Task integrations for AWS StepFunctions
Project description
Tasks for AWS Step Functions
---
The APIs of higher level constructs in this module are experimental and under active development. They are subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model and breaking changes will be announced in the release notes. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
AWS Step Functions is a web service that enables you to coordinate the components of distributed applications and microservices using visual workflows. You build applications from individual components that each perform a discrete function, or task, allowing you to scale and change applications quickly.
A Task state represents a single unit of work performed by a state machine. All work in your state machine is performed by tasks.
This module is part of the AWS Cloud Development Kit project.
Table Of Contents
Task
A Task state represents a single unit of work performed by a state machine. In the
CDK, the exact work to be In the CDK, the exact work to be done is determined by
a class that implements IStepFunctionsTask.
AWS Step Functions integrates with some AWS services so that you can call API actions, and coordinate executions directly from the Amazon States Language in Step Functions. You can directly call and pass parameters to the APIs of those services.
Paths
In the Amazon States Language, a path is a string beginning with $ that you
can use to identify components within JSON text.
Learn more about input and output processing in Step Functions here
InputPath
Both InputPath and Parameters fields provide a way to manipulate JSON as it
moves through your workflow. AWS Step Functions applies the InputPath field first,
and then the Parameters field. You can first filter your raw input to a selection
you want using InputPath, and then apply Parameters to manipulate that input
further, or add new values. If you don't specify an InputPath, a default value
of $ will be used.
The following example provides the field named input as the input to the Task
state that runs a Lambda function.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
submit_job = tasks.LambdaInvoke(stack, "Invoke Handler",
lambda_function=submit_job_lambda,
input_path="$.input"
)
OutputPath
Tasks also allow you to select a portion of the state output to pass to the next
state. This enables you to filter out unwanted information, and pass only the
portion of the JSON that you care about. If you don't specify an OutputPath,
a default value of $ will be used. This passes the entire JSON node to the next
state.
The response from a Lambda function includes the response from the function as well as other metadata.
The following example assigns the output from the Task to a field named result
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
submit_job = tasks.LambdaInvoke(stack, "Invoke Handler",
lambda_function=submit_job_lambda,
output_path="$.Payload.result"
)
ResultPath
The output of a state can be a copy of its input, the result it produces (for
example, output from a Task state’s Lambda function), or a combination of its
input and result. Use ResultPath to control which combination of these is
passed to the state output. If you don't specify an ResultPath, a default
value of $ will be used.
The following example adds the item from calling DynamoDB's getItem API to the state
input and passes it to the next state.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.DynamoGetItem(self, "PutItem",
item={"MessageId": {"s": "12345"}},
table_name="my-table",
result_path="$.Item"
)
⚠️ The OutputPath is computed after applying ResultPath. All service integrations
return metadata as part of their response. When using ResultPath, it's not possible to
merge a subset of the task output to the input.
Task parameters from the state JSON
Most tasks take parameters. Parameter values can either be static, supplied directly
in the workflow definition (by specifying their values), or a value available at runtime
in the state machine's execution (either as its input or an output of a prior state).
Parameter values available at runtime can be specified via the Data class,
using methods such as JsonPath.stringAt().
The following example provides the field named input as the input to the Lambda function
and invokes it asynchronously.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
submit_job = tasks.LambdaInvoke(stack, "Invoke Handler",
lambda_function=submit_job_lambda,
payload=sfn.JsonPath.StringAt("$.input"),
invocation_type=tasks.InvocationType.EVENT
)
Each service integration has its own set of parameters that can be supplied.
Evaluate Expression
Use the EvaluateExpression to perform simple operations referencing state paths. The
expression referenced in the task will be evaluated in a Lambda function
(eval()). This allows you to not have to write Lambda code for simple operations.
Example: convert a wait time from milliseconds to seconds, concat this in a message and wait:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
convert_to_seconds = tasks.EvaluateExpression(self, "Convert to seconds",
expression="$.waitMilliseconds / 1000",
result_path="$.waitSeconds"
)
create_message = tasks.EvaluateExpression(self, "Create message",
# Note: this is a string inside a string.
expression="`Now waiting ${$.waitSeconds} seconds...`",
runtime=lambda.Runtime.NODEJS_10_X,
result_path="$.message"
)
publish_message = tasks.SnsPublish(self, "Publish message",
topic=topic,
message=sfn.TaskInput.from_data_at("$.message"),
result_path="$.sns"
)
wait = sfn.Wait(self, "Wait",
time=sfn.WaitTime.seconds_path("$.waitSeconds")
)
sfn.StateMachine(self, "StateMachine",
definition=convert_to_seconds.next(create_message).next(publish_message).next(wait)
)
The EvaluateExpression supports a runtime prop to specify the Lambda
runtime to use to evaluate the expression. Currently, the only runtime
supported is lambda.Runtime.NODEJS_10_X.
Batch
Step Functions supports Batch through the service integration pattern.
SubmitJob
The SubmitJob API submits an AWS Batch job from a job definition.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_batch as batch
import aws_cdk.aws_stepfunctions_tasks as tasks
batch_queue = batch.JobQueue(self, "JobQueue",
compute_environments=[JobQueueComputeEnvironment(
order=1,
compute_environment=batch.ComputeEnvironment(self, "ComputeEnv",
compute_resources=ComputeResources(vpc=vpc)
)
)
]
)
batch_job_definition = batch.JobDefinition(self, "JobDefinition",
container=JobDefinitionContainer(
image=ecs.ContainerImage.from_asset(path.resolve(__dirname, "batchjob-image"))
)
)
task = tasks.BatchSubmitJob(self, "Submit Job",
job_definition=batch_job_definition,
job_name="MyJob",
job_queue=batch_queue
)
DynamoDB
You can call DynamoDB APIs from a Task state.
Read more about calling DynamoDB APIs here
GetItem
The GetItem operation returns a set of attributes for the item with the given primary key.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.DynamoGetItem(self, "Get Item",
key={"message_id": tasks.DynamoAttributeValue.from_string("message-007")},
table=table
)
PutItem
The PutItem operation creates a new item, or replaces an old item with a new item.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.DynamoPutItem(self, "PutItem",
item={
"MessageId": tasks.DynamoAttributeValue.from_string("message-007"),
"Text": tasks.DynamoAttributeValue.from_string(sfn.JsonPath.string_at("$.bar")),
"TotalCount": tasks.DynamoAttributeValue.from_number(10)
},
table=table
)
DeleteItem
The DeleteItem operation deletes a single item in a table by primary key.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_stepfunctions as sfn
import aws_cdk.aws_stepfunctions_tasks as tasks
tasks.DynamoDeleteItem(self, "DeleteItem",
key={"MessageId": tasks.DynamoAttributeValue.from_string("message-007")},
table=table,
result_path=sfn.JsonPath.DISCARD
)
UpdateItem
The UpdateItem operation edits an existing item's attributes, or adds a new item to the table if it does not already exist.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.DynamoUpdateItem(self, "UpdateItem",
key={"MessageId": tasks.DynamoAttributeValue.from_string("message-007")},
table=table,
expression_attribute_values={
":val": tasks.DynamoAttributeValue.number_from_string(sfn.JsonPath.string_at("$.Item.TotalCount.N")),
":rand": tasks.DynamoAttributeValue.from_number(20)
},
update_expression="SET TotalCount = :val + :rand"
)
ECS
Step Functions supports ECS/Fargate through the service integration pattern.
RunTask
RunTask starts a new task using the specified task definition.
EC2
The EC2 launch type allows you to run your containerized applications on a cluster of Amazon EC2 instances that you manage.
When a task that uses the EC2 launch type is launched, Amazon ECS must determine where to place the task based on the requirements specified in the task definition, such as CPU and memory. Similarly, when you scale down the task count, Amazon ECS must determine which tasks to terminate. You can apply task placement strategies and constraints to customize how Amazon ECS places and terminates tasks. Learn more about task placement
The following example runs a job from a task definition on EC2
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_ecs as ecs
import aws_cdk.aws_stepfunctions_tasks as tasks
import aws_cdk.aws_stepfunctions as sfn
vpc = ec2.Vpc.from_lookup(stack, "Vpc",
is_default=True
)
cluster = ecs.Cluster(stack, "Ec2Cluster", vpc=vpc)
cluster.add_capacity("DefaultAutoScalingGroup",
instance_type=ec2.InstanceType("t2.micro"),
vpc_subnets=SubnetSelection(subnet_type=ec2.SubnetType.PUBLIC)
)
task_definition = ecs.TaskDefinition(stack, "TD",
compatibility=ecs.Compatibility.EC2
)
task_definition.add_container("TheContainer",
image=ecs.ContainerImage.from_registry("foo/bar"),
memory_limit_mi_b=256
)
run_task = tasks.EcsRunTask(stack, "Run",
integration_pattern=sfn.IntegrationPattern.RUN_JOB,
cluster=cluster,
task_definition=task_definition,
launch_target=tasks.EcsEc2LaunchTarget(
placement_strategies=[
ecs.PlacementStrategy.spread_across_instances(),
ecs.PlacementStrategy.packed_by_cpu(),
ecs.PlacementStrategy.randomly()
],
placement_constraints=[
ecs.PlacementConstraint.member_of("blieptuut")
]
)
)
Fargate
AWS Fargate is a serverless compute engine for containers that works with Amazon Elastic Container Service (ECS). Fargate makes it easy for you to focus on building your applications. Fargate removes the need to provision and manage servers, lets you specify and pay for resources per application, and improves security through application isolation by design. Learn more about Fargate
The Fargate launch type allows you to run your containerized applications without the need to provision and manage the backend infrastructure. Just register your task definition and Fargate launches the container for you.
The following example runs a job from a task definition on Fargate
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_ecs as ecs
import aws_cdk.aws_stepfunctions_tasks as tasks
import aws_cdk.aws_stepfunctions as sfn
vpc = ec2.Vpc.from_lookup(stack, "Vpc",
is_default=True
)
cluster = ecs.Cluster(stack, "FargateCluster", vpc=vpc)
task_definition = ecs.TaskDefinition(stack, "TD",
memory_mi_b="512",
cpu="256",
compatibility=ecs.Compatibility.FARGATE
)
container_definition = task_definition.add_container("TheContainer",
image=ecs.ContainerImage.from_registry("foo/bar"),
memory_limit_mi_b=256
)
run_task = tasks.EcsRunTask(stack, "RunFargate",
integration_pattern=sfn.IntegrationPattern.RUN_JOB,
cluster=cluster,
task_definition=task_definition,
container_overrides=[ContainerOverride(
container_definition=container_definition,
environment=[TaskEnvironmentVariable(name="SOME_KEY", value=sfn.JsonPath.string_at("$.SomeKey"))]
)],
launch_target=tasks.EcsFargateLaunchTarget()
)
EMR
Step Functions supports Amazon EMR through the service integration pattern. The service integration APIs correspond to Amazon EMR APIs but differ in the parameters that are used.
Read more about the differences when using these service integrations.
Create Cluster
Creates and starts running a cluster (job flow).
Corresponds to the runJobFlow API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cluster_role = iam.Role(stack, "ClusterRole",
assumed_by=iam.ServicePrincipal("ec2.amazonaws.com")
)
service_role = iam.Role(stack, "ServiceRole",
assumed_by=iam.ServicePrincipal("elasticmapreduce.amazonaws.com")
)
auto_scaling_role = iam.Role(stack, "AutoScalingRole",
assumed_by=iam.ServicePrincipal("elasticmapreduce.amazonaws.com")
)
auto_scaling_role.assume_role_policy.add_statements(
iam.PolicyStatement(
effect=iam.Effect.ALLOW,
principals=[
iam.ServicePrincipal("application-autoscaling.amazonaws.com")
],
actions=["sts:AssumeRole"
]
))
tasks.EmrCreateCluster(stack, "Create Cluster",
instances={},
cluster_role=cluster_role,
name=sfn.TaskInput.from_data_at("$.ClusterName").value,
service_role=service_role,
auto_scaling_role=auto_scaling_role,
integration_pattern=sfn.ServiceIntegrationPattern.FIRE_AND_FORGET
)
Termination Protection
Locks a cluster (job flow) so the EC2 instances in the cluster cannot be terminated by user intervention, an API call, or a job-flow error.
Corresponds to the setTerminationProtection API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.EmrSetClusterTerminationProtection(stack, "Task",
cluster_id="ClusterId",
termination_protected=False
)
Terminate Cluster
Shuts down a cluster (job flow).
Corresponds to the terminateJobFlows API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.EmrTerminateCluster(stack, "Task",
cluster_id="ClusterId"
)
Add Step
Adds a new step to a running cluster.
Corresponds to the addJobFlowSteps API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.EmrAddStep(stack, "Task",
cluster_id="ClusterId",
name="StepName",
jar="Jar",
action_on_failure=tasks.ActionOnFailure.CONTINUE
)
Cancel Step
Cancels a pending step in a running cluster.
Corresponds to the cancelSteps API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.EmrCancelStep(stack, "Task",
cluster_id="ClusterId",
step_id="StepId"
)
Modify Instance Fleet
Modifies the target On-Demand and target Spot capacities for the instance fleet with the specified InstanceFleetName.
Corresponds to the modifyInstanceFleet API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
sfn.EmrModifyInstanceFleetByName(stack, "Task",
cluster_id="ClusterId",
instance_fleet_name="InstanceFleetName",
target_on_demand_capacity=2,
target_spot_capacity=0
)
Modify Instance Group
Modifies the number of nodes and configuration settings of an instance group.
Corresponds to the modifyInstanceGroups API in EMR.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.EmrModifyInstanceGroupByName(stack, "Task",
cluster_id="ClusterId",
instance_group_name=sfn.JsonPath.string_at("$.InstanceGroupName"),
instance_group={
"instance_count": 1
}
)
Glue
Step Functions supports AWS Glue through the service integration pattern.
You can call the StartJobRun API from a Task state.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
GlueStartJobRun(stack, "Task",
job_name="my-glue-job",
arguments={
"key": "value"
},
timeout=cdk.Duration.minutes(30),
notify_delay_after=cdk.Duration.minutes(5)
)
Lambda
Invoke a Lambda function.
You can specify the input to your Lambda function through the payload attribute.
By default, Step Functions invokes Lambda function with the state input (JSON path '$')
as the input.
The following snippet invokes a Lambda Function with the state input as the payload
by referencing the $ path.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda
import aws_cdk.aws_stepfunctions as sfn
import aws_cdk.aws_stepfunctions_tasks as tasks
my_lambda = lambda.Function(self, "my sample lambda",
code=Code.from_inline("exports.handler = async () => {\n return {\n statusCode: '200',\n body: 'hello, world!'\n };\n };"),
runtime=Runtime.NODEJS_12_X,
handler="index.handler"
)
tasks.LambdaInvoke(self, "Invoke with state input",
lambda_function=my_lambda
)
When a function is invoked, the Lambda service sends these response elements back.
⚠️ The response from the Lambda function is in an attribute called Payload
The following snippet invokes a Lambda Function by referencing the $.Payload path
to reference the output of a Lambda executed before it.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.LambdaInvoke(self, "Invoke with empty object as payload",
lambda_function=my_lambda,
payload=sfn.TaskInput.from_object()
)
# use the output of myLambda as input
tasks.LambdaInvoke(self, "Invoke with payload field in the state input",
lambda_function=my_other_lambda,
payload=sfn.TaskInput.from_data_at("$.Payload")
)
The following snippet invokes a Lambda and sets the task output to only include the Lambda function response.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.LambdaInvoke(self, "Invoke and set function response as task output",
lambda_function=my_lambda,
payload=sfn.TaskInput.from_data_at("$"),
output_path="$.Payload"
)
You can have Step Functions pause a task, and wait for an external process to return a task token. Read more about the callback pattern
To use the callback pattern, set the token property on the task. Call the Step
Functions SendTaskSuccess or SendTaskFailure APIs with the token to
indicate that the task has completed and the state machine should resume execution.
The following snippet invokes a Lambda with the task token as part of the input to the Lambda.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
tasks.LambdaInvoke(stack, "Invoke with callback",
lambda_function=my_lambda,
integration_pattern=sfn.IntegrationPattern.WAIT_FOR_TASK_TOKEN,
payload=sfn.TaskInput.from_object(
token=sfn.JsonPath.task_token,
input=sfn.JsonPath.string_at("$.someField")
)
)
⚠️ The task will pause until it receives that task token back with a SendTaskSuccess or SendTaskFailure
call. Learn more about Callback with the Task
Token.
SageMaker
Step Functions supports AWS SageMaker through the service integration pattern.
Create Training Job
You can call the CreateTrainingJob API from a Task state.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
sfn.SagemakerTrainTask(self, "TrainSagemaker",
training_job_name=sfn.JsonPath.string_at("$.JobName"),
role=role,
algorithm_specification={
"algorithm_name": "BlazingText",
"training_input_mode": tasks.InputMode.FILE
},
input_data_config=[{
"channel_name": "train",
"data_source": {
"s3_data_source": {
"s3_data_type": tasks.S3DataType.S3_PREFIX,
"s3_location": tasks.S3Location.from_json_expression("$.S3Bucket")
}
}
}],
output_data_config={
"s3_output_location": tasks.S3Location.from_bucket(s3.Bucket.from_bucket_name(stack, "Bucket", "mybucket"), "myoutputpath")
},
resource_config={
"instance_count": 1,
"instance_type": ec2.InstanceType.of(ec2.InstanceClass.P3, ec2.InstanceSize.XLARGE2),
"volume_size": cdk.Size.gibibytes(50)
},
stopping_condition={
"max_runtime": cdk.Duration.hours(1)
}
)
Create Transform Job
You can call the CreateTransformJob API from a Task state.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
sfn.SagemakerTransformTask(self, "Batch Inference",
transform_job_name="MyTransformJob",
model_name="MyModelName",
role=role,
transform_input={
"transform_data_source": {
"s3_data_source": {
"s3_uri": "s3://inputbucket/train",
"s3_data_type": S3DataType.S3Prefix
}
}
},
transform_output={
"s3_output_path": "s3://outputbucket/TransformJobOutputPath"
},
transform_resources={
"instance_count": 1,
"instance_type": ec2.InstanceType.of(ec2.InstanceClass.M4, ec2.InstanceSize.XLarge)
}
)
SNS
Step Functions supports Amazon SNS through the service integration pattern.
You can call the Publish API from a Task state to publish to an SNS topic.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_sns as sns
import aws_cdk.aws_stepfunctions as sfn
import aws_cdk.aws_stepfunctions_tasks as tasks
# ...
topic = sns.Topic(self, "Topic")
# Use a field from the execution data as message.
task1 = tasks.SnsPublish(self, "Publish1",
topic=topic,
integration_pattern=sfn.IntegrationPattern.REQUEST_RESPONSE,
message=sfn.TaskInput.from_data_at("$.state.message")
)
# Combine a field from the execution data with
# a literal object.
task2 = tasks.SnsPublish(self, "Publish2",
topic=topic,
message=sfn.TaskInput.from_object({
"field1": "somedata",
"field2": sfn.JsonPath.string_at("$.field2")
})
)
Step Functions
You can manage AWS Step Functions executions.
AWS Step Functions supports it's own StartExecution API as a service integration.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# Define a state machine with one Pass state
child = sfn.StateMachine(stack, "ChildStateMachine",
definition=sfn.Chain.start(sfn.Pass(stack, "PassState"))
)
# Include the state machine in a Task state with callback pattern
task = StepFunctionsStartExecution(stack, "ChildTask",
state_machine=child,
integration_pattern=sfn.IntegrationPattern.WAIT_FOR_TASK_TOKEN,
input=sfn.TaskInput.from_object(
token=sfn.JsonPath.task_token,
foo="bar"
),
name="MyExecutionName"
)
# Define a second state machine with the Task state above
sfn.StateMachine(stack, "ParentStateMachine",
definition=task
)
SQS
Step Functions supports Amazon SQS
You can call the SendMessage API from a Task state
to send a message to an SQS queue.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_stepfunctions as sfn
import aws_cdk.aws_stepfunctions_tasks as tasks
import aws_cdk.aws_sqs as sqs
# ...
queue = sqs.Queue(self, "Queue")
# Use a field from the execution data as message.
task1 = tasks.SqsSendMessage(self, "Send1",
queue=queue,
message_body=sfn.TaskInput.from_data_at("$.message")
)
# Combine a field from the execution data with
# a literal object.
task2 = tasks.SqsSendMessage(self, "Send2",
queue=queue,
message_body=sfn.TaskInput.from_object({
"field1": "somedata",
"field2": sfn.JsonPath.string_at("$.field2")
})
)
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