Introduction

In microservices architectures, a single request often traverses multiple services, making it challenging to track performance issues and failures. Distributed tracing provides end-to-end visibility into request flows, helping developers diagnose latency issues and optimize performance.

This guide will cover:

✅ What is distributed tracing and why it matters?
✅ Integrating Flask with OpenTelemetry for tracing
✅ Visualizing traces using Jaeger
✅ Best practices for tracing in Flask applications

Why Distributed Tracing?

Without tracing, debugging microservices can feel like solving a puzzle without all the pieces. Logs and metrics help, but they lack context about how requests propagate through the system.

✅ Pinpoint bottlenecks in service-to-service communication
✅ Analyze request latency across multiple services
✅ Identify failures and errors in distributed environments
✅ Optimize API performance and database queries

Setting Up OpenTelemetry in Flask

We’ll use OpenTelemetry, an open-source observability framework, to collect and export trace data.

Step 1: Install Required Packages

pip install flask opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation-flask opentelemetry-exporter-jaeger

Instrumenting a Flask Application

Step 2: Create a Basic Flask App

from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route("/")
def home():
return jsonify(message="Welcome to the traced Flask app!")

if __name__ == "__main__":
app.run(debug=True)

Integrating OpenTelemetry

Step 3: Configure OpenTelemetry in Flask

Modify the Flask app to include tracing instrumentation.

from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import (
BatchSpanProcessor,
ConsoleSpanExporter,
)
from opentelemetry.instrumentation.flask import FlaskInstrumentor

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

# Configure Span Processor
span_processor = BatchSpanProcessor(ConsoleSpanExporter())  # Export traces to console
trace.get_tracer_provider().add_span_processor(span_processor)

# Instrument Flask
FlaskInstrumentor().instrument_app(app)

Now, every Flask request will generate a trace logged to the console.

Setting Up a Jaeger Tracing Backend

To visualize traces, we’ll use Jaeger, an open-source tracing system.

Step 4: Run Jaeger in Docker

docker run -d --name jaeger \
-p 16686:16686 -p 6831:6831/udp \
jaegertracing/all-in-one:latest
  • Jaeger UI: http://localhost:16686/
  • Tracing data port: 6831/udp

Exporting Traces to Jaeger

Step 5: Configure Flask to Send Traces to Jaeger

Modify app.py to export traces.

from opentelemetry.exporter.jaeger.thrift import JaegerExporter

# Configure Jaeger Exporter
jaeger_exporter = JaegerExporter(
agent_host_name="localhost",
agent_port=6831,
)

# Replace ConsoleSpanExporter with Jaeger
trace.get_tracer_provider().add_span_processor(
BatchSpanProcessor(jaeger_exporter)
)

Now, traces will be visible in Jaeger’s UI at http://localhost:16686/.

Tracing External HTTP Requests

Step 6: Trace Outgoing API Calls

import requests
from opentelemetry.instrumentation.requests import RequestsInstrumentor

RequestsInstrumentor().instrument()

@app.route("/external")
def call_external():
response = requests.get("https://jsonplaceholder.typicode.com/todos/1")
return response.json()

This will trace outbound HTTP requests made by Flask services.

Tracing Database Queries

Step 7: Instrument SQLAlchemy Queries

pip install opentelemetry-instrumentation-sqlalchemy

from sqlalchemy import create_engine
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor

engine = create_engine("sqlite:///test.db")
SQLAlchemyInstrumentor().instrument(engine=engine)

This ensures database queries are also captured in traces.

Viewing Traces in Jaeger

  • Open Jaeger UI at http://localhost:16686/
  • Select flask service and click Find Traces
  • Explore request timelines and latency breakdowns

Best Practices for Distributed Tracing

✅ Use meaningful span names (/users/login, /orders/{id})
✅ Propagate context across microservices
✅ Tag important metadata (user_id, order_id)
✅ Limit excessive spans to avoid performance issues
✅ Anonymize sensitive data before exporting traces

Conclusion

🚀 Key Takeaways:

✅ Implemented OpenTelemetry for tracing Flask requests
✅ Configured Jaeger to visualize trace data
✅ Traced HTTP requests and database queries
✅ Followed best practices for efficient tracing

By integrating distributed tracing, you gain deep insights into microservices interactions, performance bottlenecks, and debugging information.

💡 What’s Next? Share your thoughts in the comments! 🚀