tempo

name	tempo
license	Apache-2.0
description	Grafana Tempo distributed tracing backend. Covers TraceQL query language (span selectors, attribute scopes, pipeline operators, structural operators, metrics functions), trace ingestion via OTLP/Jaeger/Zipkin, Tempo architecture (distributor/ingester/compactor/querier/metrics-generator), full configuration reference with YAML, metrics-from-traces (span metrics, service graphs, TraceQL metrics), deployment modes (monolithic/microservices/Helm/Kubernetes), multi-tenancy, performance tuning, caching, and HTTP API. Use when working with distributed traces, writing TraceQL queries, deploying Tempo, configuring trace pipelines, or setting up Grafana-Tempo integrations (traces-to-logs, traces-to-metrics, traces-to-profiles).

Grafana Tempo - Distributed Tracing Backend

Grafana Tempo is an open-source, high-scale distributed tracing backend. It is:

• Cost-efficient: only requires object storage (S3, GCS, Azure) to operate
• Deeply integrated: with Grafana, Mimir, Prometheus, Loki, and Pyroscope
• Protocol-agnostic: accepts OTLP, Jaeger, Zipkin, OpenCensus, Kafka

Quick Reference Links

• TraceQL Language Reference - query syntax, operators, examples, metrics functions
• Configuration Reference - all YAML config blocks with defaults
• Architecture and Operations - components, deployment, tuning
• Metrics from Traces - span metrics, service graphs, TraceQL metrics
• API Reference - HTTP endpoints, ingestion, search, metrics queries

---

What is Distributed Tracing?

A trace represents the lifecycle of a request as it passes through multiple services. It consists of:

• Spans: Individual units of work with start time, duration, attributes, and status
• Trace ID: Shared identifier across all spans in a request
• Parent-child relationships: Spans form a tree showing causality

Traces enable:

• Root cause analysis for service outages
• Understanding service dependencies
• Identifying latency bottlenecks
• Correlating events across microservices

---

Architecture Overview

Applications
    |
    | (OTLP 4317/4318, Jaeger 14250/14268, Zipkin 9411)
    v
[Distributor]  ----  hashes traceID, routes to N partitions
    |
 [Kafka]
    |---> [Live Stores]  (storage of recent data)
    |
    |---> [Block Builders] (Parquet block assembly, flush to object storage)
    |
    |---> [Metrics Generator]  (optional: derives RED metrics -> Prometheus)
    
Query path:
Grafana  -->  [Query Frontend]  (shards queries)
                    |
              [Querier pool]
              /           \
    [Live Stores]   [Object Storage]
    (recent)        (historical blocks)

Core Components

Component	Role	Default Ports
Distributor	Receives spans, routes by traceID hash	4317 (gRPC), 4318 (HTTP)
Live Store	Buffers recent data on local disk and serves queries	-
Query Frontend	Query orchestrator, shards across queriers	3200 (HTTP)
Querier	Executes search jobs against storage	-
Compactor	Merges blocks, enforces retention	-
Block Builder	Creates the final parquet blocks and flushes to object storage	-
Metrics Generator	Derives RED metrics from spans	-

---

TraceQL - The Query Language

TraceQL queries filter traces by span properties. Structure: { filters } | pipeline

Attribute Scopes

span.http.status_code        # span-level attribute
resource.service.name        # resource-level attribute (from SDK)
event.name                   # event-level attribute
name                         # intrinsic: span operation name
status                       # intrinsic: ok | error | unset
duration                     # intrinsic: span duration
kind                         # intrinsic: server | client | producer | consumer | internal
traceDuration                # intrinsic: entire trace duration
rootServiceName              # intrinsic: service of the root span
rootName                     # intrinsic: operation name of the root span

Operators

=   !=   >   <   >=   <=      # comparison
=~  !~                         # regex match (Go RE2)
&&  ||  !                      # logical

Essential Examples

# All errors
{ status = error }

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

# HTTP 5xx errors
{ span.http.status_code >= 500 }

# Count errors per trace (more than 2)
{ status = error } | count() >= 2

# Select specific fields
{ status = error } | select(span.http.url, duration, resource.service.name)

# Structural: server span with downstream error
{ kind = server } >> { status = error }

# Both conditions present (any relationship)
{ span.db.system = "redis" } && { span.db.system = "postgresql" }

# Find most recent (deterministic)
{ resource.service.name = "api" } with (most_recent=true)

TraceQL Metrics

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

# P99 latency
{ kind = server } | quantile_over_time(duration, .99) by (resource.service.name)

---

Deployment

Quick Start (Docker Compose)

git clone https://github.com/grafana/tempo.git
cd tempo/example/docker-compose/local
mkdir tempo-data
docker compose up -d
# Grafana at http://localhost:3000, Tempo API at http://localhost:3200

Kubernetes (Helm)

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

---

Sending Traces to Tempo

Via Grafana Alloy (Recommended)

// 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 }
  }
}

Via OpenTelemetry Collector

exporters:
  otlp:
    endpoint: tempo:4317
    tls:
      insecure: true
    # For multi-tenancy:
    headers:
      x-scope-orgid: my-tenant

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [otlp]

Direct HTTP (OTLP)

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}]}]}]}'

---

Metrics from Traces

Enable Metrics Generator

metrics_generator:
  storage:
    path: /var/tempo/generator/wal
    remote_write:
      - url: http://prometheus:9090/api/v1/write
        send_exemplars: true

overrides:
  defaults:
    metrics_generator:
      processors: [service-graphs, span-metrics]

Processor Types

Service Graphs: Visualizes service topology and latency

• Output: traces_service_graph_request_total, traces_service_graph_request_failed_total, duration histograms

Span Metrics: RED metrics per span

• Output: traces_spanmetrics_calls_total, traces_spanmetrics_duration_seconds_*
• Labels: service, span_name, span_kind, status_code + custom dimensions

Local Blocks: Enables TraceQL metrics queries on recent data

---

Multi-Tenancy

# Enable in Tempo config
multitenancy_enabled: true

All requests require X-Scope-OrgID header.

# OpenTelemetry Collector
exporters:
  otlp:
    headers:
      x-scope-orgid: tenant-id

# Grafana datasource
jsonData:
  httpHeaderName1: "X-Scope-OrgID"
secureJsonData:
  httpHeaderValue1: "tenant-id"

---

Grafana Integration

Data Source Configuration

datasources:
  - name: Tempo
    type: tempo
    url: http://tempo:3200
    jsonData:
      # Link traces to logs
      tracesToLogsV2:
        datasourceUid: loki-uid
        filterByTraceID: true
        tags: [{key: "service.name", value: "app"}]

      # Link traces to metrics
      tracesToMetrics:
        datasourceUid: prometheus-uid
        tags: [{key: "service.name", value: "service"}]
        queries:
          - name: Error Rate
            query: 'sum(rate(traces_spanmetrics_calls_total{$$__tags, status_code="STATUS_CODE_ERROR"}[5m]))'

      # Link traces to profiles (Pyroscope)
      tracesToProfiles:
        datasourceUid: pyroscope-uid
        tags: [{key: "service.name", value: "service_name"}]

      # Service map from span metrics
      serviceMap:
        datasourceUid: prometheus-uid

Key Grafana Features

• Explore > Tempo: Search by TraceQL, trace ID, or tag filters
• Service Graph tab: Visual service topology with RED metrics
• Traces Drilldown: /a/grafana-exploretraces-app - no TraceQL required
• Exemplars: Click metric spike -> jump directly to responsible trace
• Derived fields in Loki: Click trace ID in log -> jump to trace in Tempo

---

API Quick Reference

# Search traces
GET /api/search?q={status=error}&limit=20&start=<unix>&end=<unix>

# Get trace by ID
GET /api/traces/<traceID>
GET /api/v2/traces/<traceID>

# List all tag names
GET /api/search/tags

# Get values for a tag
GET /api/search/tag/service.name/values

# TraceQL metrics (time series)
GET /api/metrics/query_range?q={status=error}|rate()&start=...&end=...&step=60

# Health check
GET /ready

---

Performance Tuning Summary

Problem	Solution
Slow searches	Scale queriers horizontally; scale compactors to reduce block count
High memory on queriers	Reduce max_concurrent_queries; lower target_bytes_per_job
High memory on ingesters	Reduce max_block_bytes; lower per-tenant trace limits
Slow attribute queries	Add dedicated Parquet columns for frequent attributes
Cache miss rate high	Increase cache size; tune cache_min_compaction_level
Rate limited (429)	Raise max_outstanding_per_tenant or increase per-tenant ingestion limits
Memcached connection errors	Increase memcached connection limit (-c 4096)

---

Best Practices

Instrumentation

• Follow OpenTelemetry semantic conventions for attribute names
• Use span. prefix for span attributes, resource. for process context
• Keep attributes meaningful - avoid metrics/logs as span attributes
• Limit attributes to max ~128 per span (OTel default)
• Use span linking for batch processing (instead of huge fan-out traces)
• Create spans for: external calls, significant loops, operations with variable latency
• Avoid creating spans for every function call

Deployment

• Use replication factor 3 for production HA
• Object storage required for distributed deployments (not local)
• Enable dedicated attribute columns for your most-queried attributes
• Set appropriate block retention per tenant via overrides
• Monitor tempo_ingester_live_traces to detect memory pressure early

Querying

• Use time bounds (start/end) to limit search scope
• Use structural operators for root cause analysis patterns
• Prefer attribute != nil for existence checks
• Use with (most_recent=true) when you need deterministic recent results
• Scope tag discovery with a TraceQL query to reduce noise

---

Ports Reference

Port	Protocol	Purpose
3200	HTTP	Tempo API (queries, search, health)
9095	gRPC	Internal component communication
4317	gRPC	OTLP trace ingestion
4318	HTTP	OTLP trace ingestion
14268	HTTP	Jaeger Thrift HTTP ingestion
14250	gRPC	Jaeger gRPC ingestion
6831	UDP	Jaeger Thrift Compact
6832	UDP	Jaeger Thrift Binary
9411	HTTP	Zipkin ingestion
7946	TCP/UDP	Memberlist gossip