[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CSWC8E70FC2NSQ2EQTGD7G":3,"skill-same:01M6CSWC8E70FC2NSQ2EQTGD7G":1273},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":41,"repositoryHtmlUrl":42,"repositoryHomepageUrl":42,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6CSWC8E70FC2NSQ2EQTGD7G","distributed-tracing",null,"Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fdistributed-tracing\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署","SCENARIO","使用场景",[24,25,26,27,28,29,30],"distributed","distributed tracing","tracing","implement","microservices","flow","community","2026-11-10 10:12:30",false,"---\nname: distributed-tracing\ndescription: \"Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.\"\nrisk: critical\nsource: community\ndate_added: \"2026-02-27\"\n---\n\n# Distributed Tracing\n\nImplement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.\n\n## Do not use this skill when\n\n- The task is unrelated to distributed tracing\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Purpose\n\nTrack requests across distributed systems to understand latency, dependencies, and failure points.\n\n## Use this skill when\n\n- Debug latency issues\n- Understand service dependencies\n- Identify bottlenecks\n- Trace error propagation\n- Analyze request paths\n\n## Distributed Tracing Concepts\n\n### Trace Structure\n```\nTrace (Request ID: abc123)\n ↓\nSpan (frontend) [100ms]\n ↓\nSpan (api-gateway) [80ms]\n ├→ Span (auth-service) [10ms]\n └→ Span (user-service) [60ms]\n └→ Span (database) [40ms]\n```\n\n### Key Components\n- **Trace** - End-to-end request journey\n- **Span** - Single operation within a trace\n- **Context** - Metadata propagated between services\n- **Tags** - Key-value pairs for filtering\n- **Logs** - Timestamped events within a span\n\n## Jaeger Setup\n\n### Kubernetes Deployment\n\n```bash\n# Deploy Jaeger Operator\nkubectl create namespace observability\nkubectl create -f https:\u002F\u002Fgithub.com\u002Fjaegertracing\u002Fjaeger-operator\u002Freleases\u002Fdownload\u002Fv1.51.0\u002Fjaeger-operator.yaml -n observability\n\n# Deploy Jaeger instance\nkubectl apply -f - \u003C\u003CEOF\napiVersion: jaegertracing.io\u002Fv1\nkind: Jaeger\nmetadata:\n name: jaeger\n namespace: observability\nspec:\n strategy: production\n storage:\n type: elasticsearch\n options:\n es:\n server-urls: http:\u002F\u002Felasticsearch:9200\n ingress:\n enabled: true\nEOF\n```\n\n### Docker Compose\n\n```yaml\nversion: '3.8'\nservices:\n jaeger:\n image: jaegertracing\u002Fall-in-one:latest\n ports:\n - \"5775:5775\u002Fudp\"\n - \"6831:6831\u002Fudp\"\n - \"6832:6832\u002Fudp\"\n - \"5778:5778\"\n - \"16686:16686\" # UI\n - \"14268:14268\" # Collector\n - \"14250:14250\" # gRPC\n - \"9411:9411\" # Zipkin\n environment:\n - COLLECTOR_ZIPKIN_HOST_PORT=:9411\n```\n\n**Reference:** See `references\u002Fjaeger-setup.md`\n\n## Application Instrumentation\n\n### OpenTelemetry (Recommended)\n\n#### Python (Flask)\n```python\nfrom opentelemetry import trace\nfrom opentelemetry.exporter.jaeger.thrift import JaegerExporter\nfrom opentelemetry.sdk.resources import SERVICE_NAME, Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.instrumentation.flask import FlaskInstrumentor\nfrom flask import Flask\n\n# Initialize tracer\nresource = Resource(attributes={SERVICE_NAME: \"my-service\"})\nprovider = TracerProvider(resource=resource)\nprocessor = BatchSpanProcessor(JaegerExporter(\n agent_host_name=\"jaeger\",\n agent_port=6831,\n))\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n\n# Instrument Flask\napp = Flask(__name__)\nFlaskInstrumentor().instrument_app(app)\n\n@app.route('\u002Fapi\u002Fusers')\ndef get_users():\n tracer = trace.get_tracer(__name__)\n\n with tracer.start_as_current_span(\"get_users\") as span:\n span.set_attribute(\"user.count\", 100)\n # Business logic\n users = fetch_users_from_db()\n return {\"users\": users}\n\ndef fetch_users_from_db():\n tracer = trace.get_tracer(__name__)\n\n with tracer.start_as_current_span(\"database_query\") as span:\n span.set_attribute(\"db.system\", \"postgresql\")\n span.set_attribute(\"db.statement\", \"SELECT * FROM users\")\n # Database query\n return query_database()\n```\n\n#### Node.js (Express)\n```javascript\nconst { NodeTracerProvider } = require('@opentelemetry\u002Fsdk-trace-node');\nconst { JaegerExporter } = require('@opentelemetry\u002Fexporter-jaeger');\nconst { BatchSpanProcessor } = require('@opentelemetry\u002Fsdk-trace-base');\nconst { registerInstrumentations } = require('@opentelemetry\u002Finstrumentation');\nconst { HttpInstrumentation } = require('@opentelemetry\u002Finstrumentation-http');\nconst { ExpressInstrumentation } = require('@opentelemetry\u002Finstrumentation-express');\n\n\u002F\u002F Initialize tracer\nconst provider = new NodeTracerProvider({\n resource: { attributes: { 'service.name': 'my-service' } }\n});\n\nconst exporter = new JaegerExporter({\n endpoint: 'http:\u002F\u002Fjaeger:14268\u002Fapi\u002Ftraces'\n});\n\nprovider.addSpanProcessor(new BatchSpanProcessor(exporter));\nprovider.register();\n\n\u002F\u002F Instrument libraries\nregisterInstrumentations({\n instrumentations: [\n new HttpInstrumentation(),\n new ExpressInstrumentation(),\n ],\n});\n\nconst express = require('express');\nconst app = express();\n\napp.get('\u002Fapi\u002Fusers', async (req, res) => {\n const tracer = trace.getTracer('my-service');\n const span = tracer.startSpan('get_users');\n\n try {\n const users = await fetchUsers();\n span.setAttributes({ 'user.count': users.length });\n res.json({ users });\n } finally {\n span.end();\n }\n});\n```\n\n#### Go\n```go\npackage main\n\nimport (\n \"context\"\n \"go.opentelemetry.io\u002Fotel\"\n \"go.opentelemetry.io\u002Fotel\u002Fexporters\u002Fjaeger\"\n \"go.opentelemetry.io\u002Fotel\u002Fsdk\u002Fresource\"\n sdktrace \"go.opentelemetry.io\u002Fotel\u002Fsdk\u002Ftrace\"\n semconv \"go.opentelemetry.io\u002Fotel\u002Fsemconv\u002Fv1.4.0\"\n)\n\nfunc initTracer() (*sdktrace.TracerProvider, error) {\n exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(\n jaeger.WithEndpoint(\"http:\u002F\u002Fjaeger:14268\u002Fapi\u002Ftraces\"),\n ))\n if err != nil {\n return nil, err\n }\n\n tp := sdktrace.NewTracerProvider(\n sdktrace.WithBatcher(exporter),\n sdktrace.WithResource(resource.NewWithAttributes(\n semconv.SchemaURL,\n semconv.ServiceNameKey.String(\"my-service\"),\n )),\n )\n\n otel.SetTracerProvider(tp)\n return tp, nil\n}\n\nfunc getUsers(ctx context.Context) ([]User, error) {\n tracer := otel.Tracer(\"my-service\")\n ctx, span := tracer.Start(ctx, \"get_users\")\n defer span.End()\n\n span.SetAttributes(attribute.String(\"user.filter\", \"active\"))\n\n users, err := fetchUsersFromDB(ctx)\n if err != nil {\n span.RecordError(err)\n return nil, err\n }\n\n span.SetAttributes(attribute.Int(\"user.count\", len(users)))\n return users, nil\n}\n```\n\n**Reference:** See `references\u002Finstrumentation.md`\n\n## Context Propagation\n\n### HTTP Headers\n```\ntraceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01\ntracestate: congo=t61rcWkgMzE\n```\n\n### Propagation in HTTP Requests\n\n#### Python\n```python\nfrom opentelemetry.propagate import inject\n\nheaders = {}\ninject(headers) # Injects trace context\n\nresponse = requests.get('http:\u002F\u002Fdownstream-service\u002Fapi', headers=headers)\n```\n\n#### Node.js\n```javascript\nconst { propagation } = require('@opentelemetry\u002Fapi');\n\nconst headers = {};\npropagation.inject(context.active(), headers);\n\naxios.get('http:\u002F\u002Fdownstream-service\u002Fapi', { headers });\n```\n\n## Tempo Setup (Grafana)\n\n### Kubernetes Deployment\n\n```yaml\napiVersion: v1\nkind: ConfigMap\nmetadata:\n name: tempo-config\ndata:\n tempo.yaml: |\n server:\n http_listen_port: 3200\n\n distributor:\n receivers:\n jaeger:\n protocols:\n thrift_http:\n grpc:\n otlp:\n protocols:\n http:\n grpc:\n\n storage:\n trace:\n backend: s3\n s3:\n bucket: tempo-traces\n endpoint: s3.amazonaws.com\n\n querier:\n frontend_worker:\n frontend_address: tempo-query-frontend:9095\n---\napiVersion: apps\u002Fv1\nkind: Deployment\nmetadata:\n name: tempo\nspec:\n replicas: 1\n template:\n spec:\n containers:\n - name: tempo\n image: grafana\u002Ftempo:latest\n args:\n - -config.file=\u002Fetc\u002Ftempo\u002Ftempo.yaml\n volumeMounts:\n - name: config\n mountPath: \u002Fetc\u002Ftempo\n volumes:\n - name: config\n configMap:\n name: tempo-config\n```\n\n**Reference:** See `assets\u002Fjaeger-config.yaml.template`\n\n## Sampling Strategies\n\n### Probabilistic Sampling\n```yaml\n# Sample 1% of traces\nsampler:\n type: probabilistic\n param: 0.01\n```\n\n### Rate Limiting Sampling\n```yaml\n# Sample max 100 traces per second\nsampler:\n type: ratelimiting\n param: 100\n```\n\n### Adaptive Sampling\n```python\nfrom opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased\n\n# Sample based on trace ID (deterministic)\nsampler = ParentBased(root=TraceIdRatioBased(0.01))\n```\n\n## Trace Analysis\n\n### Finding Slow Requests\n\n**Jaeger Query:**\n```\nservice=my-service\nduration > 1s\n```\n\n### Finding Errors\n\n**Jaeger Query:**\n```\nservice=my-service\nerror=true\ntags.http.status_code >= 500\n```\n\n### Service Dependency Graph\n\nJaeger automatically generates service dependency graphs showing:\n- Service relationships\n- Request rates\n- Error rates\n- Average latencies\n\n## Best Practices\n\n1. **Sample appropriately** (1-10% in production)\n2. **Add meaningful tags** (user_id, request_id)\n3. **Propagate context** across all service boundaries\n4. **Log exceptions** in spans\n5. **Use consistent naming** for operations\n6. **Monitor tracing overhead** (\u003C1% CPU impact)\n7. **Set up alerts** for trace errors\n8. **Implement distributed context** (baggage)\n9. **Use span events** for important milestones\n10. **Document instrumentation** standards\n\n## Integration with Logging\n\n### Correlated Logs\n```python\nimport logging\nfrom opentelemetry import trace\n\nlogger = logging.getLogger(__name__)\n\ndef process_request():\n span = trace.get_current_span()\n trace_id = span.get_span_context().trace_id\n\n logger.info(\n \"Processing request\",\n extra={\"trace_id\": format(trace_id, '032x')}\n )\n```\n\n## Troubleshooting\n\n**No traces appearing:**\n- Check collector endpoint\n- Verify network connectivity\n- Check sampling configuration\n- Review application logs\n\n**High latency overhead:**\n- Reduce sampling rate\n- Use batch span processor\n- Check exporter configuration\n\n## Reference Files\n\n- `references\u002Fjaeger-setup.md` - Jaeger installation\n- `references\u002Finstrumentation.md` - Instrumentation patterns\n- `assets\u002Fjaeger-config.yaml.template` - Jaeger configuration\n\n## Related Skills\n\n- `prometheus-configuration` - For metrics\n- `grafana-dashboards` - For visualization\n- `slo-implementation` - For latency SLOs\n","# Distributed Tracing\n\nImplement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.\n\n## Do not use this skill when\n\n- The task is unrelated to distributed tracing\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Purpose\n\nTrack requests across distributed systems to understand latency, dependencies, and failure points.\n\n## Use this skill when\n\n- Debug latency issues\n- Understand service dependencies\n- Identify bottlenecks\n- Trace error propagation\n- Analyze request paths\n\n## Distributed Tracing Concepts\n\n### Trace Structure\n```\nTrace (Request ID: abc123)\n ↓\nSpan (frontend) [100ms]\n ↓\nSpan (api-gateway) [80ms]\n ├→ Span (auth-service) [10ms]\n └→ Span (user-service) [60ms]\n └→ Span (database) [40ms]\n```\n\n### Key Components\n- **Trace** - End-to-end request journey\n- **Span** - Single operation within a trace\n- **Context** - Metadata propagated between services\n- **Tags** - Key-value pairs for filtering\n- **Logs** - Timestamped events within a span\n\n## Jaeger Setup\n\n### Kubernetes Deployment\n\n```bash\n# Deploy Jaeger Operator\nkubectl create namespace observability\nkubectl create -f https:\u002F\u002Fgithub.com\u002Fjaegertracing\u002Fjaeger-operator\u002Freleases\u002Fdownload\u002Fv1.51.0\u002Fjaeger-operator.yaml -n observability\n\n# Deploy Jaeger instance\nkubectl apply -f - \u003C\u003CEOF\napiVersion: jaegertracing.io\u002Fv1\nkind: Jaeger\nmetadata:\n name: jaeger\n namespace: observability\nspec:\n strategy: production\n storage:\n type: elasticsearch\n options:\n es:\n server-urls: http:\u002F\u002Felasticsearch:9200\n ingress:\n enabled: true\nEOF\n```\n\n### Docker Compose\n\n```yaml\nversion: '3.8'\nservices:\n jaeger:\n image: jaegertracing\u002Fall-in-one:latest\n ports:\n - \"5775:5775\u002Fudp\"\n - \"6831:6831\u002Fudp\"\n - \"6832:6832\u002Fudp\"\n - \"5778:5778\"\n - \"16686:16686\" # UI\n - \"14268:14268\" # Collector\n - \"14250:14250\" # gRPC\n - \"9411:9411\" # Zipkin\n environment:\n - COLLECTOR_ZIPKIN_HOST_PORT=:9411\n```\n\n**Reference:** See `references\u002Fjaeger-setup.md`\n\n## Application Instrumentation\n\n### OpenTelemetry (Recommended)\n\n#### Python (Flask)\n```python\nfrom opentelemetry import trace\nfrom opentelemetry.exporter.jaeger.thrift import JaegerExporter\nfrom opentelemetry.sdk.resources import SERVICE_NAME, Resource\nfrom opentelemetry.sdk.trace import TracerProvider\nfrom opentelemetry.sdk.trace.export import BatchSpanProcessor\nfrom opentelemetry.instrumentation.flask import FlaskInstrumentor\nfrom flask import Flask\n\n# Initialize tracer\nresource = Resource(attributes={SERVICE_NAME: \"my-service\"})\nprovider = TracerProvider(resource=resource)\nprocessor = BatchSpanProcessor(JaegerExporter(\n agent_host_name=\"jaeger\",\n agent_port=6831,\n))\nprovider.add_span_processor(processor)\ntrace.set_tracer_provider(provider)\n\n# Instrument Flask\napp = Flask(__name__)\nFlaskInstrumentor().instrument_app(app)\n\n@app.route('\u002Fapi\u002Fusers')\ndef get_users():\n tracer = trace.get_tracer(__name__)\n\n with tracer.start_as_current_span(\"get_users\") as span:\n span.set_attribute(\"user.count\", 100)\n # Business logic\n users = fetch_users_from_db()\n return {\"users\": users}\n\ndef fetch_users_from_db():\n tracer = trace.get_tracer(__name__)\n\n with tracer.start_as_current_span(\"database_query\") as span:\n span.set_attribute(\"db.system\", \"postgresql\")\n span.set_attribute(\"db.statement\", \"SELECT * FROM users\")\n # Database query\n return query_database()\n```\n\n#### Node.js (Express)\n```javascript\nconst { NodeTracerProvider } = require('@opentelemetry\u002Fsdk-trace-node');\nconst { JaegerExporter } = require('@opentelemetry\u002Fexporter-jaeger');\nconst { BatchSpanProcessor } = require('@opentelemetry\u002Fsdk-trace-base');\nconst { registerInstrumentations } = require('@opentelemetry\u002Finstrumentation');\nconst { HttpInstrumentation } = require('@opentelemetry\u002Finstrumentation-http');\nconst { ExpressInstrumentation } = require('@opentelemetry\u002Finstrumentation-express');\n\n\u002F\u002F Initialize tracer\nconst provider = new NodeTracerProvider({\n resource: { attributes: { 'service.name': 'my-service' } }\n});\n\nconst exporter = new JaegerExporter({\n endpoint: 'http:\u002F\u002Fjaeger:14268\u002Fapi\u002Ftraces'\n});\n\nprovider.addSpanProcessor(new BatchSpanProcessor(exporter));\nprovider.register();\n\n\u002F\u002F Instrument libraries\nregisterInstrumentations({\n instrumentations: [\n new HttpInstrumentation(),\n new ExpressInstrumentation(),\n ],\n});\n\nconst express = require('express');\nconst app = express();\n\napp.get('\u002Fapi\u002Fusers', async (req, res) => {\n const tracer = trace.getTracer('my-service');\n const span = tracer.startSpan('get_users');\n\n try {\n const users = await fetchUsers();\n span.setAttributes({ 'user.count': users.length });\n res.json({ users });\n } finally {\n span.end();\n }\n});\n```\n\n#### Go\n```go\npackage main\n\nimport (\n \"context\"\n \"go.opentelemetry.io\u002Fotel\"\n \"go.opentelemetry.io\u002Fotel\u002Fexporters\u002Fjaeger\"\n \"go.opentelemetry.io\u002Fotel\u002Fsdk\u002Fresource\"\n sdktrace \"go.opentelemetry.io\u002Fotel\u002Fsdk\u002Ftrace\"\n semconv \"go.opentelemetry.io\u002Fotel\u002Fsemconv\u002Fv1.4.0\"\n)\n\nfunc initTracer() (*sdktrace.TracerProvider, error) {\n exporter, err := jaeger.New(jaeger.WithCollectorEndpoint(\n jaeger.WithEndpoint(\"http:\u002F\u002Fjaeger:14268\u002Fapi\u002Ftraces\"),\n ))\n if err != nil {\n return nil, err\n }\n\n tp := sdktrace.NewTracerProvider(\n sdktrace.WithBatcher(exporter),\n sdktrace.WithResource(resource.NewWithAttributes(\n semconv.SchemaURL,\n semconv.ServiceNameKey.String(\"my-service\"),\n )),\n )\n\n otel.SetTracerProvider(tp)\n return tp, nil\n}\n\nfunc getUsers(ctx context.Context) ([]User, error) {\n tracer := otel.Tracer(\"my-service\")\n ctx, span := tracer.Start(ctx, \"get_users\")\n defer span.End()\n\n span.SetAttributes(attribute.String(\"user.filter\", \"active\"))\n\n users, err := fetchUsersFromDB(ctx)\n if err != nil {\n span.RecordError(err)\n return nil, err\n }\n\n span.SetAttributes(attribute.Int(\"user.count\", len(users)))\n return users, nil\n}\n```\n\n**Reference:** See `references\u002Finstrumentation.md`\n\n## Context Propagation\n\n### HTTP Headers\n```\ntraceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01\ntracestate: congo=t61rcWkgMzE\n```\n\n### Propagation in HTTP Requests\n\n#### Python\n```python\nfrom opentelemetry.propagate import inject\n\nheaders = {}\ninject(headers) # Injects trace context\n\nresponse = requests.get('http:\u002F\u002Fdownstream-service\u002Fapi', headers=headers)\n```\n\n#### Node.js\n```javascript\nconst { propagation } = require('@opentelemetry\u002Fapi');\n\nconst headers = {};\npropagation.inject(context.active(), headers);\n\naxios.get('http:\u002F\u002Fdownstream-service\u002Fapi', { headers });\n```\n\n## Tempo Setup (Grafana)\n\n### Kubernetes Deployment\n\n```yaml\napiVersion: v1\nkind: ConfigMap\nmetadata:\n name: tempo-config\ndata:\n tempo.yaml: |\n server:\n http_listen_port: 3200\n\n distributor:\n receivers:\n jaeger:\n protocols:\n thrift_http:\n grpc:\n otlp:\n protocols:\n http:\n grpc:\n\n storage:\n trace:\n backend: s3\n s3:\n bucket: tempo-traces\n endpoint: s3.amazonaws.com\n\n querier:\n frontend_worker:\n frontend_address: tempo-query-frontend:9095\n---\napiVersion: apps\u002Fv1\nkind: Deployment\nmetadata:\n name: tempo\nspec:\n replicas: 1\n template:\n spec:\n containers:\n - name: tempo\n image: grafana\u002Ftempo:latest\n args:\n - -config.file=\u002Fetc\u002Ftempo\u002Ftempo.yaml\n volumeMounts:\n - name: config\n mountPath: \u002Fetc\u002Ftempo\n volumes:\n - name: config\n configMap:\n name: tempo-config\n```\n\n**Reference:** See `assets\u002Fjaeger-config.yaml.template`\n\n## Sampling Strategies\n\n### Probabilistic Sampling\n```yaml\n# Sample 1% of traces\nsampler:\n type: probabilistic\n param: 0.01\n```\n\n### Rate Limiting Sampling\n```yaml\n# Sample max 100 traces per second\nsampler:\n type: ratelimiting\n param: 100\n```\n\n### Adaptive Sampling\n```python\nfrom opentelemetry.sdk.trace.sampling import ParentBased, TraceIdRatioBased\n\n# Sample based on trace ID (deterministic)\nsampler = ParentBased(root=TraceIdRatioBased(0.01))\n```\n\n## Trace Analysis\n\n### Finding Slow Requests\n\n**Jaeger Query:**\n```\nservice=my-service\nduration > 1s\n```\n\n### Finding Errors\n\n**Jaeger Query:**\n```\nservice=my-service\nerror=true\ntags.http.status_code >= 500\n```\n\n### Service Dependency Graph\n\nJaeger automatically generates service dependency graphs showing:\n- Service relationships\n- Request rates\n- Error rates\n- Average latencies\n\n## Best Practices\n\n1. **Sample appropriately** (1-10% in production)\n2. **Add meaningful tags** (user_id, request_id)\n3. **Propagate context** across all service boundaries\n4. **Log exceptions** in spans\n5. **Use consistent naming** for operations\n6. **Monitor tracing overhead** (\u003C1% CPU impact)\n7. **Set up alerts** for trace errors\n8. **Implement distributed context** (baggage)\n9. **Use span events** for important milestones\n10. **Document instrumentation** standards\n\n## Integration with Logging\n\n### Correlated Logs\n```python\nimport logging\nfrom opentelemetry import trace\n\nlogger = logging.getLogger(__name__)\n\ndef process_request():\n span = trace.get_current_span()\n trace_id = span.get_span_context().trace_id\n\n logger.info(\n \"Processing request\",\n extra={\"trace_id\": format(trace_id, '032x')}\n )\n```\n\n## Troubleshooting\n\n**No traces appearing:**\n- Check collector endpoint\n- Verify network connectivity\n- Check sampling configuration\n- Review application logs\n\n**High latency overhead:**\n- Reduce sampling rate\n- Use batch span processor\n- Check exporter configuration\n\n## Reference Files\n\n- `references\u002Fjaeger-setup.md` - Jaeger 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Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[1379],{"categoryNo":1308,"categoryName":1309,"taxonomyType":21,"taxonomyTypeDesc":22},[1381,1382,1383,1384,1385,1386,1387,1388,30,1389,1390,1391,1392,1393,1394,29,1395,1396,1397,1317],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","verification","code","rust"]