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#
# Licensed under the Apache License, Version 2.0 (the "License"). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the "license" file accompanying this file. This file is
# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
"""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
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