Deploy the Guestbook Application with the Tone Analyzer¶

In this section, you will re-create the guestbook application, but this time with more components in a multi-tier architecture. This application uses the v2 version of the go application we used previously in the workshop as our web front end, and adds on 1) a Redis master for storage, 2) a replicated set of Redis slaves, and 3) a Python Flask application that calls the Watson Tone Analyzer service deployed in IBM Cloud. For all of these components, there are Kubernetes replication controllers, pods, and services. One of the main concerns with building a multi-tier application on Kubernetes, such as this one, is resolving dependencies between all of these seperately deployed components.

In a multiple tier application, there are two primary ways that service dependencies can be resolved. The v2/guestbook/main.go code provides examples of each. For Redis, the master endpoint is discovered through environment variables. These environment variables are set when the Redis services are started, so the service resources need to be created before the guestbook replication controller starts the guestbook pods. For the analyzer service, an http request is made to a hostname, which allows for resource discovery at the time when the request is made. Consequently, we'll follow a specific order when creating the application components. First up, the Redis components will be created, then the guestbook application, and finally the analyzer microservice.

Setup¶

Continue by working in the web terminal. Change to the v2 folder where the deployment files reside:

cd $HOME/digidevcon-iks/v2

Create the Redis master pod¶

Use the redis-master-deployment.yaml file to create a replication controller and Redis master pod. The pod runs a Redis key-value server in a container. Using a replication controller is the preferred way to launch long-running pods, even for 1 replica, so that the pod inherits benefits from the self-healing mechanism in Kubernetes (i.e. keeps the pods alive).

Use the redis-master-deployment.yaml file to create the Redis master deployment in your Kubernetes cluster:

kubectl create -f redis-master-deployment.yaml

$ kubectl create -f redis-master-deployment.yaml
deployment.apps/redis-master created

To verify that the redis-master controller is up, list the deployment and replicaset you created in the cluster with the kubectl get command (if you don't specify a --namespace, the current project/namespace will be used):
```
kubectl get deploy
```
this will show the current deployments in the namespace
```
$ kubectl get deploy
NAME           READY   UP-TO-DATE   AVAILABLE   AGE
redis-master   1/1     1            1           15s
```
```
kubectl get replicaset
```
this will show the current deployments in the namespace
```
$ kubectl get replicaset
NAME                      DESIRED   CURRENT   READY   AGE
redis-master-7b7968db76   1         1         1       41s
```
Result: The deployment creates the replicaset, which then creates the single Redis master pod.

Verify that the redis-master pod is running, by listing the pods you created in cluster:

kubectl get pods

$ kubectl get pods
NAME                            READY   STATUS    RESTARTS   AGE
redis-master-7b7968db76-8mjqg   1/1     Running   0          67s

Result: You'll see a single Redis master pod (may take up to thirty seconds).

Create the Redis master service¶

A Kubernetes service is a named load balancer that proxies traffic to one or more pods. The services in a Kubernetes cluster are discoverable inside other pods via environment variables or DNS.

Services find the pods to load balance based on pod labels. The pod that you created in previous step has the label app=redis and role=master. The selector field of the service determines which pods will receive the traffic sent to the service.

Use the redis-master-service.yaml file to create the service in your Kubernetes cluster:

kubectl create -f redis-master-service.yaml

$ kubectl create -f redis-master-service.yaml
service/redis-master created

To verify that the redis-master service is up, list the services you created in the cluster:
```
kubectl get services
```
```
$ kubectl get services
NAME           TYPE        CLUSTER-IP    EXTERNAL-IP   PORT(S)    AGE
kubernetes     ClusterIP   172.21.0.1    <none>        443/TCP    8h
redis-master   ClusterIP   172.21.36.3   <none>        6379/TCP   10s
...
```
Result: All new pods will see the redis-master service running on the host ($REDIS_MASTER_SERVICE_HOST environment variable) at port 6379, or running on redis-master:6379. After the service is created, the service proxy on each node is configured to set up a proxy on the specified port (in our example, that's port 6379).

Create the Redis slave pods¶

The Redis master we created earlier is a single pod (REPLICAS = 1), while the Redis read slaves we are creating here are 'replicated' pods with 2 instances that will be started. In Kubernetes, a replication controller is responsible for managing the multiple instances of a replicated pod.

Use the file redis-slave-deployment.yaml to create the replication controller:

kubectl create -f redis-slave-deployment.yaml

$ kubectl create -f redis-slave-deployment.yaml
deployment.apps/redis-slave created

To verify that the redis-slave controller is running:
```
kubectl get rs
```
```
$ kubectl get rs
NAME                      DESIRED   CURRENT   READY   AGE
redis-master-7b7968db76   1         1         1       2m48s
redis-slave-6944587c87    2         2         2       36s
```
Result: The deployment creates the replicaset, which then creates configures the Redis slave pods through the redis-master service (name:port pair, in our example that's redis-master:6379).

Verify that the Redis master and slaves pods are running:

kubectl get pods

$ kubectl get pods
NAME                            READY   STATUS    RESTARTS   AGE
redis-master-7b7968db76-8mjqg   1/1     Running   0          3m25s
redis-slave-6944587c87-4gvgj    1/1     Running   0          73s
redis-slave-6944587c87-h66wp    1/1     Running   0          73s
...

Result: You see the single Redis master and two Redis slave pods.

Create the Redis slave service¶

Just like the master, we want to have a service to proxy connections to the read slaves. In this case, in addition to discovery, the Redis slave service provides transparent load balancing to clients.

Use the redis-slave-service.yaml file to create the Redis slave service:

kubectl create -f redis-slave-service.yaml

$ kubectl create -f redis-slave-service.yaml
service/redis-slave created

To verify that the redis-slave service is up, list the services you created:

kubectl get services

$ kubectl get services
NAME           TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)    AGE
kubernetes     ClusterIP   172.21.0.1      <none>        443/TCP    8h
redis-master   ClusterIP   172.21.36.3     <none>        6379/TCP   2m32s
redis-slave    ClusterIP   172.21.251.35   <none>        6379/TCP   7s

Result: The service is created and accessible at redis-slave:6379 by pods running in the project

Create the guestbook pods¶

This is a simple Go net/http (negroni based) server that is configured to talk to either the slave or master services depending on whether the request is a read or a write. The pods we are creating expose a simple JSON interface and serves a jQuery-Ajax based UI. Like the Redis read slaves, these pods are also managed by a replication controller.

Use the guestbook-deployment.yaml file to create the guestbook replication controller:
```
kubectl create -f guestbook-deployment.yaml
```
```
$ kubectl create -f guestbook-deployment.yaml
deployment.apps/guestbook-v2 created
```
Tip: If you want to modify the guestbook code it can be found in the guestbook directory, along with its Makefile. If you have pushed your custom image be sure to update the image property accordingly in the guestbook-deployment.yaml.

Verify that the guestbook deployment is running:

kubectl get deploy

$ kubectl get deploy
NAME           READY   UP-TO-DATE   AVAILABLE   AGE
guestbook-v2   3/3     3            3           24s
redis-master   1/1     1            1           5m37s
redis-slave    2/2     2            2           3m58s

Verify that the guestbook pods are running (it might take up to thirty seconds to create the pods):

kubectl get pods

$ kubectl get pods
NAME                            READY   STATUS    RESTARTS   AGE
guestbook-v2-75fbf9c4d-c2m6s    1/1     Running   0          25s
guestbook-v2-75fbf9c4d-nx2f2    1/1     Running   0          25s
guestbook-v2-75fbf9c4d-p2x97    1/1     Running   0          25s
redis-master-7b7968db76-8mjqg   1/1     Running   0          5m10s
redis-slave-6944587c87-4gvgj    1/1     Running   0          2m58s
redis-slave-6944587c87-h66wp    1/1     Running   0          2m58s

Result: You see a single Redis master, two Redis slaves, and three guestbook pods.

Create and expose the guestbook service¶

Just like the others, we create a service to group the guestbook pods. Since guestbook uses a web application protocol we will expose it for access outside the cluster using a service of type `NodePort.

Use the guestbook-service.yaml file to create the guestbook service:
```
kubectl create -f guestbook-service.yaml
```

Verify that the guestbook service is up by listing the services in the cluster:

kubectl get services

$ kubectl get services
NAME           TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
guestbook      NodePort    172.21.128.59   <none>        3000:30796/TCP   35s
kubernetes     ClusterIP   172.21.0.1      <none>        443/TCP          8h
redis-master   ClusterIP   172.21.36.3     <none>        6379/TCP         13m
redis-slave    ClusterIP   172.21.251.35   <none>        6379/TCP         11m

Result: The service is created, and exposed as a NodePort and in this example is listening on 30796.

Create the analyzer pod¶

This is a simple Python Flask application that creates a POST endpoint /tone and takes the input text and sends it to the Watson Tone Analyzer service. In the analyzer-deployment.yaml the spec for the pod defines environment variables for the service credentials by reading the secret binding-tone created by the IBM Cloud operator.

Use the analyzer-deployment.yaml file to create the analyzer replication controller:

kubectl create -f analyzer-deployment.yaml

$ kubectl create -f analyzer-deployment.yaml
deployment.apps/analyzer created

Tip: If you want to modify the analyzer code it can be found in the analyzer directory, along with its Makefile. If you have pushed your custom image be sure to update the image property accordingly in the analyzer-deployment.yaml.

Verify that the guestbook deployment is running:

kubectl get deploy

$ kubectl get deploy
NAME           READY   UP-TO-DATE   AVAILABLE   AGE
analyzer       0/1     1            0           16s
guestbook-v2   3/3     3            3           14m
redis-master   1/1     1            1           19m
redis-slave    2/2     2            2           17m

Create the analyzer service¶

Create a service so that the guestbook application can call the analyzer pod

Use the analyzer-service.yaml file to create the analyzer service:
```
kubectl create -f analyzer-service.yaml
```

To verify that the analyzer service is up, list the services created in the cluster:

kubectl get services

$ kubectl get services
NAME           TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
analyzer       ClusterIP   172.21.125.6    <none>        80/TCP           38s
guestbook      NodePort    172.21.128.59   <none>        3000:30796/TCP   5m54s
kubernetes     ClusterIP   172.21.0.1      <none>        443/TCP          8h
redis-master   ClusterIP   172.21.36.3     <none>        6379/TCP         19m
redis-slave    ClusterIP   172.21.251.35   <none>        6379/TCP         16m

Result: The service is created

View the guestbook¶

You can now play with the guestbook that you just created by opening it in a browser, use the IP and NodePort for your deployment. Find the IP address for your cluster using this command (use the Public IP):

ibmcloud ks workers -c mycluster

$ ibmcloud ks workers -c mycluster
ID                                                     Public IP         Private IP      Flavor   State    Status   Zone    Version
kube-bmotc1dd0i2tk1jloing-mycluster-default-0000008d   184.172.252.167   10.76.195.211   free     normal   Ready    hou02   1.14.7_1535

In this example the IP is: 184.172.252.167

Get the nodeport (it will be different from the first exercise):

kubectl get service guestbook

$ kubectl get service guestbook
NAME        TYPE       CLUSTER-IP     EXTERNAL-IP   PORT(S)          AGE
guestbook   NodePort   172.21.64.67   <none>        3000:30796/TCP   9m16s

For this value of IP address and NodePort, you would use a url like http://184.172.252.167:30796 to access the guestbook.

Result: The guestbook displays in your browser:

Guestbook

Cleanup¶

After you're done playing with the guestbook, you can cleanup by deleting the guestbook service and removing the associated resources that were created, including routes, forwarding rules, target pools, and Kubernetes replication controllers and services.

Delete all the resources sourced by the files in the v2 directory:

kubectl delete -f .

$ kubectl delete -f .
deployment.apps "analyzer" deleted
service "analyzer" deleted
deployment.apps "guestbook-v2" deleted
service "guestbook" deleted
deployment.apps "redis-master" deleted
service "redis-master" deleted
deployment.apps "redis-slave" deleted
service "redis-slave" deleted