I've been using testbed, webtest, and nose to test my Python GAE app, and it is a great setup. I'm now implementing something similar to Nick's great example of using the deferred library, but I can't figure out a good way to test the parts of the code triggered by DeadlineExceededError.
Since this is in the context of a taskqueue, it would be painful to construct a test that took more than 10 minutes to run. Is there a way to temporarily set the taskqueue time limit to a few seconds for the purpose of testing? Or perhaps some other way to elegantly test the execution of code in the except DeadlineExceededError block?
Abstract the "GAE context" for your code. in production provide real "GAE implementation" for testing provide a mock own that will raise the DeadlineExceededError. The test should not depend on any timeout, should be fast.
Sample abstraction (just glue):
class AbstractGAETaskContext(object):
def task_spired(): pass # this will throw exception in mock impl
# here you define any method that you call into GAE, to be mocked
def defered(...): pass
If you don't like abstraction, you can do monkey patching for testing only, also you need to define the task_expired function to be your hook for testing.
task_expired should be called during your task implementation function.
*UPDATED*This the 3rd solution:
First I want to mention that the Nick's sample implementation is not so great, the Mapper class has to many responsabilities(deferring, query data, update in batch); and this make the test hard to made, a lot of mocks need to be defined. So I extract the deferring responsabilities in a separate class. You only want to test that deferring mechanism, what actually is happen(the update, query, etc) should be handled in other test.
Here is deffering class, also this no more depends on GAE:
class DeferredCall(object):
def __init__(self, deferred):
self.deferred = deferred
def run(self, long_execution_call, context, *args, **kwargs):
''' long_execution_call should return a tuple that tell us how was terminate operation, with timeout and the context where was abandoned '''
next_context, timeouted = long_execution_call(context, *args, **kwargs)
if timeouted:
self.deferred(self.run, next_context, *args, **kwargs)
Here is the test module:
class Test(unittest.TestCase):
def test_defer(self):
calls = []
def mock_deferrer(callback, *args, **kwargs):
calls.append((callback, args, kwargs))
def interrupted(self, context):
return "new_context", True
d = DeferredCall()
d.run(interrupted, "init_context")
self.assertEquals(1, len(calls), 'a deferred call should be')
def test_no_defer(self):
calls = []
def mock_deferrer(callback, *args, **kwargs):
calls.append((callback, args, kwargs))
def completed(self, context):
return None, False
d = DeferredCall()
d.run(completed, "init_context")
self.assertEquals(0, len(calls), 'no deferred call should be')
How will look the Nick's Mapper implementation:
class Mapper:
...
def _continue(self, start_key, batch_size):
... # here is same code, nothing was changed
except DeadlineExceededError:
# Write any unfinished updates to the datastore.
self._batch_write()
# Queue a new task to pick up where we left off.
##deferred.defer(self._continue, start_key, batch_size)
return start_key, True ## make compatible with DeferredCall
self.finish()
return None, False ## make it comaptible with DeferredCall
runner = _continue
Code where you register the long running task; this only depend on the GAE deferred lib.
import DeferredCall
import PersonMapper # this inherits the Mapper
from google.appengine.ext import deferred
mapper = PersonMapper()
DeferredCall(deferred).run(mapper.run)
Related
I have a database handler that utilizes SQLAlchemy ORM to communicate with a database. As part of SQLAlchemy's recommended practices, I interact with the session by using it as a context manager. How can I test what a function called inside the context manager using that context manager has done?
EDIT: I realized the file structure mattered due to the complexity in introduced. I re-structured the code below to more closely mirror what the end file structure will be like, and what a common production repo in my environment would look like, with code being defined in one file and tests in a completely separate file.
For example:
Code File (delete_things_from_table.py):
from db_handler import delete, SomeTable
def delete_stuff(handler):
stmt = delete(SomeTable)
with handler.Session.begin() as session:
session.execute(stmt)
session.commit()
Test File:
import pytest
import delete_things_from_table as dlt
from db_handler import Handler
def test_delete_stuff():
handler = db_handler()
dlt.delete_stuff(handler):
# Test that session.execute was called
# Test the value of 'stmt'
# Test that session.commit was called
I am not looking for a solution specific to SQLAlchemy; I am only utilizing this to highlight what I want to test within a context manager, and any strategies for testing context managers are welcome.
After sleeping on it, I came up with a solution. I'd love additional/less complex solutions if there are any available, but this works:
import pytest
import delete_things_from_table as dlt
from db_handler import Handler
class MockSession:
def __init__(self):
self.execute_params = []
self.commit_called = False
def execute(self, *args, **kwargs):
self.execute_params.append(["call", args, kwargs])
return self
def commit(self):
self.commit_called = True
return self
def begin(self):
return self
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
pass
def test_delete_stuff(monkeypatch):
handler = db_handler()
# Parens in 'MockSession' below are Important, pass an instance not the class
monkeypatch.setattr(handler, Session, MockSession())
dlt.delete_stuff(handler):
# Test that session.execute was called
assert len(handler.Session.execute_params)
# Test the value of 'stmt'
assert str(handler.Session.execute_params[0][1][0]) == "DELETE FROM some_table"
# Test that session.commit was called
assert handler.Session.commit_called
Some key things to note:
I created a static mock instead of a MagicMock as it's easier to control the methods/data flow with a custom mock class
Since the SQLAlchemy session context manager requires a begin() to start the context, my mock class needed a begin. Returning self in begin allows us to test the values later.
context managers rely on on the magic methods __enter__ and __exit__ with the argument signatures you see above.
The mocked class contains mocked methods which alter instance variables allowing us to test later
This relies on monkeypatch (there are other ways I'm sure), but what's important to note is that when you pass your mock class you want to patch in an instance of the class and not the class itself. The parentheses make a world of difference.
I don't think it's an elegant solution, but it's working. I'll happily take any suggestions for improvement.
Currently we able to test positive cases following way:
class AuthServer(auth_grpc.AuthServicer):
def __init__(self, *args, **kwargs):
print("Initializing auth server..")
super(AuthServer, self).__init__(*args, **kwargs)
def register(self, request, context):
return auth_messages.registerResponse(uuid="Test")
With pytest fixtures looking following way:
#pytest.fixture(scope="session")
def server():
return AuthServer()
#pytest.fixture(scope="session")
def context():
return DummyGRPCTestContext()
In test case environment accessed following way:
def test_user_registration(server, context, user):
request = auth_messages.registerRequest(**user)
result = server.register(request, context)
print("RESULT %s " % result)
However, if we want test negative case and changing grpc servicer method to following:
def register(self, request, context):
context.set_code(grpc.StatusCode.ALREADY_EXISTS)
context.set_details("User already exists")
return auth_messages.registerResponse()
We're failing in errors, related to dummy context.
Where we can get grpc context, that can be easily plugged into test environment?
Contexts like this one look complex and not ready for plug and test.
grpc.ServicerContext is just an interface; within your test code you should be able to write your own implementation of it and pass that to your servicer under test.
It's true that at this time we don't provide in grpc_testing a grpc.ServicerContext implementation ready to be used by tests and passed to systems under test, but it's also not entirely clear that we could provide one that would be simply implemented and also valuable to a large number of tests. There's a large behavioral space of how servicers under test use grpc.ServicerContext objects and there's another large behavioral space of how authors write tests of servicers.
I am using nose test generators feature to run the same test with different contexts. Since it requires the following boiler plate for each test:
class TestSample(TestBase):
def test_sample(self):
for context in contexts:
yield self.check_sample, context
def check_sample(self, context):
"""The real test logic is implemented here"""
pass
I decided to write the following decorator:
def with_contexts(contexts=None):
if contexts is None:
contexts = ['twitter', 'linkedin', 'facebook']
def decorator(f):
#wraps(f)
def wrapper(self, *args, **kwargs):
for context in contexts:
yield f, self, context # The line which causes the error
return wrapper
return decorator
The decorator is used in the following manner:
class TestSample(TestBase):
#with_contexts()
def test_sample(self, context):
"""The real test logic is implemented here"""
var1 = self.some_valid_attribute
When the tests executed an error is thrown specifying that the attribute which is being accessed is not available. However If I change the line which calls the method to the following it works fine:
yield getattr(self, f.__name__), service
I understand that the above snippet creates a bound method where as in the first one self is passed manually to the function. However as far as my understanding goes the first snippet should work fine too. I would appreciate if anyone could clarify the issue.
The title of the question is related to calling instance methods in decorators in general but I have kept the description specific to my context.
You can use functools.partial to tie the wrapped function to self, just like a method would be:
from functools import partial
def decorator(f):
#wraps(f)
def wrapper(self, *args, **kwargs):
for context in contexts:
yield partial(f, self), context
return wrapper
Now you are yielding partials instead, which, when called as yieldedvalue(context), will call f(self, context).
As far as I can tell, some things don't fit together. First, your decorator goes like
def with_contexts(contexts=None):
if contexts is None:
contexts = ['twitter', 'linkedin', 'facebook']
def decorator(f):
#wraps(f)
def wrapper(self, *args, **kwargs):
for context in contexts:
yield f, self, context # The line which causes the error
return wrapper
return decorator
but you use it like
#with_contexts
def test_sample(self, context):
"""The real test logic is implemented here"""
var1 = self.some_valid_attribute
This is wrong: this calls with_context(test_sample), but you need with_context()(test_sample). So do
#with_contexts()
def test_sample(self, context):
"""The real test logic is implemented here"""
var1 = self.some_valid_attribute
even if you don't provide the contexts argument.
Second, you decorate the wrong function: your usage shows that the test function yields the check function for each context. The function you want to wrap does the job of the check function, but you have to name it after the test function.
Applying self to a method can be done with partial as Martijn writes, but it can as well be done the way Python does it under the hood: with
method.__get__(self, None)
or maybe better
method.__get__(self, type(self))
you can achieve the same. (Maybe your original version works as well, with yielding the function to be called and the arguments to use. It was not clear to me that this is the way it works.)
I have a signal_handler connected through a decorator, something like this very simple one:
#receiver(post_save, sender=User,
dispatch_uid='myfile.signal_handler_post_save_user')
def signal_handler_post_save_user(sender, *args, **kwargs):
# do stuff
What I want to do is to mock it with the mock library http://www.voidspace.org.uk/python/mock/ in a test, to check how many times django calls it. My code at the moment is something like:
def test_cache():
with mock.patch('myapp.myfile.signal_handler_post_save_user') as mocked_handler:
# do stuff that will call the post_save of User
self.assert_equal(mocked_handler.call_count, 1)
The problem here is that the original signal handler is called even if mocked, most likely because the #receiver decorator is storing a copy of the signal handler somewhere, so I'm mocking the wrong code.
So the question: how do I mock my signal handler to make my test work?
Note that if I change my signal handler to:
def _support_function(*args, **kwargs):
# do stuff
#receiver(post_save, sender=User,
dispatch_uid='myfile.signal_handler_post_save_user')
def signal_handler_post_save_user(sender, *args, **kwargs):
_support_function(*args, **kwargs)
and I mock _support_function instead, everything works as expected.
Possibly a better idea is to mock out the functionality inside the signal handler rather than the handler itself. Using the OP's code:
#receiver(post_save, sender=User, dispatch_uid='myfile.signal_handler_post_save_user')
def signal_handler_post_save_user(sender, *args, **kwargs):
do_stuff() # <-- mock this
def do_stuff():
... do stuff in here
Then mock do_stuff:
with mock.patch('myapp.myfile.do_stuff') as mocked_handler:
self.assert_equal(mocked_handler.call_count, 1)
So, I ended up with a kind-of solution: mocking a signal handler simply means to connect the mock itself to the signal, so this exactly is what I did:
def test_cache():
with mock.patch('myapp.myfile.signal_handler_post_save_user', autospec=True) as mocked_handler:
post_save.connect(mocked_handler, sender=User, dispatch_uid='test_cache_mocked_handler')
# do stuff that will call the post_save of User
self.assertEquals(mocked_handler.call_count, 1) # standard django
# self.assert_equal(mocked_handler.call_count, 1) # when using django-nose
Notice that autospec=True in mock.patch is required in order to make post_save.connect to correctly work on a MagicMock, otherwise django will raise some exceptions and the connection will fail.
You can mock a django signal by mocking the ModelSignal class at django.db.models.signals.py like this:
#patch("django.db.models.signals.ModelSignal.send")
def test_overwhelming(self, mocker_signal):
obj = Object()
That should do the trick. Note that this will mock ALL signals no matter which object you are using.
If by any chance you use the mocker library instead, it can be done like this:
from mocker import Mocker, ARGS, KWARGS
def test_overwhelming(self):
mocker = Mocker()
# mock the post save signal
msave = mocker.replace("django.db.models.signals")
msave.post_save.send(KWARGS)
mocker.count(0, None)
with mocker:
obj = Object()
It's more lines but it works pretty well too :)
take a look at mock_django . It has support for signals
https://github.com/dcramer/mock-django/blob/master/tests/mock_django/signals/tests.py
In django 1.9 you can mock all receivers with something like this
# replace actual receivers with mocks
mocked_receivers = []
for i, receiver in enumerate(your_signal.receivers):
mock_receiver = Mock()
your_signal.receivers[i] = (receiver[0], mock_receiver)
mocked_receivers.append(mock_receiver)
... # whatever your test does
# ensure that mocked receivers have been called as expected
for mocked_receiver in mocked_receivers:
assert mocked_receiver.call_count == 1
mocked_receiver.assert_called_with(*your_args, sender="your_sender", signal=your_signal, **your_kwargs)
This replaces all receivers with mocks, eg ones you've registered, ones pluggable apps have registered and ones that django itself has registered. Don't be suprised if you use this on post_save and things start breaking.
You may want to inspect the receiver to determine if you actually want to mock it.
There is a way to mock django signals with a small class.
You should keep in mind that this would only mock the function as a django signal handler and not the original function; for example, if a m2mchange trigers a call to a function that calls your handler directly, mock.call_count would not be incremented. You would need a separate mock to keep track of those calls.
Here is the class in question:
class LocalDjangoSignalsMock():
def __init__(self, to_mock):
"""
Replaces registered django signals with MagicMocks
:param to_mock: list of signal handlers to mock
"""
self.mocks = {handler:MagicMock() for handler in to_mock}
self.reverse_mocks = {magicmock:mocked
for mocked,magicmock in self.mocks.items()}
django_signals = [signals.post_save, signals.m2m_changed]
self.registered_receivers = [signal.receivers
for signal in django_signals]
def _apply_mocks(self):
for receivers in self.registered_receivers:
for receiver_index in xrange(len(receivers)):
handler = receivers[receiver_index]
handler_function = handler[1]()
if handler_function in self.mocks:
receivers[receiver_index] = (
handler[0], self.mocks[handler_function])
def _reverse_mocks(self):
for receivers in self.registered_receivers:
for receiver_index in xrange(len(receivers)):
handler = receivers[receiver_index]
handler_function = handler[1]
if not isinstance(handler_function, MagicMock):
continue
receivers[receiver_index] = (
handler[0], weakref.ref(self.reverse_mocks[handler_function]))
def __enter__(self):
self._apply_mocks()
return self.mocks
def __exit__(self, *args):
self._reverse_mocks()
Example usage
to_mock = [my_handler]
with LocalDjangoSignalsMock(to_mock) as mocks:
my_trigger()
for mocked in to_mock:
assert(mocks[mocked].call_count)
# 'function {0} was called {1}'.format(
# mocked, mocked.call_count)
As you mentioned,
mock.patch('myapp.myfile._support_function') is correct but mock.patch('myapp.myfile.signal_handler_post_save_user') is wrong.
I think the reason is:
When init you test, some file import the signal's realization python file, then #receive decorator create a new signal connection.
In the test, mock.patch('myapp.myfile._support_function') will create another signal connection, so the original signal handler is called even if mocked.
Try to disconnect the signal connection before mock.patch('myapp.myfile._support_function'), like
post_save.disconnect(signal_handler_post_save_user)
with mock.patch("review.signals. signal_handler_post_save_user", autospec=True) as handler:
#do stuff
I'm creating a task (by subclassing celery.task.Task) that creates a connection to Twitter's streaming API. For the Twitter API calls, I am using tweepy. As I've read from the celery-documentation, 'a task is not instantiated for every request, but is registered in the task registry as a global instance.' I was expecting that whenever I call apply_async (or delay) for the task, I will be accessing the task that was originally instantiated but that doesn't happen. Instead, a new instance of the custom task class is created. I need to be able to access the original custom task since this is the only way I can terminate the original connection created by the tweepy API call.
Here's some piece of code if this would help:
from celery import registry
from celery.task import Task
class FollowAllTwitterIDs(Task):
def __init__(self):
# requirements for creation of the customstream
# goes here. The CustomStream class is a subclass
# of tweepy.streaming.Stream class
self._customstream = CustomStream(*args, **kwargs)
#property
def customstream(self):
if self._customstream:
# terminate existing connection to Twitter
self._customstream.running = False
self._customstream = CustomStream(*args, **kwargs)
def run(self):
self._to_follow_ids = function_that_gets_list_of_ids_to_be_followed()
self.customstream.filter(follow=self._to_follow_ids, async=False)
follow_all_twitterids = registry.tasks[FollowAllTwitterIDs.name]
And for the Django view
def connect_to_twitter(request):
if request.method == 'POST':
do_stuff_here()
.
.
.
follow_all_twitterids.apply_async(args=[], kwargs={})
return
Any help would be appreciated. :D
EDIT:
For additional context for the question, the CustomStream object creates an httplib.HTTPSConnection instance whenever the filter() method is called. This connection needs to be closed whenever there is another attempt to create one. The connection is closed by setting customstream.running to False.
The task should only be instantiated once, if you think it is not for some reason,
I suggest you add a
print("INSTANTIATE")
import traceback
traceback.print_stack()
to the Task.__init__ method, so you could tell where this would be happening.
I think your task could be better expressed like this:
from celery.task import Task, task
class TwitterTask(Task):
_stream = None
abstract = True
def __call__(self, *args, **kwargs):
try:
return super(TwitterTask, self).__call__(stream, *args, **kwargs)
finally:
if self._stream:
self._stream.running = False
#property
def stream(self):
if self._stream is None:
self._stream = CustomStream()
return self._stream
#task(base=TwitterTask)
def follow_all_ids():
ids = get_list_of_ids_to_follow()
follow_all_ids.stream.filter(follow=ids, async=false)