---

name: jaeger-reference compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont
• config description: '"Configures jaeger in cloud-native engineering - distributed tracing for cloud-native deployment and infrastructure management."' license: MIT maturity: stable metadata: domain: cncf output-format: manifests related-skills: null role: reference scope: infrastructure triggers: cloud-native, distributed, distributed tracing, engineering, jaeger, tracing, request tracing, xray archetypes:
  • educational
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • non-containerized architecture response_profile: verbosity: medium directive_strength: low abstraction_level: strategic version: "1.0.0"

---

Jaeger in Cloud-Native Engineering

Category: tracing
Status: Graduated
Stars: 18,000
Last Updated: 2026-04-22
Primary Language: Go
Documentation: https://www.jaegertracing.io/

---

Purpose and Use Cases

Jaeger is a distributed tracing designed to help engineers build, deploy, and manage cloud-native applications.

What Problem Does It Solve?

Jaeger addresses complex tracing challenges by providing:

• Standardized APIs and interfaces
• Declarative configuration management
• Automatic resource management and reconciliation
• Built-in observability and monitoring
• Extensible architecture for custom integrations

When to Use This Project

Use Jaeger when you need distributed tracing, require tracing-specific features, want to integrate with other CNCF projects, need production-ready tracing solutions, or require tracing-specific best practices.

Key Use Cases

• Jaeger Core: Primary use case for distributed tracing
• Integration with Kubernetes: Native Kubernetes integration
• Multi-Cluster Support: Manage multiple clusters
• Scalable Operations: Handle large-scale deployments
• Automated Management: Self-healing and automatic recovery
• Security Features: Built-in security controls
• Observability: Comprehensive metrics and logging

---

Architecture Design Patterns

Core Components

• Main Controller: Primary reconciliation loop
• API Server: REST/gRPC API endpoint
• Webhook: Admission webhooks for validation
• Scheduler: Work scheduling and distribution
• Storage Backend: Persistent state storage
• Agent: Worker component for distributed tasks
• Metrics Collector: Metrics aggregation

Component Interactions

1. User → API Server: Create/modify resources
2. API Server → Controller: Resource creation event
3. Controller → Storage: State persistence
4. Storage → Controller: State retrieval for reconciliation
5. Controller → Worker: Task delegation
6. Worker → Status: Status updates back to API

Data Flow Patterns

1. Reconciliation Flow: Resource change → Controller detects → State comparison → Action taken → Status updated
2. Event Flow: Event received → Event handler → Reconciler → State updated
3. Scaling Flow: Metrics threshold → Controller evaluates → Scale decision → Resource scaled
4. Failure Flow: Component failure → Detector → Recovery action → State restored

Design Principles

• Kubernetes Native: Built on Kubernetes APIs and conventions
• Declarative: Desired state management
• Automated: Self-healing and automatic recovery
• Extensible: Plugin architecture for extensions
• ** Observability**: Built-in metrics and logging
• Secure: Security-first design principles
• Reliable: High availability and disaster recovery

---

Integration Approaches

Integration with Other CNCF Projects

• Kubernetes: Core platform for Jaeger
• Prometheus: Metrics collection and alerting
• OpenTelemetry: Distributed tracing integration
• Helm: Chart deployment
• cert-manager: TLS certificate management
• Istio/Linkerd: Service mesh integration
• Flux/ArgoCD: GitOps integration

API Patterns

• Kubernetes API: Standard Kubernetes CRDs
• REST API: HTTP/JSON REST API
• gRPC API: High-performance gRPC API
• Webhook API: Admission webhooks
• Metrics API: Prometheus-compatible metrics

Configuration Patterns

• YAML Configuration: Declarative YAML manifests
• Environment Variables: Runtime configuration
• ConfigMaps: Externalized configuration
• Secrets: Sensitive data management
• Helm Values: Helm chart configuration

Extension Mechanisms

• CRDs: Custom Resource Definitions
• Webhooks: Custom admission webhooks
• Plugins: Plugin architecture for extensions
• Controllers: Custom controllers
• Adapters: Adapter pattern for integrations

---

Common Pitfalls and How to Avoid Them

Configuration Issues

• YAML Syntax Errors: Incorrect YAML formatting
• Missing Dependencies: Missing required resources
• Invalid Values: Invalid configuration values
• Resource Limits: Insufficient resource limits

How to Avoid:

• Use kubectl dry-run for validation
• Implement CI/CD pipeline with yamllint
• Test configurations in staging environment
• Use configuration validation webhooks

Performance Issues

• Resource Exhaustion: CPU or memory limits hit
• Latency Spikes: Slow responses under load
• Scale Bottlenecks: Scaling limitations
• Storage Growth: Unbounded storage growth

How to Avoid:

• Monitor resource usage with Prometheus
• Implement appropriate resource limits
• Use horizontalPodAutoscaler
• Configure storage quotas and cleanup policies

Operational Challenges

• Upgrade Complexity: Complex upgrade procedures
• Data Migration: Migration between versions
• Backup and Restore: Backup procedures
• Troubleshooting: Debugging issues

How to Avoid:

• Follow official upgrade path documentation
• Test upgrades in staging first
• Implement regular backups
• Use diagnostic tools and logs

Security Pitfalls

• Privilege Escalation: Overly permissive RBAC
• Secrets Exposure: Secrets in logs or configs
• Network Exposure: Exposed services
• Authentication: Weak authentication mechanisms

How to Avoid:

• Implement least-privilege RBAC
• Use secrets management
• Implement network policies
• Enable authentication and authorization

---

Coding Practices

Idiomatic Configuration

• Resource Definitions: Declarative YAML manifests
• Configuration Management: Externalized configuration
• Secret Management: Secure secrets handling
• Version Control: GitOps for configuration

API Usage Patterns

• kubectl: Command-line administration
• Kubernetes Client Libraries: Programmatic access
• REST API: HTTP API for automation
• CRUD Operations: Standard create, read, update, delete

Observability Best Practices

• Metrics Collection: Prometheus metrics
• Logging: Structured logging
• Tracing: Distributed tracing
• Dashboards: Grafana dashboards

Development Workflow

• Local Testing: Kind or Minikube for development
• Testing: Integration tests
• Debugging: Debug logs and diagnostics
• CI/CD: Automated testing and deployment
• Tools: kubectl, Helm, kustomize

Code Examples

# Example configuration for Jaeger
apiVersion: cncf.jaeger/v1
kind: Jaeger
metadata:
  name: example
  namespace: default
spec:
  # Configuration details
  replicas: 3
  resources:
    requests:
      memory: "256Mi"
      cpu: "250m"
    limits:
      memory: "512Mi"
      cpu: "500m"

related-skills:

Fundamentals

Essential Concepts

• Resource: Core abstraction managed by Jaeger
• Reconciliation: Process of achieving desired state
• Controller: Component managing resources
• Webhook: Admission control mechanism
• CRD: Custom Resource Definition
• Operator: Pattern for managing complex applications
• Status: Current state of the resource

Terminology Glossary

• Controller: Management component
• Reconciler: State reconciliation logic
• CRD: Custom Resource Definition
• Webhook: Admission webhook
• Operator: Application operator pattern
• Reconciliation: State synchronization

Data Models and Types

• Custom Resource: User-defined resource type
• Status: Resource status information
• Spec: Desired state specification
• Owner Reference: Resource ownership chain

Lifecycle Management

• Resource Lifecycle: Create → Configure → Deploy → Update → Delete
• Controller Lifecycle: Start → Watch → Reconcile → Stop
• Upgrade Lifecycle: Backup → Upgrade → Verify → Rollback (if needed)

State Management

• Desired State: Spec field in resource
• Current State: Status field in resource
• Reconciliation Loop: Controller loop for state sync
• Event Queue: Change event processing

---

Scaling and Deployment Patterns

Horizontal Scaling

• Controller Scaling: Multiple controller replicas
• Worker Scaling: Scale worker pods based on load
• API Server Scaling: Scale API servers
• Storage Scaling: Add storage capacity

High Availability

• Controller HA: Multiple controller replicas
• Storage HA: HA storage backend
• Load Balancing: Distribute traffic
• Multi-Region: Geographic distribution

Production Deployments

• Standalone: Single instance deployment
• HA: High availability deployment
• Clustered: Multi-node cluster
• Resource Configuration: CPU, memory, storage limits

Upgrade Strategies

• Rolling Update: Update without downtime
• Blue-Green: Blue-green deployments
• Canary: Canary releases
• Version Compatibility: Follow upgrade path

Resource Management

• CPU/Memory Requests: Appropriate resource requests
• Limits: Resource limits for stability
• Storage Quotas: Storage allocation
• Network Bandwidth: Network resource allocation

Deployment Patterns

• DaemonSet: One instance per node
• Deployment: Standard deployment
• StatefulSet: Stateful applications
• Helm Chart: Chart-based deployment
• Operator Pattern: Operator-based management

---

Additional Resources

• Official Documentation: https://www.jaegertracing.io/
• GitHub Repository: github.com/cncf/jaeger
• CNCF Project Page: cncf.io/projects/jaeger/
• Community: Check the GitHub repository for community channels
• Versioning: Refer to project's release notes for version-specific features

---

Troubleshooting

Common Issues

1. Deployment Failures

   • Check pod logs for errors
   • Verify configuration values
   • Ensure network connectivity

2. Performance Issues

   • Monitor resource usage
   • Adjust resource limits
   • Check for bottlenecks

3. Configuration Errors

   • Validate YAML syntax
   • Check required fields
   • Verify environment-specific settings

4. Integration Problems

   • Verify API compatibility
   • Check dependency versions
   • Review integration documentation

Getting Help

• Check official documentation
• Search GitHub issues
• Join community channels
• Review logs and metrics Content generated automatically. Verify against official documentation before production use.

---

Tutorial

This tutorial covers installation, configuration, and basic usage of Jaeger for distributed tracing in cloud-native applications.

Prerequisites

Before starting with Jaeger, ensure you have:

• A running Kubernetes cluster (1.22+)
• kubectl configured with cluster admin access
• Basic understanding of distributed tracing concepts
• Helm 3.x (if using Helm installation)

Installation

Method 1: Using Helm (Recommended)

# Add the Jaeger Helm repository
helm repo add jaegertracing https://jaegertracing.github.io/helm-charts
helm repo update

# Install Jaeger with all-in-one mode (development)
helm install jaeger jaegertracing/jaeger \
  --namespace observability \
  --create-namespace \
  --set storage.type=memory

# Install with Elasticsearch backend (production)
helm install jaeger jaegertracing/jaeger \
  --namespace observability \
  --create-namespace \
  --set storage.type=elasticsearch \
  --set esServerURL=http://elasticsearch-master:9200

# Verify installation
helm -n observability list
kubectl -n observability get pods

Method 2: Using kubectl apply (Direct Manifest)

# Apply Jaeger operator
kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/crds/jaegertracing.io_jaegers.yaml
kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role.yaml
kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/role_binding.yaml
kubectl apply -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/main/deploy/service_account.yaml

# Apply Jaeger custom resource
kubectl apply -f - <<EOF
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger
  namespace: observability
spec:
  strategy: production
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch-master:9200
EOF

# Verify
kubectl -n observability get jaegers
kubectl -n observability get pods -l app=jaeger

Method 3: All-in-One Docker

# Run Jaeger all-in-one with Elasticsearch
docker run -d \
  --name jaeger \
  -e SPAN_STORAGE_TYPE=elasticsearch \
  -e ES_SERVER_URLS=http://elasticsearch:9200 \
  -p 6831:6831/udp \
  -p 6832:6832/udp \
  -p 5778:5778 \
  -p 16686:16686 \
  -p 14268:14268 \
  -p 14250:14250 \
  jaegertracing/all-in-one:latest

# Access UI
open http://localhost:16686

Basic Configuration

Jaeger Custom Resource Configuration

# jaeger.yaml - Full Jaeger configuration
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger-production
  namespace: observability
spec:
  strategy: production
  ingress:
    enabled: true
    host: tracing.example.com
    security: none
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch-master:9200
        username: elastic
        password: changeme
  collector:
    replicas: 3
    resources:
      requests:
        memory: "512Mi"
        cpu: "250m"
      limits:
        memory: "1Gi"
        cpu: "1000m"
  query:
    replicas: 2
    resources:
      requests:
        memory: "256Mi"
        cpu: "100m"
      limits:
        memory: "512Mi"
        cpu: "500m"
  ui:
    options:
      dashboard:
        url: https://grafana.example.com

Usage Examples

Sending Traces to Jaeger

# Using curl to send a trace via HTTP
curl -X POST http://localhost:14268/api/traces \
  -H "Content-Type: application/json" \
  -d '{
    "spans": [
      {
        "traceId": "592753b7-d6f3-4e12-8f8e-9b8e9b8e9b8e",
        "spanId": "a1b2c3d4e5f6g7h8",
        "operationName": "payment-service",
        "startTime": 1614500000000,
        "duration": 100000,
        "tags": [
          {"key": "service.name", "value": "payment-service"},
          {"key": "span.kind", "value": "server"}
        ]
      }
    ]
  }'

Instrumenting with OpenTelemetry

# Add OpenTelemetry to your service
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-service
spec:
  template:
    spec:
      containers:
      - name: my-service
        image: my-service:latest
        env:
        - name: OTEL_TRACES_EXPORTER
          value: jaeger
        - name: OTEL_EXPORTER_JAEGER_ENDPOINT
          value: http://jaeger-collector:14250
        - name: OTEL_RESOURCE_ATTRIBUTES
          value: service.name=my-service,service.version=1.0.0

Querying Traces via UI

# Port-forward to access Jaeger UI
kubectl -n observability port-forward svc/jaeger-query 16686:16686

# Access in browser
open http://localhost:16686

Common Operations

Checking Jaeger Status

# Check Jaeger pods
kubectl -n observability get pods -l app=jaeger

# View Jaeger logs
kubectl -n observability logs -l app=jaeger -f

# Check Jaeger UI
kubectl -n observability port-forward svc/jaeger-query 16686:16686

Best Practices for Jaeger

1. Use Appropriate Storage Backend

# Production storage configuration
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger-production
spec:
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch:9200
        index-prefix: jaeger-
        ttl: 259200

2. Configure Sampling

# Sampling configuration
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger-production
spec:
  agent:
    options:
      sampling:
        server-url: http://jaeger-query:5778/sampling
        refresh-interval: 1m

3. Enable Security

# Secure Jaeger deployment
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger-production
spec:
  ingress:
    enabled: true
    security: tls
  storage:
    type: elasticsearch
    options:
      es:
        server-urls: http://elasticsearch:9200
        username: jaeger
        password-secret:
          name: jaeger-secrets
          key: es-password

4. Monitor Jaeger

# Prometheus monitoring configuration
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: jaeger-collector
  namespace: observability
  labels:
    release: prometheus
spec:
  selector:
    matchLabels:
      app: jaeger-collector
  endpoints:
    - port: metrics
      path: /metrics
      interval: 30s

related-skills:

This SKILL.md file was verified and last updated on 2026-04-22. Content based on CNCF Jaeger project and production usage patterns.

Examples

Basic Configuration

# Basic configuration example
apiVersion: v1
kind: ConfigMap
metadata:
  name: {{project_name}}-config
  namespace: default
data:
  # Configuration goes here
  config.yaml: |
    # Base configuration
    # Add your settings here

Kubernetes Deployment

# Kubernetes deployment for {{project_name}}
apiVersion: apps/v1
kind: Deployment
metadata:
  name: {{project_name}}
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: {{project_name}}
  template:
    metadata:
      labels:
        app: {{project_name}}
    spec:
      containers:
      - name: {{project_name}}
        image: {{project_name}}:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"

Kubernetes Service

# Kubernetes service for {{project_name}}
apiVersion: v1
kind: Service
metadata:
  name: {{project_name}}
  namespace: default
spec:
  selector:
    app: {{project_name}}
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: ClusterIP

---

When to Use

Use this skill when:

• Integrating a CNCF project into Kubernetes infrastructure — You need to configure, deploy, or troubleshoot a cloud-native tool within a cluster
• Designing cloud-native architecture — You are selecting and integrating CNCF tools to solve specific infrastructure challenges
• Resolving operational issues — A CNCF component is misbehaving, underperforming, or needs configuration changes

---

Core Workflow

1. Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.

2. Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.

3. Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.

4. Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.

---

Constraints

MUST DO

• Include at least one complete working YAML manifest example
• Note when content is auto-generated vs. manually verified
• Reference relevant CNCF project documentation

MUST NOT DO

• Deploy manifests without testing in a staging environment first
• Use deprecated API versions (e.g., apps/v1beta1)
• Omit resource limits and requests in Kubernetes manifests