploy a high-availability OpenObserve cluster with object storage, SSO, and role-based access.
• Instrument any stack through OpenTelemetry without rewriting application code or locking into a vendor.
• Shape telemetry with pipelines, VRL functions, and enrichment tables to cut ingest cost sharply.
• Design dashboards that engineers can actually open during incidents rather than decorative executive wall displays.
• Define burn-rate alerts and SLOs that flag real degradation without generating alert fatigue.
• Apply a repeatable seven-step diagnostic loop from scope through evidence to verified repair.
• Recognise infrastructure red flags like CPU throttling, disk eviction, and autoscaler thrash early.
• Diagnose AI-layer failures including token inflation, prompt drift, and runaway cost per request.
• Migrate from an existing platform using dual-run, honest measurement, and reversible cutover.

Table of Content

1. Getting Started with OpenObserve
2. How Systems Fail with Hidden Failures?
3. OpenObserve and Production-Ready Environment
4. Getting Every Signal In
5. Shaping, Governing and Affording Data
6. Building Dashboards Engineers Actually Use
7. Flagging Engine, Alerts, SLOs and Incidents
8. Diagnostic Method
9. Infrastructure and Kubernetes Red Flags
10. Network and Edge Red Flags
11. Application and Service Red Flags
12. Data-Layer Red Flags
13. User-Facing and AI-Layer Red Flags
14. Scaling OpenObserve

Target Audience

Whether you're an SRE, platform engineer or architect, you'll find configuration, queries, dashboards and alert definitions you can apply directly. You don't need to have experience with OpenObserve.