我的问题已在 GitHub 问题 (https://github.com/graphql-python/graphene-sqlalchemy/issues/134) 上得到解答。
这个想法是从info 参数（类型为graphql.execution.base.ResolveInfo）中识别出请求的字段，该参数通过get_field_names 函数传递给解析器函数，如下所示：
def get_field_names(info):
"""
Parses a query info into a list of composite field names.
For example the following query:
{
carts {
edges {
node {
id
name
...cartInfo
}
}
}
}
fragment cartInfo on CartType { whatever }
Will result in an array:
[
'carts',
'carts.edges',
'carts.edges.node',
'carts.edges.node.id',
'carts.edges.node.name',
'carts.edges.node.whatever'
]
"""
fragments = info.fragments
def iterate_field_names(prefix, field):
name = field.name.value
if isinstance(field, FragmentSpread):
_results = []
new_prefix = prefix
sub_selection = fragments[field.name.value].selection_set.selections
else:
_results = [prefix + name]
new_prefix = prefix + name + "."
if field.selection_set:
sub_selection = field.selection_set.selections
else:
sub_selection = []
for sub_field in sub_selection:
_results += iterate_field_names(new_prefix, sub_field)
return _results
results = iterate_field_names('', info.field_asts[0])
return results
以上函数取自https://github.com/graphql-python/graphene/issues/348#issuecomment-267717809。该问题包含此功能的其他版本，但我觉得这是最完整的。
并使用已识别的字段来限制 SQLAlchemy 查询中检索到的字段，如下所示：
fields = get_field_names(info=info)
query = TypeAuthor.get_query(info=info).options(load_only(*relation_fields))
当应用于上述示例查询时：
query GetAuthor{
author(authorId: 1) {
nameFirst
}
}
get_field_names 函数将返回 ['author', 'author.nameFirst']。但是，由于“原始”SQLAlchemy ORM 字段是蛇形大小写的，因此需要更新 get_field_names 查询以删除 author 前缀并通过 graphene.utils.str_converters.to_snake_case 函数转换字段名。
长话短说，上述方法会产生如下原始 SQL 查询：
INFO:sqlalchemy.engine.base.Engine:SELECT authors.author_id AS authors_author_id, authors.name_first AS authors_name_first
FROM authors
WHERE authors.author_id = ?
LIMIT ? OFFSET ?
2018-06-09 13:22:16,396 INFO sqlalchemy.engine.base.Engine (1, 1, 0)
更新
如果有人来到这里想知道如何实现我已经开始实现我自己的get_query_fields 函数版本：
from typing import List, Dict, Union, Type
import graphql
from graphql.language.ast import FragmentSpread
from graphql.language.ast import Field
from graphene.utils.str_converters import to_snake_case
import sqlalchemy.orm
from demo.orm_base import OrmBaseMixin
def extract_requested_fields(
info: graphql.execution.base.ResolveInfo,
fields: List[Union[Field, FragmentSpread]],
do_convert_to_snake_case: bool = True,
) -> Dict:
"""Extracts the fields requested in a GraphQL query by processing the AST
and returns a nested dictionary representing the requested fields.
Note:
This function should support arbitrarily nested field structures
including fragments.
Example:
Consider the following query passed to a resolver and running this
function with the `ResolveInfo` object passed to the resolver.
>>> query = "query getAuthor{author(authorId: 1){nameFirst, nameLast}}"
>>> extract_requested_fields(info, info.field_asts, True)
{'author': {'name_first': None, 'name_last': None}}
Args:
info (graphql.execution.base.ResolveInfo): The GraphQL query info passed
to the resolver function.
fields (List[Union[Field, FragmentSpread]]): The list of `Field` or
`FragmentSpread` objects parsed out of the GraphQL query and stored
in the AST.
do_convert_to_snake_case (bool): Whether to convert the fields as they
appear in the GraphQL query (typically in camel-case) back to
snake-case (which is how they typically appear in ORM classes).
Returns:
Dict: The nested dictionary containing all the requested fields.
"""
result = {}
for field in fields:
# Set the `key` as the field name.
key = field.name.value
# Convert the key from camel-case to snake-case (if required).
if do_convert_to_snake_case:
key = to_snake_case(name=key)
# Initialize `val` to `None`. Fields without nested-fields under them
# will have a dictionary value of `None`.
val = None
# If the field is of type `Field` then extract the nested fields under
# the `selection_set` (if defined). These nested fields will be
# extracted recursively and placed in a dictionary under the field
# name in the `result` dictionary.
if isinstance(field, Field):
if (
hasattr(field, "selection_set") and
field.selection_set is not None
):
# Extract field names out of the field selections.
val = extract_requested_fields(
info=info,
fields=field.selection_set.selections,
)
result[key] = val
# If the field is of type `FragmentSpread` then retrieve the fragment
# from `info.fragments` and recursively extract the nested fields but
# as we don't want the name of the fragment appearing in the result
# dictionary (since it does not match anything in the ORM classes) the
# result will simply be result of the extraction.
elif isinstance(field, FragmentSpread):
# Retrieve referened fragment.
fragment = info.fragments[field.name.value]
# Extract field names out of the fragment selections.
val = extract_requested_fields(
info=info,
fields=fragment.selection_set.selections,
)
result = val
return result
它将 AST 解析为 dict，保留查询的结构并（希望）匹配 ORM 的结构。
运行info 对象的查询，例如：
query getAuthor{
author(authorId: 1) {
nameFirst,
nameLast
}
}
生产
{'author': {'name_first': None, 'name_last': None}}
而像这样的更复杂的查询：
query getAuthor{
author(nameFirst: "Brandon") {
...authorFields
books {
...bookFields
}
}
}
fragment authorFields on TypeAuthor {
nameFirst,
nameLast
}
fragment bookFields on TypeBook {
title,
year
}
产生：
{'author': {'books': {'title': None, 'year': None},
'name_first': None,
'name_last': None}}
现在，这些字典可用于定义主表上的字段（在本例中为 Author），因为它们将具有 None 的值，例如 name_first 或关系上的字段该主表的字段，例如 books 关系上的字段 title。
自动应用这些字段的简单方法可以采用以下函数的形式：
def apply_requested_fields(
info: graphql.execution.base.ResolveInfo,
query: sqlalchemy.orm.Query,
orm_class: Type[OrmBaseMixin]
) -> sqlalchemy.orm.Query:
"""Updates the SQLAlchemy Query object by limiting the loaded fields of the
table and its relationship to the ones explicitly requested in the GraphQL
query.
Note:
This function is fairly simplistic in that it assumes that (1) the
SQLAlchemy query only selects a single ORM class/table and that (2)
relationship fields are only one level deep, i.e., that requestd fields
are either table fields or fields of the table relationship, e.g., it
does not support fields of relationship relationships.
Args:
info (graphql.execution.base.ResolveInfo): The GraphQL query info passed
to the resolver function.
query (sqlalchemy.orm.Query): The SQLAlchemy Query object to be updated.
orm_class (Type[OrmBaseMixin]): The ORM class of the selected table.
Returns:
sqlalchemy.orm.Query: The updated SQLAlchemy Query object.
"""
# Extract the fields requested in the GraphQL query.
fields = extract_requested_fields(
info=info,
fields=info.field_asts,
do_convert_to_snake_case=True,
)
# We assume that the top level of the `fields` dictionary only contains a
# single key referring to the GraphQL resource being resolved.
tl_key = list(fields.keys())[0]
# We assume that any keys that have a value of `None` (as opposed to
# dictionaries) are fields of the primary table.
table_fields = [
key for key, val in fields[tl_key].items()
if val is None
]
# We assume that any keys that have a value being a dictionary are
# relationship attributes on the primary table with the keys in the
# dictionary being fields on that relationship. Thus we create a list of
# `[relatioship_name, relationship_fields]` lists to be used in the
# `joinedload` definitions.
relationship_fieldsets = [
[key, val.keys()]
for key, val in fields[tl_key].items()
if isinstance(val, dict)
]
# Assemble a list of `joinedload` definitions on the defined relationship
# attribute name and the requested fields on that relationship.
options_joinedloads = []
for relationship_fieldset in relationship_fieldsets:
relationship = relationship_fieldset[0]
rel_fields = relationship_fieldset[1]
options_joinedloads.append(
sqlalchemy.orm.joinedload(
getattr(orm_class, relationship)
).load_only(*rel_fields)
)
# Update the SQLAlchemy query by limiting the loaded fields on the primary
# table as well as by including the `joinedload` definitions.
query = query.options(
sqlalchemy.orm.load_only(*table_fields),
*options_joinedloads
)
return query