# Creating basic queries (VTL)
**Note**
We now primarily support the APPSYNC\$1JS runtime and its documentation. Please consider using the APPSYNC\$1JS runtime and its guides [here](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers-js.html).
GraphQL resolvers connect the fields in a type’s schema to a data source. Resolvers are the mechanism by which requests are fulfilled. AWS AppSync can automatically create and connect resolvers from a schema or create a schema and connect resolvers from an existing table without you needing to write any code.
Resolvers in AWS AppSync use JavaScript to convert a GraphQL expression into a format the data source can use. Alternatively, mapping templates can be written in [Apache Velocity Template Language (VTL)](https://velocity.apache.org/engine/2.0/vtl-reference.html) to convert a GraphQL expression into a format the data source can use.
This section will show you how to configure resolvers using VTL. An introductory tutorial-style programming guide for writing resolvers can be found in [Resolver mapping template programming guide](resolver-mapping-template-reference-programming-guide.md#aws-appsync-resolver-mapping-template-reference-programming-guide), and helper utilities available to use when programming can be found in [Resolver mapping template context reference](resolver-context-reference.md#aws-appsync-resolver-mapping-template-context-reference). AWS AppSync also has built-in test and debug flows that you can use when you’re editing or authoring from scratch. For more information, see [Test and debug resolvers](test-debug-resolvers.md#aws-appsync-test-debug-resolvers).
We recommend following this guide before attempting to to use any of the aforementioned tutorials.
In this section, we will walk through how to create a resolver, add a resolver for mutations, and use advanced configurations.
## Create your first resolver
Following the examples from the previous sections, the first step is to create a resolver for your `Query` type.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Schema**.
1. On the right-hand side of the page, there's a window called **Resolvers**. This box contains a list of the types and fields as defined in your **Schema** window on the left-hand side of the page. You're able to attach resolvers to fields. For example, under the **Query** type, choose **Attach** next to the `getTodos` field.
1. On the **Create Resolver** page, choose the data source you created in the [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html) guide. In the **Configure mapping templates** window, you can choose both the generic request and response mapping templates using the drop-down list to the right or write your own.
**Note**
The pairing of a request mapping template to a response mapping template is called a unit resolver. Unit resolvers are typically meant to perform rote operations; we recommend using them only for singular operations with a small number of data sources. For more complex operations, we recommend using pipeline resolvers, which can execute multiple operations with multiple data sources sequentially.
For more information about the difference between request and response mapping templates, see [Unit resolvers](https://docs.aws.amazon.com//appsync/latest/devguide/resolver-mapping-template-reference-overview.html#unit-resolvers).
For more information about using pipeline resolvers, see [Pipeline resolvers](pipeline-resolvers.md#aws-appsync-pipeline-resolvers).
1. For common use cases, the AWS AppSync console has built-in templates that you can use for getting items from data sources (e.g., all item queries, individual lookups, etc.). For example, on the simple version of the schema from [Designing your schema](designing-your-schema.md#aws-appsync-designing-your-schema) where `getTodos` didn’t have pagination, the request mapping template for listing items is as follows:
```
{
"version" : "2017-02-28",
"operation" : "Scan"
}
```
1. You always need a response mapping template to accompany the request. The console provides a default with the following passthrough value for lists:
```
$util.toJson($ctx.result.items)
```
In this example, the `context` object (aliased as `$ctx`) for lists of items has the form `$context.result.items`. If your GraphQL operation returns a single item, it would be `$context.result`. AWS AppSync provides helper functions for common operations, such as the `$util.toJson` function listed previously, to format responses properly. For a full list of functions, see [Resolver mapping template utility reference](resolver-util-reference.md#aws-appsync-resolver-mapping-template-util-reference).
1. Choose **Save Resolver**.
------
#### [ API ]
1. Create a resolver object by calling the [https://docs.aws.amazon.com/appsync/latest/APIReference/API_CreateResolver.html](https://docs.aws.amazon.com/appsync/latest/APIReference/API_CreateResolver.html) API.
1. You can modify your resolver's fields by calling the [https://docs.aws.amazon.com/appsync/latest/APIReference/API_UpdateResolver.html](https://docs.aws.amazon.com/appsync/latest/APIReference/API_UpdateResolver.html) API.
------
#### [ CLI ]
1. Create a resolver by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html) command.
You'll need to type in 6 parameters for this particular command:
1. The `api-id` of your API.
1. The `type-name` of the type that you want to modify in your schema. In the console example, this was `Query`.
1. The `field-name` of the field that you want to modify in your type. In the console example, this was `getTodos`.
1. The `data-source-name` of the data source you created in the [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html) guide.
1. The `request-mapping-template`, which is the body of the request. In the console example, this was:
```
{
"version" : "2017-02-28",
"operation" : "Scan"
}
```
1. The `response-mapping-template`, which is the body of the response. In the console example, this was:
```
$util.toJson($ctx.result.items)
```
An example command may look like this:
```
aws appsync create-resolver --api-id abcdefghijklmnopqrstuvwxyz --type-name Query --field-name getTodos --data-source-name TodoTable --request-mapping-template "{ "version" : "2017-02-28", "operation" : "Scan", }" --response-mapping-template ""$"util.toJson("$"ctx.result.items)"
```
An output will be returned in the CLI. Here's an example:
```
{
"resolver": {
"kind": "UNIT",
"dataSourceName": "TodoTable",
"requestMappingTemplate": "{ version : 2017-02-28, operation : Scan, }",
"resolverArn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Query/resolvers/getTodos",
"typeName": "Query",
"fieldName": "getTodos",
"responseMappingTemplate": "$util.toJson($ctx.result.items)"
}
}
```
1. To modify a resolver's fields and/or mapping templates, run the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-resolver.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-resolver.html) command.
With the exception of the `api-id` parameter, the parameters used in the `create-resolver` command will be overwritten by the new values from the `update-resolver` command.
------
## Adding a resolver for mutations
The next step is to create a resolver for your `Mutation` type.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Schema**.
1. Under the **Mutation** type, choose **Attach** next to the `addTodo` field.
1. On the **Create Resolver** page, choose the data source you created in the [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html) guide.
1. In the **Configure mapping templates** window, you'll need to modify the request template because this is a mutation where you’re adding a new item to DynamoDB. Use the following request mapping template:
```
{
"version" : "2017-02-28",
"operation" : "PutItem",
"key" : {
"id" : $util.dynamodb.toDynamoDBJson($ctx.args.id)
},
"attributeValues" : $util.dynamodb.toMapValuesJson($ctx.args)
}
```
1. AWS AppSync automatically converts arguments defined in the `addTodo` field from your GraphQL schema into DynamoDB operations. The previous example stores records in DynamoDB using a key of `id`, which is passed through from the mutation argument as `$ctx.args.id`. All of the other fields you pass through are automatically mapped to DynamoDB attributes with `$util.dynamodb.toMapValuesJson($ctx.args)`.
For this resolver, use the following response mapping template:
```
$util.toJson($ctx.result)
```
AWS AppSync also supports test and debug workflows for editing resolvers. You can use a mock `context` object to see the transformed value of the template before invoking. Optionally, you can view the full request execution to a data source interactively when you run a query. For more information, see [Test and debug resolvers](test-debug-resolvers.md#aws-appsync-test-debug-resolvers) and [Monitoring and logging](monitoring.md#aws-appsync-monitoring).
1. Choose **Save Resolver**.
------
#### [ API ]
You can also do this with APIs by utilizing the commands in the [Create your first resolver](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers.html#create-your-first-resolver) section and the parameter details from this section.
------
#### [ CLI ]
You can also do this in the CLI by utilizing the commands in the [Create your first resolver](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers.html#create-your-first-resolver) section and the parameter details from this section.
------
At this point, if you’re not using the advanced resolvers you can begin using your GraphQL API as outlined in [Using your API](using-your-api.md#aws-appsync-using-your-api).
## Advanced resolvers
If you are following the Advanced section and you’re building a sample schema in [Designing your schema](designing-your-schema.md#aws-appsync-designing-your-schema) to do a paginated scan, use the following request template for the `getTodos` field instead:
```
{
"version" : "2017-02-28",
"operation" : "Scan",
"limit": $util.defaultIfNull(${ctx.args.limit}, 20),
"nextToken": $util.toJson($util.defaultIfNullOrBlank($ctx.args.nextToken, null))
}
```
For this pagination use case, the response mapping is more than just a passthrough because it must contain both the *cursor* (so that the client knows what page to start at next) and the result set. The mapping template is as follows:
```
{
"todos": $util.toJson($context.result.items),
"nextToken": $util.toJson($context.result.nextToken)
}
```
The fields in the preceding response mapping template should match the fields defined in your `TodoConnection` type.
For the case of relations where you have a `Comments` table and you’re resolving the comments field on the `Todo` type (which returns a type of `[Comment]`), you can use a mapping template that runs a query against the second table. To do this, you must have already created a data source for the `Comments` table as outlined in [Attaching a data source](attaching-a-data-source.md#aws-appsync-getting-started-build-a-schema-from-scratch).
**Note**
We’re using a query operation against a second table for illustrative purposes only. You could use another operation against DynamoDB instead. In addition, you could pull the data from another data source, such as AWS Lambda or Amazon OpenSearch Service, because the relation is controlled by your GraphQL schema.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Schema**.
1. Under the **Todo** type, choose **Attach** next to the `comments` field.
1. On the **Create Resolver** page, choose your **Comments** table data source. The default name for the **Comments** table from the quickstart guides is `AppSyncCommentTable`, but it may vary depending on what name you gave it.
1. Add the following snippet to your request mapping template:
```
{
"version": "2017-02-28",
"operation": "Query",
"index": "todoid-index",
"query": {
"expression": "todoid = :todoid",
"expressionValues": {
":todoid": {
"S": $util.toJson($context.source.id)
}
}
}
}
```
1. The `context.source` references the parent object of the current field that’s being resolved. In this example, `source.id` refers to the individual `Todo` object, which is then used for the query expression.
You can use the passthrough response mapping template as follows:
```
$util.toJson($ctx.result.items)
```
1. Choose **Save Resolver**.
1. Finally, back on the **Schema** page in the console, attach a resolver to the `addComment` field, and specify the data source for the `Comments` table. The request mapping template in this case is a simple `PutItem` with the specific `todoid` that is commented on as an argument, but you use the `$utils.autoId()` utility to create a unique sort key for the comment as follows:
```
{
"version": "2017-02-28",
"operation": "PutItem",
"key": {
"todoid": { "S": $util.toJson($context.arguments.todoid) },
"commentid": { "S": "$util.autoId()" }
},
"attributeValues" : $util.dynamodb.toMapValuesJson($ctx.args)
}
```
Use a passthrough response template as follows:
```
$util.toJson($ctx.result)
```
------
#### [ API ]
You can also do this with APIs by utilizing the commands in the [Create your first resolver](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers.html#create-your-first-resolver) section and the parameter details from this section.
------
#### [ CLI ]
You can also do this in the CLI by utilizing the commands in the [Create your first resolver](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers.html#create-your-first-resolver) section and the parameter details from this section.
------
# Disabling VTL mapping templates with direct Lambda resolvers (VTL)
**Note**
We now primarily support the APPSYNC\$1JS runtime and its documentation. Please consider using the APPSYNC\$1JS runtime and its guides [here](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers-js.html).
With direct Lambda resolvers, you can circumvent the use of VTL mapping templates when using AWS Lambda data sources. AWS AppSync can provide a default payload to your Lambda function as well as a default translation from a Lambda function's response to a GraphQL type. You can choose to provide a request template, a response template, or neither and AWS AppSync will handle it accordingly.
To learn more about the default request payload and response translation that AWS AppSync provides, see the [Direct Lambda resolver reference](resolver-mapping-template-reference-lambda.md#direct-lambda-resolvers). For more information on setting up an AWS Lambda data source and setting up an IAM Trust Policy, see [Attaching a data source](attaching-a-data-source.md).
## Configure direct Lambda resolvers
The following sections will show you how to attach Lambda data sources and add Lambda resolvers to your fields.
### Add a Lambda data source
Before you can activate direct Lambda resolvers, you must add a Lambda data source.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Data sources**.
1. Choose **Create data source**.
1. For **Data source name**, enter a name for your data source, such as **myFunction**.
1. For **Data source type**, choose **AWS Lambda function**.
1. For **Region**, choose the appropriate region.
1. For **Function ARN**, choose the Lambda function from the dropdown list. You can search for the function name or manually enter the ARN of the function you want to use.
1. Create a new IAM role (recommended) or choose an existing role that has the `lambda:invokeFunction` IAM permission. Existing roles need a trust policy, as explained in the [Attaching a data source](attaching-a-data-source.md) section.
The following is an example IAM policy that has the required permissions to perform operations on the resource:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [ "lambda:invokeFunction" ],
"Resource": [
"arn:aws:lambda:us-west-2:123456789012:function:myFunction",
"arn:aws:lambda:us-west-2:123456789012:function:myFunction:*"
]
}
]
}
```
------
1. Choose the **Create** button.
------
#### [ CLI ]
1. Create a data source object by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-data-source.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-data-source.html) command.
You'll need to type in 4 parameters for this particular command:
1. The `api-id` of your API.
1. The `name` of your data source. In the console example, this is the **Data source name**.
1. The `type` of data source. In the console example, this is **AWS Lambda function**.
1. The `lambda-config`, which is the **Function ARN** in the console example.
**Note**
There are other parameters such as `Region` that must be configured but will usually default to your CLI configuration values.
An example command may look like this:
```
aws appsync create-data-source --api-id abcdefghijklmnopqrstuvwxyz --name myFunction --type AWS_LAMBDA --lambda-config lambdaFunctionArn=arn:aws:lambda:us-west-2:102847592837:function:appsync-lambda-example
```
An output will be returned in the CLI. Here's an example:
```
{
"dataSource": {
"dataSourceArn": "arn:aws:appsync:us-west-2:102847592837:apis/abcdefghijklmnopqrstuvwxyz/datasources/myFunction",
"type": "AWS_LAMBDA",
"name": "myFunction",
"lambdaConfig": {
"lambdaFunctionArn": "arn:aws:lambda:us-west-2:102847592837:function:appsync-lambda-example"
}
}
}
```
1. To modify a data source's attributes, run the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-data-source.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-data-source.html) command.
With the exception of the `api-id` parameter, the parameters used in the `create-data-source` command will be overwritten by the new values from the `update-data-source` command.
------
### Activate direct Lambda resolvers
After creating a Lambda data source and setting up the appropriate IAM role to allow AWS AppSync to invoke the function, you can link it to a resolver or pipeline function.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Schema**.
1. In the **Resolvers** window, choose a field or operation and then select the **Attach** button.
1. In the **Create new resolver** page, choose the Lambda function from the dropdown list.
1. In order to leverage direct Lambda resolvers, confirm that request and response mapping templates are disabled in the **Configure mapping templates** section.
1. Choose the **Save Resolver** button.
------
#### [ CLI ]
+ Create a resolver by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html) command.
You'll need to type in 6 parameters for this particular command:
1. The `api-id` of your API.
1. The `type-name` of the type in your schema.
1. The `field-name` of the field in your schema.
1. The `data-source-name`, or your Lambda function's name.
1. The `request-mapping-template`, which is the body of the request. In the console example, this was disabled:
```
" "
```
1. The `response-mapping-template`, which is the body of the response. In the console example, this was also disabled:
```
" "
```
An example command may look like this:
```
aws appsync create-resolver --api-id abcdefghijklmnopqrstuvwxyz --type-name Subscription --field-name onCreateTodo --data-source-name LambdaTest --request-mapping-template " " --response-mapping-template " "
```
An output will be returned in the CLI. Here's an example:
```
{
"resolver": {
"resolverArn": "arn:aws:appsync:us-west-2:102847592837:apis/abcdefghijklmnopqrstuvwxyz/types/Subscription/resolvers/onCreateTodo",
"typeName": "Subscription",
"kind": "UNIT",
"fieldName": "onCreateTodo",
"dataSourceName": "LambdaTest"
}
}
```
------
When you disable your mapping templates, there are several additional behaviors that will occur in AWS AppSync:
+ By disabling a mapping template, you are signalling to AWS AppSync that you accept the default data translations specified in the [Direct Lambda resolver reference](resolver-mapping-template-reference-lambda.md#direct-lambda-resolvers).
+ By disabling the request mapping template, your Lambda data source will receive a payload consisting of the entire [Context](resolver-context-reference.md) object.
+ By disabling the response mapping template, the result of your Lambda invocation will be translated depending on the version of the request mapping template or if the request mapping template is also disabled.
# Testing and debugging resolvers in AWS AppSync (VTL)
**Note**
We now primarily support the APPSYNC\$1JS runtime and its documentation. Please consider using the APPSYNC\$1JS runtime and its guides [here](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers-js.html).
AWS AppSync executes resolvers on a GraphQL field against a data source. As described in [Resolver mapping template overview](resolver-mapping-template-reference-overview.md#aws-appsync-resolver-mapping-template-reference-overview), resolvers communicate with data sources by using a templating language. This enables you to customize the behavior and apply logic and conditions before and after communicating with the data source. For an introductory tutorial-style programming guide for writing resolvers, see the [Resolver mapping template programming guide](resolver-mapping-template-reference-programming-guide.md#aws-appsync-resolver-mapping-template-reference-programming-guide).
To help developers write, test, and debug these resolvers, the AWS AppSync console also provides tools to create a GraphQL request and response with mock data down to the individual field resolver. Additionally, you can perform queries, mutations, and subscriptions in the AWS AppSync console and see a detailed log stream from Amazon CloudWatch of the entire request. This includes results from a data source.
## Testing with mock data
When a GraphQL resolver is invoked, it contains a `context` object that contains information about the request. This includes arguments from a client, identity information, and data from the parent GraphQL field. It also contains the results from the data source, which can be used in the response template. For more information about this structure and the available helper utilities to use when programming, see the [Resolver Mapping Template Context Reference](resolver-context-reference.md#aws-appsync-resolver-mapping-template-context-reference).
When writing or editing a resolver, you can pass a *mock* or *test context* object into the console editor. This enables you to see how both the request and the response templates evaluate without actually running against a data source. For example, you can pass a test `firstname: Shaggy` argument and see how it evaluates when using `$ctx.args.firstname` in your template code. You could also test the evaluation of any utility helpers such as `$util.autoId()` or `util.time.nowISO8601()`.
### Testing resolvers
This example will use the AWS AppSync console to test resolvers.
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **APIs dashboard**, choose your GraphQL API.
1. In the **Sidebar**, choose **Schema**.
1. If you haven't done so already, under the type and next to the field, choose **Attach** to add your resolver.
For more information on how to build a conplete resolver, see [Configuring resolvers](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers.html).
Otherwise, select the resolver that's already in the field.
1. At the top of the **Edit resolver** page, choose **Select test context**, choose **Create new context**.
1. Select a sample context object or populate the JSON manually in the **Execution context** window below.
1. Enter in a **Text context name**.
1. Choose the **Save** button.
1. At the top of the **Edit Resolver** page, choose **Run test**.
For a more practical example, suppose you have an app storing a GraphQL type of `Dog` that uses automatic ID generation for objects and stores them in Amazon DynamoDB. You also want to write some values from the arguments of a GraphQL mutation, and allow only specific users to see a response. The following shows what the schema might look like:
```
type Dog {
breed: String
color: String
}
type Mutation {
addDog(firstname: String, age: Int): Dog
}
```
When you add a resolver for the `addDog` mutation, you can populate a context object like the following example. The following has arguments from the client of `name` and `age`, and a `username` populated in the `identity` object:
```
{
"arguments" : {
"firstname": "Shaggy",
"age": 4
},
"source" : {},
"result" : {
"breed" : "Miniature Schnauzer",
"color" : "black_grey"
},
"identity": {
"sub" : "uuid",
"issuer" : " https://cognito-idp.{region}.amazonaws.com/{userPoolId}",
"username" : "Nadia",
"claims" : { },
"sourceIp" :[ "x.x.x.x" ],
"defaultAuthStrategy" : "ALLOW"
}
}
```
You can test this using the following request and response mapping templates:
**Request Template**
```
{
"version" : "2017-02-28",
"operation" : "PutItem",
"key" : {
"id" : { "S" : "$util.autoId()" }
},
"attributeValues" : $util.dynamodb.toMapValuesJson($ctx.args)
}
```
**Response Template**
```
#if ($context.identity.username == "Nadia")
$util.toJson($ctx.result)
#else
$util.unauthorized()
#end
```
The evaluated template has the data from your test context object and the generated value from `$util.autoId()`. Additionally, if you were to change the `username` to a value other than `Nadia`, the results won’t be returned because the authorization check would fail. For more information about fine grained access control, see [Authorization use cases](security-authorization-use-cases.md#aws-appsync-security-authorization-use-cases).
### Testing mapping templates with AWS AppSync's APIs
You can use the `EvaluateMappingTemplate` API command to remotely test your mapping templates with mocked data. To get started with the command, make sure you have added the `appsync:evaluateMappingTemplate` permission to your policy. For example:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "appsync:evaluateMappingTemplate",
"Resource": "arn:aws:appsync:us-east-1:111122223333:*"
}
]
}
```
------
You can leverage the command by using the [AWS CLI](https://aws.amazon.com/cli/) or [AWS SDKs](https://aws.amazon.com/tools/). For example, take the `Dog` schema and its request/response mapping templates from the previous section. Using the CLI on your local station, save the request template to a file named `request.vtl`, then save the `context` object to a file named `context.json`. From your shell, run the following command:
```
aws appsync evaluate-mapping-template --template file://request.vtl --context file://context.json
```
The command returns the following response:
```
{
"evaluationResult": "{\n \"version\" : \"2017-02-28\",\n \"operation\" : \"PutItem\",\n \"key\" : {\n \"id\" : { \"S\" : \"afcb4c85-49f8-40de-8f2b-248949176456\" }\n },\n \"attributeValues\" : {\"firstname\":{\"S\":\"Shaggy\"},\"age\":{\"N\":4}}\n}\n"
}
```
The `evaluationResult` contains the results of testing your provided template with the provided `context`. You can also test your templates using the AWS SDKs. Here's an example using the AWS SDK for JavaScript V2:
```
const AWS = require('aws-sdk')
const client = new AWS.AppSync({ region: 'us-east-2' })
const template = fs.readFileSync('./request.vtl', 'utf8')
const context = fs.readFileSync('./context.json', 'utf8')
client
.evaluateMappingTemplate({ template, context })
.promise()
.then((data) => console.log(data))
```
Using the SDK, you can easily incorporate tests from your favorite test suite to validate your template's behavior. We recommend creating tests using the [Jest Testing Framework](https://jestjs.io/), but any testing suite works. The following snippet shows a hypothetical validation run. Note that we expect the evaluation response to be valid JSON, so we use `JSON.parse` to retrieve JSON from the string response:
```
const AWS = require('aws-sdk')
const fs = require('fs')
const client = new AWS.AppSync({ region: 'us-east-2' })
test('request correctly calls DynamoDB', async () => {
const template = fs.readFileSync('./request.vtl', 'utf8')
const context = fs.readFileSync('./context.json', 'utf8')
const contextJSON = JSON.parse(context)
const response = await client.evaluateMappingTemplate({ template, context }).promise()
const result = JSON.parse(response.evaluationResult)
expect(result.key.id.S).toBeDefined()
expect(result.attributeValues.firstname.S).toEqual(contextJSON.arguments.firstname)
})
```
This yields the following result:
```
Ran all test suites.
> jest
PASS ./index.test.js
✓ request correctly calls DynamoDB (543 ms)
Test Suites: 1 passed, 1 total
Tests: 1 passed, 1 total
Snapshots: 0 total
Time: 1.511 s, estimated 2 s
```
## Debugging a live query
There’s no substitute for an end-to-end test and logging to debug a production application. AWS AppSync lets you log errors and full request details using Amazon CloudWatch. Additionally, you can use the AWS AppSync console to test GraphQL queries, mutations, and subscriptions and live stream log data for each request back into the query editor to debug in real time. For subscriptions, the logs display connection-time information.
To perform this, you need to have Amazon CloudWatch logs enabled in advance, as described in [Monitoring and logging](monitoring.md#aws-appsync-monitoring). Next, in the AWS AppSync console, choose the **Queries** tab and then enter a valid GraphQL query. In the lower-right section, click and drag the **Logs** window to open the logs view. At the top of the page, choose the play arrow icon to run your GraphQL query. In a few moments, your full request and response logs for the operation are streamed to this section and you can view then in the console.
# Configuring and using pipeline resolvers in AWS AppSync (VTL)
**Note**
We now primarily support the APPSYNC\$1JS runtime and its documentation. Please consider using the APPSYNC\$1JS runtime and its guides [here](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers-js.html).
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](resolver-mapping-template-reference-overview.md#aws-appsync-resolver-mapping-template-reference-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:
1. Mutation.signUp resolver request mapping template
1. validateEmail function
1. saveUser function
1. Mutation.signUp resolver response mapping template
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](tutorial-pipeline-resolvers.md#aws-appsync-tutorial-pipeline-resolvers)