Metadata-Version: 2.1
Name: aws-cdk.aws-stepfunctions
Version: 1.47.1
Summary: The CDK Construct Library for AWS::StepFunctions
Home-page: https://github.com/aws/aws-cdk
Author: Amazon Web Services
License: Apache-2.0
Project-URL: Source, https://github.com/aws/aws-cdk.git
Description: ## AWS Step Functions Construct Library
        
        <!--BEGIN STABILITY BANNER-->---
        
        
        ![cfn-resources: Stable](https://img.shields.io/badge/cfn--resources-stable-success.svg?style=for-the-badge)
        
        > All classes with the `Cfn` prefix in this module ([CFN Resources](https://docs.aws.amazon.com/cdk/latest/guide/constructs.html#constructs_lib)) are always stable and safe to use.
        
        ![cdk-constructs: Experimental](https://img.shields.io/badge/cdk--constructs-experimental-important.svg?style=for-the-badge)
        
        > 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](https://semver.org/) 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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.
        
        ---
        <!--END STABILITY BANNER-->
        
        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](https://docs.aws.amazon.com/step-functions/latest/dg/job-status-poller-sample.html)):
        
        ### TypeScript example
        
        ```python
        # 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:
        
        ```python
        # 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](https://states-language.net/spec.html). 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](https://docs.aws.amazon.com/step-functions/latest/dg/connect-to-resource.html) 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.
        
        ```python
        # 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={"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`.
        
        ```python
        # 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.DataAt("$.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](https://docs.aws.amazon.com/step-functions/latest/dg/amazon-states-language-pass-state.html)
        
        ### 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.
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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.
        
        ```python
        # 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.
        
        ```python
        # 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.
        
        ```python
        # 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.
        
        ```python
        # 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.Data.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](https://docs.aws.amazon.com/step-functions/latest/dg/concepts-service-integrations.html) 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](https://docs.aws.amazon.com/step-functions/latest/dg/tutorial-code-snippet.html#tutorial-code-snippet-1) 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.
        
        ```python
        # 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`:
        
        ```python
        # 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`:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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.
        
        ```python
        # 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.
        
        ```python
        # 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:
        
        ```python
        # 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:
        
        ```python
        # 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.
        
        ```python
        # 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
        
        ```python
        # 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")
        ```
        
        ## Future work
        
        Contributions welcome:
        
        * [ ] A single `LambdaTask` class that is both a `Lambda` and a `Task` in one
          might make for a nice API.
        * [ ] Expression parser for Conditions.
        * [ ] Simulate state machines in unit tests.
        
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