Signal Studio field guide
ClickHouse Observability at Scale
Build a Unified OpenTelemetry Platform for Logs, Metrics, and a Million Spans per Second
Design OpenTelemetry and ClickHouse observability around signal meaning, ingestion, query performance, replay, and operating limits.
For: Observability platform engineers, SREs evaluating a ClickHouse-backed telemetry platform, Data engineers responsible for high-volume operational data
What this book helps you do
A useful observability platform must connect an investigation from a symptom to inspectable evidence while remaining usable under load. This book develops a ClickHouse and OpenTelemetry design across collection, storage, queries, signal correlation, alerting, and operations. It makes capacity assumptions and unfinished implementation boundaries explicit so that readers can distinguish a design from a measured production result.
Problems this book helps you solve
- An ingestion benchmark is fast, but incident queries become unusable under load.
- A successful export is being treated as proof that records are durably queryable.
- Trace, log, and metric screens disagree about identity or population.
- Metric aggregation loses units, temporality, or reset information.
- Replay repairs missing source records but inflates derived counts.
- An empty panel hides whether telemetry is delayed, expired, sampled, or restricted.
- Large searches consume the capacity needed for fresh ingestion.
Start with a practical question
Use a focused guide for the immediate problem, then return here when you need the complete operating method.
- How do I size a million-spans-per-second observability workload?
- Should an OpenTelemetry pipeline use persistent queues or a broker?
- How should ClickHouse tables follow observability investigation queries?
- How do I preserve OpenTelemetry metric meaning in ClickHouse?
- How can replay double-count a ClickHouse materialized view?
- How should a unified investigation explain missing telemetry?
- How do I protect ClickHouse ingestion from expensive investigations?
Decisions you will be able to make
- Which investigation journey should drive the platform design.
- What each collection and storage acknowledgement actually promises.
- Which fields, layouts, and derived structures serve the required queries.
- How to preserve metric type, population, units, and time semantics.
- What capacity assumptions require measurement before selecting infrastructure.
- How to operate replay, retention, alerts, and interactive queries together.
- What a reproducible laboratory can prove and what remains unimplemented.
Who this book is for
- Teams planning or reviewing an OpenTelemetry and ClickHouse observability architecture.
- Engineers who want decision worksheets and a bounded laboratory alongside design explanations.
Who this book is not for
- Readers expecting a turnkey platform or a verified million-spans-per-second deployment.
- Readers seeking only an introductory SQL or instrumentation tutorial.
Reading path
- Chapters 1–3: Product and signal contractsDefine the investigation, collection responsibilities, and the meaning each stored signal must preserve.
- Chapter 4: Ingestion and capacityTurn rates into workload assumptions and specify acknowledgement, buffering, and replay boundaries.
- Chapters 5–7: Traces, logs, and metricsDesign each signal for the questions it can validly answer.
- Chapters 8–9: Queries and correlationAccelerate concrete queries and connect evidence without disguising incomplete coverage.
- Chapters 10–11: Operations and healthProtect the platform under contention and make its own failures observable.
- Chapter 12 and appendices: Build and proveUse the laboratory, capacity worksheet, and implementation boundaries to plan evidence-producing work.
Follow one investigation across the platform
A service chart is only the beginning of an investigation. The next step may need representative traces, a complete request reconstruction, relevant logs, and a clear account of what is missing. The book uses that reader journey to organize the storage and query decisions, so a technical optimization can be judged by the operation it improves.
Make the scale claim inspectable
The subtitle names a demanding design target. The text turns it into explicit record-rate, size, retention, and failure assumptions. Readers can replace those assumptions with their own measurements. It does not present the fictional scenario or the small laboratory as proof that a complete system has achieved that throughput.
Read alongside the existing agent books
Readers instrumenting model and tool execution can first consult Observability for AI Agents. Readers using telemetry for incident reasoning can continue with AI Agents for SRE. This volume focuses on the underlying collection, storage, query, and operating product.
Evidence and method
The book uses official ClickHouse and OpenTelemetry sources, an original fictional investigation, explicit capacity assumptions, and a bounded local laboratory. Its million-span scenario is planning arithmetic, not a throughput benchmark. The complete platform and adapter behavior require implementation and workload-specific validation.
Continue with the Kindle edition
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Read a sample
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Resources
The related guides contain original inline checklists and decision tables; no manuscript excerpt is republished.
Errata
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