Deploying a Flink application

This Getting Started guide walks you through how to deploy a Flink application on a Kubernetes cluster using the CSA Operator.

What is Apache Flink?: Flink is a distributed processing engine and a scalable data analytics framework. You can use Flink to process data streams at a large scale and to deliver real-time analytical insights about your processed data with your streaming application.
Flink is designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. Furthermore, Flink provides communication, fault tolerance, and data distribution for distributed computations over data streams. A large variety of enterprises choose Flink as a stream processing platform due to its ability to handle scale, stateful stream processing, and event time.

This Getting Started guide walks you through how to deploy a Flink application on a Kubernetes cluster using the CSA Operator. The Getting Started guide uses the Flink Kubernetes Tutorial from the Flink tutorials public repository.

Clone the Flink Tutorials repository, and build the tutorial Docker image that contains the built Flink JAR file.

git clone https://github.com/cloudera/flink-tutorials.git -b CSA-OPERATOR-1.0.0
cd flink-tutorials/flink-kubernetes-tutorial
mvn clean package
docker build -t flink-kubernetes-tutorial .

Tag the tutorial image to attach the required information about the Docker registry that you will use in the next step.

docker image tag flink-kubernetes-tutorial [***REGISTRY HOST***]:[***PORT***]/[***PROJECT***]/flink-kubernetes-tutorial:latest

Push the newly tagged tutorial image to your Docker registry. This way the Kubernetes nodes can download the image from the (local or public) Docker registry.
```
docker push [***REGISTRY HOST***]:[***PORT***]/[***PROJECT***]/flink-kubernetes-tutorial:latest
```
You will see the Image successfully pushed message when the image is correctly pushed.

Create the FlinkDeployment configuration file by saving the following example as flink-deployment.yaml. Make sure to set spec.image to the image that you have pushed in the previous step.

apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:
  name: flink-kubernetes-tutorial
spec:
  image:  [***REGISTRY HOST***]:[***PORT***]/[***PROJECT***]/flink-kubernetes-tutorial:latest
  flinkVersion: v1_18
  flinkConfiguration:
    taskmanager.numberOfTaskSlots: "4"
  serviceAccount: flink
  mode: native
  jobManager:
    resource:
      memory: "2048m"
      cpu: 1
  taskManager:
    resource:
      memory: "2048m"
      cpu: 1
  job:
    args: ["--rowsPerSec", "10"]
    jarURI: local:///opt/flink/usrlib/flink-kubernetes-tutorial.jar
    parallelism: 4
    state: running
    upgradeMode: stateless

Run the Flink job by applying the FlinkDeployment configuration file to the cluster using the following command:
```
kubectl -n flink apply -f flink-deployment.yaml
```
The Flink Operator automatically recognizes the new resource and starts the execution of the Flink job, which is reflected by JOB STATUS switching to RUNNING:
```
kubectl -n flink get FlinkDeployment
NAME                        JOB STATUS   LIFECYCLE STATE
flink-kubernetes-tutorial   RUNNING      STABLE
```
Configure access to the Flink Dashboard at http://localhost:8081 (or IP address/domain name of your machine, if it differs from localhost) using port-forwarding with the following command:
```
kubectl -n flink port-forward service/flink-kubernetes-tutorial-rest 8081:8081
```
All requests to the http://localhost:8081 URL will be forwarded to the Flink service while the kubectl command is running. To stop the port-forwarding, exit the kubectl command by pressing CTRL+C.