distributed-tracing

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging …

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技能内容

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

import logging
from 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')}
    )