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Scalars
Scalar types represent concrete values at the leaves of a query. For example
in the following query the name field will resolve to a scalar type
(in this case it's a String
type):
{ user { name }}
{ "data": { "user": { "name": "Patrick" } }}
There are several built-in scalars, and you can define custom scalars too. (Enums are also leaf values.) The built in scalars are:
String
, maps to Python’sstr
Int
, a signed 32-bit integer, maps to Python’sint
Float
, a signed double-precision floating-point value, maps to Python’sfloat
Boolean
, true or false, maps to Python’sbool
ID
, a specialisedString
for representing unique object identifiersDate
, an ISO-8601 encoded dateDateTime
, an ISO-8601 encoded datetimeTime
, an ISO-8601 encoded timeDecimal
, a Decimal value serialized as a stringUUID
, a UUID value serialized as a stringVoid
, always null, maps to Python’sNone
Fields can return built-in scalars by using the Python equivalent:
import datetimeimport decimalimport uuidimport strawberry
@strawberry.typeclass Product: id: uuid.UUID name: str stock: int is_available: bool available_from: datetime.date same_day_shipping_before: datetime.time created_at: datetime.datetime price: decimal.Decimal void: None
type Product { id: UUID! name: String! stock: Int! isAvailable: Boolean! availableFrom: Date! sameDayShippingBefore: Time! createdAt: DateTime! price: Decimal! void: Void}
Scalar types can also be used as inputs:
import datetimeimport strawberry
@strawberry.typeclass Query: @strawberry.field def one_week_from(self, date_input: datetime.date) -> datetime.date: return date_input + datetime.timedelta(weeks=1)
Custom scalars
You can create custom scalars for your schema to represent specific types in your data model. This can be helpful to let clients know what kind of data they can expect for a particular field.
To define a custom scalar you need to give it a name and functions that tell Strawberry how to serialize and deserialise the type.
For example here is a custom scalar type to represent a Base64 string:
import base64from typing import NewType
import strawberry Base64 = strawberry.scalar( NewType("Base64", bytes), serialize=lambda v: base64.b64encode(v).decode("utf-8"), parse_value=lambda v: base64.b64decode(v).encode("utf-8"),)
@strawberry.typeclass Query: @strawberry.field def base64(self) -> Base64: return Base64(b"hi")
schema = strawberry.Schema(Query)
result = schema.execute_sync("{ base64 }")
assert results.data == {"base64": "aGk="}
The Base16
, Base32
and Base64
scalar types are available in strawberry.scalars
from strawberry.scalars import Base16, Base32, Base64
Example JSONScalar
import jsonfrom typing import Any, NewType
import strawberry JSON = strawberry.scalar( NewType("JSON", object), description="The `JSON` scalar type represents JSON values as specified by ECMA-404", serialize=lambda v: v, parse_value=lambda v: v,)
Usage:
@strawberry.typeclass Query: @strawberry.field def data(self, info) -> JSON: return {"hello": {"a": 1}, "someNumbers": [1, 2, 3]}
query ExampleDataQuery { data}
{ "data": { "hello": { "a": 1 }, "someNumbers": [1, 2, 3] }}
The JSON
scalar type is available in strawberry.scalars
from strawberry.scalars import JSON
Overriding built in scalars
To override the behaviour of the built in scalars you can pass a map of overrides to your schema.
Here is a full example of replacing the built in DateTime
scalar with one that
serializes all datetimes as unix timestamps:
from datetime import datetime, timezoneimport strawberry
# Define your custom scalarEpochDateTime = strawberry.scalar( datetime, serialize=lambda value: int(value.timestamp()), parse_value=lambda value: datetime.fromtimestamp(int(value), timezone.utc),)
@strawberry.typeclass Query: @strawberry.field def current_time(self) -> datetime: return datetime.now()
schema = strawberry.Schema( Query, scalar_overrides={ datetime: EpochDateTime, },)result = schema.execute_sync("{ currentTime }")assert result.data == {"currentTime": 1628683200}