Deploy the Example to a GKE cluster

Once the application is built you need to publish the docker images to some registry that stores, manages and secures your application images. In this example we will use the GCloud registry

Follow these steps to complete the process of publishing:

  • Set up the target environment of publishing in your local build process. Create a file named target-env.sbt in the root of your project folder (the same level where you have build.sbt). The file needs to have the following 2 lines:

// Google Cloud Registry
ThisBuild / cloudflowDockerRegistry := Some("")
ThisBuild / cloudflowDockerRepository := Some("<gcloud project id>")

Here project id refers to the ID of your Google Cloud Platform project. This can be found on the project page of your Google Cloud Platform.

  • Publish the docker image of your project to the above registry. This has 3 steps:

  • First make sure that you have access to the cluster gcloud container clusters get-credentials <cluster-name>

  • And that you have access to the Google docker registry gcloud auth configure-docker

  • Then build and publish the application sbt buildApp

Once the image is successfully published, you will see something like the following as output of buildApp in your console:

[info] Successfully built and published the following image:
[success] Cloudflow application CR generated in /.../sensor-data-scala/target/sensor-data-scala.json
[success] Use the following command to deploy the Cloudflow application:
[success] kubectl cloudflow deploy /path/to/sensor-data-scala/target/sensor-data-scala.json
[success] Total time: 41 s, completed Jun 10, 2020 10:10:10 PM

At this point, the Docker image containing the streamlets has been published to the Docker repository. The sensor-data-scala.json file that the build produces contains the application descriptor. We use the Cloudflow CLI to deploy that application description to a cluster.

Deploy Application to the Cluster

This is quite straightforward and the output of buildApp actually tells you what command to run for deployment. In addition you need to supply the credentials to the Docker repository along with the CLI deploy command.

$ kubectl cloudflow deploy /path/to/sensor-data-scala/target/sensor-data-scala.json -u oauth2accesstoken -p "$(gcloud auth print-access-token)"

If the command goes through you will see the following output:

[Done] Deployment of application `sensor-data-scala` has started.

Once all streamlets are up in their pods, you can see them running using the following command:

$ kubectl cloudflow status sensor-data-scala
Name:             sensor-data-scala
Namespace:        sensor-data-scala
Version:          484-199a3c1-dirty
Created:          2020-06-10 22:28:32 +0200 CEST
Status:           Running

STREAMLET         POD                                              READY             STATUS            RESTARTS
http-ingress      sensor-data-scala-http-ingress-6f4b677c84-8gtk7  1/1               Running           0
invalid-logger    sensor-data-scala-invalid-logger-f76667866-lffmk 1/1               Running           0
metrics           sensor-data-scala-metrics-859d9cf974-vrpzs       1/1               Running           0
valid-logger      sensor-data-scala-valid-logger-6946746b4b-bpqdc  1/1               Running           0
validation        sensor-data-scala-validation-b7f85669d-kkpx9     1/1               Running           0

Note that all streamlets run in a namespace that matches the name of the application.

Congratulations! You have deployed and started your first Cloudflow application.

What’s next

Now, we are ready to Exercise the example.