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@jasonphillips
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python graphql-tools imitation
import graphql
# build_executable schema
#
# accepts schema_definition (string) and resolvers (object) in style of graphql-tools
# returns a schema ready for execution
def build_executable_schema(schema_definition, resolvers):
ast = graphql.parse(schema_definition)
schema = graphql.build_ast_schema(ast)
for typeName in resolvers:
fieldType = schema.get_type(typeName)
for fieldName in resolvers[typeName]:
if fieldType is graphql.GraphQLScalarType:
fieldType.fields[fieldName].resolver = resolvers[typeName][fieldName]
continue
field = fieldType.fields[fieldName]
field.resolver = resolvers[typeName][fieldName]
if not fieldType.fields: continue
for remaining in fieldType.fields:
if not fieldType.fields[remaining].resolver:
fieldType.fields[remaining].resolver = \
lambda value, info, _r=remaining, **args: value[_r]
return schema
import graphql
import json
from graphql_tools import build_executable_schema
source_schema = """
schema {
query: RootQuery
}
type RootQuery {
officers: [Officer]
}
type Officer {
title: String
first: String
last: String
uniform: Uniform
}
type Uniform {
pips: Float
}
"""
resolvers = {
'RootQuery': {
# returning direct values, but would normally load fetch db with some info.context
'officers': lambda value, info, **args: [
dict(first='william', last='riker'),
dict(first='geordi', last='laforge'),
]
},
'Officer': {
# only declaring the field here which is computed
'title': lambda value, info, **args: 'Officer: %(first)s %(last)s' % value,
# and the connection
'uniform': lambda value, info, **args: value['first']=='geordi' and {'i':2.5} or {'i':3},
},
'Uniform': {
'pips': lambda value, info, **args: value['i'],
}
}
my_schema = build_executable_schema(source_schema, resolvers)
executed = graphql.graphql(my_schema, """
query Example {
officers {
first
last
title
uniform { pips }
}
}
""")
print(json.dumps(executed.data, indent=4))
{
"officers": [
{
"first": "william",
"last": "riker",
"title": "Officer: william riker",
"uniform": {
"pips": 3.0
}
},
{
"first": "geordi",
"last": "laforge",
"title": "Officer: geordi laforge",
"uniform": {
"pips": 2.5
}
}
]
}
@jasonphillips
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Minimal implementation of graphql-tools in Python.

Requires the graphql-core library (install with pip).

@linonetwo
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Why not publish it as a pip package?

@linonetwo
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And how to use with graphene?

@mingzhou
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mingzhou commented Apr 4, 2018

How could I use it with graphene?

@jasonphillips
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jasonphillips commented Apr 5, 2018

I've iterated on this idea a bit within a small project recently, and can hopefully find an opportunity from there to write it as a more complete package than the sketch above. I'll try to prioritize that in coming weeks and see what I can publish; will notify here when something comes to fruition.

(But as for usage with graphene, I'm not so sure -- this is more of an alternative to graphene's style of schema creation than an extension, as graphene's authors have likewise said, eg. here: graphql-python/graphene#704 )

@mingzhou
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mingzhou commented Apr 9, 2018

Great Job!
I opened the issue graphql-python/graphene#704 .

I think it's a better idea to design schema by DSL format. I don't know much about why we have to get a extra graphene schema layer. Even though the graphene schema layer is necessary, we still must have a DSL => graphene schema parser.

I'm quite confused that why there are two types of definition. Graphene schema definition graphene.types.schema.Schema and graphql-core schema definition graphql.type.schema.GraphQLSchema.

Since graphene.types.schema.Schema is extended from graphql.type.schema.GraphQLSchema, how could I get graphene.types.schema.Schema from graphq.parse(DSL file)?

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