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 identifiers -
Date
, an ISO-8601 encoded date -
DateTime
, an ISO-8601 encoded datetime -
Time
, an ISO-8601 encoded time -
Decimal
, a Decimal value serialized as a string -
UUID
, a UUID value serialized as a string -
Void
, always null, maps to Python’sNone
-
JSON
, a JSON value as specified in ECMA-404 standard, maps to Python’sdict
-
Base16
,Base32
,Base64
, represents hexadecimal strings encoded withBase16
/Base32
/Base64
. As specified in RFC4648 . Maps to Python’sstr
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}
Replacing datetime with the popular pendulum
library
To override with a pendulum instance you’d want to serialize and parse_value like the above example. Let’s throw them in a class this time.
In addition we’ll be using the Union
clause to combine possible input types.
Since pendulum isn’t typed yet, we’ll have to silence mypy’s errors using
# type: ignore
import pendulumfrom datetime import datetime
class DateTime: """ This class is used to convert the pendulum.DateTime type to a string and back to a pendulum.DateTime type """
@staticmethod def serialize(dt: Union[pendulum.DateTime, datetime]) -> str: # type: ignore try: return dt.isoformat() except ValueError: return dt.to_iso8601_string() # type: ignore
@staticmethod def parse_value(value: str) -> Union[pendulum.DateTime, datetime]: # type: ignore return pendulum.parse(value) # type: ignore
date_time = strawberry.scalar( Union[pendulum.DateTime, datetime], # type: ignore name="datetime", description="A date and time", serialize=DateTime.serialize, parse_value=DateTime.parse_value,)
BigInt (64-bit integers)
Python by default allows, integer size to be 2^64. However the graphql spec has capped it to 2^32.
This will inevitably raise errors. Instead of using strings on the client as a workaround, you could use the following scalar:
# This is needed because GraphQL does not support 64 bit integersBigInt = strawberry.scalar( Union[int, str], # type: ignore serialize=lambda v: int(v), parse_value=lambda v: str(v), description="BigInt field",)
You can adapt your schema to automatically use this scalar for all integers by
using the scalar_overrides
parameter
Only use this override if you expect most of your integers to be 64-bit. Since
most GraphQL schemas follow standardized design patterns and most clients
require additional effort to handle all numbers as strings, it makes more
sense to reserve BigInt for numbers that actually exceed the 32-bit limit. You
can achieve this by annotating BigInt
instead of int
in your resolvers
handling large python integers.
user_schema = strawberry.Schema( query=Query, mutation=Mutation, subscription=Subscription, scalar_overrides={datetime: date_time, int: BigInt},)