Jaeger — CNCF Distributed Tracing Platform

What it is

Jaeger is a CNCF-graduated distributed tracing system designed for monitoring and troubleshooting microservice-based architectures. It tracks requests as they flow across multiple services, showing the full call chain with timing data for each hop.

Jaeger helps developers identify latency hotspots, understand service dependencies, and diagnose root causes of failures in complex distributed systems. It supports OpenTelemetry natively and stores trace data in Elasticsearch, Cassandra, or Kafka.

How it saves time or tokens

Debugging latency in a microservice architecture without distributed tracing means grepping logs across dozens of services. Jaeger provides a visual timeline of every service call in a request, immediately showing where time is spent.

For AI-assisted debugging, Jaeger's structured trace data can be exported as JSON and fed to an LLM for analysis. The model can identify patterns like cascading timeouts or retry storms that are hard to spot manually.

Additionally, the project's well-structured documentation and active community mean developers spend less time troubleshooting integration issues. When AI coding assistants generate code for this tool, they can reference established patterns from the documentation, producing correct implementations with fewer iterations and lower token costs.

How to use

1. Run Jaeger all-in-one for development:

docker run -d --name jaeger \
  -p 16686:16686 \
  -p 4317:4317 \
  -p 4318:4318 \
  jaegertracing/all-in-one:latest

1. Instrument your application with OpenTelemetry:

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.trace.export import BatchSpanProcessor

provider = TracerProvider()
provider.add_span_processor(
    BatchSpanProcessor(OTLPSpanExporter(endpoint='http://localhost:4317'))
)
trace.set_tracer_provider(provider)

1. Open the Jaeger UI at http://localhost:16686 to search and visualize traces.

1. Use the service dependency graph to understand how your services connect.

Example

tracer = trace.get_tracer('my-service')

with tracer.start_as_current_span('process-order') as span:
    span.set_attribute('order.id', '12345')
    result = call_payment_service(order)
    span.set_attribute('payment.status', result.status)

Related on TokRepo

• AI Tools for Monitoring — Monitoring and observability tools
• AI Tools for DevOps — DevOps and infrastructure tools

Common pitfalls

• Tracing every request in production. At high throughput, tracing everything overwhelms storage. Use sampling (1% or adaptive) to capture representative traces without drowning in data.
• Not propagating trace context across service boundaries. If any service in the chain does not propagate the trace ID, the trace breaks into disconnected fragments.
• Using the all-in-one deployment in production. It stores traces in memory and loses them on restart. Use Elasticsearch or Cassandra for production storage.
• Failing to review community discussions and changelogs before upgrading. Breaking changes in major versions can disrupt existing workflows. Pin versions in production and test upgrades in staging first.