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Distributed Tracing Beispiele
Umfassende Beispiele für verteiltes Tracing mit Jaeger, OpenTelemetry und modernen Observability Tools
Beispiele
Einträge in dieser Sammlung
Jaeger Distributed Tracing Setup
Vollständige Jaeger-Konfiguration mit Collectors, Agents und UI für verteiltes Tracing
Schwierigkeit
6/10
Geschätzte Zeit
20 min
Schlagwörter
jaeger, collector, agent, deployment, sampling, storage
# Jaeger Configuration File
# Complete setup for production distributed tracing
version: '3.8'
services:
# Jaeger Collector
jaeger-collector:
image: jaegertracing/jaeger-collector:1.45
environment:
- SPAN_STORAGE_TYPE=elasticsearch
- ES_SERVER_URLS=http://elasticsearch:9200
- ES_USERNAME=elastic
- ES_PASSWORD=changeme
- ES_INDEX_PREFIX=jaeger
- COLLECTOR_ZIPKIN_HOST_PORT=:9411
- COLLECTOR_OTLP_ENABLED=true
ports:
- "14269:14269"
- "14268:14268"
- "14250:14250"
- "9411:9411"
depends_on:
- elasticsearch
# Jaeger Agent
jaeger-agent:
image: jaegertracing/jaeger-agent:1.45
command: ["--reporter.grpc.host-port=jaeger-collector:14250"]
ports:
- "6831:6831/udp"
- "6832:6832/udp"
depends_on:
- jaeger-collector
# Jaeger Query Service
jaeger-query:
image: jaegertracing/jaeger-query:1.45
environment:
- SPAN_STORAGE_TYPE=elasticsearch
- ES_SERVER_URLS=http://elasticsearch:9200
- ES_USERNAME=elastic
- ES_PASSWORD=changeme
- ES_INDEX_PREFIX=jaeger
- JAEGER_UI_CONFIG_JSON=/etc/jaeger-ui-config.json
ports:
- "16686:16686"
- "16687:16687"
volumes:
- ./jaeger-ui-config.json:/etc/jaeger-ui-config.json
depends_on:
- elasticsearch
# Elasticsearch Backend
elasticsearch:
image: docker.elastic.co/elasticsearch/elasticsearch:8.8.0
environment:
- discovery.type=single-node
- ES_JAVA_OPTS=-Xms1g -Xmx1g
- xpack.security.enabled=false
ports:
- "9200:9200"
volumes:
- elasticsearch_data:/usr/share/elasticsearch/data
volumes:
elasticsearch_data:- format
- YAML
- version
- 1.x
- components
- ["collector","agent","query-service","ui"]
- storage
- elasticsearch
- features
- ["sampling","compression","batch-processing"]
OpenTelemetry Auto-Instrumentation
Auto-Instrumentation-Konfiguration für mehrere Sprachen mit benutzerdefinierten Spans
Schwierigkeit
7/10
Geschätzte Zeit
25 min
Schlagwörter
opentelemetry, instrumentation, auto-instrument, telemetry, metrics, tracing
# OpenTelemetry Auto-Instrumentation Configuration
# Example configuration for Java applications
# Environment Variables
export OTEL_SERVICE_NAME=my-service
export OTEL_RESOURCE_ATTRIBUTES=service.name=my-service,service.version=1.0.0
export OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4317
export OTEL_TRACES_SAMPLER=traceidratio
export OTEL_TRACES_SAMPLER_ARG=1.0
# Java Agent Configuration
-javaagent:/app/opentelemetry-javaagent.jar -Dotel.service.name=my-service -Dotel.resource.attributes=service.name=my-service,service.version=1.0.0 -Dotel.exporter.otlp.endpoint=http://otel-collector:4317 -Dotel.traces.sampler=traceidratio -Dotel.traces.sampler.arg=1.0 -Dotel.javaagent.extensions=io.opentelemetry.extension.noopapi.NoOpApiExtension
# Custom Span Creation (Java Example)
import io.opentelemetry.api.OpenTelemetry;
import io.opentelemetry.api.trace.Span;
import io.opentelemetry.api.trace.Tracer;
Tracer tracer = OpenTelemetry.getGlobalTracer("my-service");
Span span = tracer.spanBuilder("custom-operation")
.setAttribute("user.id", "12345")
.setAttribute("operation.type", "business")
.startSpan();
// Do work here...
span.end();- format
- JSON
- specification
- OpenTelemetry
- languages
- ["java","python","nodejs","go","dotnet"]
- features
- ["auto-instrumentation","custom-spans","baggage","propagation"]
Microservice Request Flow Trace
Beispiel für verteilte Traces über mehrere Microservices
Schwierigkeit
5/10
Geschätzte Zeit
10 min
Schlagwörter
microservices, request-flow, correlation, context-propagation, span-tree
{
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"spans": [
{
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"span_id": "550e8400-e29b-41d4-a716-446655440001",
"parent_span_id": null,
"operation_name": "HTTP GET /api/orders",
"service_name": "api-gateway",
"start_time": 1701954645123000000,
"end_time": 1701954645156000000,
"duration_ms": 33,
"tags": {
"http.method": "GET",
"http.url": "/api/orders",
"http.status_code": "200",
"component": "netty"
},
"logs": [
{
"timestamp": 1701954645140000000,
"level": "info",
"message": "Request authenticated successfully"
}
]
},
{
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"span_id": "550e8400-e29b-41d4-a716-446655440002",
"parent_span_id": "550e8400-e29b-41d4-a716-446655440001",
"operation_name": "UserService.GetUser",
"service_name": "user-service",
"start_time": 1701954645130000000,
"end_time": 1701954645145000000,
"duration_ms": 15,
"tags": {
"user.id": "12345",
"db.system": "postgresql",
"operation.type": "query"
}
},
{
"trace_id": "550e8400-e29b-41d4-a716-446655440000",
"span_id": "550e8400-e29b-41d4-a716-446655440003",
"parent_span_id": "550e8400-e29b-41d4-a716-446655440001",
"operation_name": "OrderService.GetOrders",
"service_name": "order-service",
"start_time": 1701954645132000000,
"end_time": 1701954645152000000,
"duration_ms": 20,
"tags": {
"user.id": "12345",
"operation.type": "business"
}
}
]
}- format
- JSON
- services
- ["api-gateway","user-service","order-service","payment-service","notification-service"]
- spanTypes
- ["http","database","cache","message-queue"]
- complexity
- multi-service
Performance-Engpass-Analyse
Erweiterte Konfiguration zur Identifizierung von Performance-Engpässen
Schwierigkeit
8/10
Geschätzte Zeit
30 min
Schlagwörter
performance, bottleneck, analysis, optimization, error-tracking, metrics
# Performance Analysis Configuration
# Example setup for performance bottleneck detection
# Custom Span Attributes
attributes:
- database.query.duration
- cache.hit_ratio
- memory.usage
- cpu.utilization
# Performance Thresholds
thresholds:
slow_query: 100ms
high_memory: 80%
high_cpu: 90%
# Alert Configuration
alerts:
- name: Slow Database Queries
condition: span.duration > 100ms
severity: warning- format
- YAML
- features
- ["span-attributes","error-tracking","slow-queries","memory-usage","cpu-profiling"]
- alerts
- ["threshold-based","anomaly-detection"]
- integrations
- ["prometheus","grafana"]
OpenTelemetry Collector Pipeline
Konfiguration zur Verarbeitung und zum Export von Traces zu mehreren Backends
Schwierigkeit
7/10
Geschätzte Zeit
25 min
Schlagwörter
otel-collector, pipeline, processors, exporters, receivers
# OpenTelemetry Collector Configuration
receivers:
otlp:
protocols:
grpc:
http:
processors:
memory_limiter:
check_interval: 5s
limit_mib: 512
batch:
timeout: 5s
send_batch_size: 1024
attributes:
actions:
- key: environment
value: production
action: insert
exporters:
otlp/jaeger:
endpoint: jaeger-collector:4317
tls:
insecure: true
logging:
loglevel: info
service:
pipelines:
traces:
receivers: [otlp]
processors: [memory_limiter, batch, attributes]
exporters: [otlp/jaeger, logging]- format
- YAML
- version
- 0.x
- components
- ["receivers","processors","exporters","extensions"]
- backends
- ["jaeger","zipkin","prometheus","elasticsearch"]
- features
- ["batch-processing","sampling","filtering","transformation"]
Cross-Service Context Propagation
Implementierungsbeispiel der Kontextpropagation zwischen Microservices
Schwierigkeit
6/10
Geschätzte Zeit
15 min
Schlagwörter
context-propagation, headers, cross-service, correlation, baggage
// Context propagation across services (Node.js + OpenTelemetry)
import { context, propagation, trace } from '@opentelemetry/api'
const tracer = trace.getTracer('orders-service')
export async function handleIncomingRequest(req, res) {
// Extract trace context from inbound HTTP headers
const parentContext = propagation.extract(context.active(), req.headers)
await context.with(parentContext, async () => {
const span = tracer.startSpan('handle-order-request')
try {
span.setAttribute('http.route', req.url)
span.setAttribute('service.name', 'orders-service')
// Inject context when calling downstream service
const headers = {}
const spanContext = trace.setSpan(context.active(), span)
propagation.inject(spanContext, headers)
const response = await fetch('http://inventory-service/check', {
method: 'POST',
headers: {
...headers,
'content-type': 'application/json'
},
body: JSON.stringify({ sku: req.body.sku, quantity: req.body.quantity })
})
if (!response.ok) {
span.setAttribute('error', true)
span.setAttribute('http.status_code', response.status)
}
res.status(200).json({ ok: true })
} catch (error) {
span.recordException(error)
span.setAttribute('error', true)
res.status(500).json({ ok: false })
} finally {
span.end()
}
})
}- format
- JavaScript
- apis
- ["@opentelemetry/api","@opentelemetry/sdk-node"]
- concepts
- ["context","baggage","propagators","extract","inject"]
- protocols
- ["http","grpc","messaging"]
Werkzeuge
Tools, die oft mit diesem Beispiel genutzt werden
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