Distributed Tracing
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Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.
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Frontmatter — 2 properties
| name | distributed-tracing |
|---|---|
| description | Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems. |
| 1 | --- |
| 2 | name: distributed-tracing |
| 3 | description: Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems. |
| 4 | ---A5 — No allowed-tools declared — no way to tell what this skill may touch |
| 5 | |
| 6 | # Distributed Tracing |
| 7 | |
| 8 | Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices. |
| 9 | |
| 10 | ## Purpose |
| 11 | |
| 12 | Track requests across distributed systems to understand latency, dependencies, and failure points. |
| 13 | |
| 14 | ## When to Use |
| 15 | |
| 16 | - Debug latency issues |
| 17 | - Understand service dependencies |
| 18 | - Identify bottlenecks |
| 19 | - Trace error propagation |
| 20 | - Analyze request paths |
| 21 | |
| 22 | ## Detailed patterns and worked examples |
| 23 | |
| 24 | Detailed pattern documentation lives in `references/details.md`. Read that file when the navigation tier above is insufficient. |
| 25 | |
| 26 | ## Best Practices |
| 27 | |
| 28 | 1. **Sample appropriately** (1-10% in production) |
| 29 | 2. **Add meaningful tags** (user_id, request_id) |
| 30 | 3. **Propagate context** across all service boundaries |
| 31 | 4. **Log exceptions** in spans |
| 32 | 5. **Use consistent naming** for operations |
| 33 | 6. **Monitor tracing overhead** (<1% CPU impact) |
| 34 | 7. **Set up alerts** for trace errors |
| 35 | 8. **Implement distributed context** (baggage) |
| 36 | 9. **Use span events** for important milestones |
| 37 | 10. **Document instrumentation** standards |
| 38 | |
| 39 | ## Integration with Logging |
| 40 | |
| 41 | ### Correlated Logs |
| 42 | |
| 43 | ```python |
| 44 | import logging |
| 45 | from opentelemetry import trace |
| 46 | |
| 47 | logger = logging.getLogger(__name__) |
| 48 | |
| 49 | def process_request(): |
| 50 | span = trace.get_current_span() |
| 51 | trace_id = span.get_span_context().trace_id |
| 52 | |
| 53 | logger.info( |
| 54 | "Processing request", |
| 55 | extra={"trace_id": format(trace_id, '032x')} |
| 56 | ) |
| 57 | ``` |
| 58 | |
| 59 | ## Troubleshooting |
| 60 | |
| 61 | **No traces appearing:** |
| 62 | |
| 63 | - Check collector endpoint |
| 64 | - Verify network connectivity |
| 65 | - Check sampling configuration |
| 66 | - Review application logs |
| 67 | |
| 68 | **High latency overhead:** |
| 69 | |
| 70 | - Reduce sampling rate |
| 71 | - Use batch span processor |
| 72 | - Check exporter configuration |
| 73 | |
| 74 | |
| 75 | ## Related Skills |
| 76 | |
| 77 | - `prometheus-configuration` - For metrics |
| 78 | - `grafana-dashboards` - For visualization |
| 79 | - `slo-implementation` - For latency SLOs |
| 80 |
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