Spark Native integration

Spark have two valuable options for deploying on Kubernetes, using the official spark CLI and using a third-party(Google) operator. Cloudflow Contrib integration enables both of those options to deploy Spark Streamlet with a Cloudflow application.

Building Spark Native Streamlets

To build Spark Native Streamlets you need to add an additional sbt plugin along with the Cloudflow one in plugins.sbt:

addSbtPlugin("com.lightbend.cloudflow" % "contrib-sbt-spark" % "0.1.1")

And use the spark Native sbt plugin functionalities in your streamlet sbt sub-project functionalities:

  .enablePlugins(CloudflowApplicationPlugin, CloudflowSparkPlugin, CloudflowNativeSparkPlugin)
    .settings(
      baseDockerInstructions := sparkNativeCloudflowDockerInstructions.value,
      libraryDependencies ~= fixSparkNativeCloudflowDeps
    )

Now you can develop and use your spark streamlets as described in the official cloudflow documentation.

Operating spark streamlets in a cluster

Once you have run buildApp and you have the compiled Blueprint of your application you can deploy it using the kubectl cloudflow plugin and, in case it’s necessary, adding the option to ignore checks on the spark operator:

kubectl cloudflow deploy your-application-cr.json --unmanaged-runtimes=spark

Now you can notice that the spark streamlets are marked as <external> and their status is Unknown running the kubectl cloudflow status your-application command:

+------------+--------------------------------+
| Name:      |     call-record-aggregator     |
| Namespace: |     call-record-aggregator     |
| Version:   | 0.0.3-9-9fdc6574-20210427-1207 |
| Created:   |      2021-04-27T10:54:06Z      |
| Status:    |            Running             |
+------------+--------------------------------+
+----------------+--------------------------------------------------------+-------+---------+----------+
| STREAMLET      | POD                                                    | READY | STATUS  | RESTARTS |
+----------------+--------------------------------------------------------+-------+---------+----------+
| cdr-aggregator | <external>                                             |  0/0  | Unknown |    0     |
| cdr-generator1 | <external>                                             |  0/0  | Unknown |    0     |
| cdr-generator2 | <external>                                             |  0/0  | Unknown |    0     |
| cdr-ingress    | call-record-aggregator-cdr-ingress-7965d4bdb8-x8r66    |  1/1  | Running |    0     |
| console-egress | call-record-aggregator-console-egress-557f74d65f-k765t |  1/1  | Running |    0     |
| error-egress   | call-record-aggregator-error-egress-55d8ffc79d-2cqxn   |  1/1  | Running |    0     |
| split          | call-record-aggregator-split-bf98f8dfc-pgt5j           |  1/1  | Running |    0     |
+----------------+--------------------------------------------------------+-------+---------+----------+

To help you manage spark streamlets we have developed some example scripts they are contained in the example-scripts folder at the root of Cloudflow Contrib public repository.

For Spark you have two sets of scripts spark-cli that directly uses the Spark Cli and spark-operator to use the Spark K8s Operator.

Spark Cli

The following scripts are expected to be present on the PATH:

  • bash

  • jq

  • kubectl

  • spark cli

In the spark-cli sub-folder the first script available is setup-example-rbac.sh and this first step needs to be performed once on any cluster you want to deploy spark streamlets, refer to the upstream documentation for further details.

Spark Operator

The following scripts are expected to be present on the PATH:

  • bash

  • jq

  • kubectl

In the spark-operator sub-folder the first script available is setup-example-rbac.sh and this first step needs to be performed once on any cluster you want to deploy spark streamlets, refer to the upstream documentation for further details.

The second step is to setup the spark-operator following the steps described here.

Alternatively the script setup-spark-operator.sh provides a full example of setting up the spark-operator relying on opinionated defaults.

Common workflows

Inside spark-cli and spark-operator you find 3 folders to map 3 different use-cases, note that the order of the operations matter:

  • deploy a new Cloudflow application to a cluster:

    • deploy the Cloudflow application using the kubectl cloudflow command

    • cd into the deploy folder and run ./deploy-application.sh application-name service-account-name

  • undeploy a deployed Cloudflow application:

    • cd into the undeploy folder and run ./undeploy-application.sh application-name

    • undeploy the Cloudflow application using the kubectl cloudflow command

  • redeploy a pre-existing Cloudflow application:

    • deploy/configure the Cloudflow application using the kubectl cloudflow command

    • cd into the redeploy folder and run ./redeploy-application.sh application-name service-account-name

The provided scripts are deliberately simple and intended to be used as starting point for you to customize those operations based on your needs. The structure always resemble 3 steps:

  • fetch the Cloudflow application informations from the cluster

  • generate commands

  • executes the generated commands