Configuring and using pipeline resolvers in AWS AppSync (VTL)

AWS AppSync executes resolvers on a GraphQL field. In some cases, applications require executing multiple operations to resolve a single GraphQL field. With pipeline resolvers, developers can now compose operations called Functions and execute them in sequence. Pipeline resolvers are useful for applications that, for instance, require performing an authorization check before fetching data for a field.

A pipeline resolver is composed of a Before mapping template, an After mapping template, and a list of Functions. Each Function has a request and response mapping template that it executes against a data source. As a pipeline resolver delegates execution to a list of functions, it is therefore not linked to any data source. Unit resolvers and functions are primitives that execute operations against data sources. See the Resolver mapping template overview for more information.

Step 1: Creating a pipeline resolver

In the AWS AppSync console, go to the Schema page.

Save the following schema:

schema {
    query: Query
    mutation: Mutation
}

type Mutation {
    signUp(input: Signup): User
}

type Query {
    getUser(id: ID!): User
}

input Signup {
    username: String!
    email: String!
}

type User {
    id: ID!
    username: String
    email: AWSEmail
}

We are going to wire a pipeline resolver to the signUp field on the Mutation type. In the Mutation type on the right side, choose Attach next to the signUp mutation field. On the create resolver page, click on Actions, then Update runtime. Choose Pipeline Resolver, then choose VTL, then choose Update. The page should now show three sections: a Before mapping template text area, a Functions section, and an After mapping template text area.

Our pipeline resolver signs up a user by first validating the email address input and then saving the user in the system. We are going to encapsulate the email validation inside a validateEmail function, and the saving of the user inside a saveUser function. The validateEmail function executes first, and if the email is valid, then the saveUser function executes.

The execution flow will be as follow:

Because we will probably reuse the validateEmail function in other resolvers on our API, we want to avoid accessing $ctx.args because these will change from one GraphQL field to another. Instead, we can use the $ctx.stash to store the email attribute from the signUp(input: Signup) input field argument.

BEFORE mapping template:

## store email input field into a generic email key
$util.qr($ctx.stash.put("email", $ctx.args.input.email))
{}

The console provides a default passthrough AFTER mapping template that will we use:

$util.toJson($ctx.result)

Choose Create or Save to update the resolver.

Step 2: Creating a function

From the pipeline resolver page, in the Functions section, click on Add function, then Create new function. It is also possible to create functions without going through the resolver page; to do this, in the AWS AppSync console, go to the Functions page. Choose the Create function button. Let’s create a function that checks if an email is valid and comes from a specific domain. If the email is not valid, the function raises an error. Otherwise, it forwards whatever input it was given.

On the new function page, choose Actions, then Update runtime. Choose VTL, then Update. Make sure you have created a data source of the NONE type. Choose this data source in the Data source name list. For function name, enter in validateEmail. In the function code area, overwrite everything with this snippet:

#set($valid = $util.matches("^[a-zA-Z0-9_.+-]+@(?:(?:[a-zA-Z0-9-]+\.)?[a-zA-Z]+\.)?(myvaliddomain)\.com", $ctx.stash.email))
#if (!$valid)
    $util.error("$ctx.stash.email is not a valid email.")
#end
{
    "payload": { "email": $util.toJson(${ctx.stash.email}) }
}

Paste this into the response mapping template:

$util.toJson($ctx.result)

Review your changes, then choose Create. We just created our validateEmail function. Repeat these steps to create the saveUser function with the following request and response mapping templates (For the sake of simplicity, we use a NONE data source and pretend the user has been saved in the system after the function executes.):

Request mapping template:

## $ctx.prev.result contains the signup input values. We could have also
## used $ctx.args.input.
{
    "payload": $util.toJson($ctx.prev.result)
}

Response mapping template:

## an id is required so let's add a unique random identifier to the output
$util.qr($ctx.result.put("id", $util.autoId()))
$util.toJson($ctx.result)

We just created our saveUser function.

Step 3: Adding a function to a pipeline resolver

Our functions should have been added automatically to the pipeline resolver we just created. If this wasn't the case, or you created the functions through the Functions page, you can click on Add function on the resolver page to attach them. Add both the validateEmail and saveUser functions to the resolver. The validateEmail function should be placed before the saveUser function. As you add more functions, you can use the move up and move down options to reorganize the order of execution of your functions. Review your changes, then choose Save.

Step 4: Executing a query

In the AWS AppSync console, go to the Queries page. In the explorer, ensure that you're using your mutation. If you aren't, choose Mutation in the drop-down list, then choose +. Enter the following query:

mutation {
  signUp(input: {
    email: "nadia@myvaliddomain.com"
    username: "nadia"
  }) {
    id
    email
  }
}

This should return something like:

{
  "data": {
    "signUp": {
      "id": "256b6cc2-4694-46f4-a55e-8cb14cc5d7fc",
      "email": "nadia@myvaliddomain.com"
    }
  }
}

We have successfully signed up our user and validated the input email using a pipeline resolver. To follow a more complete tutorial focusing on pipeline resolvers, you can go to Tutorial: Pipeline Resolvers