我正在为基于龙卷风的Web应用程序编写测试模块。该应用程序使用motor作为mongodb连接器,我希望我的测试在临时数据库上运行。我在连接器客户端的 delegate_class 上使用了一个模拟技术,如下所示:
import json
import mock
import motor
import tornado.ioloop
import tornado.testing
import mongomock
import myapp
patch_motor_client = mock.patch('motor.motor_tornado.MotorClient.__delegate_class__', new=mongomock.MongoClient)
patch_motor_database = mock.patch('motor.motor_tornado.MotorDatabase.__delegate_class__', new=mock.MagicMock)
patch_motor_client.start()
patch_motor_database.start()
class TestHandlerBase(tornado.testing.AsyncHTTPTestCase):
"""
Base test handler
"""
def setUp(self):
# Create your Application for testing
self.application = myapp.app.Application()
super(TestHandlerBase, self).setUp()
def get_app(self):
return self.application
def get_new_ioloop(self):
return tornado.ioloop.IOLoop.instance()
class TestMyHandler(TestHandlerBase):
def test_post_ok(self):
"""
POST a resource is OK
"""
post_args = {
'data': 'some data here..'
}
response = self.fetch('myapi/v1/scripts', method='POST', body=json.dumps(post_args))
# assert status is 201
self.assertEqual(response.code, 201)
当我启动测试时,我收到此错误:
File "/data/.virtualenvs/myapp/lib/python3.5/site-packages/motor/core.py", line 162, in __getitem__
return db_class(self, name)
File "/data/.virtualenvs/myapp/lib/python3.5/site-packages/motor/core.py", line 217, in __init__
client.delegate, name, **kwargs)
File "/data/.virtualenvs/myapp/lib/python3.5/site-packages/pymongo/database.py", line 102, in __init__
read_concern or client.read_concern)
File "/data/.virtualenvs/myapp/lib/python3.5/site-packages/pymongo/common.py", line 614, in __init__
raise TypeError("codec_options must be an instance of "
TypeError: codec_options must be an instance of bson.codec_options.CodecOptions
目前我无法使其发挥作用,我想知道我想要做什么,甚至可能使用当前版本的电机(1.2.1),mongomock(3.8。 0)和龙卷风(4.5.3),还是我错过了什么?
感谢你的所有建议。
答案 0 :(得分:0)
我只能将其与沉重的猴子补丁配合使用(我猜想模拟补丁是类似的,但我对还原更改不感兴趣)。
我发现了以下问题:
__delegate_class__
,而是实例化实际的pymongo Database
或Collection
(例如delegate = _delegate or Collection(database.delegate, name)
)_refresh
或__data
,因为它们需要干预其操作(由于它们是IO)。 Mongomock游标要简单得多,并且没有这样的属性并像这样解决他们:
db = mongomock.Database(mongomock.MongoClient(), 'db_name') # Monkeypatch get_collection so that collections are motor-wrapped def create_motor_wrapped_mock_collection( name, codec_options=None, read_preference=None, write_concern=None, read_concern=None): if read_concern: raise NotImplementedError('Mongomock does not handle read_concern yet') collection = db._collections.get(name) if collection is None: delegate = mongomock.Collection(db, name, write_concern=write_concern) # wont be used, as we patch get_io_loop, but the MotorCollection ctor checks type fake_client = motor.motor_tornado.MotorClient() fake_db = motor.motor_tornado.MotorDatabase(fake_client, 'db_name') motor_collection = motor.motor_tornado.MotorCollection(fake_db, name, _delegate=delegate) collection = db._collections[name] = motor_collection collection.get_io_loop = lambda: tornado.ioloop.IOLoop.current() return collection db.get_collection = create_motor_wrapped_mock_collection # Then use db in your code or patch it in
def _prepare_for_motor_wrapping(cls, wrapper_cls): # Motor expects all attributes to exist on a delegate, to generate wrapped methods/attributes, even the ones we # won't need. This patches in dummy attributes/methods so that Motor wrapping can succeed def gen_fake_method(name, on): def fake_method(*args, **kwargs): raise NotImplementedError(name + ' on ' + on) return fake_method attrs = list(wrapper_cls.__dict__.items()) + list(motor.core.AgnosticBaseProperties.__dict__.items()) for k, v in attrs: attr_name = getattr(v, 'attr_name', None) or k if not hasattr(cls, attr_name) and isinstance(v, motor.metaprogramming.MotorAttributeFactory): if isinstance(v, motor.metaprogramming.ReadOnlyProperty): setattr(cls, attr_name, None) elif isinstance(v, motor.metaprogramming.Async) or isinstance(v, motor.metaprogramming.Unwrap): setattr(cls, attr_name, gen_fake_method(attr_name, cls.__name__)) else: raise RuntimeError('Dont know how to fake %s' % v) # We must clear the cache, as classes might have been generated already during some previous import motor.metaprogramming._class_cache = {} _prepare_for_motor_wrapping(mongomock.Database, motor.core.AgnosticDatabase) motor.motor_tornado.MotorDatabase = motor.motor_tornado.create_motor_class(motor.core.AgnosticDatabase) _prepare_for_motor_wrapping(mongomock.Collection, motor.core.AgnosticCollection) motor.motor_tornado.MotorCollection = motor.motor_tornado.create_motor_class(motor.core.AgnosticCollection)
由于某些原因,必须保留MotorClient。
def _patch_aggregate_cursor(): def curs_to_docs(docs_future, curs_future): curs = curs_future.result() docs_future.set_result(list(curs)) def to_list(self, *args): mock_cursor_future = self.collection._async_aggregate(self.pipeline) docs_future = self._framework.get_future(self.get_io_loop()) self._framework.add_future( self.get_io_loop(), mock_cursor_future, curs_to_docs, docs_future) return docs_future motor.core.AgnosticAggregationCursor.to_list = to_list def _patch_generic_cursor(): def to_list(self, *args): docs = list(self.delegate) docs_future = self._framework.get_future(self.get_io_loop()) docs_future.set_result(docs) return docs_future motor.core.AgnosticCursor.to_list = to_list
所有这些可能都是不完整且脆弱的,所以我让您判断是否值得付出努力。