13 docs tagged with "tempo"

Choose deployment mode: single-binary vs microservices. Configure object storage backends and retention policies. Optimize performance, sharding, and handle...

Collector pipeline syntax: receivers → processors → exporters. Common ports: Grafana 3000, Tempo 3200, OTLP gRPC 4317, OTLP HTTP 4318, Loki 3100, Prometheus...

Deploy a multi-service app and instrument all services. Route traces via Collector to Tempo; debug real issues using TraceQL and service graphs.

Deploy Tempo on Kubernetes using Helm. Configure HA, storage, authentication, and multi-tenancy. Integrate with GitOps (Argo CD/Flux) and CI/CD.

Link traces with logs (Loki) and metrics (Prometheus/Mimir). Query traces using TraceQL and build service graphs. Enable spanmetrics and exemplars.

Apply instrumentation and naming best practices. Diagnose common Collector → Tempo issues. Reduce noise and control costs while preserving signal.

Explain logs, metrics, and traces and how they complement each other. Describe the anatomy of a trace and span, and context propagation. Understand common tr...

Auto-instrument a sample application in your preferred language. Configure OTel SDK exporters to the Collector. Verify traces arrive in Grafana Explore.

Welcome to the complete Grafana Tempo course. This path takes you from fundamentals of observability, through OpenTelemetry instrumentation, into Tempo archi...

Explain Tempo’s architecture and index-free design. Compare Tempo to Jaeger/Zipkin/X-Ray/vendor backends. Understand push ingestion and query flow.

Distributed tracing helps you see the full journey of a request as it moves across multiple services in a system. It shows where time is spent and where prob...

Understand OTel SDKs, auto vs manual instrumentation. Build a Collector pipeline: receivers → processors → exporters. Position Tempo within the pipeline.

Stand up Tempo, Grafana, and the OTel Collector using Docker Compose. Optionally add Loki and Prometheus for full-stack correlation. Configure Grafana to que...