AWS Step Functions Construct Library

AWS CDK v1 has reached End-of-Support on 2023-06-01. This package is no longer being updated, and users should migrate to AWS CDK v2.

For more information on how to migrate, see the Migrating to AWS CDK v2 guide.

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

import aws_cdk.aws_lambda as lambda_

# submit_lambda: lambda.Function
# get_status_lambda: lambda.Function


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:

start_state = sfn.Pass(self, "StartState")

sfn.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.

Accessing State (the JsonPath class)

Every State Machine execution has State Machine Data: a JSON document containing keys and values that is fed into the state machine, gets modified as the state machine progresses, and finally is produced as output.

You can pass fragments of this State Machine Data into Tasks of the state machine. To do so, use the static methods on the JsonPath class. For example, to pass the value that's in the data key of OrderId to a Lambda function as you invoke it, use JsonPath.stringAt('$.OrderId'), like so:

import aws_cdk.aws_lambda as lambda_

# order_fn: lambda.Function


submit_job = tasks.LambdaInvoke(self, "InvokeOrderProcessor",
    lambda_function=order_fn,
    payload=sfn.TaskInput.from_object({
        "OrderId": sfn.JsonPath.string_at("$.OrderId")
    })
)

The following methods are available:

You can also call intrinsic functions using the methods on JsonPath:

Amazon States Language

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

• Task
• Pass
• Wait
• Choice
• Parallel
• Succeed
• Fail
• Map
• 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.

# Makes the current JSON state { ..., "subObject": { "hello": "world" } }
pass = sfn.Pass(self, "Add Hello World",
    result=sfn.Result.from_object({"hello": "world"}),
    result_path="$.subObject"
)

# Set the next state
next_state = sfn.Pass(self, "NextState")
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.

pass = sfn.Pass(self, "Filter input and inject data",
    parameters={ # input to the pass state
        "input": sfn.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.

# Wait until it's the time mentioned in the the state object's "triggerTime"
# field.
wait = sfn.Wait(self, "Wait For Trigger Time",
    time=sfn.WaitTime.timestamp_path("$.triggerTime")
)

# Set the next state
start_the_work = sfn.Pass(self, "StartTheWork")
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:

choice = sfn.Choice(self, "Did it work?")

# Add conditions with .when()
success_state = sfn.Pass(self, "SuccessState")
failure_state = sfn.Pass(self, "FailureState")
choice.when(sfn.Condition.string_equals("$.status", "SUCCESS"), success_state)
choice.when(sfn.Condition.number_greater_than("$.attempts", 5), failure_state)

# Use .otherwise() to indicate what should be done if none of the conditions match
try_again_state = sfn.Pass(self, "TryAgainState")
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:

choice = sfn.Choice(self, "What color is it?")
handle_blue_item = sfn.Pass(self, "HandleBlueItem")
handle_red_item = sfn.Pass(self, "HandleRedItem")
handle_other_item_color = sfn.Pass(self, "HanldeOtherItemColor")
choice.when(sfn.Condition.string_equals("$.color", "BLUE"), handle_blue_item)
choice.when(sfn.Condition.string_equals("$.color", "RED"), handle_red_item)
choice.otherwise(handle_other_item_color)

# Use .afterwards() to join all possible paths back together and continue
ship_the_item = sfn.Pass(self, "ShipTheItem")
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.

Available Conditions

see step function comparison operators

• Condition.isPresent - matches if a json path is present
• Condition.isNotPresent - matches if a json path is not present
• Condition.isString - matches if a json path contains a string
• Condition.isNotString - matches if a json path is not a string
• Condition.isNumeric - matches if a json path is numeric
• Condition.isNotNumeric - matches if a json path is not numeric
• Condition.isBoolean - matches if a json path is boolean
• Condition.isNotBoolean - matches if a json path is not boolean
• Condition.isTimestamp - matches if a json path is a timestamp
• Condition.isNotTimestamp - matches if a json path is not a timestamp
• Condition.isNotNull - matches if a json path is not null
• Condition.isNull - matches if a json path is null
• Condition.booleanEquals - matches if a boolean field has a given value
• Condition.booleanEqualsJsonPath - matches if a boolean field equals a value in a given mapping path
• Condition.stringEqualsJsonPath - matches if a string field equals a given mapping path
• Condition.stringEquals - matches if a field equals a string value
• Condition.stringLessThan - matches if a string field sorts before a given value
• Condition.stringLessThanJsonPath - matches if a string field sorts before a value at given mapping path
• Condition.stringLessThanEquals - matches if a string field sorts equal to or before a given value
• Condition.stringLessThanEqualsJsonPath - matches if a string field sorts equal to or before a given mapping
• Condition.stringGreaterThan - matches if a string field sorts after a given value
• Condition.stringGreaterThanJsonPath - matches if a string field sorts after a value at a given mapping path
• Condition.stringGreaterThanEqualsJsonPath - matches if a string field sorts after or equal to value at a given mapping path
• Condition.stringGreaterThanEquals - matches if a string field sorts after or equal to a given value
• Condition.numberEquals - matches if a numeric field has the given value
• Condition.numberEqualsJsonPath - matches if a numeric field has the value in a given mapping path
• Condition.numberLessThan - matches if a numeric field is less than the given value
• Condition.numberLessThanJsonPath - matches if a numeric field is less than the value at the given mapping path
• Condition.numberLessThanEquals - matches if a numeric field is less than or equal to the given value
• Condition.numberLessThanEqualsJsonPath - matches if a numeric field is less than or equal to the numeric value at given mapping path
• Condition.numberGreaterThan - matches if a numeric field is greater than the given value
• Condition.numberGreaterThanJsonPath - matches if a numeric field is greater than the value at a given mapping path
• Condition.numberGreaterThanEquals - matches if a numeric field is greater than or equal to the given value
• Condition.numberGreaterThanEqualsJsonPath - matches if a numeric field is greater than or equal to the value at a given mapping path
• Condition.timestampEquals - matches if a timestamp field is the same time as the given timestamp
• Condition.timestampEqualsJsonPath - matches if a timestamp field is the same time as the timestamp at a given mapping path
• Condition.timestampLessThan - matches if a timestamp field is before the given timestamp
• Condition.timestampLessThanJsonPath - matches if a timestamp field is before the timestamp at a given mapping path
• Condition.timestampLessThanEquals - matches if a timestamp field is before or equal to the given timestamp
• Condition.timestampLessThanEqualsJsonPath - matches if a timestamp field is before or equal to the timestamp at a given mapping path
• Condition.timestampGreaterThan - matches if a timestamp field is after the timestamp at a given mapping path
• Condition.timestampGreaterThanJsonPath - matches if a timestamp field is after the timestamp at a given mapping path
• Condition.timestampGreaterThanEquals - matches if a timestamp field is after or equal to the given timestamp
• Condition.timestampGreaterThanEqualsJsonPath - matches if a timestamp field is after or equal to the timestamp at a given mapping path
• Condition.stringMatches - matches if a field matches a string pattern that can contain a wild card () e.g: log-.txt or LATEST. No other characters other than "" have any special meaning - * can be escaped: \

Parallel

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

parallel = sfn.Parallel(self, "Do the work in parallel")

# Add branches to be executed in parallel
ship_item = sfn.Pass(self, "ShipItem")
send_invoice = sfn.Pass(self, "SendInvoice")
restock = sfn.Pass(self, "Restock")
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
send_failure_notification = sfn.Pass(self, "SendFailureNotification")
parallel.add_catch(send_failure_notification)

# What to do in case everything succeeded
close_order = sfn.Pass(self, "CloseOrder")
parallel.next(close_order)

Succeed

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

success = sfn.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.

success = sfn.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.

map = sfn.Map(self, "Map State",
    max_concurrency=1,
    items_path=sfn.JsonPath.string_at("$.inputForMap")
)
map.iterator(sfn.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.

import aws_cdk.aws_dynamodb as dynamodb


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

final_status = sfn.Pass(self, "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=Duration.seconds(30)
)

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

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:

step1 = sfn.Pass(self, "Step1")
step2 = sfn.Pass(self, "Step2")
step3 = sfn.Pass(self, "Step3")
step4 = sfn.Pass(self, "Step4")
step5 = sfn.Pass(self, "Step5")
step6 = sfn.Pass(self, "Step6")
step7 = sfn.Pass(self, "Step7")
step8 = sfn.Pass(self, "Step8")
step9 = sfn.Pass(self, "Step9")
step10 = sfn.Pass(self, "Step10")
choice = sfn.Choice(self, "Choice")
condition1 = sfn.Condition.string_equals("$.status", "SUCCESS")
parallel = sfn.Parallel(self, "Parallel")
finish = sfn.Pass(self, "Finish")

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)

sfn.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:

step1 = sfn.Pass(self, "Step1")
step2 = sfn.Pass(self, "Step2")
step3 = sfn.Pass(self, "Step3")

definition = sfn.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:

from aws_cdk.core import Stack
from constructs import Construct
import aws_cdk.aws_stepfunctions as sfn

class MyJob(sfn.StateMachineFragment):

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

        choice = sfn.Choice(self, "Choice").when(sfn.Condition.string_equals("$.branch", "left"), sfn.Pass(self, "Left Branch")).when(sfn.Condition.string_equals("$.branch", "right"), sfn.Pass(self, "Right Branch"))

        # ...

        self.start_state = choice
        self.end_states = choice.afterwards().end_states

class MyStack(Stack):
    def __init__(self, scope, id):
        super().__init__(scope, id)
        # Do 3 different variants of MyJob in parallel
        parallel = sfn.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())

        sfn.StateMachine(self, "MyStateMachine",
            definition=parallel
        )

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:

activity = sfn.Activity(self, "Activity")

# Read this CloudFormation Output from your application and use it to poll for work on
# the activity.
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:

activity = sfn.Activity(self, "Activity")

role = iam.Role(self, "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:

# task: sfn.Task

cloudwatch.Alarm(self, "TaskAlarm",
    metric=task.metric_failed(),
    threshold=1,
    evaluation_periods=1
)

There are also metrics on the complete state machine:

# state_machine: sfn.StateMachine

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:

cloudwatch.Alarm(self, "ThrottledAlarm",
    metric=sfn.StateTransitionMetric.metric_throttled_events(),
    threshold=10,
    evaluation_periods=2
)

Error names

Step Functions identifies errors in the Amazon States Language using case-sensitive strings, known as error names. The Amazon States Language defines a set of built-in strings that name well-known errors, all beginning with the States. prefix.

• States.ALL - A wildcard that matches any known error name.

• States.Runtime - An execution failed due to some exception that could not be processed. Often these are caused by errors at runtime, such as attempting to apply InputPath or OutputPath on a null JSON payload. A States.Runtime error is not retriable, and will always cause the execution to fail. A retry or catch on States.ALL will NOT catch States.Runtime errors.

• States.DataLimitExceeded - A States.DataLimitExceeded exception will be thrown for the following:

  • When the output of a connector is larger than payload size quota.
  • When the output of a state is larger than payload size quota.
  • When, after Parameters processing, the input of a state is larger than the payload size quota.
  • See the AWS documentation to learn more about AWS Step Functions Quotas.

• States.HeartbeatTimeout - A Task state failed to send a heartbeat for a period longer than the HeartbeatSeconds value.

• States.Timeout - A Task state either ran longer than the TimeoutSeconds value, or failed to send a heartbeat for a period longer than the HeartbeatSeconds value.

• States.TaskFailed- A Task state failed during the execution. When used in a retry or catch, States.TaskFailed acts as a wildcard that matches any known error name except for States.Timeout.

Logging

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

import aws_cdk.aws_logs as logs


log_group = logs.LogGroup(self, "MyLogGroup")

sfn.StateMachine(self, "MyStateMachine",
    definition=sfn.Chain.start(sfn.Pass(self, "Pass")),
    logs=sfn.LogOptions(
        destination=log_group,
        level=sfn.LogLevel.ALL
    )
)

X-Ray tracing

Enable X-Ray tracing for StateMachine:

sfn.StateMachine(self, "MyStateMachine",
    definition=sfn.Chain.start(sfn.Pass(self, "Pass")),
    tracing_enabled=True
)

See the AWS documentation to learn more about AWS Step Functions's X-Ray support.

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.

# definition: sfn.IChainable
role = iam.Role(self, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)
state_machine = sfn.StateMachine(self, "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.

# definition: sfn.IChainable
role = iam.Role(self, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)
state_machine = sfn.StateMachine(self, "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:

# definition: sfn.IChainable
role = iam.Role(self, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)
state_machine = sfn.StateMachine(self, "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:

# definition: sfn.IChainable
role = iam.Role(self, "Role",
    assumed_by=iam.ServicePrincipal("lambda.amazonaws.com")
)
state_machine = sfn.StateMachine(self, "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.

# definition: sfn.IChainable
user = iam.User(self, "MyUser")
state_machine = sfn.StateMachine(self, "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

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