'Arg2'
],
code=step_code
),
cluster_id=sfn.TaskInput.from_data_at('$.LaunchClusterResult.ClusterId').value)
phase_1.branch(step_task)
# Define an AddStep Task for the Validation Step
validate_phase_1 = emr_chains.AddStepWithArgumentOverrides(
stack, 'ValidatePhase1',
emr_step=emr_code.EMRStep(
name='Validate Phase 1',
jar='s3://us-west-2.elasticmapreduce/libs/script-runner/script-runner.jar',
args=[
f'{step_code.s3_path}/phase_1/test_validation.sh',
'Arg1',
'Arg2'
],
code=step_code
),
cluster_id=sfn.TaskInput.from_data_at('$.LaunchClusterResult.ClusterId').value,
result_path='$.ValidatePhase1Result',
fail_chain=fail)
# Create a Parallel Task for the Phase 2 Steps
phase_2 = sfn.Parallel(stack, 'Phase2', result_path='$.Result.Phase2')
# Add a Failure catch to our Parallel phase
phase_2.add_catch(terminate_failed_cluster, errors=['States.ALL'], result_path='$.Error')
def __init__(self, scope: core.Construct, id: str, emr_launch_stack,
artifact_bucket, output_bucket, **kwargs):
super().__init__(scope, id, **kwargs)
launch_function = emr_launch_stack.launch_function
# Create DynamoDB table for tracking
dynamo_table = dynamo.Table(
self,
"dynamotable",
partition_key=dynamo.Attribute(name="BatchId",
type=dynamo.AttributeType.STRING),
sort_key=dynamo.Attribute(name="Name",
type=dynamo.AttributeType.STRING),
billing_mode=dynamo.BillingMode.PAY_PER_REQUEST)
emr_role = aws_iam.Role.from_role_arn(
self, "emr_role_iam", role_arn=emr_launch_stack.instance_role_arn)
emr_role.add_to_policy(
aws_iam.PolicyStatement(actions=["dynamodb:*"],
resources=[dynamo_table.table_arn]))
emr_role.add_to_policy(
aws_iam.PolicyStatement(actions=[
"logs:CreateLogStream", "logs:DescribeLogStreams",
"logs:CreateLogGroup", "logs:PutLogEvents", "ec2:DescribeTags"
],
resources=["*"]))
# SNS Topics for Success/Failures messages from our Pipeline
success_topic = sns.Topic(self, 'SuccessTopic')
failure_topic = sns.Topic(self, 'FailureTopic')
# Upload artifacts to S3
step_code = s3d.BucketDeployment(
self,
id='sparkscript',
destination_bucket=artifact_bucket,
destination_key_prefix='steps',
sources=[
s3d.Source.asset('infrastructure/emr_orchestration/steps/')
])
# Create a Chain to receive Failure messages
fail = emr_chains.Fail(self,
'FailChain',
message=sfn.TaskInput.from_data_at('$.Error'),
subject='Pipeline Failure',
topic=failure_topic)
# # Define a Task to Terminate the Cluster on failure
terminate_failed_cluster = emr_tasks.TerminateClusterBuilder.build(
self,
'TerminateFailedCluster',
name='Terminate Failed Cluster',
cluster_id=sfn.TaskInput.from_data_at(
'$.LaunchClusterResult.ClusterId').value,
result_path='$.TerminateResult').add_catch(fail,
errors=['States.ALL'],
result_path='$.Error')
terminate_failed_cluster.next(fail)
# Use a NestedStateMachine to launch the cluster
launch_cluster = emr_chains.NestedStateMachine(
self,
'NestedStateMachine',
name='Launch Cluster StateMachine',
state_machine=launch_function.state_machine,
fail_chain=fail)
pyspark_step = emr_chains.AddStepWithArgumentOverrides(
self,
'PySparkSceneDetection',
emr_step=emr_code.EMRStep(
name=f'Scene Detection - PySpark Job',
jar='command-runner.jar',
args=[
'spark-submit', '--master', 'yarn', '--deploy-mode',
'cluster', '--packages',
'com.audienceproject:spark-dynamodb_2.12:1.1.2',
os.path.join(f's3://{artifact_bucket.bucket_name}',
'steps', 'scene_detection.py'), '--batch-id',
'DynamoDB.BatchId', '--batch-metadata-table-name',
dynamo_table.table_name, '--output-bucket',
output_bucket.bucket_name, '--synchronized-table-name',
'synchronized-signals'
]),
cluster_id=sfn.TaskInput.from_data_at(
'$.LaunchClusterResult.ClusterId').value,
result_path='$.PySparkResult',
fail_chain=terminate_failed_cluster)
# Define a Task to Terminate the Cluster
terminate_cluster = emr_tasks.TerminateClusterBuilder.build(
self,
'TerminateCluster',
name='Terminate Cluster',
cluster_id=sfn.TaskInput.from_data_at(
'$.LaunchClusterResult.ClusterId').value,
result_path='$.TerminateResult').add_catch(fail,
errors=['States.ALL'],
result_path='$.Error')
# A Chain for Success notification when the pipeline completes
success = emr_chains.Success(
self,
'SuccessChain',
message=sfn.TaskInput.from_data_at('$.TerminateResult'),
subject='Pipeline Succeeded',
topic=success_topic)
# Assemble the Pipeline
definition = sfn.Chain \
.start(launch_cluster) \
.next(pyspark_step) \
.next(terminate_cluster) \
.next(success)
# Create the State Machine
self.state_machine = sfn.StateMachine(
self,
'SceneDetectionStateMachine',
state_machine_name='scene-detection-pipeline',
definition=definition)
self.dynamo_table = dynamo_table
def test_add_step_with_argument_overrides():
default_fragment_json = {
'Type':
'Parallel',
'End':
True,
'Branches': [{
'StartAt': 'test-fragment: test-step - Override Args',
'States': {
'test-fragment: test-step - Override Args': {
'Next':
'test-fragment: test-step',
'Catch': [{
'ErrorEquals': ['States.ALL'],
'ResultPath': '$.Error',
'Next': 'test-fail'
}],
'Parameters': {
'ExecutionInput.$': '$$.Execution.Input',
'StepName': 'test-step',
'Args': ['Arg1', 'Arg2']
},
'Type':
'Task',
'Resource': {
'Fn::GetAtt': ['OverrideStepArgsE9376C9F', 'Arn']
},
'ResultPath':
'$.test-fragmentResultArgs'
},
'test-fragment: test-step': {
'End':
True,
'Catch': [{
'ErrorEquals': ['States.ALL'],
'ResultPath': '$.Error',
'Next': 'test-fail'
}],
'Parameters': {
'ClusterId': 'test-cluster-id',
'Step': {
'Name': 'test-step',
'ActionOnFailure': 'CONTINUE',
'HadoopJarStep': {
'Jar': 'Jar',
'MainClass': 'Main',
'Args.$': '$.test-fragmentResultArgs',
'Properties': []
}
}
},
'Type':
'Task',
'Resource': {
'Fn::Join': [
'',
[
'arn:', {
'Ref': 'AWS::Partition'
}, ':states:::elasticmapreduce:addStep.sync'
]
]
}
},
'test-fail': {
'Type': 'Fail'
}
}
}]
}
stack = core.Stack(core.App(), 'test-stack')
fragment = emr_chains.AddStepWithArgumentOverrides(
stack,
'test-fragment',
emr_step=emr_code.EMRStep('test-step', 'Jar', 'Main',
['Arg1', 'Arg2']),
cluster_id='test-cluster-id',
fail_chain=sfn.Fail(stack, 'test-fail'))
print_and_assert(default_fragment_json, fragment)