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| """The step definitions for workflow.""" |
| from __future__ import absolute_import |
|
|
| from typing import List, Union, Optional |
|
|
| from sagemaker.workflow.entities import ( |
| RequestType, |
| ) |
| from sagemaker.workflow.properties import ( |
| Properties, |
| ) |
| from sagemaker.workflow.step_collections import StepCollection |
| from sagemaker.workflow.steps import Step, StepTypeEnum, CacheConfig |
|
|
|
|
| class EMRStepConfig: |
| """Config for a Hadoop Jar step.""" |
|
|
| def __init__( |
| self, jar, args: List[str] = None, main_class: str = None, properties: List[dict] = None |
| ): |
| """Create a definition for input data used by an EMR cluster(job flow) step. |
| |
| See AWS documentation on the ``StepConfig`` API for more details on the parameters. |
| |
| Args: |
| args(List[str]): |
| A list of command line arguments passed to |
| the JAR file's main function when executed. |
| jar(str): A path to a JAR file run during the step. |
| main_class(str): The name of the main class in the specified Java file. |
| properties(List(dict)): A list of key-value pairs that are set when the step runs. |
| """ |
| self.jar = jar |
| self.args = args |
| self.main_class = main_class |
| self.properties = properties |
|
|
| def to_request(self) -> RequestType: |
| """Convert EMRStepConfig object to request dict.""" |
| config = {"HadoopJarStep": {"Jar": self.jar}} |
| if self.args is not None: |
| config["HadoopJarStep"]["Args"] = self.args |
| if self.main_class is not None: |
| config["HadoopJarStep"]["MainClass"] = self.main_class |
| if self.properties is not None: |
| config["HadoopJarStep"]["Properties"] = self.properties |
|
|
| return config |
|
|
|
|
| class EMRStep(Step): |
| """EMR step for workflow.""" |
|
|
| def __init__( |
| self, |
| name: str, |
| display_name: str, |
| description: str, |
| cluster_id: str, |
| step_config: EMRStepConfig, |
| depends_on: Optional[List[Union[str, Step, StepCollection]]] = None, |
| cache_config: CacheConfig = None, |
| ): |
| """Constructs a EMRStep. |
| |
| Args: |
| name(str): The name of the EMR step. |
| display_name(str): The display name of the EMR step. |
| description(str): The description of the EMR step. |
| cluster_id(str): The ID of the running EMR cluster. |
| step_config(EMRStepConfig): One StepConfig to be executed by the job flow. |
| depends_on (List[Union[str, Step, StepCollection]]): A list of `Step`/`StepCollection` |
| names or `Step` instances or `StepCollection` instances that this `EMRStep` |
| depends on. |
| cache_config(CacheConfig): A `sagemaker.workflow.steps.CacheConfig` instance. |
| |
| """ |
| super(EMRStep, self).__init__(name, display_name, description, StepTypeEnum.EMR, depends_on) |
|
|
| emr_step_args = {"ClusterId": cluster_id, "StepConfig": step_config.to_request()} |
| self.args = emr_step_args |
| self.cache_config = cache_config |
|
|
| root_property = Properties(step_name=name, shape_name="Step", service_name="emr") |
| root_property.__dict__["ClusterId"] = cluster_id |
| self._properties = root_property |
|
|
| @property |
| def arguments(self) -> RequestType: |
| """The arguments dict that is used to call `AddJobFlowSteps`. |
| |
| NOTE: The AddFlowJobSteps request is not quite the args list that workflow needs. |
| The Name attribute in AddJobFlowSteps cannot be passed; it will be set during runtime. |
| In addition to that, we will also need to include emr job inputs and output config. |
| """ |
| return self.args |
|
|
| @property |
| def properties(self) -> RequestType: |
| """A Properties object representing the EMR DescribeStepResponse model""" |
| return self._properties |
|
|
| def to_request(self) -> RequestType: |
| """Updates the dictionary with cache configuration.""" |
| request_dict = super().to_request() |
| if self.cache_config: |
| request_dict.update(self.cache_config.config) |
| return request_dict |
|
|
