Self-Managed Deployments#


You can create a self-managed deployment for an active self-managed environment. When using a self-managed deployment, you take full control of procuring the resources needed to run engine instances.

When you create a self-managed deployment, you define the engine type, version, and configuration to deploy. You also select the installation type to use - either a tarball or a Docker image

For more details, refer to the StreamSets DataOps Platform Documentation.

Using the Data Collector Engine#


The SDK is designed to mirror the UI workflow. This section shows you how to create a self-managed deployment for Data Collector using the Docker Image installation type in the UI and how to achieve the same using the StreamSets DataOps Platform SDK for Python code step by step.

Define the Deployment#

In the UI, a deployment is defined as seen below:

../../../_images/creation_define_deployment_sdc2.png

The same effect can be achieved using the streamsets.sdk.sch_models.DeploymentBuilder class in the SDK. To instantiate a DeploymentBuilder object, use the streamsets.sdk.ControlHub.get_deployment_builder() method and then provide configuration options to the streamsets.sdk.sch_models.DeploymentBuilder.build() method of the builder as seen below:

deployment_builder = sch.get_deployment_builder(deployment_type='SELF')

# sample_environment is an instance of streamsets.sdk.sch_models.SelfManagedEnvironment
deployment = deployment_builder.build(deployment_name='Sample Deployment',
                                      environment=sample_environment,
                                      engine_type='DC',
                                      engine_version='4.1.0',
                                      deployment_tags=['self-managed-tag'])

Configure the Engine#

In the UI, a deployment’s engines are configured as seen below:

../../../_images/creation_configure_engine2.png

In the above UI, when you click on 3 stage libraries selected, the following dialog opens and allows you to select stage libraries.

../../../_images/creation_configure_engine_stage_lib_selection_screen.png

In the above UI, once you select JDBC and click on any of the ‘+’ signs, then it shows the following:

../../../_images/creation_configure_engine_stage_lib_selection_as_a_list.png

Selecting stage libraries for a deployment is also possible using the SDK. The stage_libs property of the streamsets.sdk.sch_models.DeploymentEngineConfiguration attribute in a Deployment object allows specification of additional stage libraries in the '<library_name>' format, or optionally the '<library_name>:<library_version>' format.

Note

If a version is omitted for a stage library, it will default to the engine version that was configured for the deployment.

There are several methods available for modifying the stage libraries of a deployment. If you know the complete list of stage libraries you want to add to a deployment, you can specify them as a list and set the stage_libs attribute directly as seen below:

Warning

Attempting to add multiple versions of the same stage library to a deployment’s engine configuration will result in an error when you attempt to add or update a deployment on the StreamSets DataOps Platform.

# Stage libraries can be supplied both with and without version specified. Any without a version will default
# to the version of the engine selected for the deployment
deployment.engine_configuration.stage_libs = ['jdbc', 'aws:4.1.0', 'cdp_7_1:4.3.0', 'basic:4.3.0', 'dev']

The stage_libs attribute operates like a traditional list object, with accompanying append(), extend(), and remove() methods. If you are looking to add a single stage library to a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.append() method, using the same library:version syntax from above:

# Adding a single additional library to the stage library configuration
deployment.engine_configuration.stage_libs.append('aws')

If you would prefer to add a list of additional stage libraries to a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.extend() method, which also follows the same library:version syntax from above:

# Extending the list of stage libraries by adding two additional stages
deployment.engine_configuration.stage_libs.extend(['cassandra_3:4.3.0', 'elasticsearch_7'])

Finally, if you would like to remove a single stage library from a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.remove() method. The removal of a stage library from a deployment’s engine configuration intentionally requires a version to be supplied, so as to not accidentally remove an unintended stage library:

# Removing a single library from the stage library configuration by supplying the library name and version
deployment.engine_configuration.stage_libs.remove('aws:4.1.0')

Configure the Install Type#

In the UI, the Install Type for a deployment is configured as seen below:

../../../_images/select_install_type.png

To set the Install Type for the engine in a deployment via the SDK, the install_type property can be configured to either 'TARBALL' or 'DOCKER' depending on your needs:

# Set a deployment's install type to use a tarball
deployment.install_type = 'TARBALL'

# Or, set a deployment's install type to use the docker image
deployment.install_type = 'DOCKER'

Review and Launch the Deployment#

In the UI, a deployment can be reviewed and launched as seen below:

../../../_images/creation_review_and_launch_sdc.png

The same effect can be achieved by using the SDK as seen below:

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Start & Generate Install Script'
sch.start_deployment(deployment)

Complete example for the Data Collector Engine#


To create a new streamsets.sdk.sch_models.SelfManagedDeployment object and add it to Control Hub, use the streamsets.sdk.sch_models.DeploymentBuilder class. Use the streamsets.sdk.ControlHub.get_deployment_builder() method to instantiate the builder object:

deployment_builder = sch.get_deployment_builder(deployment_type='SELF')

Next, retrieve the streamsets.sdk.sch_models.SelfManagedEnvironment object which represents an active self-managed environment where engine instances will be deployed, pass it to the streamsets.sdk.sch_models.DeploymentBuilder.build() method along with other parameters, and pass the resulting streamsets.sdk.sch_models.SelfManagedDeployment object to the streamsets.sdk.ControlHub.add_deployment() method:

# sample_environment is an instance of streamsets.sdk.sch_models.SelfManagedEnvironment
deployment = deployment_builder.build(deployment_name='Sample Deployment',
                                      environment=sample_environment,
                                      engine_type='DC',
                                      engine_version='4.1.0',
                                      deployment_tags=['self-managed-tag'])
deployment.install_type = 'TARBALL'
# deployment.install_type = 'DOCKER'

# Optional - add sample stage libs
deployment.engine_configuration.stage_libs = ['jdbc', 'aws:4.1.0', 'cdp_7_1:4.3.0', 'basic:4.3.0', 'dev']
# deployment.engine_configuration.stage_libs.append('aws')
# deployment.engine_configuration.stage_libs.extend(['cassandra_3:4.3.0', 'elasticsearch_7'])

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Start & Generate Install Script'
sch.start_deployment(deployment)

Using the Transformer Engine#


The SDK is designed to mirror the UI workflow. This section shows you how to create a self-managed deployment for Transformer using the Docker Image installation type in the UI and how to achieve the same using StreamSets DataOps Platform SDK for Python code step by step.

Define the Deployment#

In the UI, a deployment is defined as seen below:

../../../_images/creation_define_deployment_transformer1.png

The same effect can be achieved by using the SDK as seen below:

deployment_builder = sch.get_deployment_builder(deployment_type='SELF')

# sample_environment is an instance of streamsets.sdk.sch_models.SelfManagedEnvironment
deployment = deployment_builder.build(deployment_name='Sample Deployment',
                                      environment=sample_environment,
                                      engine_type='TF',
                                      engine_version='4.1.0',
                                      scala_binary_version='2.11',
                                      deployment_tags=['self-managed-tag'])

Configure the Engine#

In the UI, a deployment’s engines are configured as seen below:

../../../_images/creation_configure_engine2.png

In the above UI, when you click on 3 stage libraries selected, the following dialog opens and allows you to select stage libraries.

../../../_images/creation_configure_engine_transformer_stage_lib_selection_screen.png

In the above UI, once you select JDBC and click on any of the ‘+’ signs, then it shows the following:

../../../_images/creation_configure_engine_transformer_stage_lib_selection_as_a_list.png

Selecting stage libraries for a deployment is also possible using the SDK. The stage_libs property of the streamsets.sdk.sch_models.DeploymentEngineConfiguration attribute in a Deployment object allows specification of additional stage libraries in the '<library_name>' format, or optionally the '<library_name>:<library_version>' format.

Note

If a version is omitted for a stage library, it will default to the engine version that was configured for the deployment.

There are several methods available for modifying the stage libraries of a deployment. If you know the complete list of stage libraries you want to add to a deployment, you can specify them as a list and set the stage_libs attribute directly as seen below:

Warning

Attempting to add multiple versions of the same stage library to a deployment’s engine configuration will result in an error when you attempt to add or update a deployment on the StreamSets DataOps Platform.

# Stage libraries can be supplied both with and without version specified. Any without a version will default
# to the version of the engine selected for the deployment
deployment.engine_configuration.stage_libs = ['file', 'aws_3_2_0:4.2.0', 'jdbc', 'kafka:4.2.0']

The stage_libs attribute operates like a traditional list object, with accompanying append(), extend(), and remove() methods. If you are looking to add a single stage library to a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.append() method, using the same library:version syntax from above:

# Adding a single additional library to the stage library configuration
deployment.engine_configuration.stage_libs.append('hive:4.2.0')

If you wouldd prefer to add a list of additional stage libraries to a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.extend() method, which also follows the same library:version syntax from above:

# Extending the list of stage libraries by adding two additional stages
deployment.engine_configuration.stage_libs.extend(['redshift-no-dependency:4.2.0', 'azure_3_2_0'])

Finally, if you would like to remove a single stage library from a deployment’s engine configuration, you can utilize the streamsets.sdk.sch_models.DeploymentStageLibraries.remove() method. The removal of a stage library from a deployment’s engine configuration intentionally requires a version to be supplied, so as to not accidentally remove an unintended stage library:

# Removing a single library from the stage library configuration by supplying the library name and version
deployment.engine_configuration.stage_libs.remove('kafka:4.2.0')

Configure the Install Type#

In the UI, Install Type for a deployment is configured as seen below:

../../../_images/select_install_type.png

To set the Install Type for the engine in a deployment via the SDK, the install_type property can be configured to either 'TARBALL' or 'DOCKER' depending on your needs:

# Set a deployment's install type to use the docker image
deployment.install_type = 'DOCKER'

# Or, set a deployment's install type to use a tarball
deployment.install_type = 'TARBALL'

Review and Launch the Deployment#

In the UI, a deployment can be reviewed and launched as seen below:

../../../_images/creation_review_and_launch_transformer.png

The same effect can be achieved by using the SDK as seen below:

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Start & Generate Install Script'
sch.start_deployment(deployment)

Complete example for the Transformer Engine#


To create a new streamsets.sdk.sch_models.SelfManagedDeployment object and add it to Control Hub, use the streamsets.sdk.sch_models.DeploymentBuilder class. Use the streamsets.sdk.ControlHub.get_deployment_builder() method to instantiate the builder object:

deployment_builder = sch.get_deployment_builder(deployment_type='SELF')

Next, retrieve the streamsets.sdk.sch_models.SelfManagedEnvironment object which represents an active self-managed environment where engine instances will be deployed, pass it to the streamsets.sdk.sch_models.DeploymentBuilder.build() method along with other parameters, and pass the resulting streamsets.sdk.sch_models.SelfManagedDeployment object to the streamsets.sdk.ControlHub.add_deployment() method:

# sample_environment is an instance of streamsets.sdk.sch_models.SelfManagedEnvironment
deployment = deployment_builder.build(deployment_name='Sample Deployment',
                                      environment=sample_environment,
                                      engine_type='TF',
                                      engine_version='4.1.0',
                                      scala_binary_version='2.11',
                                      deployment_tags=['self-managed-tag'])
deployment.install_type = 'DOCKER'
# deployment.install_type = 'TARBALL'

# Optional - add sample stage libs
deployment.engine_configuration.stage_libs = ['file', 'aws_3_2_0:4.2.0', 'jdbc', 'kafka:4.2.0']
# deployment.engine_configuration.stage_libs.append('hive:4.2.0')
# deployment.engine_configuration.stage_libs.extend(['redshift-no-dependency:4.2.0', 'azure_3_2_0'])

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Start & Generate Install Script'
sch.start_deployment(deployment)