Distributed Tracing

by wshobson46891e7e60daNo licenseListed Oct 8, 2026Updated Oct 8, 2026

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.

Instructions onlyDevOps & Cloud

AI-generated overview

Guides implementation of distributed tracing with Jaeger and Tempo to trace requests across microservices.

What it does: This skill provides guidance for instrumenting distributed tracing across microservices using Jaeger and Tempo. It covers sampling, span tags, context propagation, correlated logs, and troubleshooting missing traces or latency overhead. It also points to a reference file with detailed patterns and worked examples.
When to use it: Use it when debugging latency or errors in microservices, mapping service dependencies, or adding observability to a distributed system. It is also relevant when analyzing request flows and identifying performance bottlenecks.
Requirements: No scripts are included; it is instructions only. Following it assumes access to tracing tooling such as Jaeger or Tempo and, for the logging example, OpenTelemetry and Python.

Distributed Tracing

Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.

Purpose

Track requests across distributed systems to understand latency, dependencies, and failure points.

When to Use

Debug latency issues
Understand service dependencies
Identify bottlenecks
Trace error propagation
Analyze request paths

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Sample appropriately (1-10% in production)
Add meaningful tags (user_id, request_id)
Propagate context across all service boundaries
Log exceptions in spans
Use consistent naming for operations
Monitor tracing overhead (<1% CPU impact)
Set up alerts for trace errors
Implement distributed context (baggage)
Use span events for important milestones
Document instrumentation standards

Integration with Logging

Correlated Logs

python

import loggingfrom opentelemetry import trace
logger = logging.getLogger(__name__)
def process_request():    span = trace.get_current_span()    trace_id = span.get_span_context().trace_id
    logger.info(        "Processing request",        extra={"trace_id": format(trace_id, '032x')}    )

import loggingfrom opentelemetry import trace
logger = logging.getLogger(__name__)
def process_request():    span = trace.get_current_span()    trace_id = span.get_span_context().trace_id
    logger.info(        "Processing request",        extra={"trace_id": format(trace_id, '032x')}    )

Troubleshooting

No traces appearing:

Check collector endpoint
Verify network connectivity
Check sampling configuration
Review application logs

High latency overhead:

Reduce sampling rate
Use batch span processor
Check exporter configuration

Related Skills

prometheus-configuration - For metrics
grafana-dashboards - For visualization
slo-implementation - For latency SLOs

Source and attribution

Source:wshobson/agentsinplugins/observability-monitoring/skills/distributed-tracingat commit46891e7

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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Distributed Tracing

Distributed Tracing

Purpose

When to Use

Detailed patterns and worked examples

Best Practices

Integration with Logging

Correlated Logs

Troubleshooting

Related Skills

Source and attribution

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