/tempo

name: tempo
license: Apache-2.0
description: Stand up Grafana Tempo as a cost-efficient distributed-tracing backend that only needs object storage, and write TraceQL queries against it. Covers OTLP + Jaeger + Zipkin ingestion, the distributor → live-store → block-builder → object-storage write path, metrics-generator for RED spanmetrics + service graphs, Helm `tempo-distributed` deployment, multi-tenant `X-Scope-OrgID`, TraceQL span / resource / event scopes, structural operators (`>>`, `<<`), `rate()` + `quantile_over_time` metrics, and the traces-to-logs / metrics / profiles datasource links. Use when deploying Tempo, writing a TraceQL query for slow / errored requests, debugging "no traces showing in Explore", sizing queriers / compactors, configuring S3 / GCS / Azure block storage, or wiring trace ↔ log ↔ profile correlation — even when the user says "tracing backend", "find slow requests", "show me the service graph", "store traces in S3", "Jaeger compatible store", or "what called this span" without naming Tempo.

Grafana Tempo

> **Docs**: https://grafana.com/docs/tempo/latest/

Cost-efficient distributed tracing. Accepts OTLP / Jaeger / Zipkin / OpenCensus / Kafka. Stores Parquet blocks in S3/GCS/Azure.

Prerequisites

• Docker (quick start) or Kubernetes (production)
• Object storage bucket (S3/GCS/Azure) for distributed deployments
• An OTLP-emitting app or `tempo-cli` for synthetic traffic
• A Grafana stack with a Tempo datasource for querying

Common Workflows

1. Stand up Tempo locally + verify ingestion

# 1. Start the official Docker Compose example
git clone https://github.com/grafana/tempo.git
cd tempo/example/docker-compose/local
mkdir -p tempo-data
docker compose up -d

# 2. Verify readiness
curl -sf http://localhost:3200/ready                              # → "ready"

# 3. Send a synthetic OTLP span (full payload in scratch terminal)
curl -X POST -H 'Content-Type: application/json' \
  http://localhost:4318/v1/traces \
  -d '{"resourceSpans":[{"resource":{"attributes":[{"key":"service.name","value":{"stringValue":"my-service"}}]},
       "scopeSpans":[{"spans":[{"traceId":"5B8EFFF798038103D269B633813FC700","spanId":"EEE19B7EC3C1B100",
       "name":"my-op","startTimeUnixNano":1689969302000000000,"endTimeUnixNano":1689969302500000000,"kind":2}]}]}]}'

# 4. Verify the trace landed (ingestion-counter > 0 and the trace is fetchable)
curl -s http://localhost:3200/metrics | grep tempo_distributor_spans_received_total | head
curl -s http://localhost:3200/api/v2/traces/5B8EFFF798038103D269B633813FC700 | jq '.batches | length'
# Expect > 0.

# 5. In Grafana → Explore → Tempo, run TraceQL: {resource.service.name="my-service"}

2. Send traces from an app via Alloy

// alloy.river
otelcol.receiver.otlp "default" {
  grpc { endpoint = "0.0.0.0:4317" }
  http { endpoint = "0.0.0.0:4318" }
  output { traces = [otelcol.exporter.otlp.tempo.input] }
}

otelcol.exporter.otlp "tempo" {
  client {
    endpoint = "tempo:4317"
    tls { insecure = true }
  }
}

# Verify Alloy forwarded successfully
curl -s http://localhost:12345/metrics | grep otelcol_exporter_sent_spans
# Then: same Grafana → Explore → Tempo check.

3. Write + run TraceQL

# Slow requests from a service
{ resource.service.name = "frontend" && duration > 1s }

# Server span that has a downstream error (structural)
{ kind = server } >> { status = error }

# Error rate per service (metrics)
{ status = error } | rate() by (resource.service.name)

Full operator + scope cheat sheet, intrinsics list, metric functions: [`references/traceql.md`](references/traceql.md).

# Via the API
curl -sG --data-urlencode 'q={resource.service.name="frontend" && duration > 1s}' \
  --data-urlencode "start=$(date -d '1h ago' +%s)" --data-urlencode "end=$(date +%s)" \
  http://localhost:3200/api/search | jq '.traces | length'

4. Deploy on Kubernetes (Helm)

helm repo add grafana https://grafana.github.io/helm-charts
helm install tempo grafana/tempo-distributed --version 1.61.3 \
  --set storage.trace.backend=s3 \
  --set storage.trace.s3.bucket=my-tempo-bucket \
  --set storage.trace.s3.region=us-east-1

# Verify every pod is Ready (distributor, ingester, querier, query-frontend, compactor)
kubectl get pods -n default -l app.kubernetes.io/instance=tempo
kubectl port-forward svc/tempo-query-frontend 3200:3200 &
curl -sf http://localhost:3200/ready

Multi-tenancy

multitenancy_enabled: true
# All requests must include header: X-Scope-OrgID: <tenant-id>

Full architecture, ports, performance tuning, metrics-generator config, multi-tenant client snippets, traces-to-logs/metrics/profiles datasource: [`references/architecture-and-operations.md`](references/architecture-and-operations.md).

Troubleshooting

• `/ready` → 503 → ingester still joining; check `tempo_ingester_*` metrics + logs
• 429 on push → raise `max_outstanding_per_tenant` or per-tenant ingest limits
• "no traces showing in Explore" → confirm `X-Scope-OrgID` matches between writer and Grafana datasource
• TraceQL slow → narrow `start`/`end`, add a service.name filter, enable dedicated Parquet columns for hot attributes

Resources

• [Tempo docs](https://grafana.com/docs/tempo/latest/)
• [TraceQL reference](https://grafana.com/docs/tempo/latest/traceql/)
• [`references/traceql.md`](references/traceql.md) — full TraceQL cheat sheet
• [`references/architecture-and-operations.md`](references/architecture-and-operations.md) — components, ports, Helm, tuning, datasource links