Amazon EC2 Deployments#


You can create an Amazon EC2 deployment for an active AWS environment.

When you create an Amazon EC2 deployment, you define the engine type, version, and configuration to deploy to the Amazon VPC specified in the environment. You also specify the number of engine instances to deploy. Each engine instance runs on a dedicated EC2 instance.

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

Creating Deployment for Data Collector#


The SDK is designed to mirror the UI workflow. This section shows you how to create an EC2 deployment for Data Collector 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.png

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

deployment_builder = sch.get_deployment_builder(deployment_type='EC2')

# sample_environment is an instance of streamsets.sdk.sch_models.AWSEnvironment
deployment = builder.build(deployment_name='Sample Deployment DC-EXISTING_KEY_PAIR_NAME'),
                           environment=sample_environment,
                           engine_type='DC',
                           engine_version='4.1.0',
                           deployment_tags=['ec2-deployment-tag'])

Configure the Engine#

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

../../../_images/creation_configure_engine1.png
In the above UI, when you click on 4 stage libraries selected, the following 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, 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 EC2 Autoscaling Group#

In the UI, the EC2 Autoscaling Group for a deployment is configured as seen below:

../../../_images/creation_configure_ec2_autoscaling_group.png

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

deployment.engine_instances = 1
deployment.ec2_instance_type = 'm4.large'
# Optional instance_profile, not required if environment has default instance profile set
deployment.instance_profile = <AWS instance profile>
deployment.aws_tags = 'owner=stf'

Configure EC2 SSH Access#

In the UI, the EC2 SSH Access for a deployment is configured as seen below:

../../../_images/creation_configure_ec2_ssh_access.png

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

deployment.ssh_key_source = 'EXISTING_KEY_PAIR_NAME' # The other valid value is 'NONE'
if ssh_key_source == 'EXISTING_KEY_PAIR_NAME':
    deployment.key_pair_name = <SSH key pair name>

Review and Launch the Deployment#

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

../../../_images/creation_review_and_launch1.png

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

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Launch Deployment'
sch.start_deployment(deployment)

Complete example for Data Collector#


To create a new streamsets.sdk.sch_models.EC2Deployment 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='EC2')

Next, retrieve the streamsets.sdk.sch_models.AWSEnvironment object which represents an active AWS 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.EC2Deployment object to the streamsets.sdk.ControlHub.add_deployment() method:

# sample_environment is an instance of streamsets.sdk.sch_models.AWSEnvironment
deployment = builder.build(deployment_name='Sample Deployment DC-EXISTING_KEY_PAIR_NAME'),
                           environment=sample_environment,
                           engine_type='DC',
                           engine_version='4.1.0',
                           deployment_tags=['ec2-deployment-tag'])

deployment.engine_instances = 1
deployment.ec2_instance_type = 'm4.large'
# Optional instance_profile, not required if environment has default instance profile set
deployment.instance_profile = <AWS instance profile>
deployment.ssh_key_source = 'EXISTING_KEY_PAIR_NAME' # The other valid value is 'NONE'
if ssh_key_source == 'EXISTING_KEY_PAIR_NAME':
    deployment.key_pair_name = <SSH key pair name>
deployment.aws_tags = 'owner=stf'

# 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 'Launch Deployment'
sch.start_deployment(deployment)

Creating Deployment for Transformer#


The SDK is designed to mirror the UI workflow. This section shows you how to create an EC2 deployment for Transformer 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_transformer_define_deployment.png

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

deployment_builder = sch.get_deployment_builder(deployment_type='EC2')

# sample_environment is an instance of streamsets.sdk.sch_models.AWSEnvironment
deployment = builder.build(deployment_name='Sample Deployment TF-EXISTING_KEY_PAIR_NAME',
                           environment=sample_environment,
                           engine_type='TF',
                           engine_version='4.1.0',
                           scala_binary_version='2.12',
                           deployment_tags=['ec2-deployment-tag'])

Configure the Engine#

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

../../../_images/creation_configure_engine1.png

In the above UI, when you click on 4 stage libraries selected, the following 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, 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 EC2 Autoscaling Group#

In the UI, the EC2 Autoscaling Group for a deployment is configured as seen below:

../../../_images/creation_configure_ec2_autoscaling_group.png

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

deployment.engine_instances = 1
deployment.ec2_instance_type = 'm4.large'
# Optional instance_profile, not required if environment has default instance profile set
deployment.instance_profile = <AWS instance profile>
deployment.aws_tags = 'owner=stf'

Configure EC2 SSH Access#

In the UI, the EC2 SSH Access for a deployment is configured as seen below:

../../../_images/creation_configure_ec2_ssh_access.png

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

deployment.ssh_key_source = 'EXISTING_KEY_PAIR_NAME' # The other valid value is 'NONE'
if ssh_key_source == 'EXISTING_KEY_PAIR_NAME':
    deployment.key_pair_name = <SSH key pair name>

Review and Launch the Deployment#

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

../../../_images/creation_review_and_launch1.png

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

sch.add_deployment(deployment)
# Optional - equivalent to clicking on 'Launch Deployment'
sch.start_deployment(deployment)

Complete example for Transformer#


To create a new streamsets.sdk.sch_models.EC2Deployment 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='EC2')

Next, retrieve the streamsets.sdk.sch_models.AWSEnvironment object which represents an active AWS 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.EC2Deployment object to the streamsets.sdk.ControlHub.add_deployment() method:

# sample_environment is an instance of streamsets.sdk.sch_models.AWSEnvironment
deployment = builder.build(deployment_name='Sample Deployment TF-EXISTING_KEY_PAIR_NAME',
                           deployment_tags=['ec2-deployment-tag'],
                           engine_type='TF',
                           engine_version='4.1.0',
                           scala_binary_version='2.12',
                           environment=sample_environment)

# add sample stage libs
deployment.engine_configuration.select_stage_libraries = ['streamsets-spark-jdbc-lib:4.1.0']

deployment.engine_instances = 1
deployment.ec2_instance_type = 'm4.large'
# Optional instance_profile, not required if environment has default instance profile set
deployment.instance_profile = <AWS instance profile>
deployment.ssh_key_source = 'EXISTING_KEY_PAIR_NAME' # The other valid value is 'NONE'
if ssh_key_source == 'EXISTING_KEY_PAIR_NAME':
    deployment.key_pair_name = <SSH key pair name>
deployment.aws_tags = 'owner=stf'

# 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 'Launch Deployment'
sch.start_deployment(deployment)