Python schema-first GraphQL library based on GraphQL-core.
Python 3.7+
pip install python-gql
import graphql
from gql import gql, make_schema, query, mutate
type_defs = gql("""
type Query {
hello(name: String!): String!
}
type Post {
author: String!
comment: String!
}
type Mutation {
addPost(author: String, comment: String): Post!
}
""")
@query
def hello(parent, info, name: str) -> str:
return name
@mutate
def add_post(parent, info, author: str = None, comment: str = None) -> dict:
return {'author': author, 'comment': comment}
schema = make_schema(type_defs)
q = """
query {
hello(name: "graphql")
}
"""
result = graphql.graphql_sync(schema, q)
print(result.data)
# result: {'hello': 'graphql'}
q = """
mutation {
addPost(author: "syfun", comment: "This is a good library.") {
author
comment
}
}
"""
result = graphql.graphql_sync(schema, q)
print(result.data)
# result: {'addPost': {'author': 'syfun', 'comment': 'This is a good library.'}}
This library is schema-first
, so you must build a schema explicitly.
Here, we have two methods to build a schema, by a type definitions
or a schema file
.
from gql import gql, make_schema
type_defs = gql("""
type Query {
hello(name: String!): String!
}
""")
schema = make_schema(type_defs)
gql
function will check your type definitions syntax.
from gql import make_schema_from_file
schema = make_schema_from_file('./schema.graphql')
In Python,
decorator
is my favorite function, it save my life!
We can use query
, mutation
, subscribe
to bind functions to GraphQL resolvers.
@query
def hello(parent, info, name: str) -> str:
return name
These decorators will auto convert the snake function to camel one.
# add_port => addPost
@mutate
def add_post(parent, info, author: str = None, comment: str = None) -> dict:
return {'author': author, 'comment': comment}
When the funcation name different from the resolver name, you can give a name argument to these decorators.
@query('hello')
def hello_function(parent, info, name: str) -> str:
return name
About subscribe
, please see gql-subscriptions.
Use enum_type
decorator with a python Enum class.
from enum import Enum
from gql import enum_type
@enum_type
class Gender(Enum):
MALE = 1
FEMALE = 2
Use scalar_type
decorator with a python class.
from gql import scalar_type
@scalar_type
class JSONString:
description = "The `JSONString` represents a json string."
@staticmethod
def serialize(value: Any) -> str:
return json.dumps(value)
@staticmethod
def parse_value(value: Any) -> dict:
if not isinstance(value, str):
raise TypeError(f'JSONString cannot represent non string value: {inspect(value)}')
return json.loads(value)
@staticmethod
def parse_literal(ast, _variables=None):
if isinstance(ast, StringValueNode):
return json.loads(ast.value)
return INVALID
from gql import gql, make_schema, query, SchemaDirectiveVisitor
from gql.resolver import default_field_resolver
type_defs = gql("""
directive @upper on FIELD_DEFINITION
type Query {
hello(name: String!): String! @upper
}
""")
class UpperDirective(SchemaDirectiveVisitor):
def visit_field_definition(self, field, object_type):
original_resolver = field.resolve or default_field_resolver
def resolve_upper(obj, info, **kwargs):
result = original_resolver(obj, info, **kwargs)
if result is None:
return None
return result.upper()
field.resolve = resolve_upper
return field
schema = make_schema(type_defs, directives={'upper': UpperDirective})
Thanks to Ariadne