MicroservicesHow-ToBeginner · 4 min read

How to Use Jaeger for Tracing in Microservices

To use Jaeger for tracing, instrument your microservices with a tracing library that supports OpenTelemetry or OpenTracing, configure them to send trace data to the Jaeger backend, and run the Jaeger UI to visualize traces. This helps track requests across services and identify performance bottlenecks.

📐

Syntax

Using Jaeger involves three main parts:

Tracer Initialization: Set up a tracer in your service code to start and finish spans.
Span Creation: Create spans around operations to record timing and metadata.
Exporter Configuration: Configure the tracer to send data to the Jaeger collector or agent.

Example syntax in code shows how to initialize and use the tracer.

python

from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

# Initialize tracer provider
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)

# Configure Jaeger exporter
jaeger_exporter = JaegerExporter(
    agent_host_name='localhost',
    agent_port=6831
)

# Add span processor to send spans to Jaeger
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(jaeger_exporter)
)

# Create a span
with tracer.start_as_current_span("example-operation") as span:
    span.set_attribute("key", "value")
    # Your operation code here

💻

Example

This example shows a simple Python microservice instrumented with OpenTelemetry to send traces to Jaeger running locally. It creates a span around a simulated operation.

python

from opentelemetry import trace
from opentelemetry.exporter.jaeger.thrift import JaegerExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
import time

# Setup tracer provider and exporter
trace.set_tracer_provider(TracerProvider())
tracer = trace.get_tracer(__name__)
jaeger_exporter = JaegerExporter(agent_host_name='localhost', agent_port=6831)
trace.get_tracer_provider().add_span_processor(BatchSpanProcessor(jaeger_exporter))

# Simulate a traced operation
with tracer.start_as_current_span("process_request") as span:
    span.set_attribute("http.method", "GET")
    print("Processing request...")
    time.sleep(1)  # Simulate work
    span.set_attribute("http.status_code", 200)

print("Trace sent to Jaeger")

Output

Processing request... Trace sent to Jaeger

⚠️

Common Pitfalls

Not running Jaeger backend: Without the Jaeger collector and UI running, traces won't be visible.
Incorrect exporter configuration: Wrong host or port prevents trace data from reaching Jaeger.
Missing span creation: Forgetting to create spans means no trace data is recorded.
Sampling too aggressive: Setting sampling rate too low can miss important traces.

Always verify Jaeger services are running and your tracer is properly configured.

python

## Wrong exporter config example (wrong port)
jaeger_exporter = JaegerExporter(agent_host_name='localhost', agent_port=9999)  # Wrong port

## Correct exporter config
jaeger_exporter = JaegerExporter(agent_host_name='localhost', agent_port=6831)  # Default port

📊

Quick Reference

Step	Description
Install Jaeger backend	Run Jaeger all-in-one or separate services locally or in your environment.
Add tracing library	Use OpenTelemetry or OpenTracing SDK in your microservices.
Configure exporter	Set tracer to send data to Jaeger agent or collector.
Create spans	Wrap code blocks with spans to record trace data.
View traces	Open Jaeger UI to search and analyze traces.

✅

Key Takeaways

Instrument your microservices with OpenTelemetry or OpenTracing to create spans.

Configure the tracer exporter to send trace data to the running Jaeger backend.

Run Jaeger collector and UI to collect and visualize traces.

Ensure correct host and port settings for the Jaeger exporter to avoid data loss.

Use spans to capture meaningful operations and attributes for better trace insights.