Metadata-Version: 2.1
Name: aws-cdk.aws-stepfunctions
Version: 1.29.0
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-->---
        
        
        ![Stability: Experimental](https://img.shields.io/badge/stability-Experimental-important.svg?style=for-the-badge)
        
        > **This is a *developer preview* (public beta) module.**
        >
        > All classes with the `Cfn` prefix in this module ([CFN Resources](https://docs.aws.amazon.com/cdk/latest/guide/constructs.html#constructs_lib))
        > are auto-generated from CloudFormation. They are stable and safe to use.
        >
        > However, all other classes, i.e., higher level constructs, are under active development and subject to non-backward
        > compatible changes or removal in any future version. These are not subject to the [Semantic Versioning](https://semver.org/) model.
        > This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.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
        
        submit_lambda = lambda.Function(self, "SubmitLambda", ...)
        get_status_lambda = lambda.Function(self, "CheckLambda", ...)
        
        submit_job = sfn.Task(self, "Submit Job",
            task=tasks.InvokeFunction(submit_lambda),
            # Put Lambda's result here in the execution's state object
            result_path="$.guid"
        )
        
        wait_x = sfn.Wait(self, "Wait X Seconds",
            duration=sfn.WaitTime.seconds_path("$.waitSeconds")
        )
        
        get_status = sfn.Task(self, "Get Job Status",
            task=tasks.InvokeFunction(get_status_lambda),
            # Pass just the field named "guid" into the Lambda, put the
            # Lambda's result in a field called "status"
            input_path="$.guid",
            result_path="$.status"
        )
        
        job_failed = sfn.Fail(self, "Job Failed",
            cause="AWS Batch Job Failed",
            error="DescribeJob returned FAILED"
        )
        
        final_status = sfn.Task(self, "Get Final Job Status",
            task=tasks.InvokeFunction(get_status_lambda),
            # Use "guid" field as input, output of the Lambda becomes the
            # entire state machine output.
            input_path="$.guid"
        )
        
        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)
        )
        ```
        
        ## 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`
        * `Pass`
        * `Wait`
        * `Choice`
        * `Parallel`
        * `Succeed`
        * `Fail`
        
        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` package. A
        couple of the tasks available are:
        
        * `tasks.InvokeActivity` -- start an Activity (Activities represent a work
          queue that you poll on a compute fleet you manage yourself)
        * `tasks.InvokeFunction` -- invoke a Lambda function with function ARN
        * `tasks.RunBatchJob` -- run a Batch job
        * `tasks.RunLambdaTask` -- call Lambda as integrated service with magic ARN
        * `tasks.RunGlueJobTask` -- call Glue Job as integrated service
        * `tasks.PublishToTopic` -- publish a message to an SNS topic
        * `tasks.SendToQueue` -- send a message to an SQS queue
        * `tasks.RunEcsFargateTask`/`ecs.RunEcsEc2Task` -- run a container task,
          depending on the type of capacity.
        * `tasks.SagemakerTrainTask` -- run a SageMaker training job
        * `tasks.SagemakerTransformTask` -- run a SageMaker transform job
        * `tasks.StartExecution` -- call StartExecution to a state machine of Step Functions
        * `tasks.EvaluateExpression` -- evaluate an expression referencing state paths
        
        Except `tasks.InvokeActivity` and `tasks.InvokeFunction`, the [service integration
        pattern](https://docs.aws.amazon.com/step-functions/latest/dg/connect-to-resource.html)
        (`integrationPattern`) are supposed to be given as parameter when customers want
        to call integrated services within a Task state. The default value is `FIRE_AND_FORGET`.
        
        #### Task parameters from the state json
        
        Many tasks take parameters. The values for those can either be supplied
        directly in the workflow definition (by specifying their values), or at
        runtime by passing a value obtained from the static functions on `Data`,
        such as `Data.stringAt()`.
        
        If so, the value is taken from the indicated location in the state JSON,
        similar to (for example) `inputPath`.
        
        #### Lambda example - InvokeFunction
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        task = sfn.Task(self, "Invoke1",
            task=tasks.InvokeFunction(my_lambda),
            input_path="$.input",
            timeout=Duration.minutes(5)
        )
        
        # Add a retry policy
        task.add_retry(
            interval=Duration.seconds(5),
            max_attempts=10
        )
        
        # Add an error handler
        task.add_catch(error_handler_state)
        
        # Set the next state
        task.next(next_state)
        ```
        
        #### Lambda example - RunLambdaTask
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        task = sfn.Task(stack, "Invoke2",
            task=tasks.RunLambdaTask(my_lambda,
                integration_pattern=sfn.ServiceIntegrationPattern.WAIT_FOR_TASK_TOKEN,
                payload={
                    "token": sfn.Context.task_token
                }
            )
        )
        ```
        
        #### Glue Job example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        task = sfn.Task(stack, "ETL",
            task=tasks.RunGlueJobTask(glue_job_name,
                integration_pattern=sfn.ServiceIntegrationPattern.SYNC,
                arguments={
                    "--table-prefix": "myTable"
                }
            )
        )
        ```
        
        [Example CDK app](../aws-stepfunctions-tasks/test/integ.glue-task.ts)
        
        #### Batch example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        import aws_cdk.aws_batch as batch
        
        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 = sfn.Task(self, "Submit Job",
            task=tasks.RunBatchJob(
                job_definition=batch_job_definition,
                job_name="MyJob",
                job_queue=batch_queue
            )
        )
        ```
        
        #### SNS example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        import aws_cdk.aws_sns as sns
        
        # ...
        
        topic = sns.Topic(self, "Topic")
        
        # Use a field from the execution data as message.
        task1 = sfn.Task(self, "Publish1",
            task=tasks.PublishToTopic(topic,
                integration_pattern=sfn.ServiceIntegrationPattern.FIRE_AND_FORGET,
                message=TaskInput.from_data_at("$.state.message")
            )
        )
        
        # Combine a field from the execution data with
        # a literal object.
        task2 = sfn.Task(self, "Publish2",
            task=tasks.PublishToTopic(topic,
                message=TaskInput.from_object(
                    field1="somedata",
                    field2=Data.string_at("$.field2")
                )
            )
        )
        ```
        
        #### SQS example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        import aws_cdk.aws_sqs as sqs
        
        # ...
        
        queue = sns.Queue(self, "Queue")
        
        # Use a field from the execution data as message.
        task1 = sfn.Task(self, "Send1",
            task=tasks.SendToQueue(queue,
                message_body=TaskInput.from_data_at("$.message"),
                # Only for FIFO queues
                message_group_id="1234"
            )
        )
        
        # Combine a field from the execution data with
        # a literal object.
        task2 = sfn.Task(self, "Send2",
            task=tasks.SendToQueue(queue,
                message_body=TaskInput.from_object(
                    field1="somedata",
                    field2=Data.string_at("$.field2")
                ),
                # Only for FIFO queues
                message_group_id="1234"
            )
        )
        ```
        
        #### ECS example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        import aws_cdk.aws_ecs as ecs
        
        # See examples in ECS library for initialization of 'cluster' and 'taskDefinition'
        
        fargate_task = ecs.RunEcsFargateTask(
            cluster=cluster,
            task_definition=task_definition,
            container_overrides=[{
                "container_name": "TheContainer",
                "environment": [{
                    "name": "CONTAINER_INPUT",
                    "value": Data.string_at("$.valueFromStateData")
                }
                ]
            }
            ]
        )
        
        fargate_task.connections.allow_to_default_port(rds_cluster, "Read the database")
        
        task = sfn.Task(self, "CallFargate",
            task=fargate_task
        )
        ```
        
        #### SageMaker Transform example
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        transform_job = tasks.SagemakerTransformTask(transform_job_name, "MyTransformJob", model_name, "MyModelName", 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)
        )
        
        task = sfn.Task(self, "Batch Inference",
            task=transform_job
        )
        ```
        
        #### Step Functions example
        
        ```python
        # 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 = sfn.Task(stack, "ChildTask",
            task=tasks.ExecuteStateMachine(child,
                integration_pattern=sfn.ServiceIntegrationPattern.WAIT_FOR_TASK_TOKEN,
                input={
                    "token": sfn.Context.task_token,
                    "foo": "bar"
                },
                name="MyExecutionName"
            )
        )
        
        # Define a second state machine with the Task state above
        sfn.StateMachine(stack, "ParentStateMachine",
            definition=task
        )
        ```
        
        #### Eval example
        
        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:
        
        ```python
        # Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
        convert_to_seconds = sfn.Task(self, "Convert to seconds",
            task=tasks.EvaluateExpression(expression="$.waitMilliseconds / 1000"),
            result_path="$.waitSeconds"
        )
        
        create_message = sfn.Task(self, "Create message",
            # Note: this is a string inside a string.
            task=tasks.EvaluateExpression(expression="`Now waiting ${$.waitSeconds} seconds...`"),
            result_path="$.message"
        )
        
        publish_message = sfn.Task(self, "Publish message",
            task=tasks.PublishToTopic(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`.
        
        ### Pass
        
        A `Pass` state does no work, but it can optionally transform the execution's
        JSON state.
        
        ```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)
        ```
        
        ### 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 differen 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"
        )
        ```
        
        ## 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())
        ```
        
        ## 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)
        ```
        
        ## 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
        )
        ```
        
        ## 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.
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Typing :: Typed
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved
Requires-Python: >=3.6
Description-Content-Type: text/markdown
