Processing independant states in parallel

Up until now we have performed both of our data checking steps in a serial fashion, one after the other. But checking an applicant’s address doesn’t depend on the result from checking the applicant’s name. So, this is a great opportunity to speed things up and perform our two data check steps in parallel instead.

Step Functions has a Parallel state type which, unsurprisingly, lets a state machine perform parallel executions of multiple states. A Parallel state causes the interpreter to execute each branch starting with the state named in its StartAt field, as concurrently as possible, and wait until each branch terminates (reaches a terminal state) before processing the Parallel state’s Next field.

In this step, we will

  • Update our state machine to run the Check Name and Check Address states in parallel using the Parallel state type

  • Update our state machine’s ‘Review Required?’ Choice state to handle the results from the parallel data checks. We need to do this because the Parallel state returns each check as an element in an array in the same order the steps are specified in the Parallel state definition.

Make these changes

Let’s refactor our state machine to perform the name and address checks in parallel:

➡️ Step 1. Replace statemachine/account-application-workflow.asl.json with this content (click the gray button to copy to clipboard).

Click to view diff

➡️ Step 2. Run:

sam deploy

Try it out

Now you can try a few types of application submissions to see how they each execute:

➡️ Step 1. Submit a valid application and see it auto approve after checking the data fields in parallel. Run:

aws lambda invoke --function-name sfn-workshop-SubmitApplication --payload '{ "name": "Spock", "address": "123 Enterprise Street" }' /dev/stdout 

Here is what a valid application execution flow looks like:

Parallel check auto approving

➡️ Step 2. Submit an application with an invalid name or address (or both) and see the parallel checks result in the workflow routing to wait for a review. Run:

aws lambda invoke --function-name sfn-workshop-SubmitApplication --payload '{ "name": "Spock", "address": "ABadAddress" }' /dev/stdout 

Here is what an invalid application execution flow looks like:

Parallel check pending

➡️ Step 3. Submit an application with our test unprocessable name to see the parallel data checking state throw the error and route to the state to flag an application as unprocessable. Run:

aws lambda invoke --function-name sfn-workshop-SubmitApplication --payload '{ "name": "UNPROCESSABLE_DATA", "address": "123 Street" }' /dev/stdout 

Here is what an unprocessable application execution flow looks like:

Parallel check unprocessable

At this point, we have a well structured state machine to manage the workflow of processing new account applications for our simple banking system. If we wanted to, we could add on another step in our workflow to handle further downstream logic involved with opening up a bank account for applications that get approved. But, this is a good place to wrap up because you already have all the experience needed to continue implementing these further steps on your own, if you wish.