Datamodel (PostgreSQL)

Overview

The datamodel of your service configuration has two major roles:

• Define the underlying database schema (the models and fields are mapped to database tables).
• It is the foundation for the auto-generated CRUD operations of your Prisma client.

The datamodel is written using a subset of the GraphQL Schema Definition Language (SDL) and stored in one or more .prisma-files. These .prisma-files need to be referenced in your prisma.yml under the datamodel property. For example:

endpoint: __YOUR_PRISMA_ENDPOINT__
datamodel: datamodel.prisma
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Building blocks of the datamodel

There are several available building blocks to shape your datamodel:

• Types consist of multiple fields and typically represent entities from your application domain (e.g. User, Car, Order). Each type in your datamodel is mapped to a database table and CRUD operations are exposed in the generated Prisma client API.
• Relations describe relationships between types.
• Directives covering different use cases such as type constraints or cascading delete behaviour.

Example

A simple example datamodel.prisma file:

type Tweet {
  id: ID! @id
  createdAt: DateTime! @createdAt
  text: String!
  owner: User! @relation(link: INLINE)
  location: Location!
}

type User {
  id: ID! @id
  createdAt: DateTime! @createdAt
  updatedAt: DateTime! @updatedAt
  handle: String! @unique
  name: String
  tweets: [Tweet!]!
}

type Location {
  id: ID! @id
  latitude: Float!
  longitude: Float!
}

This example illustrates a few important concepts when working with your datamodel:

• The three types Tweet, User and Location are mapped to database tables.
• There is a bidirectional relation between User and Tweet (via the owner and tweets fields). The relation is represented via a foreign key on the Tweet type.
• There is a unidirectional relation from Tweet to Location (via the location field).
• Except for the name field on User, all fields are required in the datamodel (as indicated by the ! following the type).
• The fields annotated with the @id, @createdAt and @updatedAt directives are managed by Prisma and read-only in the exposed Prisma API.
• The @unique directive expresses a unique constraint, meaning Prisma ensures that there never will be two records with the same values for the annotated field.

Creating and updating your datamodel is as simple as writing and saving the datamodel file. Once you're happy with your datamodel, you can save the file and apply the changes to your Prisma service by running prisma deploy:

$ prisma deploy

Changes:

  Tweet (Type)
  + Created type `Tweet`
  + Created field `id` of type `ID!`
  + Created field `createdAt` of type `DateTime!`
  + Created field `text` of type `String!`
  + Created field `owner` of type `User!`
  + Created field `location` of type `Location!`

  User (Type)
  + Created type `User`
  + Created field `id` of type `ID!`
  + Created field `createdAt` of type `DateTime!`
  + Created field `updatedAt` of type `DateTime!`
  + Created field `handle` of type `String!`
  + Created field `name` of type `String`
  + Created field `tweets` of type `[Tweet!]!`

  Location (Type)
  + Created type `Location`
  + Created field `id` of type `ID!`
  + Created field `latitude` of type `Float!`
  + Created field `longitude` of type `Float!`

  TweetToUser (Relation)
  + Created an inline relation between `Tweet` and `User` in the column `owner` of table `Tweet`

  LocationToTweet (Relation)
  + Created an inline relation between `Location` and `Tweet` in the column `location` of table `Tweet`

Applying changes 1.1s

Files

You can write your datamodel in a single .prisma-file or split it accross multiple ones.

The .prisma-files containing the datamodel need to be referenced in your prisma.yml under the datamodel property. For example:

datamodel:
  - user.prisma
  - order.prisma

If there is only a single file that defines the datamodel, it can be specified as follows:

datamodel: datamodel.prisma

Object types

An object type (or short type) defines the structure for one model in your datamodel. It is used to represent entities from your application domain.

Each object type is mapped to the database. For relational databases, one table is created per type. For schemaless databases, an equivalent structure is used (e.g. a document). Note that Prisma enforces a schema even for schemaless databases!

A type has a name and one or multiple fields. Type names can only contain alphanumeric characters and need to start with an uppercase letter. They can contain at most 64 characters.

An instantiation of a type is called a node. This term refers to a node inside your data graph.

Defining an object type

A object type is defined in the datamodel with the keyword type:

type Article {
  id: ID! @id
  title: String!
  text: String
  isPublished: Boolean! @default(value: false)
}

The type defined above has the following properties:

• Name: Article
• Fields: id, title, text and isPublished (with the default value false)

id and title and isPublished are required (non-nullable) as indicated by the ! following the type, text is nullable.

Generated API operations for types

The types in your datamodel affect the available operations in the Prisma API. Here is an overview of the generated CRUD and realtime operations for every type in your Prisma API:

• Queries let you fetch one or many nodes of that type
• Mutations let you create, update or delete nodes of that type
• Subscriptions let you get notified of changes to nodes of that type (i.e. new nodes are created or existing nodes are updated or deleted)

Fields

Fields are the building blocks of a type, giving a node its shape. Every field is referenced by its name and is either scalar or a relation field.

Field names can only contain alphanumeric characters and need to start with a lowercase letter. They can contain at most 64 characters.

Scalar fields

String

A String holds text. This is the type you would use for a username, the content of a blog post or anything else that is best represented as text.

String values are currently limited to 256KB in size on Demo servers. This limit can be increased on other clusters using the cluster configuration.

Here is an example of a String scalar definition:

type User {
  name: String
}

Integer

An Int is a number that cannot have decimals. Use this to store values such as the weight of an ingredient required for a recipe or the minimum age for an event.

Int values range from -2147483648 to 2147483647.

Here is an example of an Int scalar definition:

type User {
  age: Int
}

Float

A Float is a number that can have decimals. Use this to store values such as the price of an item in a store or the result of complex calculations.

In queries or mutations, Float fields have to be specified without any enclosing characters and an optional decimal point: float: 42, float: 4.2.

Here is an example of a Float scalar definition:

type Item {
  price: Float
}

Boolean

A Boolean can have the value true or false. This is useful to keep track of settings such as whether the user wants to receive an email newsletter or if a recipe is appropriate for vegetarians.

Here is an example of a Boolean scalar definition:

type User {
  overEighteen: Boolean
}

DateTime

The DateTime type can be used to store date and/or time values. A good example might be a person's date of birth or the time/data when a specific event is happening.

Here is an example of a DateTime scalar definition:

type User {
  birthday: DateTime
}

When used as arguments in an operation, DateTime fields have to be specified in ISO 8601 format and are typically passed as strings, here are a few examples:

• "2015"
• "2015-11"
• "2015-11-22"
• "2015-11-22T13:57:31.123Z".

Enum

Like a Boolean an Enum can have one of a predefined set of values. The difference is that you can define the possible values (whereas for a Boolean the options are restriced to true and false). For example you could specify how an article should be formatted by creating an Enum with the possible values COMPACT, WIDE and COVER.

Enum values can only contain alphanumeric characters and underscores and need to start with an uppercase letter. The name of an enum value can be used in query filters and mutations. They can contain at most 191 characters.

Here is an example of an enum definition:

enum ArticleFormat {
  COMPACT
  WIDE
  COVER
}

type Article {
  format: ArticleFormat
}

Json

Sometimes you might need to store arbitrary JSON values for loosely structured data. The Json type makes sure that it is actually valid JSON and returns the value as a parsed JSON object/array instead of a string.

Json values are currently limited to 256KB in size.

Here is an example of a Json definition:

type Item {
  data: Json
}

ID

An ID value is a generated unique 25-character string based on cuid.

Fields of type ID that are annotated with the @id directive are system fields and maintained by Prisma. Only one ID field per model can be annotated with @id:

type User {
  id: ID! @id
}

Type modifiers

In a field definition, a type can be annotated with a type modifier. SDL supports two type modifiers:

• Lists: Annotate the type with a pair of enclosing [], e.g. friends: [User]
• Required fields: Annotate the type with a !, e.g. name: String!

List

Scalar fields can be marked with the list field type. A field of a relation that has the many multiplicity will also be marked as a list.

You will often find list definitions looking similar to this:

type Article {
  tags: [String!]!
}

Notice the two ! type modifiers, here is what they express:

• The first ! type modifier (right after String) means that no item in the list can be null, e.g. this value for tags would not be valid: ["Software", null, "Prisma"]
• The second ! type modifier (after the closing square bracket) means that the list itself can never be null, it might be empty though. Consequently, null is not a valid value for the tags field but [] is.

Required

Fields can be marked as required (also referred to as "non-nullable"). Required fields are marked using a ! after the field's type:

type User {
  name: String!
}

Field constraints

Fields can be configured with field constraints to add further semantics and enforce certain rules in your datamodel.

Unique

Setting the unique constraint makes sure that two records of the model in question cannot have the same value for a certain field. The only exception is the null value, meaning that multiple records can have the value null without violating the constraint. Unique fields have a unique index applied in the underlying database.

A typical example would be an email field on a User models where the assumption is that every User should have a globally unique email address.

Only the first 191 characters in a String field are considered for uniqueness and the unique check is case insensitive. Storing two different strings is not possible if the first 191 characters are the same or if they only differ in casing.

To mark a field as unique, simply append the @unique directive to its definition:

type User {
  id: ID! @id
  email: String! @unique
  name: String!
}

For every field that's annotated with @unique, you're able to query the corresponding record by providing a value for that field as a query argument.

For example, considering the above datamodel, you can now retrieve a particular User node by its email address:

const user = await prisma.user({
  email: 'alice@prisma.io',
})
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More constraints

More database constraints will be added soon. Please join the discussion in this feature request if you have wish to see certain constraints implemented in Prisma.

Default value

You can set a default value for non-list scalar fields. The value will be applied to newly created records when no value was supplied during the create-operation.

To specify a default value for a field, you can use the @default directive:

type Story {
  isPublished: Boolean @default(value: false)
  someNumber: Int! @default(value: 42)
  title: String! @default(value: "My New Post")
  publishDate: DateTime! @default(value: "2018-01-26")
  status: Status! @default(value: PUBLIC)
}

enum Status {
  PRIVATE
  PUBLIC
}

The @relation directive

When defining relations between types, you can use the @relation directive which provides meta-information about the relation. If a relation is ambiguous, you must use the @relation directive to disambiguate it.

It can take three arguments:

• name: An identifier for this relation, provided as a string.

• link: Specifies how the relation should be represented in the underlying database. The input values for this argument are defined as an enum with the following possible values:

  • INLINE: The relation is represented with foreign keys.
  • TABLE: The relation is represented via a dedicated relation table.

• onDelete: Specifies the deletion behaviour and enables cascading deletes. In case a node with related nodes gets deleted, the deletion behaviour determines what should happen to the related nodes. The input values for this argument are defined as an enum with the following possible values:

  • SET_NULL (default): Set the related node(s) to null.
  • CASCADE: Delete the related node(s). Note that is not possible to set both ends of a bidirectional relation to CASCADE.

Here is an example of a datamodel where the @relation directive is used:

type User {
  id: ID! @id
  stories: [Story!]! @relation(name: "StoriesByUser", onDelete: CASCADE)
}

type Story {
  id: ID! @id
  text: String!
  author: User @relation(name: "StoriesByUser")
}

The relation is named StoriesByUser and the deletion behaviour is as follows:

• When a User node gets deleted, all its related Story nodes will be deleted as well.
• When a Story node gets deleted, it will simply be removed from the stories list on the related User node.

Omitting the @relation directive

In the simplest case, where a relation between two types is unambiguous and the default deletion behaviour (SET_NULL) should be applied, the corresponding relation fields do not have to be annotated with the @relation directive.

Here we are defining a bidirectional one-to-many relation between the User and Story types. Since onDelete has not been provided, the default deletion behaviour is used: SET_NULL:

type User {
  id: ID! @id
  stories: [Story!]!
}

type Story {
  id: ID! @id
  text: String!
  author: User
}

The deletion behaviour in this example is as follows:

• When a User node gets deleted, the author field on all its related Story nodes will be set to null. Note that if the author field was marked as required, the operation would result in an error.
• When a Story node gets deleted, it will simply be removed from the stories list on the related User node.

Using the name argument of the @relation directive

In certain cases, your datamodel may contain ambiguous relations. For example, consider you not only want a relation to express the "author-relationship" between User and Story, but you also want a relation to express which Story nodes have been liked by a User.

In that case, you end up with two different relations between User and Story! In order to disambiguate them, you need to give the relation a name:

type User {
  id: ID! @id
  writtenStories: [Story!]! @relation(name: "WrittenStories")
  likedStories: [Story!]! @relation(name: "LikedStories")
}

type Story {
  id: ID! @id
  text: String!
  author: User! @relation(name: "WrittenStories")
  likedBy: [User!]! @relation(name: "LikedStories")
}

If the name wasn't provided in this case, there would be no way to decide whether writtenStories should relate to the author or the likedBy field.

Using the onDelete argument of the @relation directive

As mentioned above, you can specify a dedicated deletion behaviour for the related nodes. That's what the onDelete argument of the @relation directive is for.

Consider the following example:

type User {
  id: ID! @id
  comments: [Comment!]! @relation(name: "CommentAuthor", onDelete: CASCADE)
  blog: Blog @relation(name: "BlogOwner", onDelete: CASCADE)
}

type Blog {
  id: ID! @id
  comments: [Comment!]! @relation(name: "Comments", onDelete: CASCADE)
  owner: User! @relation(name: "BlogOwner", onDelete: SET_NULL)
}

type Comment {
  id: ID! @id
  blog: Blog! @relation(name: "Comments", onDelete: SET_NULL)
  author: User @relation(name: "CommentAuthor", onDelete: SET_NULL)
}

Let's investigate the deletion behaviour for the three types:

• When a User node gets deleted,

  • all related Comment nodes will be deleted.
  • the related Blog node will be deleted.

• When a Blog node gets deleted,

  • all related Comment nodes will be deleted.
  • the related User node will have its blog field set to null.

• When a Comment node gets deleted,

  • the related Blog node continues to exist and the deleted Comment node is removed from its comments list.
  • the related User node continues to exist and the deleted Comment node is removed from its comments list.

SDL directives

Directives are used to provide additional information in your datamodel. They look like this: @name(argument: "value") or simply @name when there are no arguments.

Datamodel directives

Datamodel directives describe additional information about models or fields.

@id

A record will automatically get assigned a globally unique identifier when it's created, this identifier is stored in the field annotated with the @id directive:

type User {
  id: ID! @id
}

An auto-generated id has the following properties:

• Consists of 25 alphanumeric characters (letters are always lowercase)
• Always starts with a (lowercase) letter, e.g. c
• Follows cuid (collision resistant unique identifiers) scheme

@createdAt and @updatedAt

The datamodel further provides two special field directives which you can add to your types:

• createdAt: DateTime! @createdAt: Stores the exact date and time for when a record of this object type was created.
• updatedAt: DateTime! @updatedAt: Stores the exact date and time for when a record of this object type was last updated.

If you to add this behaviour to your model, you can simply add the corresponding directives to the model definition, for example:

type User {
  id: ID! @id
  createdAt: DateTime! @createdAt
  updatedAt: DateTime! @updatedAt
}

@unique

The @unique directive marks a scalar field as unique. Unique fields will have a unique index applied in the underlying database.

type User {
  email: String @unique
}

Find more info about the @unique directive above.

@db

The @db directive can be applied to types/fields to determine the name of the table/column in the underlying database. For example:

type User @db(name: "user") {
  id: ID! @id
  name: String! @db(name: "full_name")
}

In this case, the underlying table for the User model is called user and the column representing the name field is called full_name.

Datamodel directives

Datamodel directives describe additional information about models or fields.

Unique scalar fields

The @unique directive marks a scalar field as unique. Unique fields will have a unique index applied in the underlying database.

# the `User` type has a unique `email` field
type User {
  email: String @unique
}

Find more info about the @unique directive above.

Relation fields

The directive @relation(name: String, onDelete: ON_DELETE! = SET_NULL) can be attached to a relation field.

See above for more information.

Default value for scalar fields

The directive @default(value: String!) sets a default value for a scalar field:

# the `title`, `published` and `someNumber` fields have default values `New Post`, `false` and `42`
type Post {
  title: String! @default(value: "New Post")
  published: Boolean! @default(value: false)
  someNumber: Int! @default(value: 42)
}

Naming conventions

Different objects you encounter in a Prisma service like types or relations follow separate naming conventions to help you distinguish them.

Types

The type name determines the name of derived queries and mutations as well as the argument names for nested mutations.

Here are the conventions for naming types:

• Choose type names in singular:

  • Yes: type User { ... }
  • No: type Users { ... }

Scalar and relation fields

The name of a scalar field is used in queries and in query arguments of mutations. The name of relation fields follows the same conventions and determines the argument names for relation mutations.Relation field names can only contain alphanumeric characters and need to start with an uppercase letter. They can contain at most 64 characters.

Field names are unique per type.

Here are the conventions for naming fields:

• Choose plural names for list fields:

  • Yes: friends: [User!]!
  • No: friendList: [User!]!

• Choose singular names for non-list fields:

  • Yes: post: Post!
  • No: posts: Post!

More SDL features

In this section, we describe further SDL features that are not yet supported for data modeling with Prisma.

Interfaces

"Like many type systems, [SDL] supports interfaces. An interface is an abstract type that includes a certain set of fields that a type must include to implement the interface." From the official Documentation

To learn more about when and how interfaces are coming to Prisma, check out this feature request.

Union types

"Union types are very similar to interfaces, but they don't get to specify any common fields between the types." From the official Documentation

To learn more about when and how union types are coming to Prisma, check out this feature request.