telepresence

Local development against a remote Kubernetes or OpenShift cluster

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Quick Overview

Telepresence is an open-source tool for local development of Kubernetes microservices. It allows developers to run a single service locally while connecting to a remote Kubernetes cluster, enabling faster development cycles and easier debugging of distributed applications.

Pros

Seamless local development experience with remote Kubernetes clusters
Faster development cycles by avoiding constant container rebuilds and deploys
Easy debugging of microservices in complex distributed environments
Supports various programming languages and frameworks

Cons

Initial setup can be complex for some environments
May introduce additional network latency in certain scenarios
Requires careful configuration to ensure security in production environments
Limited support for some advanced Kubernetes features

Getting Started

Install Telepresence:

# macOS
brew install datawire/blackbird/telepresence

# Linux
curl -s https://packagecloud.io/install/repositories/datawire/telepresence/script.deb.sh | sudo bash
sudo apt install telepresence

Connect to your Kubernetes cluster:

telepresence connect

Intercept a service:

telepresence intercept <service-name> --port <local-port>:<remote-port>

Run your local service and start developing!

Competitor Comparisons

emissary

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open source Kubernetes-native API gateway for microservices built on the Envoy Proxy

Pros of Emissary

Designed as a full-featured API Gateway and Ingress Controller
Offers advanced traffic management and routing capabilities
Provides built-in support for authentication and rate limiting

Cons of Emissary

Steeper learning curve due to more complex configuration options
May be overkill for simple development environments
Requires more resources to run compared to Telepresence

Code Comparison

Emissary configuration example:

---
apiVersion: getambassador.io/v3alpha1
kind: Mapping
metadata:
  name: example-mapping
spec:
  prefix: /example/
  service: example-service

Telepresence configuration example:

intercepts:
- name: example
  service: example-service
  port: 8080

Emissary focuses on defining API routes and services, while Telepresence emphasizes local development and service interception. Emissary's configuration is more detailed, reflecting its broader feature set as an API Gateway. Telepresence's configuration is simpler, tailored for development workflows and service proxying.

While both tools serve Kubernetes environments, they address different needs. Emissary is better suited for production-grade API management, whereas Telepresence excels in streamlining local development and testing against remote clusters.

istio

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Connect, secure, control, and observe services.

Pros of Istio

Comprehensive service mesh solution with advanced traffic management, security, and observability features
Supports multi-cluster and multi-cloud deployments
Provides robust load balancing, circuit breaking, and fault injection capabilities

Cons of Istio

Steeper learning curve and more complex setup compared to Telepresence
Higher resource overhead due to its extensive feature set
May introduce additional latency in certain scenarios

Code Comparison

Istio (configuring a virtual service):

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: my-service
spec:
  hosts:
  - my-service
  http:
  - route:
    - destination:
        host: my-service
        subset: v1

Telepresence (intercepting traffic):

telepresence intercept my-service --port 8080:80

Istio provides a more declarative approach to traffic management, while Telepresence offers a simpler command-line interface for local development and debugging. Istio's configuration is more verbose but offers greater flexibility, whereas Telepresence focuses on ease of use for developers working on microservices locally.

Both tools serve different purposes: Istio is a full-fledged service mesh for production environments, while Telepresence is primarily designed for local development and testing of microservices in Kubernetes clusters.

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Pros of Linkerd2

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Lightweight and has minimal performance overhead compared to other service meshes

Cons of Linkerd2

Requires cluster-wide installation and modifications, which can be complex
May introduce additional latency for inter-service communication
Limited to Kubernetes environments, not suitable for local development

Code Comparison

Linkerd2 (installing the service mesh):

linkerd install | kubectl apply -f -
linkerd inject deployment.yaml | kubectl apply -f -

Telepresence (local development setup):

telepresence connect
telepresence intercept my-service --port 8080:80

While both projects aim to improve Kubernetes development and operations, they serve different purposes. Linkerd2 is a comprehensive service mesh for production environments, while Telepresence focuses on local development and debugging of Kubernetes applications.

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Pros of Traefik

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Supports automatic HTTPS with Let's Encrypt integration
Offers dynamic configuration and service discovery

Cons of Traefik

Steeper learning curve for complex configurations
May be overkill for simple development environments
Requires more resources to run compared to Telepresence

Code Comparison

Traefik configuration (YAML):

http:
  routers:
    my-router:
      rule: "Host(`example.com`)"
      service: my-service
  services:
    my-service:
      loadBalancer:
        servers:
          - url: "http://localhost:8080"

Telepresence configuration (CLI):

telepresence connect
telepresence intercept my-service --port 8080:80

While Traefik is a powerful reverse proxy and load balancer, Telepresence focuses on local development and testing of Kubernetes services. Traefik excels in production environments, offering advanced routing and load balancing features. Telepresence, on the other hand, simplifies the development workflow by allowing developers to run services locally while connected to a remote Kubernetes cluster.

envoy

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Cloud-native high-performance edge/middle/service proxy

Pros of Envoy

More versatile and can be used as a general-purpose proxy for various protocols
Highly performant and scalable, suitable for large-scale deployments
Extensive feature set for traffic management, observability, and security

Cons of Envoy

Steeper learning curve due to its complexity and wide range of features
Requires more configuration and setup compared to Telepresence
May be overkill for simple development scenarios

Code Comparison

Telepresence configuration example:

intercepts:
- name: example
  service: example-svc
  port: 8080

Envoy configuration example:

static_resources:
  listeners:
  - address:
      socket_address:
        address: 0.0.0.0
        port_value: 8080

While Telepresence focuses on local development and testing, Envoy is a full-featured proxy designed for production environments. Telepresence offers a simpler setup for developers working on microservices, whereas Envoy provides more advanced networking capabilities but requires more extensive configuration.

minikube

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Pros of Minikube

Provides a full-fledged local Kubernetes environment
Supports multiple hypervisors and container runtimes
Offers a more realistic Kubernetes experience for testing and development

Cons of Minikube

Requires more system resources compared to Telepresence
Setup and configuration can be more complex
May not be as suitable for rapid development cycles

Code Comparison

Minikube startup:

minikube start --driver=docker
kubectl create deployment hello-minikube --image=k8s.gcr.io/echoserver:1.10
kubectl expose deployment hello-minikube --type=NodePort --port=8080

Telepresence usage:

telepresence connect
telepresence intercept hello-world --port 8080:http

Key Differences

Minikube creates a full local Kubernetes cluster, while Telepresence connects to an existing cluster
Telepresence focuses on local development and debugging, whereas Minikube is more suited for testing and simulating production environments
Minikube requires more setup and resources, but provides a more comprehensive Kubernetes experience
Telepresence offers faster development cycles and easier integration with local development environments

Both tools serve different purposes in the Kubernetes ecosystem, with Minikube being more suitable for full cluster simulation and Telepresence excelling in rapid development and debugging scenarios.

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README

Telepresence: fast, efficient local development for Kubernetes microservices

Telepresence gives developers infinite scale development environments for Kubernetes.

Key benefits

With Telepresence:

You run your services locally, using your favorite IDE and other tools
Your workstation is connected to the cluster and can access to its services

This gives developers:

A fast local dev loop, with no waiting for a container build / push / deploy
Ability to use their favorite local tools (IDE, debugger, etc.)
Ability to run large-scale applications that can't run locally

Quick Start

A few quick ways to start using Telepresence:

Telepresence Quick Start: Quick Start
Install Telepresence: Install
Contributor's Guide: Guide
Meetings: Check out our community meeting schedule for opportunities to interact with Telepresence developers

Walkthrough

Install an interceptable service:

Start with an empty cluster:

$ kubectl create deploy hello --image=registry.k8s.io/echoserver:1.4
deployment.apps/hello created
$ kubectl expose deploy hello --port 80 --target-port 8080
service/hello exposed
$ kubectl get ns,svc,deploy,po
NAME                        STATUS   AGE
namespace/kube-system       Active   53m
namespace/default           Active   53m
namespace/kube-public       Active   53m
namespace/kube-node-lease   Active   53m

NAME                 TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)   AGE
service/kubernetes   ClusterIP   10.43.0.1      <none>        443/TCP   53m
service/hello        ClusterIP   10.43.73.112   <none>        80/TCP    2m

NAME                    READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/hello   1/1     1            1           2m

NAME                        READY   STATUS    RESTARTS   AGE
pod/hello-9954f98bf-6p2k9   1/1     Running   0          2m15s

Check telepresence version

$ telepresence version
OSS Client : v2.17.0
Root Daemon: not running
User Daemon: not running

Setup Traffic Manager in the cluster

Install Traffic Manager in your cluster. By default, it will reside in the ambassador namespace:

$ telepresence helm install

Traffic Manager installed successfully

Establish a connection to the cluster (outbound traffic)

Let telepresence connect:

$ telepresence connect
Launching Telepresence Root Daemon
Launching Telepresence User Daemon
Connected to context default, namespace default (https://35.232.104.64)

A session is now active and outbound connections will be routed to the cluster. I.e. your laptop is logically "inside" a namespace in the cluster.

Since telepresence connected to the default namespace, all services in that namespace can now be reached directly by their name. You can of course also use namespaced names, e.g. curl hello.default.

$ curl hello
CLIENT VALUES:
client_address=10.244.0.87
command=GET
real path=/
query=nil
request_version=1.1
request_uri=http://hello:8080/

SERVER VALUES:
server_version=nginx: 1.10.0 - lua: 10001

HEADERS RECEIVED:
accept=*/*
host=hello
user-agent=curl/8.0.1
BODY:
-no body in request-

Intercept the service. I.e. redirect traffic to it to our laptop (inbound traffic)

Add an intercept for the hello deployment on port 9000. Here, we also start a service listening on that port:

$ telepresence intercept hello --port 9000 -- python3 -m http.server 9000
Using Deployment hello
intercepted
    Intercept name         : hello
    State                  : ACTIVE
    Workload kind          : Deployment
    Destination            : 127.0.0.1:9000
    Service Port Identifier: 80
    Volume Mount Point     : /tmp/telfs-524630891
    Intercepting           : all TCP connections
Serving HTTP on 0.0.0.0 port 9000 (http://0.0.0.0:9000/) ...

The python -m httpserver is now started on port 9000 and will run until terminated by <ctrl>-C. Access it from a browser using http://hello/ or use curl from another terminal. With curl, it presents a html listing from the directory where the server was started. Something like:

$ curl hello
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd">
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
<title>Directory listing for /</title>
</head>
<body>
<h1>Directory listing for /</h1>
<hr>
<ul>
<li><a href="file1.txt">file1.txt</a></li>
<li><a href="file2.txt">file2.txt</a></li>
</ul>
<hr>
</body>
</html>

Observe that the python service reports that it's being accessed:

127.0.0.1 - - [16/Jun/2022 11:39:20] "GET / HTTP/1.1" 200 -

Clean-up and close daemon processes

End the service with <ctrl>-C and then try curl hello or http://hello again. The intercept is gone, and the echo service responds as normal.

Now end the session too. Your desktop no longer has access to the cluster internals.

$ telepresence quit
Disconnected
$ curl hello
curl: (6) Could not resolve host: hello

The telepresence daemons are still running in the background, which is harmless. You'll need to stop them before you upgrade telepresence. That's done by passing the option -s (stop all local telepresence daemons) to the quit command.

$ telepresence quit -s
Telepresence Daemons quitting...done

What got installed in the cluster?

Telepresence installs the Traffic Manager in your cluster if it is not already present. This deployment remains unless you uninstall it.

Telepresence injects the Traffic Agent as an additional container into the pods of the workload you intercept, and will optionally install an init-container to route traffic through the agent (the init-container is only injected when the service is headless or uses a numerical targetPort). The modifications persist unless you uninstall them.

At first glance, we can see that the deployment is installed ...

$ kubectl get svc,deploy,pod
service/kubernetes   ClusterIP   10.43.0.1       <none>        443/TCP                      7d22h
service/hello        ClusterIP   10.43.145.57    <none>        80/TCP                       13m

NAME                    READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/hello   1/1     1            1           13m

NAME                         READY   STATUS    RESTARTS        AGE
pod/hello-774455b6f5-6x6vs   2/2     Running   0               10m

... and that the traffic-manager is installed in the "ambassador" namespace.

$ kubectl -n ambassador get svc,deploy,pod
NAME                      TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)    AGE
service/traffic-manager   ClusterIP   None           <none>        8081/TCP   17m
service/agent-injector    ClusterIP   10.43.72.154   <none>        443/TCP    17m

NAME                              READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/traffic-manager   1/1     1            1           17m

NAME                                  READY   STATUS    RESTARTS   AGE
pod/traffic-manager-dcd4cc64f-6v5bp   1/1     Running   0          17m

The traffic-agent is installed too, in the hello pod. Here together with an init-container, because the service is using a numerical targetPort.

$ kubectl describe pod hello-774455b6f5-6x6vs
Name:             hello-75b7c6d484-9r4xd
Namespace:        default
Priority:         0
Service Account:  default
Node:             kind-control-plane/192.168.96.2
Start Time:       Sun, 07 Jan 2024 01:01:33 +0100
Labels:           app=hello
                  pod-template-hash=75b7c6d484
                  telepresence.io/workloadEnabled=true
                  telepresence.io/workloadName=hello
Annotations:      telepresence.io/inject-traffic-agent: enabled
                  telepresence.io/restartedAt: 2024-01-07T00:01:33Z
Status:           Running
IP:               10.244.0.89
IPs:
  IP:           10.244.0.89
Controlled By:  ReplicaSet/hello-75b7c6d484
Init Containers:
  tel-agent-init:
    Container ID:  containerd://4acdf45992980e2796f0eb79fb41afb1a57808d108eb14a355cb390ccc764571
    Image:         docker.io/datawire/tel2:2.17.0
    Image ID:      docker.io/datawire/tel2@sha256:e18aed6e7bd3c15cb5a99161c164e0303d20156af68ef138faca98dc2c5754a7
    Port:          <none>
    Host Port:     <none>
    Args:
      agent-init
    State:          Terminated
      Reason:       Completed
      Exit Code:    0
      Started:      Sun, 07 Jan 2024 01:01:34 +0100
      Finished:     Sun, 07 Jan 2024 01:01:34 +0100
    Ready:          True
    Restart Count:  0
    Environment:    <none>
    Mounts:
      /etc/traffic-agent from traffic-config (rw)
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-svf4h (ro)
Containers:
  echoserver:
    Container ID:   containerd://577e140545f3106c90078e687e0db3661db815062084bb0c9f6b2d0b4f949308
    Image:          registry.k8s.io/echoserver:1.4
    Image ID:       sha256:523cad1a4df732d41406c9de49f932cd60d56ffd50619158a2977fd1066028f9
    Port:           <none>
    Host Port:      <none>
    State:          Running
      Started:      Sun, 07 Jan 2024 01:01:34 +0100
    Ready:          True
    Restart Count:  0
    Environment:    <none>
    Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-svf4h (ro)
  traffic-agent:
    Container ID:  containerd://17558b4711903f4cb580c5afafa169d314a7deaf33faa749f59d3a2f8eed80a9
    Image:         docker.io/datawire/tel2:2.17.0
    Image ID:      docker.io/datawire/tel2@sha256:e18aed6e7bd3c15cb5a99161c164e0303d20156af68ef138faca98dc2c5754a7
    Port:          9900/TCP
    Host Port:     0/TCP
    Args:
      agent
    State:          Running
      Started:      Sun, 07 Jan 2024 01:01:34 +0100
    Ready:          True
    Restart Count:  0
    Readiness:      exec [/bin/stat /tmp/agent/ready] delay=0s timeout=1s period=10s #success=1 #failure=3
    Environment:
      _TEL_AGENT_POD_IP:       (v1:status.podIP)
      _TEL_AGENT_NAME:        hello-75b7c6d484-9r4xd (v1:metadata.name)
      A_TELEPRESENCE_MOUNTS:  /var/run/secrets/kubernetes.io/serviceaccount
    Mounts:
      /etc/traffic-agent from traffic-config (rw)
      /tel_app_exports from export-volume (rw)
      /tel_app_mounts/echoserver/var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-svf4h (ro)
      /tel_pod_info from traffic-annotations (rw)
      /tmp from tel-agent-tmp (rw)
      /var/run/secrets/kubernetes.io/serviceaccount from kube-api-access-svf4h (ro)
Conditions:
  Type              Status
  Initialized       True
  Ready             True
  ContainersReady   True
  PodScheduled      True
Volumes:
  kube-api-access-svf4h:
    Type:                    Projected (a volume that contains injected data from multiple sources)
    TokenExpirationSeconds:  3607
    ConfigMapName:           kube-root-ca.crt
    ConfigMapOptional:       <nil>
    DownwardAPI:             true
  traffic-annotations:
    Type:  DownwardAPI (a volume populated by information about the pod)
    Items:
      metadata.annotations -> annotations
  traffic-config:
    Type:      ConfigMap (a volume populated by a ConfigMap)
    Name:      telepresence-agents
    Optional:  false
  export-volume:
    Type:       EmptyDir (a temporary directory that shares a pod's lifetime)
    Medium:
    SizeLimit:  <unset>
  tel-agent-tmp:
    Type:        EmptyDir (a temporary directory that shares a pod's lifetime)
    Medium:
    SizeLimit:   <unset>
QoS Class:       BestEffort
Node-Selectors:  <none>
Tolerations:     node.kubernetes.io/not-ready:NoExecute op=Exists for 300s
                 node.kubernetes.io/unreachable:NoExecute op=Exists for 300s
Events:
  Type    Reason     Age    From               Message
  ----    ------     ----   ----               -------
  Normal  Scheduled  7m40s  default-scheduler  Successfully assigned default/hello-75b7c6d484-9r4xd to kind-control-plane
  Normal  Pulled     7m40s  kubelet            Container image "docker.io/datawire/tel2:2.17.0" already present on machine
  Normal  Created    7m40s  kubelet            Created container tel-agent-init
  Normal  Started    7m39s  kubelet            Started container tel-agent-init
  Normal  Pulled     7m39s  kubelet            Container image "registry.k8s.io/echoserver:1.4" already present on machine
  Normal  Created    7m39s  kubelet            Created container echoserver
  Normal  Started    7m39s  kubelet            Started container echoserver
  Normal  Pulled     7m39s  kubelet            Container image "docker.io/datawire/tel2:2.17.0" already present on machine
  Normal  Created    7m39s  kubelet            Created container traffic-agent
  Normal  Started    7m39s  kubelet            Started container traffic-agent

Telepresence keeps track of all possible intercepts for containers that have an agent installed in the configmap telepresence-agents.

$ kubectl describe configmap telepresence-agents
Name:         telepresence-agents
Namespace:    default
Labels:       app.kubernetes.io/created-by=traffic-manager
              app.kubernetes.io/name=telepresence-agents
              app.kubernetes.io/version=2.17.0
Annotations:  <none>

Data
====
hello:
----
agentImage: localhost:5000/tel2:2.17.0
agentName: hello
containers:
- Mounts: null
  envPrefix: A_
  intercepts:
  - agentPort: 9900
    containerPort: 8080
    protocol: TCP
    serviceName: hello
    servicePort: 80
    serviceUID: 68a4ecd7-0a12-44e2-9293-dc16fb205621
    targetPortNumeric: true
  mountPoint: /tel_app_mounts/echoserver
  name: echoserver
logLevel: debug
managerHost: traffic-manager.ambassador
managerPort: 8081
namespace: default
pullPolicy: IfNotPresent
workloadKind: Deployment
workloadName: hello


BinaryData
====

Events:  <none>

Uninstalling

You can uninstall the traffic-agent from specific deployments or from all deployments. Or you can choose to uninstall everything in which case the traffic-manager and all traffic-agents will be uninstalled.

$ telepresence helm uninstall

will remove everything that was automatically installed by telepresence from the cluster.

$ telepresence uninstall hello

will remove the traffic-agent and the configmap entry.

Troubleshooting

The telepresence background processes daemon and connector both produces log files that can be very helpful when problems are encountered. The files are named daemon.log and connector.log. The location of the logs differ depending on what platform that is used:

macOS ~/Library/Logs/telepresence
Linux ~/.cache/telepresence/logs
Windows "%USERPROFILE%\AppData\Local\logs"

How it works

When Telepresence 2 connects to a Kubernetes cluster, it

Ensures Traffic Manager is installed in the cluster.
Looks for the relevant subnets in the kubernetes cluster.
Creates a Virtual Network Interface (VIF).
Assigns the cluster's subnets to the VIF.
Binds itself to VIF and starts routing traffic to the traffic-manager, or a traffic-agent if one is present.
Starts listening for, and serving DNS requests, by passing a selected portion to the traffic-manager or traffic-agent.

When a locally running application makes a network request to a service in the cluster, Telepresence will resolve the name to an address within the cluster. The operating system then sees that the TUN device has an address in the same subnet as the address of the outgoing packets and sends them to tel0. Telepresence is on the other side of tel0 and picks up the packets, injecting them into the cluster through a gRPC connection with Traffic Manager.

Troubleshooting

Visit the troubleshooting section in the Telepresence documentation for more advice: Troubleshooting

Or discuss with the community in the CNCF Slack in the #telepresence-oss channel.

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