# Structuring a GraphQL API (blank or imported APIs)
Before you create your GraphQL API from a blank template, it would help to review the concepts surrounding GraphQL. There are three fundamental components of a GraphQL API:
1. The **schema** is the file containing the shape and definition of your data. When a request is made by a client to your GraphQL service, the data returned will follow the specification of the schema. For more information, see [GraphQL schemas](schema-components.md#aws-appsync-schema-components).
1. The **data source** is attached to your schema. When a request is made, this is where the data is retrieved and modified. For more information, see [Data sources](data-source-components.md#aws-appsync-data-source-components).
1. The **resolver** sits between the schema and the data source. When a request is made, the resolver performs the operation on the data from the source, then returns the result as a response. For more information, see [Resolvers](resolver-components.md#aws-appsync-resolver-components).
![\[GraphQL API architecture showing schema, resolvers, and data sources connected via AppSync.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/appsync-architecture-graphql-api.png)
AWS AppSync manages your APIs by allowing you to create, edit, and store the code for your schemas and resolvers. Your data sources will come from external repositories such as databases, DynamoDB tables, and Lambda functions. If you're using an AWS service to store your data or are planning on doing so, AWS AppSync provides a near-seamless experience when associating data from your AWS accounts to your GraphQL APIs.
In the next section, you will learn how to create each of these components using the AWS AppSync service.
**Topics**
+ [Designing your GraphQL schema](designing-your-schema.md)
+ [Attaching a data source](attaching-a-data-source.md)
+ [Configuring AWS AppSync resolvers](resolver-config-overview.md)
+ [Using APIs with the CDK](using-your-api.md)
# Designing your GraphQL schema
The GraphQL schema is the foundation of any GraphQL server implementation. Each GraphQL API is defined by a **single** schema that contains types and fields describing how the data from requests will be populated. The data flowing through your API and the operations performed must be validated against the schema.
In general, the [GraphQL type system](https://graphql.org/learn/schema/#type-system) describes the capabilities of a GraphQL server and is used to determine if a query is valid. A server’s type system is often referred to as that server’s schema and can consist of different object types, scalar types, input types, and more. GraphQL is both declarative and strongly typed, meaning the types will be well-defined at runtime and will only return what was specified.
AWS AppSync allows you to define and configure GraphQL schemas. The following section describes how to create GraphQL schemas from scratch using AWS AppSync's services.
## Structuring a GraphQL Schema
**Tip**
We recommend reviewing the [Schemas](https://docs.aws.amazon.com//appsync/latest/devguide/schema-components.html) section before continuing.
GraphQL is a powerful tool for implementing API services. According to [GraphQL's website](https://graphql.org/), GraphQL is the following:
"*GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.*"
This section covers the very first part of your GraphQL implementation, the schema. Using the quote above, a schema plays the role of "providing a complete and understandable description of the data in your API". In other words, a GraphQL schema is a textual representation of your service's data, operations, and the relations between them. The schema is considered the main entry point for your GraphQL service implementation. Unsurprisingly, it's often one of the first things you make in your project. We recommend reviewing the [Schemas](https://docs.aws.amazon.com//appsync/latest/devguide/schema-components.html) section before continuing.
To quote the [Schemas](https://docs.aws.amazon.com//appsync/latest/devguide/schema-components.html) section, GraphQL schemas are written in the *Schema Definition Language* (SDL). SDL is composed of types and fields with an established structure:
+ **Types**: Types are how GraphQL defines the shape and behavior of the data. GraphQL supports a multitude of types that will be explained later in this section. Each type that's defined in your schema will contain its own scope. Inside the scope will be one or more fields that can contain a value or logic that will be used in your GraphQL service. Types fill many different roles, the most common being objects or scalars (primitive value types).
+ **Fields**: Fields exist within the scope of a type and hold the value that's requested from the GraphQL service. These are very similar to variables in other programming languages. The shape of the data you define in your fields will determine how the data is structured in a request/response operation. This allows developers to predict what will be returned without knowing how the backend of the service is implemented.
The simplest schemas will contain three different data categories:
1. **Schema roots**: Roots define the entry points of your schema. It points to the fields that will be performing some operation on the data like adding, deleting, or modifying something.
1. **Types**: These are base types that are used to represent the shape of the data. You can almost think of these as objects or abstract representations of something with defined characteristics. For example, you could make a `Person` object that represents a person in a database. Each person's characteristics will be defined inside the `Person` as fields. They can be anything like the person's name, age, job, address, etc.
1. **Special object types**: These are the types that define the behavior of the operations in your schema. Each special object type is defined once per schema. They are first placed in the schema root, then defined in the schema body. Each field in a special object type defines a single operation to be implemented by your resolver.
To put this into perspective, imagine you're creating a service that stores authors and the books they've written. Each author has a name and an array of books they've authored. Each book has a name and a list of associated authors. We also want the ability to add or retrieve books and authors. A simple UML representation of this relationship may look like this:
![\[UML diagram showing Author and Book classes with attributes and methods, linked by association.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/GraphQL-UML-1.png)
In GraphQL, the entities `Author` and `Book` represent two different object types in your schema:
```
type Author {
}
type Book {
}
```
`Author` contains `authorName` and `Books`, while `Book` contains `bookName` and `Authors`. These can be represented as the fields within the scope of your types:
```
type Author {
authorName: String
Books: [Book]
}
type Book {
bookName: String
Authors: [Author]
}
```
As you can see, the type representations are very close to the diagram. However, the methods are where it gets a bit trickier. These will be placed in one of a few special object types as a field. Their special object categorization depends on their behavior. GraphQL contains three fundamental special object types: queries, mutations, and subscriptions. For more information, see [Special objects](https://docs.aws.amazon.com//appsync/latest/devguide/graphql-types.html#special-object-components).
Because `getAuthor` and `getBook` are both requesting data, they will be placed in a `Query` special object type:
```
type Author {
authorName: String
Books: [Book]
}
type Book {
bookName: String
Authors: [Author]
}
type Query {
getAuthor(authorName: String): Author
getBook(bookName: String): Book
}
```
The operations are linked to the query, which itself is linked to the schema. Adding a schema root will define the special object type (`Query` in this case) as one of your entry points. This can be done using the `schema` keyword:
```
schema {
query: Query
}
type Author {
authorName: String
Books: [Book]
}
type Book {
bookName: String
Authors: [Author]
}
type Query {
getAuthor(authorName: String): Author
getBook(bookName: String): Book
}
```
Looking at the final two methods, `addAuthor` and `addBook` are adding data to your database, so they will be defined in a `Mutation` special object type. However, from the [Types](https://docs.aws.amazon.com/appsync/latest/devguide/graphql-types.html#input-components) page, we also know that inputs directly referencing Objects aren't allowed because they're strictly output types. In this case, we can't use `Author` or `Book`, so we need to make an input type with the same fields. In this example, we added `AuthorInput` and `BookInput`, both of which accept the same fields of their respective types. Then, we create our mutation using the inputs as our parameters:
```
schema {
query: Query
mutation: Mutation
}
type Author {
authorName: String
Books: [Book]
}
input AuthorInput {
authorName: String
Books: [BookInput]
}
type Book {
bookName: String
Authors: [Author]
}
input BookInput {
bookName: String
Authors: [AuthorInput]
}
type Query {
getAuthor(authorName: String): Author
getBook(bookName: String): Book
}
type Mutation {
addAuthor(input: [BookInput]): Author
addBook(input: [AuthorInput]): Book
}
```
Let's review what we just did:
1. We created a schema with the `Book` and `Author` types to represent our entities.
1. We added the fields containing the characteristics of our entities.
1. We added a query to retrieve this information from the database.
1. We added a mutation to manipulate data in the database.
1. We added input types to replace our object parameters in the mutation to comply with GraphQL's rules.
1. We added the query and mutation to our root schema so that the GraphQL implementation understands the root type location.
As you can see, the process of creating a schema takes a lot of concepts from data modeling (especially database modeling) in general. You can think of the schema as fitting the shape of the data from the source. It also serves as the model that the resolver will implement. In the following, sections, you'll learn how to make a schema using various AWS-backed tools and services.
**Note**
The examples in the following sections are not meant to run in a real application. They are only there to showcase the commands so you can build your own applications.
## Creating schemas
Your schema will be in a file called `schema.graphql`. AWS AppSync allows users to create new schemas for their GraphQL APIs using various methods. In this example, we'll be creating a blank API along with a blank schema.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. In the **Dashboard**, choose **Create API**.
1. Under **API options**, choose **GraphQL APIs**, **Design from scratch**, then **Next**.
1. For **API name**, change the prepopulated name to what your application needs.
1. For **contact details**, you can enter a point of contact to identify a manager for the API. This is an optional field.
1. Under **Private API configuration**, you can enable private API features. A private API can only be accessed from a configured VPC endpoint (VPCE). For more information, see [Private APIs](https://docs.aws.amazon.com/appsync/latest/devguide/using-private-apis.html).
We don't recommend enabling this feature for this example. Choose **Next** after reviewing your inputs.
1. Under **Create a GraphQL type**, you can choose to create a DynamoDB table to use as a data source or skip this and do it later.
For this example, choose **Create GraphQL resources later**. We will be creating a resource in a separate section.
1. Review your inputs, then choose **Create API**.
1. You will be in the dashboard of your specific API. You can tell because the API's name will be at the top of the dashboard. If this isn't the case, you can select **APIs** in the **Sidebar**, then choose your API in the **APIs dashboard**.
1. In the **Sidebar** underneath your API's name, choose **Schema**.
1. In the **Schema editor**, you can configure your `schema.graphql` file. It may be empty or filled with types generated from a model. On the right, you have the **Resolvers** section for attaching resolvers to your schema fields. We won't be looking at resolvers in this section.
------
#### [ CLI ]
**Note**
When using the CLI, make sure you have the correct permissions to access and create resources in the service. You may want to set [least-privilege](https://docs.aws.amazon.com/IAM/latest/UserGuide/best-practices.html#grant-least-privilege) policies for non-admin users who need to access the service. For more information about AWS AppSync policies, see [Identity and access management for AWS AppSync](https://docs.aws.amazon.com//appsync/latest/devguide/security-iam.html).
Additionally, we recommend reading the console version first if you haven't done so already.
1. If you haven't already done so, [install](https://docs.aws.amazon.com//cli/latest/userguide/cli-chap-getting-started.html) the AWS CLI, then add your [configuration](https://docs.aws.amazon.com//cli/latest/userguide/cli-configure-quickstart.html).
1. Create a GraphQL API object by running the [https://docs.aws.amazon.com/cli/latest/reference/appsync/create-graphql-api.html](https://docs.aws.amazon.com/cli/latest/reference/appsync/create-graphql-api.html) command.
You'll need to type in two parameters for this particular command:
1. The `name` of your API.
1. The `authentication-type`, or the type of credentials used to access the API (IAM, OIDC, etc.).
**Note**
Other parameters such as `Region` must be configured but will usually default to your CLI configuration values.
An example command may look like this:
```
aws appsync create-graphql-api --name testAPI123 --authentication-type API_KEY
```
An output will be returned in the CLI. Here's an example:
```
{
"graphqlApi": {
"xrayEnabled": false,
"name": "testAPI123",
"authenticationType": "API_KEY",
"tags": {},
"apiId": "abcdefghijklmnopqrstuvwxyz",
"uris": {
"GRAPHQL": "https://zyxwvutsrqponmlkjihgfedcba.appsync-api.us-west-2.amazonaws.com/graphql",
"REALTIME": "wss://zyxwvutsrqponmlkjihgfedcba.appsync-realtime-api.us-west-2.amazonaws.com/graphql"
},
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz"
}
}
```
1.
**Note**
This is an optional command that takes an existing schema and uploads it to the AWS AppSync service using a base-64 blob. We will not be using this command for the sake of this example.
Run the [https://docs.aws.amazon.com/cli/latest/reference/appsync/start-schema-creation.html](https://docs.aws.amazon.com/cli/latest/reference/appsync/start-schema-creation.html) command.
You'll need to type in two parameters for this particular command:
1. Your `api-id` from the previous step.
1. The schema `definition` is a base-64 encoded binary blob.
An example command may look like this:
```
aws appsync start-schema-creation --api-id abcdefghijklmnopqrstuvwxyz --definition "aa1111aa-123b-2bb2-c321-12hgg76cc33v"
```
An output will be returned:
```
{
"status": "PROCESSING"
}
```
This command will not return the final output after processing. You must use a separate command, [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/get-schema-creation-status.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/get-schema-creation-status.html), to see the result. Note that these two commands are asynchronous, so you can check the output status even while the schema is still being created.
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
1. The starting point for the CDK is a bit different. Ideally, your `schema.graphql` file should already be created. You just need to create a new file with the `.graphql` file extension. This can be an empty file.
1. In general, you may have to add the import directive to the service you're using. For example, it may follow the forms:
```
import * as x from 'x'; # import wildcard as the 'x' keyword from 'x-service'
import {a, b, ...} from 'c'; # import {specific constructs} from 'c-service'
```
To add a GraphQL API, your stack file needs to import the AWS AppSync service:
```
import * as appsync from 'aws-cdk-lib/aws-appsync';
```
**Note**
This means we're importing the entire service under the `appsync` keyword. To use this in your app, your AWS AppSync constructs will use the format `appsync.construct_name`. For instance, if we wanted to make a GraphQL API, we would say `new appsync.GraphqlApi(args_go_here)`. The following step depicts this.
1. The most basic GraphQL API will include a `name` for the API and the `schema` path.
```
const add_api = new appsync.GraphqlApi(this, 'API_ID', {
name: 'name_of_API_in_console',
schema: appsync.SchemaFile.fromAsset(path.join(__dirname, 'schema_name.graphql')),
});
```
**Note**
Let's review what this snippet does. Inside the scope of `api`, we're creating a new GraphQL API by calling `appsync.GraphqlApi(scope: Construct, id: string, props: GraphqlApiProps)`. The scope is `this`, which refers to the current object. The id is *API\$1ID*, which will be your GraphQL API's resource name in CloudFormation when it's created. The `GraphqlApiProps` contains the `name` of your GraphQL API and the `schema`. The `schema` will generate a schema (`SchemaFile.fromAsset`) by searching the absolute path (`__dirname`) for the `.graphql` file (*schema\$1name.graphql*). In a real scenario, your schema file will probably be inside the CDK app.
To use changes made to your GraphQL API, you'll have to redeploy the app.
------
## Adding types to schemas
Now that you've added your schema, you can start adding both your input and output types. Note that the types here shouldn't be used in real code; they're just examples to help you understand the process.
First, we'll create an object type. In real code, you don't have to start with these types. You can make any type you want at any time so long as you follow GraphQL's rules and syntax.
**Note**
These next few sections will be using the **schema editor**, so keep this open.
------
#### [ Console ]
+ You can create an object type using the `type` keyword along with the type's name:
```
type Type_Name_Goes_Here {}
```
Inside the type's scope, you can add fields that represent the object's characteristics:
```
type Type_Name_Goes_Here {
# Add fields here
}
```
Here's an example:
```
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
```
**Note**
In this step, we added a generic object type with a required `id` field stored as `ID`, a `title` field stored as a `String`, and a `date` field stored as an `AWSDateTime`. To see a list of types and fields and what they do, see [Schemas](https://docs.aws.amazon.com//appsync/latest/devguide/schema-components.html). To see a list of scalars and what they do, see the [Type reference](https://docs.aws.amazon.com/appsync/latest/devguide/type-reference.html).
------
#### [ CLI ]
**Note**
We recommend reading the console version first if you haven't done so already.
+ You can create an object type by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `definition`, or the content of your type. In the console example, this was:
```
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
```
1. The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync create-type --api-id abcdefghijklmnopqrstuvwxyz --definition "type Obj_Type_1{id: ID! title: String date: AWSDateTime}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "type Obj_Type_1{id: ID! title: String date: AWSDateTime}",
"name": "Obj_Type_1",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Obj_Type_1",
"format": "SDL"
}
}
```
**Note**
In this step, we added a generic object type with a required `id` field stored as `ID`, a `title` field stored as a `String`, and a `date` field stored as an `AWSDateTime`. To see a list of types and fields and what they do, see [Schemas](https://docs.aws.amazon.com//appsync/latest/devguide/schema-components.html). To see a list of scalars and what they do, see [Type reference](https://docs.aws.amazon.com/appsync/latest/devguide/type-reference.html).
On a further note, you may have realized that entering the definition directly works for smaller types but is infeasible for adding larger or multiple types. You can opt to add everything in a `.graphql` file and then [pass it as the input](https://docs.aws.amazon.com/cli/latest/userguide/cli-usage-parameters-file.html).
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
To add a type, you need to add it to your `.graphql` file. For instance, the console example was:
```
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
```
You can add your types directly to the schema like any other file.
**Note**
To use changes made to your GraphQL API, you'll have to redeploy the app.
------
The [object type](https://graphql.org/learn/schema/#object-types-and-fields) has fields that are [scalar types](https://graphql.org/learn/schema/#scalar-types) such as strings and integers. AWS AppSync also allows you to use enhanced scalar types like `AWSDateTime` in addition to the base GraphQL scalars. Also, any field that ends in an exclamation point is required.
The `ID` scalar type in particular is a unique identifier that can be either `String` or `Int`. You can control these in your resolver code for automatic assignment.
There are similarities between special object types like `Query` and "regular" object types like the example above in that they both use the `type` keyword and are considered objects. However, for the special object types (`Query`, `Mutation`, and `Subscription`), their behavior is vastly different because they are exposed as the entry points for your API. They're also more about shaping operations rather than data. For more information, see [The query and mutation types](https://graphql.org/learn/schema/#the-query-and-mutation-types).
On the topic of special object types, the next step could be to add one or more of them to perform operations on the shaped data. In a real scenario, every GraphQL schema must at least have a root query type for requesting data. You can think of the query as one of the entry points (or endpoints) for your GraphQL server. Let's add a query as an example.
------
#### [ Console ]
+ To create a query, you can simply add it to the schema file like any other type. A query would require a `Query` type and an entry in the root like this:
```
schema {
query: Name_of_Query
}
type Name_of_Query {
# Add field operation here
}
```
Note that *Name\$1of\$1Query* in a production environment will simply be called `Query` in most cases. We recommend keeping it at this value. Inside the query type, you can add fields. Each field will perform an operation in the request. As a result, most, if not all, of these fields will be attached to a resolver. However, we're not concerned with that in this section. Regarding the format of the field operation, it might look like this:
```
Name_of_Query(params): Return_Type # version with params
Name_of_Query: Return_Type # version without params
```
Here's an example:
```
schema {
query: Query
}
type Query {
getObj: [Obj_Type_1]
}
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
```
**Note**
In this step, we added a `Query` type and defined it in our `schema` root. Our `Query` type defined a `getObj` field that returns a list of `Obj_Type_1` objects. Note that `Obj_Type_1` is the object of the previous step. In production code, your field operations will normally be working with data shaped by objects like `Obj_Type_1`. In addition, fields like `getObj` will normally have a resolver to perform the business logic. That will be covered in a different section.
As an additional note, AWS AppSync automatically adds a schema root during exports, so technically you don't have to add it directly to the schema. Our service will automatically process duplicate schemas. We're adding it here as a best practice.
------
#### [ CLI ]
**Note**
We recommend reading the console version first if you haven't done so already.
1. Create a `schema` root with a `query` definition by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `definition`, or the content of your type. In the console example, this was:
```
schema {
query: Query
}
```
1. The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync create-type --api-id abcdefghijklmnopqrstuvwxyz --definition "schema {query: Query}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "schema {query: Query}",
"name": "schema",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/schema",
"format": "SDL"
}
}
```
**Note**
Note that if you didn't input something correctly in the `create-type` command, you can update your schema root (or any type in the schema) by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-type.html) command. In this example, we'll be temporarily changing the schema root to contain a `subscription` definition.
You'll need to enter a few parameters for this particular command:
The `api-id` of your API.
The `type-name` of your type. In the console example, this was `schema`.
The `definition`, or the content of your type. In the console example, this was:
```
schema {
query: Query
}
```
The schema after adding a `subscription` will look like this:
```
schema {
query: Query
subscription: Subscription
}
```
The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync update-type --api-id abcdefghijklmnopqrstuvwxyz --type-name schema --definition "schema {query: Query subscription: Subscription}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "schema {query: Query subscription: Subscription}",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/schema",
"format": "SDL"
}
}
```
Adding preformatted files will still work in this example.
1. Create a `Query` type by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `definition`, or the content of your type. In the console example, this was:
```
type Query {
getObj: [Obj_Type_1]
}
```
1. The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync create-type --api-id abcdefghijklmnopqrstuvwxyz --definition "type Query {getObj: [Obj_Type_1]}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "Query {getObj: [Obj_Type_1]}",
"name": "Query",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Query",
"format": "SDL"
}
}
```
**Note**
In this step, we added a `Query` type and defined it in your `schema` root. Our `Query` type defined a `getObj` field that returned a list of `Obj_Type_1` objects.
In the `schema` root code `query: Query`, the `query:` part indicates that a query was defined in your schema, while the `Query` part indicates the actual special object name.
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
You'll need to add your query and the schema root to the `.graphql` file. Our example looked like the example below, but you'll want to replace it with your actual schema code:
```
schema {
query: Query
}
type Query {
getObj: [Obj_Type_1]
}
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
```
You can add your types directly to the schema like any other file.
**Note**
Updating the schema root is optional. We added it to this example as a best practice.
To use changes made to your GraphQL API, you'll have to redeploy the app.
------
You've now seen an example of creating both objects and special objects (queries). You've also seen how these can be interconnected to describe data and operations. You can have schemas with only the data description and one or more queries. However, we'd like to add another operation to add data to the data source. We'll add another special object type called `Mutation` that modifies data.
------
#### [ Console ]
+ A mutation will be called `Mutation`. Like `Query`, the field operations inside `Mutation` will describe an operation and will be attached to a resolver. Also, note that we need to define it in the `schema` root because it's a special object type. Here's an example of a mutation:
```
schema {
mutation: Name_of_Mutation
}
type Name_of_Mutation {
# Add field operation here
}
```
A typical mutation will be listed in the root like a query. The mutation is defined using the `type` keyword along with the name. *Name\$1of\$1Mutation* will usually be called `Mutation`, so we recommend keeping it that way. Each field will also perform an operation. Regarding the format of the field operation, it might look like this:
```
Name_of_Mutation(params): Return_Type # version with params
Name_of_Mutation: Return_Type # version without params
```
Here's an example:
```
schema {
query: Query
mutation: Mutation
}
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
type Query {
getObj: [Obj_Type_1]
}
type Mutation {
addObj(id: ID!, title: String, date: AWSDateTime): Obj_Type_1
}
```
**Note**
In this step, we added a `Mutation` type with an `addObj` field. Let's summarize what this field does:
```
addObj(id: ID!, title: String, date: AWSDateTime): Obj_Type_1
```
`addObj` is using the `Obj_Type_1` object to perform an operation. This is apparent due to the fields, but the syntax proves this in the `: Obj_Type_1` return type. Inside `addObj`, it's accepting the `id`, `title`, and `date` fields from the `Obj_Type_1` object as parameters. As you may see, it looks a lot like a method declaration. However, we haven't described the behavior of our method yet. As stated earlier, the schema is only there to define what the data and operations will be and not how they operate. Implementing the actual business logic will come later when we create our first resolvers.
Once you're done with your schema, there's an option to export it as a `schema.graphql` file. In the **Schema editor**, you can choose **Export schema** to download the file in a supported format.
As an additional note, AWS AppSync automatically adds a schema root during exports, so technically you don't have to add it directly to the schema. Our service will automatically process duplicate schemas. We're adding it here as a best practice.
------
#### [ CLI ]
**Note**
We recommend reading the console version first if you haven't done so already.
1. Update your root schema by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/update-type.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `type-name` of your type. In the console example, this was `schema`.
1. The `definition`, or the content of your type. In the console example, this was:
```
schema {
query: Query
mutation: Mutation
}
```
1. The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync update-type --api-id abcdefghijklmnopqrstuvwxyz --type-name schema --definition "schema {query: Query mutation: Mutation}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "schema {query: Query mutation: Mutation}",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/schema",
"format": "SDL"
}
}
```
1. Create a `Mutation` type by running the [https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-type.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `definition`, or the content of your type. In the console example, this was
```
type Mutation {
addObj(id: ID!, title: String, date: AWSDateTime): Obj_Type_1
}
```
1. The `format` of your input. In this example, we're using `SDL`.
An example command may look like this:
```
aws appsync create-type --api-id abcdefghijklmnopqrstuvwxyz --definition "type Mutation {addObj(id: ID! title: String date: AWSDateTime): Obj_Type_1}" --format SDL
```
An output will be returned in the CLI. Here's an example:
```
{
"type": {
"definition": "type Mutation {addObj(id: ID! title: String date: AWSDateTime): Obj_Type_1}",
"name": "Mutation",
"arn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Mutation",
"format": "SDL"
}
}
```
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
You'll need to add your query and the schema root to the `.graphql` file. Our example looked like the example below, but you'll want to replace it with your actual schema code:
```
schema {
query: Query
mutation: Mutation
}
type Obj_Type_1 {
id: ID!
title: String
date: AWSDateTime
}
type Query {
getObj: [Obj_Type_1]
}
type Mutation {
addObj(id: ID!, title: String, date: AWSDateTime): Obj_Type_1
}
```
**Note**
Updating the schema root is optional. We added it to this example as a best practice.
To use changes made to your GraphQL API, you'll have to redeploy the app.
------
## Optional considerations - Using enums as statuses
At this point, you know how to make a basic schema. However, there are many things you could add to increase the schema's functionality. One common thing found in applications is the use of enums as statuses. You can use an enum to force a specific value from a set of values to be chosen when called. This is good for things that you know will not change drastically over long periods of time. Hypothetically speaking, we could add an enum that returns the status code or String in the response.
As an example, let's assume we're making a social media app that's storing a user's post data in the backend. Our schema contains a `Post` type that represents an individual post's data:
```
type Post {
id: ID!
title: String
date: AWSDateTime
poststatus: PostStatus
}
```
Our `Post` will contain a unique `id`, post `title`, `date` of posting, and an enum called `PostStatus` that represents the post's state as it's processed by the app. For our operations, we'll have a query that returns all post data:
```
type Query {
getPosts: [Post]
}
```
We'll also have a mutation that adds posts to the data source:
```
type Mutation {
addPost(id: ID!, title: String, date: AWSDateTime, poststatus: PostStatus): Post
}
```
Looking at our schema, the `PostStatus` enum could have several statuses. We might want the three basic states called `success` (post successfully processed), `pending` (post being processed), and `error` (post unable to be processed). To add the enum, we could do this:
```
enum PostStatus {
success
pending
error
}
```
The full schema might look like this:
```
schema {
query: Query
mutation: Mutation
}
type Post {
id: ID!
title: String
date: AWSDateTime
poststatus: PostStatus
}
type Mutation {
addPost(id: ID!, title: String, date: AWSDateTime, poststatus: PostStatus): Post
}
type Query {
getPosts: [Post]
}
enum PostStatus {
success
pending
error
}
```
If a user adds a `Post` in the application, the `addPost` operation will be called to process that data. As the resolver attached to `addPost` processes the data, it will continually update the `poststatus` with the status of the operation. When queried, the `Post` will contain the final status of the data. Keep in mind, we're only describing how we want the data to work in the schema. We're assuming a lot about the implementation of our resolver(s), which will implement the actual business logic for handling the data to fulfill the request.
## Optional considerations - Subscriptions
Subscriptions in AWS AppSync are invoked as a response to a mutation. You configure this with a `Subscription` type and `@aws_subscribe()` directive in the schema to denote which mutations invoke one or more subscriptions. For more information about configuring subscriptions, see [Real-time data](https://docs.aws.amazon.com/appsync/latest/devguide/aws-appsync-real-time-data.html).
## Optional considerations - Relations and pagination
Suppose you had a million `Posts` stored in a DynamoDB table, and you wanted to return some of that data. However, the example query given above only returns all posts. You wouldn’t want to fetch all of these every time you made a request. Instead, you would want to [paginate](https://graphql.org/learn/pagination/) through them. Make the following changes to your schema:
+ In the `getPosts` field, add two input arguments: `nextToken` (iterator) and `limit` (iteration limit).
+ Add a new `PostIterator` type containing `Posts` (retrieves the list of `Post` objects) and `nextToken` (iterator) fields.
+ Change `getPosts` so that it returns `PostIterator` and not a list of `Post` objects.
```
schema {
query: Query
mutation: Mutation
}
type Post {
id: ID!
title: String
date: AWSDateTime
poststatus: PostStatus
}
type Mutation {
addPost(id: ID!, title: String, date: AWSDateTime, poststatus: PostStatus): Post
}
type Query {
getPosts(limit: Int, nextToken: String): PostIterator
}
enum PostStatus {
success
pending
error
}
type PostIterator {
posts: [Post]
nextToken: String
}
```
The `PostIterator` type allows you to return a portion of the list of `Post` objects and a `nextToken` for getting the next portion. Inside `PostIterator`, there is a list of `Post` items (`[Post]`) that is returned with a pagination token (`nextToken`). In AWS AppSync, this would be connected to Amazon DynamoDB through a resolver and automatically generated as an encrypted token. This converts the value of the `limit` argument to the `maxResults` parameter and the `nextToken` argument to the `exclusiveStartKey` parameter. For examples and the built-in template samples in the AWS AppSync console, see [Resolver reference (JavaScript)](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-js-version.html).
# Attaching a data source in AWS AppSync
Data sources are resources in your AWS account that GraphQL APIs can interact with. AWS AppSync supports a multitude of data sources like AWS Lambda, Amazon DynamoDB, relational databases (Amazon Aurora Serverless), Amazon OpenSearch Service, and HTTP endpoints. An AWS AppSync API can be configured to interact with multiple data sources, enabling you to aggregate data in a single location. AWS AppSync can use existing AWS resources from your account or provision DynamoDB tables on your behalf from a schema definition.
The following section will show you how to attach a data source to your GraphQL API.
## Types of data sources
Now that you have created a schema in the AWS AppSync console, you can attach a data source to it. When you initially create an API, there's an option to provision an Amazon DynamoDB table during the creation of the predefined schema. However, we won't be covering that option in this section. You can see an example of this in the [Launching a schema](https://docs.aws.amazon.com//appsync/latest/devguide/schema-launch-start.html) section.
Instead, we'll be looking at all of the data sources AWS AppSync supports. There are many factors that go into picking the right solution for your application. The sections below will provide some additional context for each data source. For general information about data sources, see [Data sources](https://docs.aws.amazon.com/appsync/latest/devguide/data-source-components.html).
### Amazon DynamoDB
Amazon DynamoDB is one of AWS' main storage solutions for scalable applications. The core component of DynamoDB is the **table**, which is simply a collection of data. You will typically create tables based on entities like `Book` or `Author`. Table entry information is stored as **items**, which are groups of fields that are unique to each entry. A full item represents a row/record in the database. For example, an item for a `Book` entry might include `title` and `author` along with their values. The individual fields like the `title` and `author` are called **attributes**, which are akin to column values in relational databases.
As you can guess, tables will be used to store data from your application. AWS AppSync allows you to hook up your DynamoDB tables to your GraphQL API to manipulate data. Take this [use case](https://aws.amazon.com/blogs/mobile/new-real-time-multi-group-app-with-aws-amplify-graphql-build-a-twitter-community-clone/) from the *Front-end web and mobile blog*. This application lets users sign up for a social media app. Users can join groups and upload posts that are broadcasted to other users subscribed to the group. Their application stores user, post, and user group information in DynamoDB. The GraphQL API (managed by AWS AppSync) interfaces with the DynamoDB table. When a user makes a change in the system that will be reflected on the front-end, the GraphQL API retrieves these changes and broadcasts them to other users in real time.
### AWS Lambda
Lambda is an event-driven service that automatically builds the necessary resources to run code as a response to an event. Lambda uses **functions**, which are group statements containing the code, dependencies, and configurations for executing a resource. Functions automatically execute when they detect a **trigger**, a group of activities that invoke your function. A trigger could be anything like an application making an API call, an AWS service in your account spinning up a resource, etc. When triggered, functions will process **events**, which are JSON documents containing the data to modify.
Lambda is good for running code without having to provision the resources to run it. Take this [use case](https://aws.amazon.com/blogs/mobile/building-a-graphql-api-with-java-and-aws-lambda/) from the *Front-end web and mobile blog*. This use case is a bit similar to the one showcased in the DynamoDB section. In this application, the GraphQL API is responsible for defining the operations for things like adding posts (mutations) and fetching that data (queries). To implement the functionality of their operations (e.g., `getPost ( id: String ! ) : Post`, `getPostsByAuthor ( author: String ! ) : [ Post ]`), they use Lambda functions to process inbound requests. Under *Option 2: AWS AppSync with Lambda resolver*, they use the AWS AppSync service to maintain their schema and link a Lambda data source to one of the operations. When the operation is called, Lambda interfaces with the Amazon RDS proxy to perform the business logic on the database.
### Amazon RDS
Amazon RDS lets you quickly build and configure relational databases. In Amazon RDS, you'll create a generic **database instance** that will serve as the isolated database environment in the cloud. In this instance, you'll use a **DB engine**, which is the actual RDBMS software (PostgreSQL, MySQL, etc.). The service offloads much of the backend work by providing scalability using AWS' infrastructure, security services such as patching and encryption, and lowered administrative costs for deployments.
Take the same [use case](https://aws.amazon.com/blogs/mobile/building-a-graphql-api-with-java-and-aws-lambda/) from the Lambda section. Under *Option 3: AWS AppSync with Amazon RDS resolver*, another option presented is linking the GraphQL API in AWS AppSync to Amazon RDS directly. Using a [data API](https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/data-api.html), they associate the database with the GraphQL API. A resolver is attached to a field (usually a query, mutation, or subscription) and implements the SQL statements needed to access the database. When a request calling the field is made by the client, the resolver executes the statements and returns the response.
### Amazon EventBridge
In EventBridge, you'll create **event buses**, which are pipelines that receive events from services or applications you attach (the **event source**) and process them based on a set of rules. An **event** is some state change in an execution environment, while a **rule** is a set of filters for events. A rule follows an **event pattern**, or metadata of an event's state change (id, Region, account number, ARN(s), etc.). When an event matches the event pattern, EventBridge will send the event across the pipeline to the destination service (**target**) and trigger the action specified in the rule.
EventBridge is good for routing state-changing operations to some other service. Take this [use case](https://aws.amazon.com/blogs/mobile/appsync-eventbridge/) from the *Front-end web and mobile blog*. The example depicts an e-commerce solution that has several teams maintaining different services. One of these services provides order updates to the customer at each step of the delivery (order placed, in progress, shipped, delivered, etc.) on the front-end. However, the front-end team managing this service doesn't have direct access to the ordering system data as that's maintained by a separate backend team. The backend team's ordering system is also described as a black box, so it's hard to glean information about the way they're structuring their data. However, the backend team did set up a system that published order data through an event bus managed by EventBridge. To access the data coming from the event bus and route it to the front-end, the front-end team created a new target pointing to their GraphQL API sitting in AWS AppSync. They also created a rule to only send data relevant to the order update. When an update is made, the data from the event bus is sent to the GraphQL API. The schema in the API processes the data, then passes it to the front-end.
### None data sources
If you aren't planning on using a data source, you can set it to `none`. A `none` data source, while still explicitly categorized as a data source, isn't a storage medium. Typically, a resolver will invoke one or more data sources at some point to process the request. However, there are situations where you may not need to manipulate a data source. Setting the data source to `none` will run the request, skip the data invocation step, then run the response.
Take the same [use case](https://aws.amazon.com/blogs/mobile/appsync-eventbridge/) from the EventBridge section. In the schema, the mutation processes the status update, then sends it out to subscribers. Recalling how resolvers work, there's usually at least one data source invocation. However, the data in this scenario was already sent automatically by the event bus. This means there's no need for the mutation to perform a data source invocation; the order status can simply be handled locally. The mutation is set to `none`, which acts as a pass-through value with no data source invocation. The schema is then populated with the data, which is sent out to subscribers.
### OpenSearch
Amazon OpenSearch Service is a suite of tools to implement full-text searching, data visualization, and logging. You can use this service to query the structured data you've uploaded.
In this service, you'll create instances of OpenSearch. These are called **nodes**. In a node, you'll be adding at least one **index**. Indices conceptually are a bit like tables in relational databases. (However, OpenSearch isn't ACID compliant, so it shouldn't be used that way). You'll populate your index with data that you upload to the OpenSearch service. When your data is uploaded, it will be indexed in one or more shards that exist in the index. A **shard** is like a partition of your index that contains some of your data and can be queried separately from other shards. Once uploaded, your data will be structured as JSON files called **documents**. You can then query the node for data in the document.
### HTTP endpoints
You can use HTTP endpoints as data sources. AWS AppSync can send requests to the endpoints with the relevant information like params and payload. The HTTP response will be exposed to the resolver, which will return the final response after it finishes its operation(s).
## Adding a data source
If you created a data source, you can link it to the AWS AppSync service and, more specifically, the API.
------
#### [ Console ]
1. Sign in to the AWS Management Console and open the [AppSync console](https://console.aws.amazon.com/appsync/).
1. Choose your API in the **Dashboard**.
1. In the **Sidebar**, choose **Data Sources**.
1. Choose **Create data source**.
1. Give your data source a name. You can also give it a description, but that's optional.
1. Choose your **Data source type**.
1. For DynamoDB, you'll have to choose your Region, then the table in the Region. You can dictate interaction rules with your table by choosing to make a new generic table role or importing an existing role for the table. You can enable [versioning](https://docs.aws.amazon.com/appsync/latest/devguide/conflict-detection-and-sync.html), which can automatically create versions of data for each request when multiple clients are trying to update data at the same time. Versioning is used to keep and maintain multiple variants of data for conflict detection and resolution purposes. You can also enable automatic schema generation, which takes your data source and generates some of the CRUD, `List`, and `Query` operations needed to access it in your schema.
For OpenSearch, you'll have to choose your Region, then the domain (cluster) in the Region. You can dictate interaction rules with your domain by choosing to make a new generic table role or importing an existing role for the table.
For Lambda, you'll have to choose your Region, then the ARN of the Lambda function in the Region. You can dictate interaction rules with your Lambda function by choosing to make a new generic table role or importing an existing role for the table.
For HTTP, you'll have to enter your HTTP endpoint.
For EventBridge, you'll have to choose your Region, then the event bus in the Region. You can dictate interaction rules with your event bus by choosing to make a new generic table role or importing an existing role for the table.
For RDS, you'll have to choose your Region, then the secret store (username and password), database name, and schema.
For none, you will add a data source with no actual data source. This is for handling resolvers locally rather than through an actual data source.
**Note**
If you're importing existing roles, they need a trust policy. For more information, see the [IAM trust policy](#iam-trust-policy.title).
1. Choose **Create**.
**Note**
Alternatively, if you're creating a DynamoDB data source, you can go to the **Schema** page in the console, choose **Create Resources** at the top of the page, then fill out a predefined model to convert into a table. In this option, you will fill out or import the base type, configure the basic table data including the partition key, and review the schema changes.
------
#### [ CLI ]
+ Create your data source by running the [https://docs.aws.amazon.com/cli/latest/reference/appsync/create-data-source.html](https://docs.aws.amazon.com/cli/latest/reference/appsync/create-data-source.html) command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `name` of your table.
1. The `type` of data source. Depending on the data source type you choose, you may need to enter a `service-role-arn` and a `-config` tag.
An example command may look like this:
```
aws appsync create-data-source --api-id abcdefghijklmnopqrstuvwxyz --name data_source_name --type data_source_type --service-role-arn arn:aws:iam::107289374856:role/role_name --[data_source_type]-config {params}
```
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
To add your particular data source, you'll need to add the construct to your stack file. A list of data source types can be found here:
+ [ DynamoDbDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.DynamoDbDataSource.html)
+ [ EventBridgeDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.EventBridgeDataSource.html)
+ [ HttpDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.HttpDataSource.html)
+ [ LambdaDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.LambdaDataSource.html)
+ [ NoneDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.NoneDataSource.html)
+ [ OpenSearchDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.OpenSearchDataSource.html)
+ [ RdsDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.RdsDataSource.html)
1. In general, you may have to add the import directive to the service you're using. For example, it may follow the forms:
```
import * as x from 'x'; # import wildcard as the 'x' keyword from 'x-service'
import {a, b, ...} from 'c'; # import {specific constructs} from 'c-service'
```
For example, here's how you could import the AWS AppSync and DynamoDB services:
```
import * as appsync from 'aws-cdk-lib/aws-appsync';
import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';
```
1. Some services like RDS require some additional setup in the stack file before creating the data source (e.g., VPC creation, roles, and access credentials). Consult the examples in the relevant CDK pages for more information.
1. For most data sources, especially AWS services, you'll be creating a new instance of the data source in your stack file. Typically, this will look like the following:
```
const add_data_source_func = new service_scope.resource_name(scope: Construct, id: string, props: data_source_props);
```
For example, here's an example Amazon DynamoDB table:
```
const add_ddb_table = new dynamodb.Table(this, 'Table_ID', {
partitionKey: {
name: 'id',
type: dynamodb.AttributeType.STRING,
},
sortKey: {
name: 'id',
type: dynamodb.AttributeType.STRING,
},
tableClass: dynamodb.TableClass.STANDARD,
});
```
**Note**
Most data sources will have at least one required prop (will be denoted **without** a `?` symbol). Consult the CDK documentation to see which props are needed.
1. Next, you need to link the data source to the GraphQL API. The recommended method is to add it when you make a function for your pipeline resolver. For instance, the snippet below is a function that scans all elements in a DynamoDB table:
```
const add_func = new appsync.AppsyncFunction(this, 'func_ID', {
name: 'func_name_in_console',
add_api,
dataSource: add_api.addDynamoDbDataSource('data_source_name_in_console', add_ddb_table),
code: appsync.Code.fromInline(`
export function request(ctx) {
return { operation: 'Scan' };
}
export function response(ctx) {
return ctx.result.items;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
});
```
In the `dataSource` props, you can call the GraphQL API (`add_api`) and use one of its built-in methods (`addDynamoDbDataSource`) to make the association between the table and the GraphQL API. The arguments are the name of this link that will exist in the AWS AppSync console (`data_source_name_in_console` in this example) and the table method (`add_ddb_table`). More on this topic will be revealed in the next section when you start making resolvers.
There are alternative methods for linking a data source. You could technically add `api` to the props list in the table function. For example, here's the snippet from step 3 but with an `api` props containing a GraphQL API:
```
const add_api = new appsync.GraphqlApi(this, 'API_ID', {
...
});
const add_ddb_table = new dynamodb.Table(this, 'Table_ID', {
...
api: add_api
});
```
Alternatively, you can call the `GraphqlApi` construct separately:
```
const add_api = new appsync.GraphqlApi(this, 'API_ID', {
...
});
const add_ddb_table = new dynamodb.Table(this, 'Table_ID', {
...
});
const link_data_source = add_api.addDynamoDbDataSource('data_source_name_in_console', add_ddb_table);
```
We recommend only creating the association in the function's props. Otherwise, you'll either have to link your resolver function to the data source manually in the AWS AppSync console (if you want to keep using the console value `data_source_name_in_console`) or create a separate association in the function under another name like `data_source_name_in_console_2`. This is due to limitations in how the props process information.
**Note**
You'll have to redeploy the app to see your changes.
------
### IAM trust policy
If you’re using an existing IAM role for your data source, you need to grant that role the appropriate permissions to perform operations on your AWS resource, such as `PutItem` on an Amazon DynamoDB table. You also need to modify the trust policy on that role to allow AWS AppSync to use it for resource access as shown in the following example policy:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "appsync.amazonaws.com"
},
"Action": "sts:AssumeRole"
}
]
}
```
------
You can also add conditions to your trust policy to limit access to the data source as desired. Currently, `SourceArn` and `SourceAccount` keys can be used in these conditions. For example, the following policy limits access to your data source to the account `123456789012`:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "appsync.amazonaws.com"
},
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": {
"aws:SourceAccount": "123456789012"
}
}
}
]
}
```
------
Alternatively, you can limit access to a data source to a specific API, such as `abcdefghijklmnopq`, using the following policy:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "appsync.amazonaws.com"
},
"Action": "sts:AssumeRole",
"Condition": {
"ArnEquals": {
"aws:SourceArn": "arn:aws:appsync:us-west-2:123456789012:apis/abcdefghijklmnopq"
}
}
}
]
}
```
------
You can limit access to all AWS AppSync APIs from a specific region, such as `us-east-1`, using the following policy:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "appsync.amazonaws.com"
},
"Action": "sts:AssumeRole",
"Condition": {
"ArnEquals": {
"aws:SourceArn": "arn:aws:appsync:us-east-1:123456789012:apis/*"
}
}
}
]
}
```
------
In the next section ([Configuring Resolvers](https://docs.aws.amazon.com//appsync/latest/devguide/resolver-config-overview.html)), we'll add our resolver business logic and attach it to the fields in our schema to process the data in our data source.
For more information regarding role policy configuration, see [Modifying a role](https://docs.aws.amazon.com//IAM/latest/UserGuide/id_roles_manage_modify.html) in the *IAM User Guide*.
For more information regarding cross-account access of AWS Lambda resolvers for AWS AppSync, see [Building cross-account AWS Lambda resolvers for AWS AppSync](https://aws.amazon.com/blogs/mobile/appsync-lambda-cross-account/).
# Configuring resolvers in AWS AppSync
In the previous sections, you learned how to create your GraphQL schema and data source, then linked them together in the AWS AppSync service. In your schema, you may have established one or more fields (operations) in your query and mutation. While the schema described the kinds of data the operations would request from the data source, it never implemented how those operations would behave around the data.
An operation's behavior is always implemented in the resolver, which will be linked to the field performing the operation. For more information about how resolvers work in general, see the [Resolvers](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-components.html) page.
In AWS AppSync, your resolver is tied to a runtime, which is the environment in which your resolver executes. Runtimes dictate the language that your resolver will be written in. There are currently two supported runtimes: APPSYNC\$1JS (JavaScript) and Apache Velocity Template Language (VTL).
When implementing resolvers, there is a general structure they follow:
+ **Before step**: When a request is made by the client, the resolvers for the schema fields being used (typically your queries, mutations, subscriptions) are passed the request data. The resolver will begin processing the request data with a before step handler, which allows some preprocessing operations to be performed before the data moves through the resolver.
+ **Function(s)**: After the before step runs, the request is passed to the functions list. The first function in the list will execute against the data source. A function is a subset of your resolver's code containing its own request and response handler. A request handler will take the request data and perform operations against the data source. The response handler will process the data source's response before passing it back to the list. If there is more than one function, the request data will be sent to the next function in the list to be executed. Functions in the list will be executed serially in the order defined by the developer. Once all functions have been executed, the final result is passed to the after step.
+ **After step**: The after step is a handler function that allows you to perform some final operations on the final function's response before passing it to the GraphQL response.
This flow is an example of a pipeline resolver. Pipeline resolvers are supported in both runtimes. However, this is a simplified explanation of what pipeline resolvers can do. Also, we're describing only one possible resolver configuration. For more information about supported resolver configurations, see the [JavaScript resolvers overview](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-overview-js.html) for APPSYNC\$1JS or the [Resolver mapping template overview](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-mapping-template-reference-overview.html) for VTL.
As you can see, resolvers are modular. In order for the components of the resolver to work properly, they must be able to peer into the state of the execution from other components. From the [Resolvers](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-components.html) section, you know that each component in the resolver can be passed vital information about the state of the execution as a set of arguments (`args`, `context`, etc.). In AWS AppSync, this is handled strictly by the `context`. It's a container for the information about the field being resolved. This can include everything from arguments being passed, results, authorization data, header data, etc. For more information about the context, see the [Resolver context object reference](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-context-reference-js.html) for APPSYNC\$1JS or the [Resolver mapping template context reference](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-context-reference.html) for VTL.
The context isn't the only tool you can use to implement your resolver. AWS AppSync supports a wide range of utilities for value generation, error handling, parsing, conversion, etc. You can see a list of utilities [here](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-util-reference-js.html) for APPSYNC\$1JS or [here](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-util-reference.html) for VTL.
In the following sections, you will learn how to configure resolvers in your GraphQL API.
**Topics**
+ [Creating basic queries (JavaScript)](configuring-resolvers-js.md)
+ [Creating basic queries (VTL)](configuring-resolvers.md)
# Creating basic queries (JavaScript)
GraphQL resolvers connect the fields in a type’s schema to a data source. Resolvers are the mechanism by which requests are fulfilled.
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 describes how to configure resolvers using JavaScript. The [Resolver tutorials (JavaScript)](https://docs.aws.amazon.com/appsync/latest/devguide/tutorials-js.html) section provides in-depth tutorials on how to implement resolvers using JavaScript. The [Resolver reference (JavaScript)](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-js-version.html) section provides an explanation of utility operations that can be used with JavaScript resolvers.
We recommend following this guide before attempting to use any of the aforementioned tutorials.
In this section, we will walk through how to create and configure resolvers for queries and mutations.
**Note**
This guide assumes you have created your schema and have at least one query or mutation. If you're looking for subscriptions (real-time data), then see [this](https://docs.aws.amazon.com/appsync/latest/devguide/aws-appsync-real-time-data.html) guide.
In this section, we'll provide some general steps for configuring resolvers along with an example that uses the schema below:
```
// schema.graphql file
input CreatePostInput {
title: String
date: AWSDateTime
}
type Post {
id: ID!
title: String
date: AWSDateTime
}
type Mutation {
createPost(input: CreatePostInput!): Post
}
type Query {
getPost: [Post]
}
```
## Creating basic query resolvers
This section will show you how to make a basic query resolver.
------
#### [ 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. Enter the details of your schema and data source. See the [Designing your schema](https://docs.aws.amazon.com/appsync/latest/devguide/designing-your-schema.html) and [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html) sections for more information.
1. Next to the **Schema** editor, There's a window called **Resolvers**. This box contains a list of the types and fields as defined in your **Schema** window. You can attach resolvers to fields. You will most likely be attaching resolvers to your field operations. In this section, we'll look at simple query configurations. Under the **Query** type, choose **Attach** next to your query's field.
1. On the **Attach resolver** page, under **Resolver type**, you can choose between pipeline or unit resolvers. For more information about these types, see [Resolvers](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-components.html). This guide will make use of `pipeline resolvers`.
**Tip**
When creating pipeline resolvers, your data source(s) will be attached to the pipeline function(s). Functions are created after you create the pipeline resolver itself, which is why there's no option to set it in this page. If you're using a unit resolver, the data source is tied directly to the resolver, so you would set it in this page.
For **Resolver runtime**, choose `APPSYNC_JS` to enable the JavaScript runtime.
1. You can enable [caching](https://docs.aws.amazon.com/appsync/latest/devguide/enabling-caching.html) for this API. We recommend turning this feature off for now. Choose **Create**.
1. On the **Edit resolver** page, there's a code editor called **Resolver code** that allows you to implement the logic for the resolver handler and response (before and after steps). For more information, see the [JavaScript resolvers overview](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-overview-js.html).
**Note**
In our example, we're just going to leave the request blank and the response set to return the last data source result from the [context](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-context-reference-js.html):
```
import {util} from '@aws-appsync/utils';
export function request(ctx) {
return {};
}
export function response(ctx) {
return ctx.prev.result;
}
```
Below this section, there's a table called **Functions**. Functions allow you to implement code that can be reused across multiple resolvers. Instead of constantly rewriting or copying code, you can store the source code as a function to be added to a resolver whenever you need it.
Functions make up the bulk of a pipeline's operation list. When using multiple functions in a resolver, you set the order of the functions, and they will be run in that order sequentially. They are executed after the request function runs and before the response function begins.
To add a new function, under **Functions**, choose **Add function**, then **Create new function**. Alternatively, you may see a **Create function** button to choose instead.
1. Choose a data source. This will be the data source on which the resolver acts.
**Note**
In our example, we're attaching a resolver for `getPost`, which retrieves a `Post` object by `id`. Let's assume we already set up a DynamoDB table for this schema. Its partition key is set to the `id` and is empty.
1. Enter a `Function name`.
1. Under **Function code**, you'll need to implement the function's behavior. This might be confusing, but each function will have its own local request and response handler. The request runs, then the data source invocation is made to handle the request, then the data source response is processed by the response handler. The result is stored in the [context](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-context-reference-js.html) object. Afterward, the next function in the list will run or will be passed to the after step response handler if it's the last one.
**Note**
In our example, we're attaching a resolver to `getPost`, which gets a list of `Post` objects from the data source. Our request function will request the data from our table, the table will pass its response to the context (ctx), then the response will return the result in the context. AWS AppSync's strength lies in its interconnectedness with other AWS services. Because we're using DynamoDB, we have a [suite of operations](https://docs.aws.amazon.com/appsync/latest/devguide/js-resolver-reference-dynamodb.html) to simplify things like this. We have some boilerplate examples for other data source types as well.
Our code will look like this:
```
import { util } from '@aws-appsync/utils';
/**
* Performs a scan on the dynamodb data source
*/
export function request(ctx) {
return { operation: 'Scan' };
}
/**
* return a list of scanned post items
*/
export function response(ctx) {
return ctx.result.items;
}
```
In this step, we added two functions:
`request`: The request handler performs the retrieval operation against the data source. The argument contains the context object (`ctx`), or some data that is available to all resolvers performing a particular operation. For example, it might contain authorization data, the field names being resolved, etc. The return statement performs a [https://docs.aws.amazon.com//appsync/latest/devguide/js-resolver-reference-dynamodb.html#js-aws-appsync-resolver-reference-dynamodb-scan](https://docs.aws.amazon.com//appsync/latest/devguide/js-resolver-reference-dynamodb.html#js-aws-appsync-resolver-reference-dynamodb-scan) operation (see [here](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Scan.html) for examples). Because we're working with DynamoDB, we're allowed to use some of the operations from that service. The scan performs a basic fetch of all items in our table. The result of this operation is stored in the context object as a `result` container before being passed to the response handler. The `request` is run before the response in the pipeline.
`response`: The response handler that returns the output of the `request`. The argument is the updated context object, and the return statement is `ctx.prev.result`. At this point in the guide, you may not be familiar with this value. `ctx` refers to the context object. `prev` refers to the previous operation in the pipeline, which was our `request`. The `result` contains the result(s) of the resolver as it moves through the pipeline. If you put it all together, `ctx.prev.result` is returning the result of the last operation performed, which was the request handler.
1. Choose **Create** after you're done.
1. Back on the resolver screen, under **Functions**, choose the **Add function** drop-down and add your function to your functions list.
1. Choose **Save** to update the resolver.
------
#### [ CLI ]
**To add your function**
+ Create a function for your pipeline resolver using the `[create-function](https://docs.aws.amazon.com/cli/latest/reference/appsync/create-function.html)` command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `name` of the function in the AWS AppSync console.
1. The `data-source-name`, or the name of the data source the function will use. It must already be created and linked to your GraphQL API in the AWS AppSync service.
1. The `runtime`, or environment and language of the function. For JavaScript, the name must be `APPSYNC_JS`, and the runtime, `1.0.0`.
1. The `code`, or request and response handlers of your function. While you can type it in manually, it's far easier to add it to a .txt file (or a similar format) and then pass it in as the argument.
**Note**
Our query code will be in a file passed in as the argument:
```
import { util } from '@aws-appsync/utils';
/**
* Performs a scan on the dynamodb data source
*/
export function request(ctx) {
return { operation: 'Scan' };
}
/**
* return a list of scanned post items
*/
export function response(ctx) {
return ctx.result.items;
}
```
An example command may look like this:
```
aws appsync create-function \
--api-id abcdefghijklmnopqrstuvwxyz \
--name get_posts_func_1 \
--data-source-name table-for-posts \
--runtime name=APPSYNC_JS,runtimeVersion=1.0.0 \
--code file://~/path/to/file/{filename}.{fileType}
```
An output will be returned in the CLI. Here's an example:
```
{
"functionConfiguration": {
"functionId": "ejglgvmcabdn7lx75ref4qeig4",
"functionArn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/functions/ejglgvmcabdn7lx75ref4qeig4",
"name": "get_posts_func_1",
"dataSourceName": "table-for-posts",
"maxBatchSize": 0,
"runtime": {
"name": "APPSYNC_JS",
"runtimeVersion": "1.0.0"
},
"code": "Code output goes here"
}
}
```
**Note**
Make sure you record the `functionId` somewhere as this will be used to attach the function to the resolver.
**To create your resolver**
+ Create a pipeline function for `Query` by running the `[create-resolver](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html)` command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `type-name`, or the special object type in your schema (Query, Mutation, Subscription).
1. The `field-name`, or the field operation inside the special object type you want to attach the resolver to.
1. The `kind`, which specifies a unit or pipeline resolver. Set this to `PIPELINE` to enable pipeline functions.
1. The `pipeline-config`, or the function(s) to attach to the resolver. Make sure you know the `functionId` values of your functions. Order of listing matters.
1. The `runtime`, which was `APPSYNC_JS` (JavaScript). The `runtimeVersion` currently is `1.0.0`.
1. The `code`, which contains the before and after step handlers.
**Note**
Our query code will be in a file passed in as the argument:
```
import { util } from '@aws-appsync/utils';
/**
* Sends a request to `put` an item in the DynamoDB data source
*/
export function request(ctx) {
const { id, ...values } = ctx.args;
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({ id }),
attributeValues: util.dynamodb.toMapValues(values),
};
}
/**
* returns the result of the `put` operation
*/
export function response(ctx) {
return ctx.result;
}
```
An example command may look like this:
```
aws appsync create-resolver \
--api-id abcdefghijklmnopqrstuvwxyz \
--type-name Query \
--field-name getPost \
--kind PIPELINE \
--pipeline-config functions=ejglgvmcabdn7lx75ref4qeig4 \
--runtime name=APPSYNC_JS,runtimeVersion=1.0.0 \
--code file:///path/to/file/{filename}.{fileType}
```
An output will be returned in the CLI. Here's an example:
```
{
"resolver": {
"typeName": "Mutation",
"fieldName": "getPost",
"resolverArn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Mutation/resolvers/getPost",
"kind": "PIPELINE",
"pipelineConfig": {
"functions": [
"ejglgvmcabdn7lx75ref4qeig4"
]
},
"maxBatchSize": 0,
"runtime": {
"name": "APPSYNC_JS",
"runtimeVersion": "1.0.0"
},
"code": "Code output goes here"
}
}
```
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
A basic app will need the following things:
1. Service import directives
1. Schema code
1. Data source generator
1. Function code
1. Resolver code
From the [Designing your schema](https://docs.aws.amazon.com/appsync/latest/devguide/designing-your-schema.html) and [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html) sections, we know that the stack file will include the import directives of the form:
```
import * as x from 'x'; # import wildcard as the 'x' keyword from 'x-service'
import {a, b, ...} from 'c'; # import {specific constructs} from 'c-service'
```
**Note**
In previous sections, we only stated how to import AWS AppSync constructs. In real code, you'll have to import more services just to run the app. In our example, if we were to create a very simple CDK app, we would at least import the AWS AppSync service along with our data source, which was a DynamoDB table. We would also need to import some additional constructs to deploy the app:
```
import * as cdk from 'aws-cdk-lib';
import * as appsync from 'aws-cdk-lib/aws-appsync';
import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';
import { Construct } from 'constructs';
```
To summarize each of these:
`import * as cdk from 'aws-cdk-lib';`: This allows you to define your CDK app and constructs such as the stack. It also contains some useful utility functions for our application like manipulating metadata. If you're familiar with this import directive, but are wondering why the cdk core library is not being used here, see the [Migration](https://docs.aws.amazon.com/cdk/v2/guide/migrating-v2.html) page.
`import * as appsync from 'aws-cdk-lib/aws-appsync';`: This imports the [AWS AppSync service](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
`import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';`: This imports the [DynamoDB service](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_dynamodb-readme.html).
`import { Construct } from 'constructs';`: We need this to define the root [construct](https://docs.aws.amazon.com/cdk/v2/guide/constructs.html).
The type of import depends on the services you're calling. We recommend looking at the CDK documentation for examples. The schema at the top of the page will be a separate file in your CDK app as a `.graphql` file. In the stack file, we can associate it with a new GraphQL using the form:
```
const add_api = new appsync.GraphqlApi(this, 'graphQL-example', {
name: 'my-first-api',
schema: appsync.SchemaFile.fromAsset(path.join(__dirname, 'schema.graphql')),
});
```
**Note**
In the scope `add_api`, we're adding a new GraphQL API using the `new` keyword followed by `appsync.GraphqlApi(scope: Construct, id: string , props: GraphqlApiProps)`. Our scope is `this`, the CFN id is `graphQL-example`, and our props are `my-first-api` (name of the API in the console) and `schema.graphql` (the absolute path to the schema file).
To add a data source, you'll first have to add your data source to the stack. Then, you need to associate it with the GraphQL API using the source-specific method. The association will happen when you make your resolver function. In the meantime, let's use an example by creating the DynamoDB table using `dynamodb.Table`:
```
const add_ddb_table = new dynamodb.Table(this, 'posts-table', {
partitionKey: {
name: 'id',
type: dynamodb.AttributeType.STRING,
},
});
```
**Note**
If we were to use this in our example, we'd be adding a new DynamoDB table with the CFN id of `posts-table` and a partition key of `id (S)`.
Next, we need to implement our resolver in the stack file. Here's an example of a simple query that scans for all items in a DynamoDB table:
```
const add_func = new appsync.AppsyncFunction(this, 'func-get-posts', {
name: 'get_posts_func_1',
add_api,
dataSource: add_api.addDynamoDbDataSource('table-for-posts', add_ddb_table),
code: appsync.Code.fromInline(`
export function request(ctx) {
return { operation: 'Scan' };
}
export function response(ctx) {
return ctx.result.items;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
});
new appsync.Resolver(this, 'pipeline-resolver-get-posts', {
add_api,
typeName: 'Query',
fieldName: 'getPost',
code: appsync.Code.fromInline(`
export function request(ctx) {
return {};
}
export function response(ctx) {
return ctx.prev.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
pipelineConfig: [add_func],
});
```
**Note**
First, we created a function called `add_func`. This order of creation may seem a bit counterintuitive, but you have to create the functions in your pipeline resolver before you make the resolver itself. A function follows the form:
```
AppsyncFunction(scope: Construct, id: string, props: AppsyncFunctionProps)
```
Our scope was `this`, our CFN id was `func-get-posts`, and our props contained the actual function details. Inside props, we included:
The `name` of the function that will be present in the AWS AppSync console (`get_posts_func_1`).
The GraphQL API we created earlier (`add_api`).
The data source; this is the point where we link the data source to the GraphQL API value, then attach it to the function. We take the table we created (`add_ddb_table`) and attach it to the GraphQL API (`add_api`) using one of the `GraphqlApi` methods ([https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.GraphqlApi.html#addwbrdynamowbrdbwbrdatawbrsourceid-table-options](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.GraphqlApi.html#addwbrdynamowbrdbwbrdatawbrsourceid-table-options)). The id value (`table-for-posts`) is the name of the data source in the AWS AppSync console. For a list of source-specific methods, see the following pages:
[ DynamoDbDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.DynamoDbDataSource.html)
[ EventBridgeDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.EventBridgeDataSource.html)
[ HttpDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.HttpDataSource.html)
[ LambdaDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.LambdaDataSource.html)
[ NoneDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.NoneDataSource.html)
[ OpenSearchDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.OpenSearchDataSource.html)
[ RdsDataSource ](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync.RdsDataSource.html)
The code contains our function's request and response handlers, which is a simple scan and return.
The runtime specifies that we want to use the APPSYNC\$1JS runtime version 1.0.0. Note that this is currently the only version available for APPSYNC\$1JS.
Next, we need to attach the function to the pipeline resolver. We created our resolver using the form:
```
Resolver(scope: Construct, id: string, props: ResolverProps)
```
Our scope was `this`, our CFN id was `pipeline-resolver-get-posts`, and our props contained the actual function details. Inside the props, we included:
The GraphQL API we created earlier (`add_api`).
The special object type name; this is a query operation, so we simply added the value `Query`.
The field name (`getPost`) is the name of the field in the schema under the `Query` type.
The code contains your before and after handlers. Our example just returns whatever results were in the context after the function performed its operation.
The runtime specifies that we want to use the APPSYNC\$1JS runtime version 1.0.0. Note that this is currently the only version available for APPSYNC\$1JS.
The pipeline config contains the reference to the function we created (`add_func`).
------
To summarize what happened in this example, you saw an AWS AppSync function that implemented a request and response handler. The function was responsible for interacting with your data source. The request handler sent a `Scan` operation to AWS AppSync, instructing it on what operation to perform against your DynamoDB data source. The response handler returned the list of items (`ctx.result.items`). The list of items was then mapped to the `Post` GraphQL type automatically.
## Creating basic mutation resolvers
This section will show you how to make a basic mutation resolver.
------
#### [ 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 **Resolvers** section and the **Mutation** type, choose **Attach** next to your field.
**Note**
In our example, we're attaching a resolver for `createPost`, which adds a `Post` object to our table. Let's assume we're using the same DynamoDB table from the last section. Its partition key is set to the `id` and is empty.
1. On the **Attach resolver** page, under **Resolver type**, choose `pipeline resolvers`. As a reminder, you can find more information about resolvers [here](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-components.html). For **Resolver runtime**, choose `APPSYNC_JS` to enable the JavaScript runtime.
1. You can enable [caching](https://docs.aws.amazon.com/appsync/latest/devguide/enabling-caching.html) for this API. We recommend turning this feature off for now. Choose **Create**.
1. Choose **Add function**, then choose **Create new function**. Alternatively, you may see a **Create function** button to choose instead.
1. Choose your data source. This should be the source whose data you will manipulate with the mutation.
1. Enter a `Function name`.
1. Under **Function code**, you'll need to implement the function's behavior. This is a mutation, so the request will ideally perform some state-changing operation on the invoked data source. The result will be processed by the response function.
**Note**
`createPost` is adding, or "putting", a new `Post` in the table with our parameters as the data. We could add something like this:
```
import { util } from '@aws-appsync/utils';
/**
* Sends a request to `put` an item in the DynamoDB data source
*/
export function request(ctx) {
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({id: util.autoId()}),
attributeValues: util.dynamodb.toMapValues(ctx.args.input),
};
}
/**
* returns the result of the `put` operation
*/
export function response(ctx) {
return ctx.result;
}
```
In this step, we also added `request` and `response` functions:
`request`: The request handler accepts the context as the argument. The request handler return statement performs a [https://docs.aws.amazon.com//appsync/latest/devguide/js-resolver-reference-dynamodb.html#js-aws-appsync-resolver-reference-dynamodb-putitem](https://docs.aws.amazon.com//appsync/latest/devguide/js-resolver-reference-dynamodb.html#js-aws-appsync-resolver-reference-dynamodb-putitem) command, which is a built-in DynamoDB operation (see [here](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/getting-started-step-2.html) or [here](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/WorkingWithItems.html#WorkingWithItems.WritingData) for examples). The `PutItem` command adds a `Post` object to our DynamoDB table by taking the partition `key` value (automatically generated by `util.autoid()`) and `attributes` from the context argument input (these are the values we will pass in our request). The `key` is the `id` and `attributes` are the `date` and `title` field arguments. They're both preformatted through the [https://docs.aws.amazon.com//appsync/latest/devguide/dynamodb-helpers-in-util-dynamodb-js.html#utility-helpers-in-toMap-js](https://docs.aws.amazon.com//appsync/latest/devguide/dynamodb-helpers-in-util-dynamodb-js.html#utility-helpers-in-toMap-js) helper to work with the DynamoDB table.
`response`: The response accepts the updated context and returns the result of the request handler.
1. Choose **Create** after you're done.
1. Back on the resolver screen, under **Functions**, choose the **Add function** drop-down and add your function to your functions list.
1. Choose **Save** to update the resolver.
------
#### [ CLI ]
**To add your function**
+ Create a function for your pipeline resolver using the `[create-function](https://docs.aws.amazon.com/cli/latest/reference/appsync/create-function.html)` command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `name` of the function in the AWS AppSync console.
1. The `data-source-name`, or the name of the data source the function will use. It must already be created and linked to your GraphQL API in the AWS AppSync service.
1. The `runtime`, or environment and language of the function. For JavaScript, the name must be `APPSYNC_JS`, and the runtime, `1.0.0`.
1. The `code`, or request and response handlers of your function. While you can type it in manually, it's far easier to add it to a .txt file (or a similar format) then pass it in as the argument.
**Note**
Our query code will be in a file passed in as the argument:
```
import { util } from '@aws-appsync/utils';
/**
* Sends a request to `put` an item in the DynamoDB data source
*/
export function request(ctx) {
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({id: util.autoId()}),
attributeValues: util.dynamodb.toMapValues(ctx.args.input),
};
}
/**
* returns the result of the `put` operation
*/
export function response(ctx) {
return ctx.result;
}
```
An example command may look like this:
```
aws appsync create-function \
--api-id abcdefghijklmnopqrstuvwxyz \
--name add_posts_func_1 \
--data-source-name table-for-posts \
--runtime name=APPSYNC_JS,runtimeVersion=1.0.0 \
--code file:///path/to/file/{filename}.{fileType}
```
An output will be returned in the CLI. Here's an example:
```
{
"functionConfiguration": {
"functionId": "vulcmbfcxffiram63psb4dduoa",
"functionArn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/functions/vulcmbfcxffiram63psb4dduoa",
"name": "add_posts_func_1",
"dataSourceName": "table-for-posts",
"maxBatchSize": 0,
"runtime": {
"name": "APPSYNC_JS",
"runtimeVersion": "1.0.0"
},
"code": "Code output foes here"
}
}
```
**Note**
Make sure you record the `functionId` somewhere as this will be used to attach the function to the resolver.
**To create your resolver**
+ Create a pipeline function for `Mutation` by running the `[create-resolver](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/appsync/create-resolver.html)` command.
You'll need to enter a few parameters for this particular command:
1. The `api-id` of your API.
1. The `type-name`, or the special object type in your schema (Query, Mutation, Subscription).
1. The `field-name`, or the field operation inside the special object type you want to attach the resolver to.
1. The `kind`, which specifies a unit or pipeline resolver. Set this to `PIPELINE` to enable pipeline functions.
1. The `pipeline-config`, or the function(s) to attach to the resolver. Make sure you know the `functionId` values of your functions. Order of listing matters.
1. The `runtime`, which was `APPSYNC_JS` (JavaScript). The `runtimeVersion` currently is `1.0.0`.
1. The `code`, which contains the before and after step.
**Note**
Our query code will be in a file passed in as the argument:
```
import { util } from '@aws-appsync/utils';
/**
* Sends a request to `put` an item in the DynamoDB data source
*/
export function request(ctx) {
const { id, ...values } = ctx.args;
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({ id }),
attributeValues: util.dynamodb.toMapValues(values),
};
}
/**
* returns the result of the `put` operation
*/
export function response(ctx) {
return ctx.result;
}
```
An example command may look like this:
```
aws appsync create-resolver \
--api-id abcdefghijklmnopqrstuvwxyz \
--type-name Mutation \
--field-name createPost \
--kind PIPELINE \
--pipeline-config functions=vulcmbfcxffiram63psb4dduoa \
--runtime name=APPSYNC_JS,runtimeVersion=1.0.0 \
--code file:///path/to/file/{filename}.{fileType}
```
An output will be returned in the CLI. Here's an example:
```
{
"resolver": {
"typeName": "Mutation",
"fieldName": "createPost",
"resolverArn": "arn:aws:appsync:us-west-2:107289374856:apis/abcdefghijklmnopqrstuvwxyz/types/Mutation/resolvers/createPost",
"kind": "PIPELINE",
"pipelineConfig": {
"functions": [
"vulcmbfcxffiram63psb4dduoa"
]
},
"maxBatchSize": 0,
"runtime": {
"name": "APPSYNC_JS",
"runtimeVersion": "1.0.0"
},
"code": "Code output goes here"
}
}
```
------
#### [ CDK ]
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/home.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
The steps listed below will only show a general example of the snippet used to add a particular resource. This is **not** meant to be a working solution in your production code. We also assume you already have a working app.
+ To make a mutation, assuming you're in the same project, you can add it to the stack file like the query. Here's a modified function and resolver for a mutation that adds a new `Post` to the table:
```
const add_func_2 = new appsync.AppsyncFunction(this, 'func-add-post', {
name: 'add_posts_func_1',
add_api,
dataSource: add_api.addDynamoDbDataSource('table-for-posts-2', add_ddb_table),
code: appsync.Code.fromInline(`
export function request(ctx) {
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({id: util.autoId()}),
attributeValues: util.dynamodb.toMapValues(ctx.args.input),
};
}
export function response(ctx) {
return ctx.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
});
new appsync.Resolver(this, 'pipeline-resolver-create-posts', {
add_api,
typeName: 'Mutation',
fieldName: 'createPost',
code: appsync.Code.fromInline(`
export function request(ctx) {
return {};
}
export function response(ctx) {
return ctx.prev.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
pipelineConfig: [add_func_2],
});
```
**Note**
Since this mutation and the query are similarly structured, we'll just explain the changes we made to make the mutation.
In the function, we changed the CFN id to `func-add-post` and name to `add_posts_func_1` to reflect the fact that we're adding `Posts` to the table. In the data source, we made a new association to our table (`add_ddb_table`) in the AWS AppSync console as `table-for-posts-2` because the `addDynamoDbDataSource` method requires it. Keep in mind, this new association is still using the same table we created earlier, but we now have two connections to it in the AWS AppSync console: one for the query as `table-for-posts` and one for the mutation as `table-for-posts-2`. The code was changed to add a `Post` by generating its `id` value automatically and accepting a client's input for the rest of the fields.
In the resolver, we changed the id value to `pipeline-resolver-create-posts` to reflect the fact that we're adding `Posts` to the table. To reflect the mutation in the schema, the type name was changed to `Mutation`, and the name, `createPost`. The pipeline config was set to our new mutation function `add_func_2`.
------
To summarize what's happening in this example, AWS AppSync automatically converts arguments defined in the `createPost` field from your GraphQL schema into DynamoDB operations. The example stores records in DynamoDB using a key of `id`, which is automatically created using our `util.autoId()` helper. All of the other fields you pass to the context arguments (`ctx.args.input`) from requests made in the AWS AppSync console or otherwise will be stored as the table's attributes. Both the key and the attributes are automatically mapped to a compatible DynamoDB format using the `util.dynamodb.toMapValues(values)` helper.
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 it. Optionally, you can view the full request to a data source interactively when you run a query. For more information, see [Test and debug resolvers (JavaScript)](https://docs.aws.amazon.com/appsync/latest/devguide/test-debug-resolvers-js.html) and [Monitoring and logging](https://docs.aws.amazon.com/appsync/latest/devguide/monitoring.html#aws-appsync-monitoring).
## Advanced resolvers
If you are following the optional pagination section in [Designing your schema](designing-your-schema.md#aws-appsync-designing-your-schema), you still need to add your resolver to your request to make use of pagination. Our example used a query pagination called `getPosts` to return only a portion of the things requested at a time. Our resolver's code on that field may look like this:
```
/**
* Performs a scan on the dynamodb data source
*/
export function request(ctx) {
const { limit = 20, nextToken } = ctx.args;
return { operation: 'Scan', limit, nextToken };
}
/**
* @returns the result of the `put` operation
*/
export function response(ctx) {
const { items: posts = [], nextToken } = ctx.result;
return { posts, nextToken };
}
```
In the request, we pass in the context of the request. Our `limit` is *20*, meaning we return up to 20 `Posts` in the first query. Our `nextToken` cursor is fixed to the first `Post` entry in the data source. These are passed to the args. The request then performs a scan from the first `Post` up to the scan limit number. The data source stores the result in the context, which is passed to the response. The response returns the `Posts` it retrieved, then sets the `nextToken` is set to the `Post` entry right after the limit. The next request is sent out to do the exact same thing but starting at the offset right after the first query. Keep in mind that these sorts of requests are done sequentially and not in parallel.
# Testing and debugging resolvers in AWS AppSync (JavaScript)
AWS AppSync executes resolvers on a GraphQL field against a data source. When working with pipeline resolvers, functions interact with your data sources. As described in the [JavaScript resolvers overview](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-overview-js.html), functions communicate with data sources by using request and response handlers written in JavaScript and running on the `APPSYNC_JS` runtime. This enables you to provide custom logic and conditions before and after communicating with the data source.
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 of the entire request from Amazon CloudWatch. This includes results from the data source.
## Testing with mock data
When a GraphQL resolver is invoked, it contains a `context` object that has relevant information about the request. This includes arguments from a client, identity information, and data from the parent GraphQL field. It also stores the results from the data source, which can be used in the response handler. For more information about this structure and the available helper utilities to use when programming, see the [Resolver context object reference](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-context-reference-js.html).
When writing or editing a resolver function, 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 handlers 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 **Functions**.
1. Choose an existing function.
1. At the top of the **Update function** page, choose **Select test context**, then choose **Create new context**.
1. Select a sample context object or populate the JSON manually in the **Configure test context** window below.
1. Enter a **Text context name**.
1. Choose the **Save** button.
1. To evaluate your resolver using this mocked context object, 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 snippet shows what the schema might look like:
```
type Dog {
breed: String
color: String
}
type Mutation {
addDog(firstname: String, age: Int): Dog
}
```
You can write an AWS AppSync function and add it to your `addDog` resolver to handle the mutation. To test your AWS AppSync function, 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 your AWS AppSync function using the following code:
```
import { util } from '@aws-appsync/utils';
export function request(ctx) {
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({ id: util.autoId() }),
attributeValues: util.dynamodb.toMapValues(ctx.args),
};
}
export function response(ctx) {
if (ctx.identity.username === 'Nadia') {
console.log("This request is allowed")
return ctx.result;
}
util.unauthorized();
}
```
The evaluated request and response handler 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 request and response handlers with AWS AppSync's APIs
You can use the `EvaluateCode` API command to remotely test your code with mocked data. To get started with the command, make sure you have added the `appsync:evaluateMappingCode` permission to your policy. For example:
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": "appsync:evaluateCode",
"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 AWS AppSync function request and response handlers from the previous section. Using the CLI on your local station, save the code to a file named `code.js`, then save the `context` object to a file named `context.json`. From your shell, run the following command:
```
$ aws appsync evaluate-code \
--code file://code.js \
--function response \
--context file://context.json \
--runtime name=APPSYNC_JS,runtimeVersion=1.0.0
```
The response contains an `evaluationResult` containing the payload returned by your handler. It also contains a `logs` object, that holds the list of logs that were generated by your handler during the evaluation. This makes it easy to debug your code execution and see information about your evaluation to help troubleshoot. For example:
```
{
"evaluationResult": "{\"breed\":\"Miniature Schnauzer\",\"color\":\"black_grey\"}",
"logs": [
"INFO - code.js:13:5: \"This request is allowed\""
]
}
```
The `evaluationResult` can be parsed as JSON, which gives:
```
{
"breed": "Miniature Schnauzer",
"color": "black_grey"
}
```
Using the SDK, you can easily incorporate tests from your favorite test suite to validate your handlers' 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' })
const runtime = {name:'APPSYNC_JS',runtimeVersion:'1.0.0')
test('request correctly calls DynamoDB', async () => {
const code = fs.readFileSync('./code.js', 'utf8')
const context = fs.readFileSync('./context.json', 'utf8')
const contextJSON = JSON.parse(context)
const response = await client.evaluateCode({ code, context, runtime, function: 'request' }).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 totalTime: 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 them in the console.
# Configuring and using pipeline resolvers in AWS AppSync (JavaScript)
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.
For more information about the architecture of a JavaScript pipeline resolver, see the [JavaScript resolvers overview](https://docs.aws.amazon.com/appsync/latest/devguide/resolver-reference-overview-js.html#anatomy-of-a-pipeline-resolver-js).
## 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. Set the resolver to `pipeline resolver` and the `APPSYNC_JS` runtime, then create the resolver.
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 follows:
1. Mutation.signUp resolver request handler
1. validateEmail function
1. saveUser function
1. Mutation.signUp resolver response handler
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.
Update your resolver code by replacing your request and response functions:
```
export function request(ctx) {
ctx.stash.email = ctx.args.input.email
return {};
}
export function response(ctx) {
return ctx.prev.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.
Make sure you have created a data source of the **NONE** type. Choose this data source in the **Data source name** list. For the **function name**, enter in `validateEmail`. In the **function code** area, overwrite everything with this snippet:
```
import { util } from '@aws-appsync/utils';
export function request(ctx) {
const { email } = ctx.stash;
const valid = util.matches(
'^[a-zA-Z0-9_.+-]+@(?:(?:[a-zA-Z0-9-]+\.)?[a-zA-Z]+\.)?(myvaliddomain)\.com',
email
);
if (!valid) {
util.error(`"${email}" is not a valid email.`);
}
return { payload: { email } };
}
export function response(ctx) {
return ctx.result;
}
```
Review your inputs, then choose **Create**. We just created our **validateEmail** function. Repeat these steps to create the **saveUser** function with the following code (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.):
```
import { util } from '@aws-appsync/utils';
export function request(ctx) {
return ctx.prev.result;
}
export function response(ctx) {
ctx.result.id = util.autoId();
return 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** back on the `signUp` 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: Running 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
username
}
}
```
This should return something like:
```
{
"data": {
"signUp": {
"id": "256b6cc2-4694-46f4-a55e-8cb14cc5d7fc",
"username": "nadia"
}
}
}
```
We have successfully signed up our user and validated the input email using a pipeline resolver.
# 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)
# Using an AWS AppSync API with the AWS CDK
**Tip**
Before you use the CDK, we recommend reviewing the CDK's [official documentation](https://docs.aws.amazon.com/cdk/v2/guide/getting_started.html) along with AWS AppSync's [CDK reference](https://docs.aws.amazon.com/cdk/api/v2/docs/aws-cdk-lib.aws_appsync-readme.html).
We also recommend ensuring that your [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html) and [NPM](https://docs.npmjs.com/) installations are working on your system.
In this section, we're going to create a simple CDK application that can add and fetch items from a DynamoDB table. This is meant to be a quickstart example using some of the code from the [Designing your schema](https://docs.aws.amazon.com/appsync/latest/devguide/designing-your-schema.html), [Attaching a data source](https://docs.aws.amazon.com/appsync/latest/devguide/attaching-a-data-source.html), and [Configuring resolvers (JavaScript)](https://docs.aws.amazon.com/appsync/latest/devguide/configuring-resolvers-js.html) sections.
## Setting up a CDK project
**Warning**
These steps may not be completely accurate depending on your environment. We're assuming your system has the necessary utilities installed, a way to interface with AWS services, and proper configurations in place.
The first step is installing the AWS CDK. In your CLI, you can enter the following command:
```
npm install -g aws-cdk
```
Next, you need to create a project directory, then navigate to it. An example set of commands to create and navigate to a directory is:
```
mkdir example-cdk-app
cd example-cdk-app
```
Next, you need to create an app. Our service primarily uses TypeScript. In your project directory, enter the following command:
```
cdk init app --language typescript
```
When you do this, a CDK app along with its initialization files will be installed:
![\[Terminal output showing Git repository initialization and npm install completion.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-init-app-example.png)
Your project structure may look like this:
![\[Project directory structure showing folders and files for an example CDK app.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-init-directories.png)
You'll notice we have several important directories:
+ `bin`: The initial bin file will create the app. We won't touch this in this guide.
+ `lib`: The lib directory contains your stack files. You can think of stack files as individual units of execution. Constructs will be inside our stack files. Basically, these are resources for a service that will be spun up in CloudFormation when the app is deployed. This is where most of our coding will happen.
+ `node_modules`: This directory is created by NPM and contains all package dependencies you installed using the `npm` command.
Our initial stack file may contain something like this:
```
import * as cdk from 'aws-cdk-lib';
import { Construct } from 'constructs';
// import * as sqs from 'aws-cdk-lib/aws-sqs';
export class ExampleCdkAppStack extends cdk.Stack {
constructor(scope: Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// The code that defines your stack goes here
// example resource
// const queue = new sqs.Queue(this, 'ExampleCdkAppQueue', {
// visibilityTimeout: cdk.Duration.seconds(300)
// });
}
}
```
This is the boilerplate code to create a stack in our app. Most of our code in this example will go inside the scope of this class.
To verify that your stack file is in the app, in your app's directory, run the following command in the terminal:
```
cdk ls
```
A list of your stacks should appear. If it doesn't, then you may need to run through the steps again or check the official documentation for help.
If you want to build your code changes before deploying, you can always run the following command in the terminal:
```
npm run build
```
And, to see the changes before deploying:
```
cdk diff
```
Before we add our code to the stack file, we're going to perform a bootstrap. Bootstrapping allows us to provision resources for the CDK before the app deploys. More information about this process can be found [here](https://docs.aws.amazon.com/cdk/v2/guide/bootstrapping.html). To create a bootstrap, the command is:
```
cdk bootstrap aws://ACCOUNT-NUMBER/REGION
```
**Tip**
This step requires several IAM permissions in your account. Your bootstrap will be denied if you don't have them. If this happens, you may have to delete incomplete resources caused by the bootstrap such as the S3 bucket it generates.
Bootstrap will spin up several resources. The final message will look like this:
![\[Terminal output showing successful bootstrapping of an AWS environment.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-init-bootstrap-final.png)
This is done once per account per Region, so you won't have to do this often. The main resources of the bootstrap are the CloudFormation stack and the Amazon S3 bucket.
The Amazon S3 bucket is used to store files and IAM roles that grant permissions needed to perform deployments. The required resources are defined in an CloudFormation stack, called the bootstrap stack, which is usually named `CDKToolkit`. Like any CloudFormation stack, it appears in the CloudFormation console once it has been deployed:
![\[CDKToolkit stack with CREATE_COMPLETE status in CloudFormation console.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-init-bootstrap-cfn-console.png)
The same can be said for the bucket:
![\[S3 bucket details showing name, region, access settings, and creation date.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-init-bootstrap-bucket-console.png)
To import the services we need in our stack file, we can use the following command:
```
npm install aws-cdk-lib # V2 command
```
**Tip**
If you're having trouble with V2, you could install the individual libraries using V1 commands:
```
npm install @aws-cdk/aws-appsync @aws-cdk/aws-dynamodb
```
We don't recommend this because V1 has been deprecated.
## Implementing a CDK project - Schema
We can now start implementing our code. First, we must create our schema. You can simply create a `.graphql` file in your app:
```
mkdir schema
touch schema.graphql
```
In our example, we included a top-level directory called `schema` containing our `schema.graphql`:
![\[File structure showing a schema folder containing schema.graphql file.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-schema-directory.png)
Inside our schema, let's include a simple example:
```
input CreatePostInput {
title: String
content: String
}
type Post {
id: ID!
title: String
content: String
}
type Mutation {
createPost(input: CreatePostInput!): Post
}
type Query {
getPost: [Post]
}
```
Back in our stack file, we need to make sure the following import directives are defined:
```
import * as cdk from 'aws-cdk-lib';
import * as appsync from 'aws-cdk-lib/aws-appsync';
import * as dynamodb from 'aws-cdk-lib/aws-dynamodb';
import { Construct } from 'constructs';
```
Inside the class, we'll add code to make our GraphQL API and connect it to our `schema.graphql` file:
```
export class ExampleCdkAppStack extends cdk.Stack {
constructor(scope: Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// makes a GraphQL API
const api = new appsync.GraphqlApi(this, 'post-apis', {
name: 'api-to-process-posts',
schema: appsync.SchemaFile.fromAsset('schema/schema.graphql'),
});
}
}
```
We'll also add some code to print out the GraphQL URL, API key, and Region:
```
export class ExampleCdkAppStack extends cdk.Stack {
constructor(scope: Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// Makes a GraphQL API construct
const api = new appsync.GraphqlApi(this, 'post-apis', {
name: 'api-to-process-posts',
schema: appsync.SchemaFile.fromAsset('schema/schema.graphql'),
});
// Prints out URL
new cdk.CfnOutput(this, "GraphQLAPIURL", {
value: api.graphqlUrl
});
// Prints out the AppSync GraphQL API key to the terminal
new cdk.CfnOutput(this, "GraphQLAPIKey", {
value: api.apiKey || ''
});
// Prints out the stack region to the terminal
new cdk.CfnOutput(this, "Stack Region", {
value: this.region
});
}
}
```
At this point, we'll use deploy our app again:
```
cdk deploy
```
This is the result:
![\[Deployment output showing ExampleCdkAppStack details, including GraphQL API URL and stack region.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-schema.png)
It appears our example was successful, but let's check the AWS AppSync console just to confirm:
![\[GraphQL interface showing successful API request with response data displayed.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-schema-result-1.png)
It appears our API was created. Now, we'll check the schema attached to the API:
![\[GraphQL schema defining CreatePostInput, Post type, Mutation, and Query operations.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-schema-result-2.png)
This appears to match up with our schema code, so it was successful. Another way to confirm this from a metadata viewpoint is to look at the CloudFormation stack:
![\[CloudFormation stack showing ExampleCdkAppStack update complete and CDKToolkit creation complete.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-schema-result-3.png)
When we deploy our CDK app, it goes through CloudFormation to spin up resources like the bootstrap. Each stack within our app maps 1:1 with an CloudFormation stack. If you go back to the stack code, the stack name was grabbed from the class name `ExampleCdkAppStack`. You can see the resources it created, which also match our naming conventions in our GraphQL API construct:
![\[Expanded view of post-apis resource showing Schema, DefaultApiKey, and CDKMetadata.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-schema-result-4.png)
## Implementing a CDK project - Data source
Next, we need to add our data source. Our example will use a DynamoDB table. Inside the stack class, we'll add some code to create a new table:
```
export class ExampleCdkAppStack extends cdk.Stack {
constructor(scope: Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// Makes a GraphQL API construct
const api = new appsync.GraphqlApi(this, 'post-apis', {
name: 'api-to-process-posts',
schema: appsync.SchemaFile.fromAsset('schema/schema.graphql'),
});
//creates a DDB table
const add_ddb_table = new dynamodb.Table(this, 'posts-table', {
partitionKey: {
name: 'id',
type: dynamodb.AttributeType.STRING,
},
});
// Prints out URL
new cdk.CfnOutput(this, "GraphQLAPIURL", {
value: api.graphqlUrl
});
// Prints out the AppSync GraphQL API key to the terminal
new cdk.CfnOutput(this, "GraphQLAPIKey", {
value: api.apiKey || ''
});
// Prints out the stack region to the terminal
new cdk.CfnOutput(this, "Stack Region", {
value: this.region
});
}
}
```
At this point, let's deploy again:
```
cdk deploy
```
We should check the DynamoDB console for our new table:
![\[DynamoDB console showing ExampleCdkAppStack-poststable as Active with Provisioned capacity.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-ddb-result-1.png)
Our stack name is correct, and the table name matches our code. If we check our CloudFormation stack again, we'll now see the new table:
![\[Expanded view of a logical ID in CloudFormation showing post-apis, posts-table, and CDKMetadata.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-ddb-result-2.png)
## Implementing a CDK project - Resolver
This example will use two resolvers: one to query the table and one to add to it. Since we're using pipeline resolvers, we'll need to declare two pipeline resolvers with one function in each. In the query, we'll add the following code:
```
export class ExampleCdkAppStack extends cdk.Stack {
constructor(scope: Construct, id: string, props?: cdk.StackProps) {
super(scope, id, props);
// Makes a GraphQL API construct
const api = new appsync.GraphqlApi(this, 'post-apis', {
name: 'api-to-process-posts',
schema: appsync.SchemaFile.fromAsset('schema/schema.graphql'),
});
//creates a DDB table
const add_ddb_table = new dynamodb.Table(this, 'posts-table', {
partitionKey: {
name: 'id',
type: dynamodb.AttributeType.STRING,
},
});
// Creates a function for query
const add_func = new appsync.AppsyncFunction(this, 'func-get-post', {
name: 'get_posts_func_1',
api,
dataSource: api.addDynamoDbDataSource('table-for-posts', add_ddb_table),
code: appsync.Code.fromInline(`
export function request(ctx) {
return { operation: 'Scan' };
}
export function response(ctx) {
return ctx.result.items;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
});
// Creates a function for mutation
const add_func_2 = new appsync.AppsyncFunction(this, 'func-add-post', {
name: 'add_posts_func_1',
api,
dataSource: api.addDynamoDbDataSource('table-for-posts-2', add_ddb_table),
code: appsync.Code.fromInline(`
export function request(ctx) {
return {
operation: 'PutItem',
key: util.dynamodb.toMapValues({id: util.autoId()}),
attributeValues: util.dynamodb.toMapValues(ctx.args.input),
};
}
export function response(ctx) {
return ctx.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
});
// Adds a pipeline resolver with the get function
new appsync.Resolver(this, 'pipeline-resolver-get-posts', {
api,
typeName: 'Query',
fieldName: 'getPost',
code: appsync.Code.fromInline(`
export function request(ctx) {
return {};
}
export function response(ctx) {
return ctx.prev.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
pipelineConfig: [add_func],
});
// Adds a pipeline resolver with the create function
new appsync.Resolver(this, 'pipeline-resolver-create-posts', {
api,
typeName: 'Mutation',
fieldName: 'createPost',
code: appsync.Code.fromInline(`
export function request(ctx) {
return {};
}
export function response(ctx) {
return ctx.prev.result;
}
`),
runtime: appsync.FunctionRuntime.JS_1_0_0,
pipelineConfig: [add_func_2],
});
// Prints out URL
new cdk.CfnOutput(this, "GraphQLAPIURL", {
value: api.graphqlUrl
});
// Prints out the AppSync GraphQL API key to the terminal
new cdk.CfnOutput(this, "GraphQLAPIKey", {
value: api.apiKey || ''
});
// Prints out the stack region to the terminal
new cdk.CfnOutput(this, "Stack Region", {
value: this.region
});
}
}
```
In this snippet, we added a pipeline resolver called `pipeline-resolver-create-posts` with a function called `func-add-post` attached to it. This is the code that will add `Posts` to the table. The other pipeline resolver was called `pipeline-resolver-get-posts` with a function called `func-get-post` that retrieves `Posts` added to the table.
We'll deploy this to add it to the AWS AppSync service:
```
cdk deploy
```
Let's check the AWS AppSync console to see if they were attached to our GraphQL API:
![\[GraphQL API schema showing mutation and query fields with Pipeline resolvers.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-1.png)
It appears to be correct. In the code, both of these resolvers were attached to the GraphQL API we made (denoted by the `api` props value present in both the resolvers and functions). In the GraphQL API, the fields we attached our resolvers to were also specified in the props (defined by the `typename` and `fieldname` props in each resolver).
Let's see if the content of the resolvers is correct starting with the `pipeline-resolver-get-posts`:
![\[Code snippet showing request and response functions in a resolver, with an arrow pointing to them.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-2.png)
The before and after handlers match our `code` props value. We can also see that a function called `add_posts_func_1`, which matches the name of the function we attached in the resolver.
Let's look at the code content of that function:
![\[Function code showing request and response methods for a PutItem operation.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-3.png)
This matches up with the `code` props of the `add_posts_func_1` function. Our query was successfully uploaded, so let's check on the query:
![\[Resolver code with request and response functions, and a get_posts_func_1 function listed below.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-4.png)
These also match the code. If we look at `get_posts_func_1`:
![\[Code snippet showing two exported functions: request returning 'Scan' operation and response returning items.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-5.png)
Everything appears to be in place. To confirm this from a metadata perspective, we can check our stack in CloudFormation again:
![\[List of logical IDs for AWS resources including API, table, functions, and pipelines.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-deploy-resolver-result-6.png)
Now, we need to test this code by performing some requests.
## Implementing a CDK project - Requests
To test our app in the AWS AppSync console, we made one query and one mutation:
![\[GraphQL code snippet showing a query to get post details and a mutation to create a post.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-request-1.png)
`MyMutation` contains a `createPost` operation with the arguments `1970-01-01T12:30:00.000Z` and `first post`. It returns the `date` and `title` that we passed in as well as the automatically generated `id` value. Running the mutation yields the result:
```
{
"data": {
"createPost": {
"date": "1970-01-01T12:30:00.000Z",
"id": "4dc1c2dd-0aa3-4055-9eca-7c140062ada2",
"title": "first post"
}
}
}
```
If we check the DynamoDB table quickly, we can see our entry in the table when we scan it:
![\[DynamoDB table entry showing id, date, and title fields for a single item.\]](http://docs.aws.amazon.com/appsync/latest/devguide/images/cdk-code-request-2.png)
Back in the AWS AppSync console, if we run the query to retrieve this `Post`, we get the following result:
```
{
"data": {
"getPost": [
{
"id": "9f62c4dd-49d5-48d5-b835-143284c72fe0",
"date": "1970-01-01T12:30:00.000Z",
"title": "first post"
}
]
}
}
```