Developing and debugging services locally using telepresence
Kubernetes applications usually consist of multiple, separate services, each running in its own container. Developing and debugging these services on a remote Kubernetes cluster can be cumbersome, requiring you to get a shell on a running container in order to run debugging tools.
is a tool to ease the process of developing and debugging services locally while proxying the service to a remote Kubernetes cluster. Using telepresence
allows you to use custom tools, such as a debugger and IDE, for a local service and provides the service full access to ConfigMap, secrets, and the services running on the remote cluster.
This document describes using telepresence
to develop and debug services running on a remote cluster locally.
- Kubernetes cluster is installed
- is configured to communicate with the cluster
You can curl services using the Kubernetes syntax e.g.
When developing an application on Kubernetes, you typically program or debug a single service. The service might require access to other services for testing and debugging. One option is to use the continuous deployment pipeline, but even the fastest deployment pipeline introduces a delay in the program or debug cycle.
Use the telepresence intercept $SERVICE_NAME --port $LOCAL_PORT:$REMOTE_PORT
command to create an “intercept” for rerouting remote service traffic.
$SERVICE_NAME
is the name of your local service- is the port that your service is running on your local workstation
- And
$REMOTE_PORT
is the port your service listens to in the cluster
Running this command tells Telepresence to send remote traffic to your local service instead of the service in the remote Kubernetes cluster. Make edits to your service source code locally, save, and see the corresponding changes when accessing your remote application take effect immediately. You can also run your local service using a debugger or any other local development tool.
Telepresence installs a traffic-agent sidecar next to your existing application’s container running in the remote cluster. It then captures all traffic requests going into the Pod, and instead of forwarding this to the application in the remote cluster, it routes all traffic (when you create a ) or a subset of the traffic (when you create a personal intercept) to your local development environment.
If you’re interested in a hands-on tutorial, check out this tutorial that walks through locally developing the Guestbook application on Google Kubernetes Engine.