AWS Step Functions Construct Library

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The @aws-cdk/aws-stepfunctions package contains constructs for building serverless workflows using objects. Use this in conjunction with the @aws-cdk/aws-stepfunctions-tasks package, which contains classes used to call other AWS services.

Defining a workflow looks like this (for the Step Functions Job Poller example):

Example

# 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_lambda as lambda

submit_lambda = lambda.Function(self, "SubmitLambda", ...)
get_status_lambda = lambda.Function(self, "CheckLambda", ...)

submit_job = tasks.LambdaInvoke(self, "Submit Job",
    lambda_function=submit_lambda,
    # Lambda's result is in the attribute `Payload`
    output_path="$.Payload"
)

wait_x = sfn.Wait(self, "Wait X Seconds",
    time=sfn.WaitTime.seconds_path("$.waitSeconds")
)

get_status = tasks.LambdaInvoke(self, "Get Job Status",
    lambda_function=get_status_lambda,
    # Pass just the field named "guid" into the Lambda, put the
    # Lambda's result in a field called "status" in the response
    input_path="$.guid",
    output_path="$.Payload"
)

job_failed = sfn.Fail(self, "Job Failed",
    cause="AWS Batch Job Failed",
    error="DescribeJob returned FAILED"
)

final_status = tasks.LambdaInvoke(self, "Get Final Job Status",
    lambda_function=get_status_lambda,
    # Use "guid" field as input
    input_path="$.guid",
    output_path="$.Payload"
)

definition = submit_job.next(wait_x).next(get_status).next(sfn.Choice(self, "Job Complete?").when(sfn.Condition.string_equals("$.status", "FAILED"), job_failed).when(sfn.Condition.string_equals("$.status", "SUCCEEDED"), final_status).otherwise(wait_x))

sfn.StateMachine(self, "StateMachine",
    definition=definition,
    timeout=Duration.minutes(5)
)

You can find more sample snippets and learn more about the service integrations in the @aws-cdk/aws-stepfunctions-tasks package.

State Machine

A stepfunctions.StateMachine is a resource that takes a state machine definition. The definition is specified by its start state, and encompasses all states reachable from the start state:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
start_state = stepfunctions.Pass(self, "StartState")

stepfunctions.StateMachine(self, "StateMachine",
    definition=start_state
)

State machines execute using an IAM Role, which will automatically have all permissions added that are required to make all state machine tasks execute properly (for example, permissions to invoke any Lambda functions you add to your workflow). A role will be created by default, but you can supply an existing one as well.

Amazon States Language

This library comes with a set of classes that model the Amazon States Language. The following State classes are supported:

  • ``Task` <#task>`_

  • ``Pass` <#pass>`_

  • ``Wait` <#wait>`_

  • ``Choice` <#choice>`_

  • ``Parallel` <#parallel>`_

  • ``Succeed` <#succeed>`_

  • ``Fail` <#fail>`_

  • ``Map` <#map>`_

  • ``Custom State` <#custom-state>`_

An arbitrary JSON object (specified at execution start) is passed from state to state and transformed during the execution of the workflow. For more information, see the States Language spec.

Task

A Task represents some work that needs to be done. The exact work to be done is determine by a class that implements IStepFunctionsTask, a collection of which can be found in the @aws-cdk/aws-stepfunctions-tasks module.

The tasks in the @aws-cdk/aws-stepfunctions-tasks module support the service integration pattern that integrates Step Functions with services directly in the Amazon States language.

Pass

A Pass state passes its input to its output, without performing work. Pass states are useful when constructing and debugging state machines.

The following example injects some fixed data into the state machine through the result field. The result field will be added to the input and the result will be passed as the state’s output.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# Makes the current JSON state { ..., "subObject": { "hello": "world" } }
pass = stepfunctions.Pass(self, "Add Hello World",
    result=stepfunctions.Result.from_object(hello="world"),
    result_path="$.subObject"
)

# Set the next state
pass.next(next_state)

The Pass state also supports passing key-value pairs as input. Values can be static, or selected from the input with a path.

The following example filters the greeting field from the state input and also injects a field called otherData.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
pass = stepfunctions.Pass(self, "Filter input and inject data",
    parameters={# input to the pass state
        "input": stepfunctions.JsonPath.string_at("$.input.greeting"),
        "other_data": "some-extra-stuff"}
)

The object specified in parameters will be the input of the Pass state. Since neither Result nor ResultPath are supplied, the Pass state copies its input through to its output.

Learn more about the Pass state

Wait

A Wait state waits for a given number of seconds, or until the current time hits a particular time. The time to wait may be taken from the execution’s JSON state.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# Wait until it's the time mentioned in the the state object's "triggerTime"
# field.
wait = stepfunctions.Wait(self, "Wait For Trigger Time",
    time=stepfunctions.WaitTime.timestamp_path("$.triggerTime")
)

# Set the next state
wait.next(start_the_work)

Choice

A Choice state can take a different path through the workflow based on the values in the execution’s JSON state:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
choice = stepfunctions.Choice(self, "Did it work?")

# Add conditions with .when()
choice.when(stepfunctions.Condition.string_equal("$.status", "SUCCESS"), success_state)
choice.when(stepfunctions.Condition.number_greater_than("$.attempts", 5), failure_state)

# Use .otherwise() to indicate what should be done if none of the conditions match
choice.otherwise(try_again_state)

If you want to temporarily branch your workflow based on a condition, but have all branches come together and continuing as one (similar to how an if ... then ... else works in a programming language), use the .afterwards() method:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
choice = stepfunctions.Choice(self, "What color is it?")
choice.when(stepfunctions.Condition.string_equal("$.color", "BLUE"), handle_blue_item)
choice.when(stepfunctions.Condition.string_equal("$.color", "RED"), handle_red_item)
choice.otherwise(handle_other_item_color)

# Use .afterwards() to join all possible paths back together and continue
choice.afterwards().next(ship_the_item)

If your Choice doesn’t have an otherwise() and none of the conditions match the JSON state, a NoChoiceMatched error will be thrown. Wrap the state machine in a Parallel state if you want to catch and recover from this.

Parallel

A Parallel state executes one or more subworkflows in parallel. It can also be used to catch and recover from errors in subworkflows.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
parallel = stepfunctions.Parallel(self, "Do the work in parallel")

# Add branches to be executed in parallel
parallel.branch(ship_item)
parallel.branch(send_invoice)
parallel.branch(restock)

# Retry the whole workflow if something goes wrong
parallel.add_retry(max_attempts=1)

# How to recover from errors
parallel.add_catch(send_failure_notification)

# What to do in case everything succeeded
parallel.next(close_order)

Succeed

Reaching a Succeed state terminates the state machine execution with a succesful status.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
success = stepfunctions.Succeed(self, "We did it!")

Fail

Reaching a Fail state terminates the state machine execution with a failure status. The fail state should report the reason for the failure. Failures can be caught by encompassing Parallel states.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
success = stepfunctions.Fail(self, "Fail",
    error="WorkflowFailure",
    cause="Something went wrong"
)

Map

A Map state can be used to run a set of steps for each element of an input array. A Map state will execute the same steps for multiple entries of an array in the state input.

While the Parallel state executes multiple branches of steps using the same input, a Map state will execute the same steps for multiple entries of an array in the state input.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
map = stepfunctions.Map(self, "Map State",
    max_concurrency=1,
    items_path=stepfunctions.JsonPath.string_at("$.inputForMap")
)
map.iterator(stepfunctions.Pass(self, "Pass State"))

Custom State

It’s possible that the high-level constructs for the states or stepfunctions-tasks do not have the states or service integrations you are looking for. The primary reasons for this lack of functionality are:

  • A service integration is available through Amazon States Langauge, but not available as construct classes in the CDK.

  • The state or state properties are available through Step Functions, but are not configurable through constructs

If a feature is not available, a CustomState can be used to supply any Amazon States Language JSON-based object as the state definition.

Code Snippets are available and can be plugged in as the state definition.

Custom states can be chained together with any of the other states to create your state machine definition. You will also need to provide any permissions that are required to the role that the State Machine uses.

The following example uses the DynamoDB service integration to insert data into a DynamoDB table.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_dynamodb as ddb
import aws_cdk.core as cdk
import aws_cdk.aws_stepfunctions as sfn

# create a table
table = ddb.Table(self, "montable",
    partition_key=Attribute(
        name="id",
        type=ddb.AttributeType.STRING
    )
)

final_status = sfn.Pass(stack, "final step")

# States language JSON to put an item into DynamoDB
# snippet generated from https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-code-snippet.html#tutorial-code-snippet-1
state_json = {
    "Type": "Task",
    "Resource": "arn:aws:states:::dynamodb:putItem",
    "Parameters": {
        "TableName": table.table_name,
        "Item": {
            "id": {
                "S": "MyEntry"
            }
        }
    },
    "ResultPath": null
}

# custom state which represents a task to insert data into DynamoDB
custom = sfn.CustomState(self, "my custom task",
    state_json=state_json
)

chain = sfn.Chain.start(custom).next(final_status)

sm = sfn.StateMachine(self, "StateMachine",
    definition=chain,
    timeout=cdk.Duration.seconds(30)
)

# don't forget permissions. You need to assign them
table.grant_write_data(sm.role)

Task Chaining

To make defining work flows as convenient (and readable in a top-to-bottom way) as writing regular programs, it is possible to chain most methods invocations. In particular, the .next() method can be repeated. The result of a series of .next() calls is called a Chain, and can be used when defining the jump targets of Choice.on or Parallel.branch:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
definition = step1.next(step2).next(choice.when(condition1, step3.next(step4).next(step5)).otherwise(step6).afterwards()).next(parallel.branch(step7.next(step8)).branch(step9.next(step10))).next(finish)

stepfunctions.StateMachine(self, "StateMachine",
    definition=definition
)

If you don’t like the visual look of starting a chain directly off the first step, you can use Chain.start:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
definition = stepfunctions.Chain.start(step1).next(step2).next(step3)

State Machine Fragments

It is possible to define reusable (or abstracted) mini-state machines by defining a construct that implements IChainable, which requires you to define two fields:

  • startState: State, representing the entry point into this state machine.

  • endStates: INextable[], representing the (one or more) states that outgoing transitions will be added to if you chain onto the fragment.

Since states will be named after their construct IDs, you may need to prefix the IDs of states if you plan to instantiate the same state machine fragment multiples times (otherwise all states in every instantiation would have the same name).

The class StateMachineFragment contains some helper functions (like prefixStates()) to make it easier for you to do this. If you define your state machine as a subclass of this, it will be convenient to use:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
class MyJob(stepfunctions.StateMachineFragment):

    def __init__(self, parent, id, *, jobFlavor):
        super().__init__(parent, id)

        first = stepfunctions.Task(self, "First", ...)
        # ...
        last = stepfunctions.Task(self, "Last", ...)

        self.start_state = first
        self.end_states = [last]

# Do 3 different variants of MyJob in parallel
stepfunctions.Parallel(self, "All jobs").branch(MyJob(self, "Quick", job_flavor="quick").prefix_states()).branch(MyJob(self, "Medium", job_flavor="medium").prefix_states()).branch(MyJob(self, "Slow", job_flavor="slow").prefix_states())

A few utility functions are available to parse state machine fragments.

  • State.findReachableStates: Retrieve the list of states reachable from a given state.

  • State.findReachableEndStates: Retrieve the list of end or terminal states reachable from a given state.

Activity

Activities represent work that is done on some non-Lambda worker pool. The Step Functions workflow will submit work to this Activity, and a worker pool that you run yourself, probably on EC2, will pull jobs from the Activity and submit the results of individual jobs back.

You need the ARN to do so, so if you use Activities be sure to pass the Activity ARN into your worker pool:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
activity = stepfunctions.Activity(self, "Activity")

# Read this CloudFormation Output from your application and use it to poll for work on
# the activity.
cdk.CfnOutput(self, "ActivityArn", value=activity.activity_arn)

Activity-Level Permissions

Granting IAM permissions to an activity can be achieved by calling the grant(principal, actions) API:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
activity = stepfunctions.Activity(self, "Activity")

role = iam.Role(stack, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)

activity.grant(role, "states:SendTaskSuccess")

This will grant the IAM principal the specified actions onto the activity.

Metrics

Task object expose various metrics on the execution of that particular task. For example, to create an alarm on a particular task failing:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cloudwatch.Alarm(self, "TaskAlarm",
    metric=task.metric_failed(),
    threshold=1,
    evaluation_periods=1
)

There are also metrics on the complete state machine:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cloudwatch.Alarm(self, "StateMachineAlarm",
    metric=state_machine.metric_failed(),
    threshold=1,
    evaluation_periods=1
)

And there are metrics on the capacity of all state machines in your account:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
cloudwatch.Alarm(self, "ThrottledAlarm",
    metric=StateTransitionMetrics.metric_throttled_events(),
    threshold=10,
    evaluation_periods=2
)

Logging

Enable logging to CloudWatch by passing a logging configuration with a destination LogGroup:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
log_group = logs.LogGroup(stack, "MyLogGroup")

stepfunctions.StateMachine(stack, "MyStateMachine",
    definition=stepfunctions.Chain.start(stepfunctions.Pass(stack, "Pass")),
    logs={
        "destinations": log_group,
        "level": stepfunctions.LogLevel.ALL
    }
)

State Machine Permission Grants

IAM roles, users, or groups which need to be able to work with a State Machine should be granted IAM permissions.

Any object that implements the IGrantable interface (has an associated principal) can be granted permissions by calling:

  • stateMachine.grantStartExecution(principal) - grants the principal the ability to execute the state machine

  • stateMachine.grantRead(principal) - grants the principal read access

  • stateMachine.grantTaskResponse(principal) - grants the principal the ability to send task tokens to the state machine

  • stateMachine.grantExecution(principal, actions) - grants the principal execution-level permissions for the IAM actions specified

  • stateMachine.grant(principal, actions) - grants the principal state-machine-level permissions for the IAM actions specified

Start Execution Permission

Grant permission to start an execution of a state machine by calling the grantStartExecution() API.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
role = iam.Role(stack, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)

state_machine = stepfunction.StateMachine(stack, "StateMachine",
    definition=definition
)

# Give role permission to start execution of state machine
state_machine.grant_start_execution(role)

The following permission is provided to a service principal by the grantStartExecution() API:

  • states:StartExecution - to state machine

Read Permissions

Grant read access to a state machine by calling the grantRead() API.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
role = iam.Role(stack, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)

state_machine = stepfunction.StateMachine(stack, "StateMachine",
    definition=definition
)

# Give role read access to state machine
state_machine.grant_read(role)

The following read permissions are provided to a service principal by the grantRead() API:

  • states:ListExecutions - to state machine

  • states:ListStateMachines - to state machine

  • states:DescribeExecution - to executions

  • states:DescribeStateMachineForExecution - to executions

  • states:GetExecutionHistory - to executions

  • states:ListActivities - to *

  • states:DescribeStateMachine - to *

  • states:DescribeActivity - to *

Task Response Permissions

Grant permission to allow task responses to a state machine by calling the grantTaskResponse() API:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
role = iam.Role(stack, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)

state_machine = stepfunction.StateMachine(stack, "StateMachine",
    definition=definition
)

# Give role task response permissions to the state machine
state_machine.grant_task_response(role)

The following read permissions are provided to a service principal by the grantRead() API:

  • states:SendTaskSuccess - to state machine

  • states:SendTaskFailure - to state machine

  • states:SendTaskHeartbeat - to state machine

Execution-level Permissions

Grant execution-level permissions to a state machine by calling the grantExecution() API:

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
role = iam.Role(stack, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)

state_machine = stepfunction.StateMachine(stack, "StateMachine",
    definition=definition
)

# Give role permission to get execution history of ALL executions for the state machine
state_machine.grant_execution(role, "states:GetExecutionHistory")

Custom Permissions

You can add any set of permissions to a state machine by calling the grant() API.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
user = iam.User(stack, "MyUser")

state_machine = stepfunction.StateMachine(stack, "StateMachine",
    definition=definition
)

# give user permission to send task success to the state machine
state_machine.grant(user, "states:SendTaskSuccess")

Import

Any Step Functions state machine that has been created outside the stack can be imported into your CDK stack.

State machines can be imported by their ARN via the StateMachine.fromStateMachineArn() API

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_stepfunctions as sfn

stack = Stack(app, "MyStack")
sfn.StateMachine.from_state_machine_arn(stack, "ImportedStateMachine", "arn:aws:states:us-east-1:123456789012:stateMachine:StateMachine2E01A3A5-N5TJppzoevKQ")