---
title: Distributed Tracing
category: product
entity_type: skill
price: $15
canonical: https://forgehouse.ai/skills/distributed-tracing/
lang: en
hreflang_alt: https://forgehouse.ai/tr/skiller/distributed-tracing/
last_updated: 2026-06-20
---

# Distributed Tracing

> Implement distributed tracing with Jaeger and Tempo to track requests across microservices and…

Implements distributed tracing with Jaeger and Tempo so you can follow a single request across all your microservices and pinpoint where latency and failures occur. It covers OpenTelemetry instrumentation, context propagation, sampling strategy, and correlation of logs, metrics, and traces. Turns a foggy 'which service is slow?' question into a clear, visualized request journey.

## Use cases
- Debugging latency issues across a microservices architecture
- Understanding service dependencies and request flow
- Identifying performance bottlenecks in distributed systems
- Tracing error propagation from frontend to database
- Instrumenting Python, Node.js, or Go services with OpenTelemetry
- Correlating logs, metrics, and traces by trace ID in Grafana

## Benefits
- Cut incident resolution from hours to minutes with end-to-end request visibility
- Pinpoint the exact service and operation causing latency
- Control observability cost with smart head and tail sampling
- See the full story of any request by correlating logs, metrics, and traces

## What’s included
- Jaeger Kubernetes and Docker Compose deployment configs
- OpenTelemetry instrumentation examples for Python, Node.js, and Go
- W3C context propagation patterns across HTTP, gRPC, and message queues
- Probabilistic, rate-limiting, and adaptive sampling strategies
- Grafana Tempo setup with S3 trace storage
- Logs-metrics-traces correlation via trace ID and baggage propagation

## Who it’s for
Backend and platform engineers running microservices who need to trace requests end to end and find bottlenecks fast.

## How it runs
The instrumentation sequence the skill executes to make a request visible across every microservice it touches:
1. Stands up the tracing backend first: Jaeger via the Kubernetes operator or Docker Compose for smaller setups, or Grafana Tempo with object-storage backed trace retention.
2. Instruments each service with OpenTelemetry: a TracerProvider with the service name resource, a BatchSpanProcessor exporting to the collector, and auto-instrumentation for the framework (Flask, Express, Go HTTP) so baseline spans appear without touching business code.
3. Shapes the span hierarchy deliberately: parent spans per operation, child spans for database and downstream calls, attributes like db.system and db.statement for filtering, errors recorded on the span, and tree depth kept to 3 to 5 levels so the trace view stays readable.
4. Propagates context across every boundary: W3C traceparent headers injected into HTTP calls, interceptors for gRPC, and trace context carried in message attributes for Kafka or RabbitMQ, because a single broken handoff fragments the entire trace.
5. Sets a sampling strategy that survives production traffic: head-based 1 percent ratio sampling as the baseline, rate limiting at 100 traces per second against spikes, and tail-based sampling in the collector so error and high-latency traces are kept at 100 percent.
6. Correlates the three pillars: trace_id written into every log line and attached to metrics as exemplars, so an alert jumps from a latency histogram to the exact trace to the exact logs in clicks instead of hours.

## FAQ
### We already use a different observability backend. Is this locked to Jaeger and Tempo?
The instrumentation is OpenTelemetry, which is vendor-neutral, so the traces can go to other compatible backends. Jaeger and Tempo are the worked examples, not a hard requirement.

### Will tracing really show me where latency is, or just add noise to my logs?
Context propagation ties one request across every service into a single trace, so a slow hop is visible instead of guessed. Sampling keeps the volume sane so you get signal, not a flood.

### I run a single service, not microservices. Is this worth setting up?
The payoff comes from following a request across service boundaries, which a monolith does not have. On one service, simpler request logging or profiling usually answers the same question with less work.

## Price
$15, one-time, no subscription. VAT included.

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