AWS Step Functions allows you to create serverless workflows that orchestrate your business processes. Step Functions stores data from workflow invocations as application state. Today we are increasing the size limit of application state from 32,768 characters to 256 kilobytes of data per workflow invocation. The new limit matches payload limits for other commonly used serverless services such as Amazon SNS, Amazon SQS, and Amazon EventBridge. This means you no longer need to manage Step Functions payload limitations as a special case in your serverless applications.

Faster, cheaper, simpler state management

Previously, customers worked around limits on payload size by storing references to data, such as a primary key, in their application state. An AWS Lambda function then loaded the data via an SDK call at runtime when the data was needed. With larger payloads, you now can store complete objects directly in your workflow state. This removes the need to persist and load data from data stores such as Amazon DynamoDB and Amazon S3. You do not pay for payload size, so storing data directly in your workflow may reduce both cost and execution time of your workflows and Lambda functions. Storing data in your workflow state also reduces the amount of code you need to write and maintain.

AWS Management Console and workflow history improvements

Larger state payloads mean more data to visualize and search. To help you understand that data, we are also introducing changes to the AWS Management Console for Step Functions. We have improved load time for the Execution History page to help you get the information you need more quickly. We have also made backwards-compatible changes to the GetExecutionHistory API call. Now if you set includeExecutionData to false, GetExecutionHistory excludes payload data and returns only metadata. This allows you to debug your workflows more quickly.

Doing more with dynamic parallelism

A larger payload also allows your workflows to process more information. Step Functions workflows can process an arbitrary number of tasks concurrently using dynamic parallelism via the Map State. Dynamic parallelism enables you to iterate over a collection of related items applying the same process to each item. This is an implementation of the map procedure in the MapReduce programming model.

When to choose dynamic parallelism

Choose dynamic parallelism when performing operations on a small collection of items generated in a preliminary step. You define an Iterator, which operates on these items individually. Optionally, you can reduce the results to an aggregate item. Unlike with parallel invocations, each item in the collection is related to the other items. This means that an error in processing one item typically impacts the outcome of the entire workflow.

Example use case

Ecommerce and line of business applications offer many examples where dynamic parallelism is the right approach. Consider an order fulfillment system that receives an order and attempts to authorize payment. Once payment is authorized, it attempts to lock each item in the order for shipment. The available items are processed and their total is taken from the payment authorization. The unavailable items are marked as pending for later processing.

The following Amazon States Language (ASL) defines a Map State with a simplified Iterator that implements the order fulfillment steps described previously.

 "Map": { "Type": "Map", "ItemsPath": "$.orderItems", "ResultPath": "$.packedItems", "MaxConcurrency": 40, "Next": "Print Label", "Iterator": { "StartAt": "Lock Item", "States": { "Lock Item": { "Type": "Pass", "Result": "Item locked!", "Next": "Pull Item" }, "Pull Item": { "Type": "Pass", "Result": "Item pulled!", "Next": "Pack Item" }, "Pack Item": { "Type": "Pass", "Result": "Item packed!", "End": true } } } }

The following image provides a visualization of this workflow. A preliminary state retrieves the collection of items from a data store and loads it into the state under the orderItems key. The triple dashed lines represent the Map State which attempts to lock, pull, and pack each item individually. The result of processing each individual item impacts the next state, Print Label. As more items are pulled and packed, the total weight increases. If an item is out of stock, the total weight will decrease.

A visualization of a portion of an AWS Step Functions workflow that implements dynamic parallelism

Dynamic parallelism or the “Map State”

Larger state payload improvements

Without larger state payloads, each item in the $.orderItems object in the workflow state would be a primary key to a specific item in a DynamoDB table. Each step in the “Lock, Pull, Pack” workflow would need to read data from DynamoDB for every item in the order to access detailed item properties.

With larger state payloads, each item in the $.orderItems object can be a complete object containing the required fields for the relevant items. Not only is this faster, resulting in a better user experience, but it also makes debugging workflows easier.

Pricing and availability

Larger state payloads are available now in all commercial and AWS GovCloud (US) Regions where Step Functions is available. No changes to your workflows are required to use larger payloads, and your existing workflows will continue to run as before. The larger state is available however you invoke your Step Functions workflows, including the AWS CLI, the AWS SDKs, the AWS Step Functions Data Science SDK, and Step Functions Local.

Larger state payloads are included in existing Step Functions pricing for Standard Workflows. Because Express Workflows are priced by runtime and memory, you may see more cost on individual workflows with larger payloads. However, this increase may also be offset by the reduced cost of Lambda, DynamoDB, S3, or other AWS services.

Conclusion

Larger Step Functions payloads simplify and increase the efficiency of your workflows by eliminating function calls to persist and retrieve data. Larger payloads also allow your workflows to process more data concurrently using dynamic parallelism.

With larger payloads, you can minimize the amount of custom code you write and focus on the business logic of your workflows. Get started building serverless workflows today!