+++
title = "Getting started"
docpage = true
[menu.docs]
parent = "getting-started"
+++
Conduit has two basic components: a *data plane* comprised of lightweight
proxies, which are deployed as sidecar containers alongside your service code,
and a *control plane* of processes that coordinate and manage these proxies.
Humans interact with the service mesh via a command-line interface (CLI) or
a web app that you use to control the cluster.
In this guide, we’ll walk you through how to deploy Conduit on your Kubernetes
cluster, and how to set up a sample gRPC application.
Afterwards, check out the [Using Conduit to debug a service](/debugging-an-app) page,
where we'll walk you through how to use Conduit to investigate poorly performing services.
> Note that Conduit v{{% latestversion %}} is an alpha release. Conduit will
automatically work for most protocols. However, applications that use
WebSockets, HTTP tunneling/proxying, or protocols such as MySQL and SMTP, will
require some additional configuration. See [Adding your service to the
mesh](/adding-your-service) for details.
____
##### STEP ONE
## Set up 🌟
First, you'll need a Kubernetes cluster running 1.8 or later, and a functioning
`kubectl` command on your local machine.
To run Kubernetes on your local machine, we suggest
Minikube
--- running version 0.24.1 or later.
### When ready, make sure you're running the latest version of Kubernetes with:
#### `kubectl version --short`
### Which should display:
```
Client Version: v1.8.3
Server Version: v1.8.0
```
Confirm that both `Client Version` and `Server Version` are v1.8.0 or greater.
If not, or if `kubectl` displays an error message, your Kubernetes cluster may
not exist or may not be set up correctly.
___
##### STEP TWO
## Install the CLI 💻
If this is your first time running
Conduit, you’ll need to download the command-line interface (CLI) onto your
local machine. You’ll then use this CLI to install Conduit on a Kubernetes
cluster.
### To install the CLI, run:
#### `curl https://run.conduit.io/install | sh`
### Which should display:
```
Downloading conduit-{{% latestversion %}}-macos...
Conduit was successfully installed 🎉
Copy $HOME/.conduit/bin/conduit into your PATH. Then run
conduit install | kubectl apply -f -
to deploy Conduit to Kubernetes. Once deployed, run
conduit dashboard
to view the Conduit UI.
Visit conduit.io for more information.
```
>Alternatively, you can download the CLI directly via the
[Conduit releases page](https://github.com/runconduit/conduit/releases/v{{% latestversion %}}).
### Next, add conduit to your path with:
#### `export PATH=$PATH:$HOME/.conduit/bin`
### Verify the CLI is installed and running correctly with:
#### `conduit version`
### Which should display:
```
Client version: v{{% latestversion %}}
Server version: unavailable
```
With `Server version: unavailable`, don't worry, we haven't added the control plane... yet.
___
##### STEP THREE
## Install Conduit onto the cluster 😎
Now that you have the CLI running locally, it’s time to install the Conduit control plane
onto your Kubernetes cluster. Don’t worry if you already have things running on this
cluster---the control plane will be installed in a separate `conduit` namespace, where
it can easily be removed.
### To install conduit into your environment, run the following commands.
If you are using GKE with RBAC enabled, you must grant a ClusterRole of cluster-admin
to your Google Cloud account first, in order to install certain telemetry features in the control plane.
Note that the $USER environment variable should be the username of your
Google Cloud account.
Then run:
conduit install | kubectl apply -f -
### Which should display:
```
namespace "conduit" created
serviceaccount "conduit-controller" created
clusterrole "conduit-controller" created
clusterrolebinding "conduit-controller" created
serviceaccount "conduit-prometheus" created
clusterrole "conduit-prometheus" created
clusterrolebinding "conduit-prometheus" created
service "api" created
service "proxy-api" created
deployment "controller" created
service "web" created
deployment "web" created
service "prometheus" created
deployment "prometheus" created
configmap "prometheus-config" created
```
### To verify the Conduit server version is v{{% latestversion %}}, run:
#### `conduit version`
### Which should display:
```
Client version: v{{% latestversion %}}
Server version: v{{% latestversion %}}
```
### Now, to view the control plane locally, run:
#### `conduit dashboard`
The first command generates a Kubernetes config, and pipes it to `kubectl`.
Kubectl then applies the config to your Kubernetes cluster.
If you see something like below, Conduit is now running on your cluster. 🎉
Of course, you haven’t actually added any services to the mesh yet,
so the dashboard won’t have much to display beyond the status of the service mesh itself.
___
##### STEP FOUR
## Install the demo app 🚀
Finally, it’s time to install a demo application and add it to the service mesh.
See a live version of the demo app
### To install a local version of this demo locally and add it to Conduit, run:
#### `curl https://raw.githubusercontent.com/runconduit/conduit-examples/master/emojivoto/emojivoto.yml | conduit inject - | kubectl apply -f -`
### Which should display:
```
namespace "emojivoto" created
deployment "emoji" created
service "emoji-svc" created
deployment "voting" created
service "voting-svc" created
deployment "web" created
service "web-svc" created
deployment "vote-bot" created
```
This command downloads the Kubernetes config for an example gRPC application
where users can vote for their favorite emoji, then runs the config through
`conduit inject`. This rewrites the config to insert the Conduit data plane
proxies as sidecar containers in the application pods.
Finally, `kubectl` applies the config to the Kubernetes cluster.
As with `conduit install`, in this command, the Conduit CLI is simply doing text
transformations, with `kubectl` doing the heavy lifting of actually applying
config to the Kubernetes cluster. This way, you can introduce additional filters
into the pipeline, or run the commands separately and inspect the output of each
one.
At this point, you should have an application running on your Kubernetes
cluster, and (unbeknownst to it!) also added to the Conduit service mesh.
___
##### STEP FIVE
## Watch it run! 👟
If you glance at the Conduit dashboard, you should see all the
HTTP/2 and HTTP/1-speaking services in the demo app show up in the list of
deployments that have been added to the Conduit mesh.
### View the demo app by visiting the web service's public IP:
Find the public IP by selecting your environment below.
kubectl get svc web-svc -n emojivoto -o jsonpath="{.status.loadBalancer.ingress[0].*}"
minikube -n emojivoto service web-svc --url
Finally, let’s take a look back at our dashboard (run `conduit dashboard` if you
haven’t already). You should be able to browse all the services that are running
as part of the application to view:
- Success rates
- Request rates
- Latency distribution percentiles
- Upstream and downstream dependencies
As well as various other bits of information about live traffic. Neat, huh?
### Views available in `conduit dashboard`:
### SERVICE MESH
Displays continuous health metrics of the control plane itself, as well as
high-level health metrics of deployments in the data plane.
### DEPLOYMENTS
Lists all deployments by requests, success rate, and latency.
___
## Using the CLI 💻
Of course, the dashboard isn’t the only way to inspect what’s
happening in the Conduit service mesh. The CLI provides several interesting and
powerful commands that you should experiment with, including `conduit stat` and `conduit tap`.
### To view details per deployment, run:
#### `conduit stat deployments`
### Which should display:
```
NAME REQUEST_RATE SUCCESS_RATE P50_LATENCY P99_LATENCY
emojivoto/emoji 2.0rps 100.00% 0ms 0ms
emojivoto/voting 0.6rps 66.67% 0ms 0ms
emojivoto/web 2.0rps 95.00% 0ms 0ms
```
### To see a live pipeline of requests for your application, run:
#### `conduit tap deploy emojivoto/voting`
### Which should display:
```
req id=0:127 src=172.17.0.11:50992 dst=172.17.0.10:8080 :method=POST :authority=voting-svc.emojivoto:8080 :path=/emojivoto.v1.VotingService/VoteManInTuxedo
rsp id=0:127 src=172.17.0.11:50992 dst=172.17.0.10:8080 :status=200 latency=588µs
end id=0:127 src=172.17.0.11:50992 dst=172.17.0.10:8080 grpc-status=OK duration=9µs response-length=5B
req id=0:128 src=172.17.0.11:50992 dst=172.17.0.10:8080 :method=POST :authority=voting-svc.emojivoto:8080 :path=/emojivoto.v1.VotingService/VotePager
rsp id=0:128 src=172.17.0.11:50992 dst=172.17.0.10:8080 :status=200 latency=601µs
end id=0:128 src=172.17.0.11:50992 dst=172.17.0.10:8080 grpc-status=OK duration=11µs response-length=5B
req id=0:129 src=172.17.0.11:50992 dst=172.17.0.10:8080 :method=POST :authority=voting-svc.emojivoto:8080 :path=/emojivoto.v1.VotingService/VotePoop
...
```
___
## That’s it! 👏
For more information about Conduit, check out the
[overview doc](/docs) and the [roadmap doc](/roadmap), or hop into the #conduit channel on [the
Linkerd Slack](https://slack.linkerd.io) or browse through the
[Conduit forum](https://discourse.linkerd.io/c/conduit). You can also follow
[@runconduit](https://twitter.com/runconduit) on Twitter.
We’re just getting started building Conduit, and we’re extremely interested in your feedback!