Getting Started with DataPower and Kubernetes
This tutorial provides a walkthrough on the basics of getting up and running with DataPower in Kubernetes. Kubernetes is a popular orchestrator for Docker containers.
This tutorial uses the Google Container Engine to provide a hosted Kubernetes environment. Naturally, you can use any Kubernetes environment of your choosing. As such, I will mark the sections pertaining to the Google Container Engine (GKE) with an ‚Äú(Optional)‚ÄĚ designation. Below is a list of the tools you will be using in this tutorial:
- We use Docker to package, distribute and run applications
- IBM DataPower Gateway for Docker
- Handle the heavy lifting of orchestrating our application
- Google Container Engine (GKE) (Optional)
- A hosted Kubernetes service from the Google Cloud Platform
- Google Cloud Shell (Optional)
- A shell environment for managing Google Cloud Platform resources
Setting up Kubernetes in GKE
This section is optional if you already have Kubernetes. Otherwise, you can follow along with this section to set up Kubernetes.
First you will need a Google Cloud Platform (GCP) account. You can use an existing account or sign up for a free trial here.
Next you will need a GCP project. You may use the default project, an existing project, or create a new one as described here.
You will also need to enable some Google Cloud APIs from the Google API Console:
- Compute Engine API – To allow external network access to the application
- Container Engine API – To provide Kubernetes
Here is a screenshot of where you can enable those APIs.
In addition, you will use the Google Cloud Shell to manage your Google Cloud Platform resources. You can find a getting started for the Google Cloud Shell here. Once ready, head over to your project page in the Google Cloud Platform web console and start a Google Cloud Shell session.
To avoid having to specify the ‚Äďzone flag when required, set the ‚Äúcompute zone‚ÄĚ with:
$ gcloud compute zones list NAME REGION STATUS NEXT_MAINTENANCE TURNDOWN_DATE ... us-east1-b us-east1 UP ... $ gcloud config set compute/zone us-east1-b
Now you are ready to create the Kubernetes cluster. A cluster is a group of nodes that run Kubernetes. To provision a cluster with 3 nodes, run the following command:
$ gcloud container clusters create k8s-cluster kubeconfig entry generated for k8s-cluster. NAME ZONE MASTER_VERSION MASTER_IP MACHINE_TYPE NODE_VERSION NUM_NODES STATUS k8s-cluster us-east1-b 1.4.7 22.214.171.124 n1-standard-1 1.4.7 3 RUNNING
This might take a couple of minutes.
With your cluster up and running, you are now ready to run containers.
Get the Source Code
Now that you have Kubernetes running it‚Äôs time to get the source code for this tutorial. If you are running in GKE you can
git clone in the Google Cloud Shell. Otherwise clone the repository wherever you run
kubectl (the command line interface to interact with kubernetes clusters).
$ git clone https://github.com/ibm-datapower/datapower-tutorials.git $ cd datapower-tutorials/using-datapower-in-kubernetes/
Introduction to Kubernetes
One of the reasons that Kubernetes is widely used is because Kubernetes provides a useful set of abstractions that let you think of your applications as services instead of getting bogged down by the details of the underlying infrastructure. As a result, you will be more concerned with the state of the system and will make use of Pods, ReplicaSets, and Services which are the fundamental units that compose an application in Kubernetes. I will go through each of those briefly in this section before we move on to the world-view of deploying a composed application in Kubernetes.
In Kubernetes, all containers run in what‚Äôs called a pod. A pod represents a logical application composed of co-located and co-scheduled containers, that share certain resources. Pods are the units of deployment, replication, and scheduling. Pods, not containers, are the smallest deployable artifact in Kubernetes.
To run a pod directly, we can issue a
kubectl run command and specify an image. A better approach is to expressly declare the pod you want to deploy by writing a pod configuration file in YAML (or JSON) such as the one below:
$ cat kubernetes/pods/datapower-pod.yaml apiVersion: v1 kind: Pod metadata: name: datapower labels: app: datapower spec: containers: - name: datapower image: ibmcom/datapower:latest stdin: true tty: true env: - name: DATAPOWER_ACCEPT_LICENSE value: "true" - name: DATAPOWER_INTERACTIVE value: "true"
To create the pod, we simply run:
$ kubectl create -f kubernetes/pods/datapower-pod.yaml pod "datapower" created
We can check the status of the pod by running:
$ kubectl get pods NAME READY STATUS RESTARTS AGE datapower 1/1 Running 0 4m
Additionally, we can get more detailed information about the pod by running:
$ kubectl describe pods
Once the pod is running, we can attach to the container and log in using the familiar ‚Äúadmin/admin‚ÄĚ default credentials by running:
$ kubectl attach datapower --stdin --tty If you don't see a command prompt, try pressing enter. login: admin Password: *****
To delete the pod, you can open a new Cloud Shell session and run:
$ kubectl delete pods datapower pod "datapower" deleted
A ReplicaSet ensures that a given number of pods are running, spawning pods when there are fewer than specified or removing some if there are more than specified. ReplicaSets can also be defined in config files similar to the pod configuration file above. The following is a sample ReplicaSet configuration file:
$ cat kubernetes/replicasets/datapower-replicaset.yaml apiVersion: extensions/v1beta1 kind: ReplicaSet metadata: name: datapower spec: replicas: 3 selector: matchLabels: app: datapower template: metadata: labels: app: datapower spec: containers: - name: datapower image: ibmcom/datapower tty: true stdin: true env: - name: DATAPOWER_ACCEPT_LICENSE value: "true" - name: DATAPOWER_INTERACTIVE value: "true"
replicas: 3 declaration, here we are declaring that we want three instances of the pod defined later in the
To create the ReplicaSet above we run:
$ kubectl create -f kubernetes/replicasets/datapower-replicaset.yaml replicaset "datapower" created
We can quickly check the status of the Pods by running:
$ kubectl get replicasets -o wide NAME DESIRED CURRENT READY AGE CONTAINER(S) IMAGE(S) SELECTOR datapower 3 3 3 56s datapower ibmcom/datapower app=datapower
To demonstrate the role of ReplicaSets, let‚Äôs manually delete one of the replicas that was created.
$ kubectl get pods NAME READY STATUS RESTARTS AGE datapower-2fh7r 1/1 Running 0 4m datapower-nmwzx 1/1 Running 0 4m datapower-x5vx7 1/1 Running 1 4m $ kubectl delete pods datapower-2fh7r pod "datapower-2fh7r" deleted $ kubectl get pods NAME READY STATUS RESTARTS AGE datapower-2fh7r 1/1 Terminating 0 4m datapower-mb42t 0/1 ContainerCreating 0 5s datapower-nmwzx 1/1 Running 0 4m datapower-x5vx7 1/1 Running 1 4m
Notice how immediately after a pod was deleted, the ReplicaSet created a new pod
Once you are done with the ReplicaSet, you can run:
$ kubectl delete replicasets datapower replicaset "datapower" deleted
Now that you‚Äôve had a taste for ReplicaSets and Pods and seen how useful they are, I‚Äôd just like to add that for the most part, you won‚Äôt be interacting directly with Pod and ReplicaSet objects in Kubernetes since there is a higher-order object called a
Deployment that helps to manage Pods, replicas, updates and more in a declarative way. I will introduce Deployments in a later section.
The main idea behind Services is that we recognize that Pods are ephemeral and ReplicaSets start and delete Pods dynamically so we cannot rely on a particular IP address assigned to a Pod. If we have a set of Pods that provide a function, we need a way to reach that set of Pods that does not involve keeping track of each individual address. The
Service abstraction allows for this type of decoupling.
In order to use the service you first need to deploy the application that provides the service. To deploy the sample backend Pod, you can run:
$ kubectl create -f kubernetes/pods/backend-pod.yaml
backend Pod consists of a single container that responds with ‚ÄúHello world from [hostname]‚ÄĚ when reached on port
8080, the Pod also has a label ‚Äúapp=backend‚ÄĚ that the Service will use as a selector. The Service object looks as follows:
$ cat kubernetes/services/backend-service.yaml apiVersion: v1 kind: Service metadata: name: "backend" spec: selector: app: "backend" type: LoadBalancer ports: - port: 8080 protocol: TCP targetPort: 8080
To create the Service object, we run the familiar command:
$ kubectl create -f kubernetes/services/backend-service.yaml service "backend" created
You will have to wait for a while for the Service object to provide an external endpoint for the service. You can check the status of the Service by running:
$ kubectl get service NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE backend 10.23.248.249 126.96.36.199 8080:32621/TCP 2h kubernetes 10.23.240.1 <none> 443/TCP 2d
Once the service is up and running, Kubernetes will make the Service name available to the cluster. You can test the Service by launching a test Pod and sending a request to the Service like so:
$ kubectl create -f kubernetes/pods/busybox-test-pod.yaml pod "busybox" created $ kubectl exec --tty --stdin busybox -- wget -O - backend:8080 Connecting to backend:8080 (10.23.248.249:8080) Hello world from backend - 100% |*******************************| 25 0:00:00 ETA
Note that the request is sent to the Service name and not a specific IP address but we still receive the expected
Hello world from backend response.
Once you are done with the example above, you can delete by running:
$ kubectl delete service backend service "backend" deleted $ kubectl delete pods busybox backend pod "busybox" deleted pod "backend" deleted
Now we can begin to deploy a simple composed application. For this tutorial, we will use a similar application to the one in the Docker Compose Hello World sample. The applications consists of an nginx ‚Äėbackend‚Äô that just responds with a simple
Hello from <hostname> message and a DataPower service on the front that proxies the request and transforms the response from the backend, using Gatewayscript, by prepending ‚ÄúDataPower proxied: ‚Äú to the response from the nginx service. In order to achieve this in Kubernetes, we will:
- Provide the DataPower application configuration as a Kubernetes ConfigMap
- Deploy our application in Kubernetes
- Expose the Kubernetes Service so that our datapower can communicate to our backend without knowing all the details of the backend application
Configuring our Application Using ConfigMaps
One best practice is to separate your application code from your configuration. To this end, Kubernetes provides a
ConfigMap resource as of version 1.2. A ConfigMap is a flexible object for providing your application with non-secret configuration data. The data is presented as key-value pairs and can represent cli arguments, environment variables, and files in a mounted volume.
Let‚Äôs create a ConfigMap from our
datapower/local directories which look as follows:
$ tree datapower datapower ‚Ēú‚ĒÄ‚ĒÄ config ‚Ēā ‚Ēú‚ĒÄ‚ĒÄ auto-startup.cfg ‚Ēā ‚Ēú‚ĒÄ‚ĒÄ auto-user.cfg ‚Ēā ‚ĒĒ‚ĒÄ‚ĒÄ foo ‚Ēā ‚ĒĒ‚ĒÄ‚ĒÄ foo.cfg ‚ĒĒ‚ĒÄ‚ĒÄ local ‚ĒĒ‚ĒÄ‚ĒÄ foo ‚ĒĒ‚ĒÄ‚ĒÄ hello.js 4 directories, 4 files
These will set up our DataPower domain with application logic and services; it will also place applicaiton data, such as our gatewayscript file, in the right directory.
To create the ConfigMaps, simply
cd to the
datapower directory and type:
$ kubectl create configmap datapower-config --from-file=config/ --from-file=config/foo
$ kubectl create configmap datapower-local-foo --from-file=local/foo
And to get more details about the ConfigMaps we just created, we can type:
$ kubectl describe configmap
We are now ready to consume the
datapower-local-foo ConfigMaps in a Kubernetes deployment.
For pretty much any non-trivial application deployment, we want to have a more reproducible way to define, compose and deploy our application. Therefore, a best practice is to define our application using Deployments. A Deployment is a higher-order object in Kubernetes that manages Pods and ReplicaSets. Below is the sample deployment YAML for the datapower application we use for this tutorial:
$ cat kubernetes/deployments/datapower-deployment.yaml apiVersion: extensions/v1beta1 kind: Deployment metadata: name: datapower spec: replicas: 1 template: metadata: labels: app: datapower track: stable spec: containers: - name: datapower image: "ibmcom/datapower:latest" stdin: true tty: true ports: - name: web-mgmt containerPort: 9090 env: - name: DATAPOWER_ACCEPT_LICENSE value: "true" - name: DATAPOWER_INTERACTIVE value: "true" volumeMounts: - mountPath: /drouter/config/foo/foo.cfg name: config-volume subPath: foo.cfg - mountPath: /drouter/config/auto-startup.cfg name: config-volume subPath: auto-startup.cfg - mountPath: /drouter/config/auto-user.cfg name: config-volume subPath: auto-user.cfg - mountPath: /drouter/local/foo name: local-foo-volume volumes: - name: config-volume configMap: name: datapower-config - name: local-foo-volume configMap: name: datapower-local-foo
Note that we use the
datapower-local-foo ConfigMaps as a
volumes parameter and mount it in the
volumeMounts section. The
mountPath: /drouter/config is special in this case because DataPower will automatically execute a config file named auto-startup.cfg if found in the /drouter/config directory, which we have placed in this configMap.
There is likewise a deployment config file for our backend application which can be found under
To create the deployments we head over to the
kubernetes/deployments directory and issue the following commands:
$ kubectl create -f datapower-deployment.yaml
$ kubectl create -f backend-deployment.yaml
Exposing Our Application to the Internet
Before we continue, let‚Äôs stop and reflect for a minute on what we have accomplished so far. At this time, we have deployed an applicaiton that can recover from a failure in one of our nodes in our Kubernetes cluster. We can deploy this applicaiton in a highly reproducible way. Additionally, the application‚Äôs configuration is managed independently of the application and can be changed easily without having to rebuild our applicaiton.
Now, we want to expose the application service so that we can access it from the public internet. We can expose the datapower multi-protocol gateway service running on port 80 that was configured using our
datapower-config ConfigMap by heading over to the
kubernetes/services directory and running:
$ kubectl create -f datapower-service.yaml
Likewise, create the backend service by running:
$ kubectl create -f backend-service.yaml
This will create a Kubernetes Service that we can use to get the external IP and port we just exposed. To view it, run:
$ kubectl get service datapower
$ kubectl get service datapower NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE datapower 10.7.246.47 188.8.131.52 80/TCP 12h
Which might take a minute or two to populate the external IP. If you are following along with GKE as in this tutorial, you will also need make sure that traffic from external IPs is allowed by opening a firewall traffic rule on port 80 as follows:
$ gcloud compute firewall-rules create datapower-mpgw --allow=tcp:80
We should now be able to send a request to our service from the public internet.
$ curl -k 184.108.40.206:80 DataPower Proxied: Hello world from backend-113429680-tmq5q
Cleaning it Up
In order to prevent incurring charges in Google Cloud Platform when not using our cluster we will want to delete the Kubernetes cluster we created earlier. In a Google Cloud Shell session, type:
$ gcloud container clusters delete k8s-cluster
We hope this introduction to running DataPower in Kubernetes was helpful in building a foundation that you can use when proceeding to build more complex applications that follow industry best-practices.
From here, we are ready to explore more advanced topics. Some example next steps include:
- Services provisioning and discovery
- Scaling microservices in your application
- Managing secrets in Kubernetes
- Update rollouts
- Application health checks and monitoring
Thank you and please let us know what you think by commenting or submitting your own pull request in our GitHub repo