Lab 1: The Global Intercept¶

What will we learn?¶

• How to connect Telepresence to a kind cluster
• Creating a global intercept that routes all traffic to your local machine
• Testing intercepted traffic with curl
• Properly cleaning up intercepts

Introduction¶

• A Global Intercept redirects 100% of the traffic destined for a service to your local machine
• This is the simplest type of intercept - ideal for solo development
• No container rebuilds needed: edit locally, test instantly

graph LR
    A["Client Request"] --> B["frontend :80"]
    B --> C["Traffic Manager"]
    C -->|"Intercepted"| D["Your Local Machine :5000"]
    C -.->|"Original (bypassed)"| E["backend pod"]

    style D fill:#10b981,color:#fff
    style E fill:#ef4444,color:#fff,stroke-dasharray: 5 5
    style C fill:#f59e0b,color:#fff

Prerequisites¶

• Both kind clusters created and running (./setup-clusters.sh)
• Telepresence CLI installed
• No other Telepresence connections active (telepresence quit)

Step 01 - Connect to cluster-east¶

# Switch kubectl context
kubectl config use-context kind-cluster-east

# Connect Telepresence to the cluster
telepresence connect

# Verify connection
telepresence status

Root Daemon: Running
User Daemon: Running
...
Cluster context: kind-cluster-east

Step 02 - List Available Services¶

# See what can be intercepted
telepresence list -n telepresence-lab

You should see backend and frontend listed as interceptable workloads.

Step 03 - Start a Local Backend Process¶

Before creating the intercept, start a local server that will handle the redirected traffic:

# Save as local_backend.py
from http.server import HTTPServer, BaseHTTPRequestHandler
import json

class Handler(BaseHTTPRequestHandler):
    def do_GET(self):
        self.send_response(200)
        self.send_header('Content-Type', 'application/json')
        self.end_headers()
        response = {
            "source": "LOCAL MACHINE",
            "service": "backend",
            "message": "Hello from your laptop!",
            "path": self.path
        }
        self.wfile.write(json.dumps(response, indent=2).encode())

print("Local backend running on :5000")
HTTPServer(('0.0.0.0', 5000), Handler).serve_forever()

python3 local_backend.py

while true; do
  echo -e "HTTP/1.1 200 OK\r\nContent-Type: text/plain\r\n\r\nHello from LOCAL!" \
    | nc -l -p 5000
done

// Save as local_backend.js
const http = require('http');
http.createServer((req, res) => {
  res.writeHead(200, { 'Content-Type': 'application/json' });
  res.end(JSON.stringify({
    source: 'LOCAL MACHINE',
    service: 'backend',
    path: req.url
  }, null, 2));
}).listen(5000, () => console.log('Local backend on :5000'));

node local_backend.js

Step 04 - Create the Intercept¶

# Intercept backend service, route port 5000 to local 5000
telepresence intercept backend \
  --port 5000 \
  --namespace telepresence-lab

backend: intercepted
  Intercept name: backend-telepresence-lab
  State         : ACTIVE
  Workload kind : Deployment
  Destination   : 127.0.0.1:5000
  Intercepting  : all TCP connections

Step 05 - Verify the Intercept¶

# Call the frontend (which proxies to backend internally)
curl http://frontend.telepresence-lab.svc.cluster.local/api

# Or call the backend directly
curl http://backend.telepresence-lab.svc.cluster.local:5000/

# Check intercept status
telepresence list -n telepresence-lab

The response should come from your LOCAL process (e.g., "source": "LOCAL MACHINE") instead of the cluster’s backend pod.

Step 06 - Make Live Changes¶

Step 07 - Cleanup¶

# Leave the intercept
telepresence leave backend-telepresence-lab

# Verify intercept is removed
telepresence list -n telepresence-lab

# Disconnect
telepresence quit

Validation Checklist¶