Telepresence gives developers infinite scale development environments for Kubernetes. With Telepresence:
- You run one service locally, using your favorite IDE and other tools
- You run the rest of your application in the cloud, where there is unlimited memory and compute
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
Telepresence 2 is based on learnings from the original Telepresence architecture. Rewritten in Go, Telepresence 2 provides a simpler and more powerful user experience, improved performance, and better reliability than Telepresence 1. More details on Telepresence 2 are below.
Start with an empty cluster:
$ kubectl create deploy hello --image=k8s.gcr.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
Client v2.0.2
Let telepresence connect:
$ telepresence connect
Launching Telepresence Daemon v2.0.2 (api v3)
Connecting to traffic manager...
Connected to context 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 "inside" the cluster.
$ curl hello.default
CLIENT VALUES:
client_address=10.42.0.7
command=GET
real path=/
query=nil
request_version=1.1
request_uri=http://hello.default:8080/
SERVER VALUES:
server_version=nginx: 1.10.0 - lua: 10001
HEADERS RECEIVED:
accept=*/*
host=hello.default
user-agent=curl/7.71.1
BODY:
-no body in request-
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
State : ACTIVE
Destination : 127.0.0.1:9000
Intercepting: all connections
Serving HTTP on :: port 9000 (http://[::]: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:
::ffff:127.0.0.1 - - [17/Feb/2021 13:14:20] "GET / HTTP/1.1" 200 -
::ffff:127.0.0.1 - - [17/Feb/2021 13:16:54] "GET / HTTP/1.1" 200 -
Since telepresence is now intercepting services in the default namespace, all services in that namespace can now be reached directly by their name. You can of course still use the namespaced name too, e.g. curl hello.default
.
End the service with <ctrl>-C
and then try curl hello.default
or http://hello.default
again. The intercept is gone, and the echo service responds as normal. Using just curl hello
will no longer succeed. This is because telepresence stopped mapping the default namespace when there were no more intercepts using it.
Now end the session too. Your desktop no longer has access to the cluster internals.
$ telepresence quit
Telepresence Daemon quitting...done
$ curl hello.default
curl: (6) Could not resolve host: hello.default
There is no need to start a telepresence subshell when doing an intercept. Telepresence will automatically detect that a session is active, and if not, start one. The session then ends when the command exits, as shown in this example:
telepresence intercept hello --port 9000 -- python3 -m http.server 9000
Launching Telepresence Daemon v2.0.1-64-g814052e (api v3)
Connecting to traffic manager...
Connected to context default (https://35.202.114.63)
Using deployment hello
intercepted
State : ACTIVE
Destination : 127.0.0.1:9000
Intercepting: all connections
Serving HTTP on :: port 9000 (http://[::]:9000/) ...
::ffff:127.0.0.1 - - [17/Feb/2021 14:05:37] "GET / HTTP/1.1" 200 -
^C
Keyboard interrupt received, exiting.
Disconnecting...done
Telepresence Daemon quitting...done
At first glance, we can see that the deployment is installed ...
kubectl get svc,deploy,pod
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
service/kubernetes ClusterIP 10.43.0.1 <none> 443/TCP 25m
service/hello ClusterIP 10.43.73.112 <none> 80/TCP 23m
NAME READY UP-TO-DATE AVAILABLE AGE
deployment.apps/hello 1/1 1 1 23m
NAME READY STATUS RESTARTS AGE
pod/hello-75c8ffd99-dklkl 2/2 Running 0 15m
... 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> 8022/TCP,8081/TCP 23m
NAME READY UP-TO-DATE AVAILABLE AGE
deployment.apps/traffic-manager 1/1 1 1 23m
NAME READY STATUS RESTARTS AGE
pod/traffic-manager-596b6cdf68-sclsx 1/1 Running 0 20m
The traffic-agent is installed too, in the hello pod.
kubectl describe pod hello-75c8ffd99-dklkl
Name: hello-75c8ffd99-dklkl
Namespace: default
Priority: 0
Node: bobtester/10.88.24.2
Start Time: Wed, 17 Feb 2021 13:13:03 +0100
Labels: app=hello
pod-template-hash=75c8ffd99
Annotations: <none>
Status: Running
IP: 10.42.0.8
IPs:
IP: 10.42.0.8
Controlled By: ReplicaSet/hello-75c8ffd99
Containers:
echoserver:
Container ID: containerd://270098cea9f15fc8974603bde47fde7d36022524967d7b40a81f18324c657686
Image: k8s.gcr.io/echoserver:1.4
Image ID: sha256:523cad1a4df732d41406c9de49f932cd60d56ffd50619158a2977fd1066028f9
Port: <none>
Host Port: <none>
State: Running
Started: Wed, 17 Feb 2021 13:13:03 +0100
Ready: True
Restart Count: 0
Environment: <none>
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from default-token-zkwqq (ro)
traffic-agent:
Container ID: containerd://b0253a0e3ecc3d03991d7e92c0ab92123fc60245cc0277a7a07185933690fc4a
Image: docker.io/datawire/tel2:2.0.2
Image ID: docker.io/datawire/tel2@sha256:9002068a5dc224c029754c80e1b4616139a8f4aca5608942f75488debbe387cf
Port: 9900/TCP
Host Port: 0/TCP
Args:
agent
State: Running
Started: Wed, 17 Feb 2021 13:13:04 +0100
Ready: True
Restart Count: 0
Environment:
TELEPRESENCE_CONTAINER: echoserver
LOG_LEVEL: debug
AGENT_NAME: hello
AGENT_POD_NAME: hello-75c8ffd99-dklkl (v1:metadata.name)
AGENT_NAMESPACE: default (v1:metadata.namespace)
APP_PORT: 8080
Mounts:
/var/run/secrets/kubernetes.io/serviceaccount from default-token-zkwqq (ro)
Conditions:
Type Status
Initialized True
Ready True
ContainersReady True
PodScheduled True
Volumes:
default-token-zkwqq:
Type: Secret (a volume populated by a Secret)
SecretName: default-token-zkwqq
Optional: false
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 19m default-scheduler Successfully assigned default/hello-75c8ffd99-dklkl to bobtester
Normal Pulled 19m kubelet Container image "k8s.gcr.io/echoserver:1.4" already present on machine
Normal Created 19m kubelet Created container echoserver
Normal Started 19m kubelet Started container echoserver
Normal Pulled 19m kubelet Container image "docker.io/datawire/tel2:2.0.2" already present on machine
Normal Created 19m kubelet Created container traffic-agent
Normal Started 19m kubelet Started container traffic-agent
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 uninstall --everything
will remove everything that was automatically installed by telepresence from the cluster.
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
Telepresence will launch your command, or a shell, when you start a session. When that program ends, the session ends and Telepresence cleans up.
- Outbound: You can
curl
a service running in the cluster while a session is running. - Inbound: You can intercept a deployment, causing all requests to that deployment to go to your laptop instead.
- Namespaces: You can intercept multiple deployments in different namespaces simultaneously.
- Environment variables: The environment variables of the intercepted pod can be captured in a file or propagated to a command.
- Filesystem forwarding for volume mounts: If the intercepted service has mounted volumes, those are made available as remote mounts on the desktop during an intercept.
- Also Proxy: If you have a resource that is external to the cluster that is needed for your intercept, you can create a Headless Service (including ExternalName) that points to your resource to access it from your local machine.
- Container method
Telepresence installs the Traffic Manager in your cluster if it is not already present. This deployment remains unless you uninstall it.
Telepresence installs the Traffic Agent as an additional container in any deployment you intercept, and modifies any associated services it finds to route traffic through the agent. This modification persists unless you uninstall it.
You can launch other Telepresence sessions to the same cluster while an existing session is running, letting you intercept other deployments.