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InfoQ Homepage Articles Building Workflows with AWS Step Functions

# Building Workflows with AWS Step Functions

### Key Takeaways

• AWS Step Functions is a serverless low-code service in the AWS Cloud for building workflows.
• Using AWS Step Functions, we can build a wide variety of applications involving workflows like orchestrating microservices, automating IT and business processes, and building data and machine learning pipelines.
• We define workflows in Step Functions with building blocks like blocks for configuring actions for integrating various AWS services, parallelizing more than one action, failure management, and setting up observability mechanisms.
• We use a Domain Specific Language in JSON format using a schema called Amazon States Language (ASL) for defining the workflows in Step Functions.
• Step Functions also provide a visual interface called Workflow Studio to help us define, view, debug, and run workflows.

In this article, we will learn about the main concepts of AWS Step Functions and apply those to build a workflow for a sample business process: Order Fulfillment.

## AWS Step Functions Implement a State Machine

AWS Step Functions use a state machine to represent the workflow. A workflow consists of a set of tasks, each of which represents a discrete activity to be performed. Each task is defined by a state of the state machine.

Fundamentally, a state machine is an abstract mathematical concept of representing the possible states of a system at a particular point in time. It has traditionally been used to model state transitions in Unified Modeling Language (UML) in software engineering.

We define the state machine of a Step Function in a Domain Specific Language (DSL) called Amazon States Language (ASL). ASL is a JSON-based language used to describe state machines declaratively.

We can also see a visual representation in the Workflow Studio. Workflow Studio is a visual designer where we can drag and drop different constructs to create and edit workflows. As we build the workflow in the designer, the Workflow Studio validates and auto-generates code in ASL.

A state machine for a sample loan approval workflow looks like this in the Workflow Studio:

We can create two types of state machines: Standard and Express. The Express type is used for high-volume, event-processing workloads and can run for up to five minutes. The Standard type is the default and is used to create workflows for long-running, durable, and auditable processes. Refer to the official documentation to get a deeper understanding of the differences between the two types of state machines.

## A State Machine has States

Each workflow step is represented by a state in the state machine and connected to one or more states through transitions. A state takes an input, performs some action depending on the type of the state, and emits an output to be passed onto the next state.

States of type Task perform the units of "work" in a state machine. They are configured as an API call to one of the AWS services. The parameters to the API are either specified in the definition of the state machine or supplied at runtime.

The "work" is performed by using an activity or by calling the API actions of AWS services. The AWS Lambda function is the most common service used by Task type states by calling the invoke method of the Lambda service.

An activity is a program hosted on another system like EC2, ECS, or mobile devices. Unlike Lambda functions, activities poll Step Functions while waiting for the Task type state to execute.

Apart from the Task type state, some other types of state are:

1. Choice: Decision point with one or more branches with child states, one of which executes based on a boolean condition
2. Map: Iterate multiple times over a child state
3. Parallel: Execute one or more branches in parallel
4. Success or Fail: End the execution with success or failure
5. Pass: A placeholder where we can perform data transformations and use for debugging executions

We will come across these types of states in the example of the order fulfillment process in subsequent sections.

## Defining the State Machine for Order Fulfillment Process

Let us first define the state machine for the order fulfillment process.

We can define a state machine from the Workflow Studio in the AWS Management Console or by defining the states in ASL in the Step Functions console or by uploading a file containing the state definition in ASL.

Let us use Workflow Studio to author our state machine for this example. We will also select the type of state machine as standard because our order fulfillment process is long-running and can run for more than 5 minutes.

We will give the name: order fulfillment to our state machine and assign an IAM role that defines which resources our state machine has permission to access during execution. Our IAM policy definition is associated with the following policy:

{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"lambda:InvokeFunction"
],
"Resource": [
"*"
]
}
]
}

This policy will allow the state machine to invoke any Lambda function.

A typical order fulfillment process uses a workflow similar to this:

This workflow is triggered after an order is placed by the customer and consists of the steps shown in the diagram. A Step Functions state machine to represent this workflow will look like this in the Workflow Studio:

As we can see, we have used the following states to define this state machine:

1. Check Inventory: This is a state of type: Task and invokes a Lambda function check inventory
2. Cancel Order: This is a state of type: Task and invokes a Lambda function update order
3. Mark Order as complete: This is a state of type: Task and invokes a Lambda function update order
4. Update Inventory: This is a state of type: Task and invokes a Lambda function update inventory
5. Items available: This is a state of type: Choice with 2 branches. If items are not available the order is canceled
6. Parallel: This is a state of type: Parallel with 2 branches which the state machine executes in parallel
7. Success: End the execution with a success
8. Fail: End the execution with a failure

We can also see a start and end indicator to define the start and end positions for the execution of the state machine.

The corresponding definition of the state machine in the Amazon States Language (ASL) looks like this:

{
"Comment": "order processing",
"StartAt": "check inventory",
"States": {
"check inventory": {
"Resource": "arn:aws:states:::lambda:invoke",
"OutputPath": "$.Payload", "Parameters": { "FunctionName": "arn:aws:lambda:us-east-1:**********:function:checkInventory:$LATEST"
},
"Next": "items available?"
},
"items available?": {
"Type": "Choice",
"Choices": [
{
"Variable": "$.item.num_of_items", "NumericGreaterThanPath": "$.inventory.num_of_items",
"Next": "cancel order"
}
],
"Default": "Parallel"
},
"Parallel": {
"Type": "Parallel",
"Branches": [
{
"StartAt": "mark order as complete",
"States": {
"mark order as complete": {
"Resource": "arn:aws:states:::lambda:invoke",
"OutputPath": "$.Payload", "Parameters": { "Payload.$": "$", "FunctionName": "arn:aws:lambda:us-east-1:**********:function:updateOrder:$LATEST"
},
"End": true
}
}
},
{
"StartAt": "update inventory",
"States": {
"update inventory": {
"Resource": "arn:aws:states:::lambda:invoke",
"OutputPath": "$.Payload", "Parameters": { "Payload.$": "$", "FunctionName": "arn:aws:lambda:us-east-1:**********:function:updateInventory:$LATEST"
},
"End": true
}
}
}
],
"Next": "Success"
},
"Success": {
"Type": "Succeed"
},
"cancel order": {
"Resource": "arn:aws:states:::lambda:invoke",
"OutputPath": "$.Payload", "Parameters": { "Payload.$": "$", "FunctionName": "arn:aws:lambda:us-east-1:**********:function:updateOrder:$LATEST"
},
"Next": "Fail"
},
"Fail": {
"Type": "Fail"
}
}
}

This structure has the States field containing the collection of all the state objects with names like: check inventory, cancel order, update inventory, etc. The value of the field: StartAt is check inventory which means that the state machine starts execution from the state named check inventory.

Each state object has a Type attribute for the type of the state and a Next attribute. The Next attribute contains the name of the next state that the state machine will execute.

Other attributes of the state object are dependent on the type of the state. In this example, for each of the states of type Task, we have defined an attribute Resource with a value of arn:aws:states:::lambda:invoke to represent the API to be called. The Resource attribute takes an ARN of the AWS service to be invoked which has the format: arn:aws:states:::aws-sdk:serviceName:apiAction.[serviceIntegrationPattern]. The serviceIntegrationPattern suffix in this format can take one of the values:

1. .sync: When we use .sync as the suffix, Step Functions wait for a request to complete before progressing to the next state.
2. .waitForTaskToken: When we use this integration pattern, we can pause Step Functions indefinitely, and wait for an external process or workflow to complete.
3. Empty: If this suffix is omitted, the integration pattern is of type request-response which means the Step Functions wait for an HTTP response before progressing to the next state. All our Lambda functions will be invoked in this way since we have not provided any suffix for serviceIntegrationPattern in the Resource attribute.

We have defined the parameters of the API in an attribute Parameters which takes a set of key-value pairs. We can see the keys: FunctionName and Payload.$. The FunctionName key has a value of the name of the Lambda function while the key Payload.$ contains an expression to determine the input to be passed to the Lambda function during the execution of the state machine.

We will see the passing and manipulation of the input and output by the various states during the execution of the state machine in the next section.

## Data Manipulation with Input and Output Filters

We can invoke state machines asynchronously or synchronously depending on whether the type of workflow is standard or express. They can be invoked from the Step Functions console or the AWS Command Line Interface (CLI), or by calling the Step Functions API with the AWS SDKs.

Step Functions receive input in JSON format which is then passed to the different states in the state machine. We can configure different kinds of filters to manipulate data in each state both before and after the task processing as shown in this diagram:

Let us now see how we can use these filters by applying them to the different states of the state machine of our order fulfillment process.

Let us suppose that our order processing workflow takes the following input:

{
"order_processing_request": {
"customer": {

},
"item": {
"item_no": "I1234",
"num_of_items": 5,
"shipping_date": "23/12/2022",
},
"order_details" : {
"order_id": "ORD345567",
"order_date": "15/12/2022"
}
}
}

The input data consists of information about the customer who has placed the order, the item for which the order is placed, and the order details. This input is fed to the first state: check inventory. The state machine will execute the Lambda function: check inventory associated with this task. Here is the code for the Lambda function for check inventory:

exports.handler = async (event, context, callback) => {
const item_no = event.item_no
const num_of_items = event.num_of_items
console.log(item::: ${item_no}${num_of_items})

// TODO fetch inventory info from the database
const inventoryData = getInventoryDataForItem(item_no)

callback(null, inventoryData)
}

function getInventoryDataForItem(item_no) {
var rand = Math.random()*100
var power = Math.pow(10, 0)
quantity_in_stock =  Math.floor(rand*power)

const inventory = {
sku: "S0001",
quantity_in_stock: quantity_in_stock,
warehouse_no: "W001",
age_of_stock_in_days: 98
}
return inventory
}

It takes the item_no as input and fetches the inventory information from a data store. For this example, we are returning a hardcoded value of the inventory data.
We will prepare the input for the Lambda function using two filters:

1.    InputPath We have set this filter as $.item This filter will extract the item attribute from the input of the state machine. Here is the result of applying this filter: { "item_no": "I1234", "num_of_items": 5, "shipping_date": "23/12/2022", "shipping_address": "address_1" } 2. Parameter: This filter will prepare the input required by the Lambda function. { "item_no.$": "$.item_no", "num_of_items.$": "$.num_of_items" } Here is the result of applying the filters InputPath and Parameter to the state input: { "item_no": "I1234", "num_of_items": 5 } This is the input payload that will be used by the state machine to execute the task associated with this state. The result of the execution of the Lambda function: checkInventory is: { "sku": "S0001", "quantity_in_stock": 84, "warehouse_no": "W001", "age_of_stock_in_days": 98 } When the Lambda function is invoked by the Task type state of the state machine it will look like this: { "ExecutedVersion": "$LATEST",
"sku": "S0001",
"quantity_in_stock": 84,
"warehouse_no": "W001",
"age_of_stock_in_days": 98
},
...
},
...
},
"HttpStatusCode": 200
},
"RequestId": "ac79dacd-7c6f-41c7-bfcf-eea70b43e141"
},
"StatusCode": 200
}

If we do not apply any more filters, this payload will be passed on to the next state. In that case, we will lose the original input data containing customer and order information which will be required to execute the remaining states of the state machine. We also do not need all the fields returned in the JSON response.

For preserving the original input and extract only the relevant fields from the task result for further processing by the state machine, let us add some more filters:

1.    ResultSelector: We use this filter to construct a new JSON payload containing only the fields required for executing the remaining states in the state machine. For example, we can safely discard the fields: warehouse_no and age_of_stock_in_days which are not required for any other processing. Accordingly, we will define the ResultSelector filter with the following expression:

{
"num_items_in_inventory.$": "$.Payload.quantity_in_stock",
"item_sku.$": "$.Payload.sku"
}

This will construct a payload with only the attributes num_items_in_inventory and item_sku. Our payload after applying this filter to the execution result of the task looks like this:

{
"num_items_in_inventory": 84,
"item_sku": "S0001"
}

2.    ResultPath: We use the ResultPath filter to add the task result to the original state input. We have defined the ResultPath filter for the current state: check inventory as $.task_result. Our payload after applying this filter will be: { "order_processing_request": { "customer": { "customer_id": "C123456" }, "item": { "item_no": "I1234", "num_of_items": 5, "shipping_date": "23/12/2022", "shipping_address": "address_1" }, "order_details" : { "order_id": "ORD345567", "order_date": "15/12/2022" } }, "task_result": { "num_items_in_inventory": 84, "item_sku": "S0001" } } We can now see the output of the ResultSelector appended to the input data under the attribute task_result. 3. OutputPath: The OutputPath filter is used to extract a portion of the output to pass to the next state. For example, we will define OutputPath filter as $.order_processing_request.order_details if we need only order_details. For our example of order fulfillment, we need all the fields so we will define $ as the value of the OutputPath filter which is also the default. The state check inventory after adding these filters looks like this: { "Comment": "order processing", "StartAt": "check inventory", "States": { "check inventory": { "Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "OutputPath": "$",
"Parameters": {
"FunctionName": "arn:aws:lambda:us-east-1:***********:function:checkInventory:$LATEST", "Payload": { "item_no.$": "$.item_no", "num_of_items.$": "$.num_of_items" } }, "Next": "items available?", "InputPath": "$.order_processing_request.item",
"ResultSelector": {
"num_items_in_inventory.$": "$.Payload.quantity_in_stock",
"item_sku.$": "$.Payload.sku"
},
"ResultPath": "$.task_result" }, … … } } We can see the list of state machine executions with information such as execution id, status, and start date in the Step Functions console. We can see a graph inspector on selecting an execution, that shows states and transitions marked with colors to indicate successful tasks, failures, and tasks that are still in progress. The graph inspector resulting from the execution of our order fulfillment workflow is shown below: ## Handling Errors in Step Function Workflows In absence of any error handling, the execution of a state machine will fail whenever a state reports an error. States of type: Task provides options for configuring a retry and fallback for handling errors. ### Retrying on Error We configure retry by defining one or more retry rules, called retriers. This will allow the task to be retried for execution when errors occur during the execution of the task. Coming to our example, our Lambda function can encounter errors of type: Lambda.ServiceException, Lambda.AWSLambdaException, or Lambda.SdkClientException. To retry the task for check inventory, when these errors occur, we have configured a retry rule as shown in the ASL: { "Comment": "order processing", "StartAt": "check inventory", "States": { "check inventory": { "Type": "Task", "Resource": "arn:aws:states:::lambda:invoke", "OutputPath": "$",
"Parameters": {
"FunctionName": "arn:aws:lambda:us-east-1:***********:function:checkInventory:$LATEST", "Payload": { "item_no.$": "$.item_no", "num_of_items.$": "\$.num_of_items"
}
},
"Retry": [
{
"ErrorEquals": [
"Lambda.ServiceException",
"Lambda.AWSLambdaException",
"Lambda.SdkClientException"
],
"IntervalSeconds": 3,
"MaxAttempts": 2,
"BackoffRate": 2
}
],
"Next": "items available?",
}
}

Here we have defined the retrier with an Interval of 3 seconds, MaxAttempts: 2, and a BackoffRate of 2. The Interval is the number of seconds before the first retry attempt. MaxAttempts is the maximum number of retry attempts and BackoffRate is the multiplier by which the retry interval increases with each attempt.

### FallBack to a Different State on Error

We can revert to a fallback state when errors occur by defining rules for intercepting the errors, called "catchers." In this example, we are defining a prepare error state to which the check inventory state can fallback if it encounters an error of type: States.TaskFailed.

The state machine with a retrier and a catcher defined for the check inventory step looks like this in the Workflow Studio:

We have defined the check inventory to fallback to the cancel order state on encountering an error or type State.TaskFailed.

## Summary of the Steps used to Build the State Machine

Let us summarize the steps we used to define the workflow for our Order Fulfillment process:

1. We created a state machine of type standard since the order fulfillment is a long-running process.
2. We used an IAM role to give permission to the state machine to call different AWS Lambda functions.
3. We used states of type: Task, Parallel, Choice, Success, and Fail in the state machine to design our workflow
4. We defined the filters InputPath, Parameter, ResultSelector, ResultPath, and OutputPath to manipulate the data through the state machine during execution.
5. We attached error handling to a state by configuring a retry and a fallback condition.

## Conclusion

Running workflows for coordinating between granular services is one of the common challenges faced when building complex applications.

AWS Step Functions helps us to design workflows either visually or in an easy to read Domain Specific Language (in Amazon States Language). When executing state machines, Step Functions take care of common workflow concerns like state management, checkpointing, retries, and fallback.

We need to use the right combination of state machine building blocks to design our workflows for optimal performance. We can leverage the Step Function Workflow Collection to apply best practices to the design of our workflows.

The Step Function Workflow Collection is what AWS calls a new experience for designing workflows with opinionated templates, reusable patterns, and example applications to help build our workflows with Step Functions.

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