mirror of https://github.com/linkerd/linkerd2.git
325 lines
11 KiB
Markdown
Executable File
325 lines
11 KiB
Markdown
Executable File
+++
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title = "Getting started"
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docpage = true
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[menu.docs]
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parent = "getting-started"
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+++
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Conduit has two basic components: a *data plane* comprised of lightweight
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proxies, which are deployed as sidecar containers alongside your service code,
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and a *control plane* of processes that coordinate and manage these proxies.
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Humans interact with the service mesh via a command-line interface (CLI) or
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a web app that you use to control the cluster.
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In this guide, we’ll walk you through how to deploy Conduit on your Kubernetes
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cluster, and how to set up a sample gRPC application.
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Afterwards, check out the [Using Conduit to debug a service](/debugging-an-app) page,
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where we'll walk you through how to use Conduit to investigate poorly performing services.
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> Note that Conduit v{{% latestversion %}} is an alpha release. Conduit will
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automatically work for most protocols. However, applications that use
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WebSockets, HTTP tunneling/proxying, or protocols such as MySQL and SMTP, will
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require some additional configuration. See [Adding your service to the
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mesh](/adding-your-service) for details.
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____
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##### STEP ONE
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## Set up 🌟
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First, you'll need a Kubernetes cluster running 1.8 or later, and a functioning
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`kubectl` command on your local machine.
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To run Kubernetes on your local machine, we suggest
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<a href="https://kubernetes.io/docs/tasks/tools/install-minikube/" target="_blank">Minikube</a>
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--- running version 0.24.1 or later.
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### When ready, make sure you're running the latest version of Kubernetes with:
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#### `kubectl version --short`
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### Which should display:
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```
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Client Version: v1.8.3
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Server Version: v1.8.0
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```
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Confirm that both `Client Version` and `Server Version` are v1.8.0 or greater.
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If not, or if `kubectl` displays an error message, your Kubernetes cluster may
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not exist or may not be set up correctly.
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___
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##### STEP TWO
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## Install the CLI 💻
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If this is your first time running
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Conduit, you’ll need to download the command-line interface (CLI) onto your
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local machine. You’ll then use this CLI to install Conduit on a Kubernetes
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cluster.
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### To install the CLI, run:
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#### `curl https://run.conduit.io/install | sh`
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### Which should display:
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```
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Downloading conduit-{{% latestversion %}}-macos...
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Conduit was successfully installed 🎉
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Copy $HOME/.conduit/bin/conduit into your PATH. Then run
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conduit install | kubectl apply -f -
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to deploy Conduit to Kubernetes. Once deployed, run
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conduit dashboard
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to view the Conduit UI.
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Visit conduit.io for more information.
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```
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>Alternatively, you can download the CLI directly via the
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[Conduit releases page](https://github.com/runconduit/conduit/releases/v{{% latestversion %}}).
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### Next, add conduit to your path with:
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#### `export PATH=$PATH:$HOME/.conduit/bin`
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### Verify the CLI is installed and running correctly with:
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#### `conduit version`
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### Which should display:
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```
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Client version: v{{% latestversion %}}
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Server version: unavailable
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```
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With `Server version: unavailable`, don't worry, we haven't added the control plane... yet.
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___
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##### STEP THREE
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## Install Conduit onto the cluster 😎
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Now that you have the CLI running locally, it’s time to install the Conduit control plane
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onto your Kubernetes cluster. Don’t worry if you already have things running on this
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cluster---the control plane will be installed in a separate `conduit` namespace, where
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it can easily be removed.
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### To install conduit into your environment, run the following commands.
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<main>
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<input id="tab1" type="radio" name="tabs" checked>
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<label for="tab1">Standard</label>
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<input id="tab2" type="radio" name="tabs">
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<label for="tab2">GKE</label>
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<div class="first-tab">
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<h4 class="minikube">
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<code>conduit install | kubectl apply -f -</code>
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</h4>
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</div>
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<div class="second-tab">
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<p>First run:</p>
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<h4 class="kubernetes">
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<code>kubectl create clusterrolebinding cluster-admin-binding-$USER
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--clusterrole=cluster-admin --user=$(gcloud config get-value account)</code>
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</h4>
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<blockquote>
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If you are using GKE with RBAC enabled, you must grant a <code>ClusterRole</code> of <code>cluster-admin</code>
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to your Google Cloud account first, in order to install certain telemetry features in the control plane.
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</blockquote>
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<blockquote>
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Note that the <code>$USER</code> environment variable should be the username of your
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Google Cloud account.
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</blockquote>
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<p style="margin-top: 1rem;">Then run:</p>
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<h4>
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<code>conduit install | kubectl apply -f -</code>
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</h4>
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</div>
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</main>
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### Which should display:
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```
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namespace "conduit" created
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serviceaccount "conduit-controller" created
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clusterrole "conduit-controller" created
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clusterrolebinding "conduit-controller" created
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serviceaccount "conduit-prometheus" created
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clusterrole "conduit-prometheus" created
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clusterrolebinding "conduit-prometheus" created
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service "api" created
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service "proxy-api" created
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deployment "controller" created
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service "web" created
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deployment "web" created
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service "prometheus" created
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deployment "prometheus" created
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configmap "prometheus-config" created
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```
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### To verify the Conduit server version is v{{% latestversion %}}, run:
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#### `conduit version`
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### Which should display:
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```
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Client version: v{{% latestversion %}}
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Server version: v{{% latestversion %}}
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```
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### Now, to view the control plane locally, run:
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#### `conduit dashboard`
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The first command generates a Kubernetes config, and pipes it to `kubectl`.
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Kubectl then applies the config to your Kubernetes cluster.
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If you see something like below, Conduit is now running on your cluster. 🎉
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Of course, you haven’t actually added any services to the mesh yet,
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so the dashboard won’t have much to display beyond the status of the service mesh itself.
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___
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##### STEP FOUR
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## Install the demo app 🚀
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Finally, it’s time to install a demo application and add it to the service mesh.
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<a href="http://emoji.voto/" class="button" target="_blank">See a live version of the demo app</a>
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### To install a local version of this demo locally and add it to Conduit, run:
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#### `curl https://raw.githubusercontent.com/runconduit/conduit-examples/master/emojivoto/emojivoto.yml | conduit inject - | kubectl apply -f -`
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### Which should display:
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```
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namespace "emojivoto" created
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deployment "emoji" created
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service "emoji-svc" created
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deployment "voting" created
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service "voting-svc" created
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deployment "web" created
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service "web-svc" created
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deployment "vote-bot" created
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```
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This command downloads the Kubernetes config for an example gRPC application
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where users can vote for their favorite emoji, then runs the config through
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`conduit inject`. This rewrites the config to insert the Conduit data plane
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proxies as sidecar containers in the application pods.
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Finally, `kubectl` applies the config to the Kubernetes cluster.
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As with `conduit install`, in this command, the Conduit CLI is simply doing text
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transformations, with `kubectl` doing the heavy lifting of actually applying
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config to the Kubernetes cluster. This way, you can introduce additional filters
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into the pipeline, or run the commands separately and inspect the output of each
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one.
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At this point, you should have an application running on your Kubernetes
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cluster, and (unbeknownst to it!) also added to the Conduit service mesh.
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___
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##### STEP FIVE
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## Watch it run! 👟
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If you glance at the Conduit dashboard, you should see all the
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HTTP/2 and HTTP/1-speaking services in the demo app show up in the list of
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deployments that have been added to the Conduit mesh.
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### View the demo app by visiting the web service's public IP:
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<main>
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<p>Find the public IP by selecting your environment below.</p>
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<input id="tab3" type="radio" name="second-tabs" checked>
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<label for="tab3">Kubernetes</label>
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<input id="tab4" type="radio" name="second-tabs">
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<label for="tab4">Minikube</label>
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<div class="first-tab">
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<h4 class="kubernetes">
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<code>kubectl get svc web-svc -n emojivoto -o jsonpath="{.status.loadBalancer.ingress[0].*}"</code>
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</h4>
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</div>
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<div class="second-tab">
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<h4 class="minikube">
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<code>minikube -n emojivoto service web-svc --url</code>
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</h4>
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</div>
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</main>
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Finally, let’s take a look back at our dashboard (run `conduit dashboard` if you
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haven’t already). You should be able to browse all the services that are running
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as part of the application to view:
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- Success rates
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- Request rates
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- Latency distribution percentiles
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- Upstream and downstream dependencies
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As well as various other bits of information about live traffic. Neat, huh?
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### Views available in `conduit dashboard`:
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### SERVICE MESH
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Displays continuous health metrics of the control plane itself, as well as
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high-level health metrics of deployments in the data plane.
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### DEPLOYMENTS
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Lists all deployments by requests, success rate, and latency.
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### PODS
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Lists all pods by requests, success rate, and latency.
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### REPLICATION CONTROLLER
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Lists all replications controllers by requests, success rate, and latency.
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### GRAFANA
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For detailed metrics on all of the above resources, click any resource to browse
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to a dynamically-generated Grafana dashboard.
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___
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## Using the CLI 💻
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Of course, the dashboard isn’t the only way to inspect what’s
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happening in the Conduit service mesh. The CLI provides several interesting and
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powerful commands that you should experiment with, including `conduit stat` and `conduit tap`.
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### To view details per deployment, run:
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#### `conduit -n emojivoto stat deploy`
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### Which should display:
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```
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NAME MESHED SUCCESS RPS LATENCY_P50 LATENCY_P95 LATENCY_P99
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emoji 1/1 100.00% 2.0rps 1ms 2ms 3ms
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vote-bot 1/1 - - - - -
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voting 1/1 81.36% 1.0rps 1ms 1ms 2ms
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web 1/1 90.68% 2.0rps 4ms 5ms 5ms
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```
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### To see a live pipeline of requests for your application, run:
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#### `conduit -n emojivoto tap deploy`
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### Which should display:
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```
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req id=0:2900 src=10.1.8.151:51978 dst=10.1.8.150:80 :method=GET :authority=web-svc.emojivoto:80 :path=/api/list
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req id=0:2901 src=10.1.8.150:49246 dst=emoji-664486dccb-97kws :method=POST :authority=emoji-svc.emojivoto:8080 :path=/emojivoto.v1.EmojiService/ListAll
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rsp id=0:2901 src=10.1.8.150:49246 dst=emoji-664486dccb-97kws :status=200 latency=2146µs
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end id=0:2901 src=10.1.8.150:49246 dst=emoji-664486dccb-97kws grpc-status=OK duration=27µs response-length=2161B
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rsp id=0:2900 src=10.1.8.151:51978 dst=10.1.8.150:80 :status=200 latency=5698µs
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end id=0:2900 src=10.1.8.151:51978 dst=10.1.8.150:80 duration=112µs response-length=4558B
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req id=0:2902 src=10.1.8.151:51978 dst=10.1.8.150:80 :method=GET :authority=web-svc.emojivoto:80 :path=/api/vote
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...
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```
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___
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## That’s it! 👏
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For more information about Conduit, check out the
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[overview doc](/docs) and the [roadmap doc](/roadmap), or hop into the #conduit channel on [the
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Linkerd Slack](https://slack.linkerd.io) or browse through the
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[Conduit forum](https://discourse.linkerd.io/c/conduit). You can also follow
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[@runconduit](https://twitter.com/runconduit) on Twitter.
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We’re just getting started building Conduit, and we’re extremely interested in your feedback!
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