Unit testing allows you to check the quality of your code after you've written it, but you can also use unit testing to improve your development process as you go along. Instead of writing tests after you finish developing your application, consider writing the tests as you go. This helps you design small, maintainable, reusable units of code. It also makes it easier for you to test your code thoroughly and quickly.
When you do local unit testing, you run tests that stay inside your own development environment without involving remote components. App Engine provides testing utilities that use local implementations of datastore and other App Engine services. This means you can exercise your code's use of these services locally, without deploying your code to App Engine, by using service stubs.
A service stub is a method that simulates the behavior of the service. For example, the datastore service stub shown in Writing Datastore and Memcache Tests allows you to test your datastore code without making any requests to the real datastore. Any entity stored during a datastore unit test is held in memory, not in the datastore, and is deleted after the test run. You can run small, fast tests without any dependency on datastore itself.
This document describes how to write unit tests against several local App Engine services, then gives some information about setting up a testing framework.
- Introducing the Python testing utilities
- Writing Datastore and memcache tests
- Writing High Replication Datastore tests
- Writing task queue tests
- Writing deferred task tests
- Writing mail tests
- Changing the default environment variables
- Setting up a testing framework
Introducing the Python testing utilities
Service stubs are available for the following services:
- App Identity
- Blobstore (use
- Capability (use
- Channel (use
- Datastore (use
- Files (use
- Images (only for dev_appserver; use
- LogService (use
- Mail (use
- Memcache (use
- Task Queue (use
- URL fetch (use
- User service (use
- XMPP (use
To initialize all stubs at the same time, you can use
Writing Datastore and memcache tests
Make sure your test runner has the appropriate libraries on the Python load path, including the App Engine libraries,
yaml (included in the App Engine SDK), the application root, and any other modifications to the library path expected by application code (such as a local
./lib directory, if you have one). For example:
import sys sys.path.insert(1, 'google-cloud-sdk/platform/google_appengine') sys.path.insert(1, 'google-cloud-sdk/platform/google_appengine/lib/yaml/lib') sys.path.insert(1, 'myapp/lib')
Import the Python
unittest module and the App Engine modules that are
relevant to the services being tested—in this case
uses both the datastore and memcache. Also import the App Engine
import unittest from google.appengine.api import memcache from google.appengine.ext import ndb from google.appengine.ext import testbed
Then create a
TestModel class. In this example, a function checks to see whether an entity is stored in memcache. If no entity is found, it checks for an entity in the datastore.
(This may often be redundant in real life, since
ndb uses memcache itself behind the curtains, but it's still an OK pattern for a test).
class TestModel(ndb.Model): """A model class used for testing.""" number = ndb.IntegerProperty(default=42) text = ndb.StringProperty() class TestEntityGroupRoot(ndb.Model): """Entity group root""" pass def GetEntityViaMemcache(entity_key): """Get entity from memcache if available, from datastore if not.""" entity = memcache.get(entity_key) if entity is not None: return entity key = ndb.Key(urlsafe=entity_key) entity = key.get() if entity is not None: memcache.set(entity_key, entity) return entity
Next, create a test case. No matter what services you are testing, the test case must create a
Testbed instance and activate it. The test case must also initialize the relevant service stubs, in this case using
init_memcache_stub. (The methods for initializing other App Engine service stubs are listed in Introducing the Python Testing Utilities.)
class DatastoreTestCase(unittest.TestCase): def setUp(self): # First, create an instance of the Testbed class. self.testbed = testbed.Testbed() # Then activate the testbed, which prepares the service stubs for use. self.testbed.activate() # Next, declare which service stubs you want to use. self.testbed.init_datastore_v3_stub() self.testbed.init_memcache_stub() # Clear ndb's in-context cache between tests. # This prevents data from leaking between tests. # Alternatively, you could disable caching by # using ndb.get_context().set_cache_policy(False) ndb.get_context().clear_cache()
init_datastore_v3_stub() method with no argument uses an in-memory datastore that is initially empty. If you want to test an existing datastore entity, include its pathname as an argument to
In addition to
setUp(), include a
tearDown() method that deactivates the testbed. This restores the original stubs so that tests do not interfere with each other.
def tearDown(self): self.testbed.deactivate()
Then implement the tests.
def testInsertEntity(self): TestModel().put() self.assertEqual(1, len(TestModel.query().fetch(2)))
Now you can use
TestModel to write tests that use the datastore or memcache service stubs instead of using the real services.
For example, the method shown below creates two entities: the first entity uses the default value for the
number attribute (42), and the second uses a nondefault value for
number (17). The method then builds a query for
TestModel entities, but only for those with the default value of
After retrieving all matching entities, the method tests that exactly one entity was found, and that the
number attribute value of that entity is the default value.
def testFilterByNumber(self): root = TestEntityGroupRoot(id="root") TestModel(parent=root.key).put() TestModel(number=17, parent=root.key).put() query = TestModel.query(ancestor=root.key).filter( TestModel.number == 42) results = query.fetch(2) self.assertEqual(1, len(results)) self.assertEqual(42, results.number)
As another example, the following method creates an entity and retrieves it using the
GetEntityViaMemcache() function that we created above. The method then tests that an entity was returned, and that its
number value is the same as for the previously created entity.
def testGetEntityViaMemcache(self): entity_key = TestModel(number=18).put().urlsafe() retrieved_entity = GetEntityViaMemcache(entity_key) self.assertNotEqual(None, retrieved_entity) self.assertEqual(18, retrieved_entity.number)
And finally, invoke
if __name__ == '__main__': unittest.main()
You can run these tests simply by running the script:
% python tests.py
Writing High Replication Datastore tests
If your app uses the High Replication Datastore (HRD), you may want to write tests that verify your application's behavior in the face of eventual consistency.
db.testbed exposes options that make this easy:
from google.appengine.datastore import datastore_stub_util # noqa class HighReplicationTestCaseOne(unittest.TestCase): def setUp(self): # First, create an instance of the Testbed class. self.testbed = testbed.Testbed() # Then activate the testbed, which prepares the service stubs for use. self.testbed.activate() # Create a consistency policy that will simulate the High Replication # consistency model. self.policy = datastore_stub_util.PseudoRandomHRConsistencyPolicy( probability=0) # Initialize the datastore stub with this policy. self.testbed.init_datastore_v3_stub(consistency_policy=self.policy) # Initialize memcache stub too, since ndb also uses memcache self.testbed.init_memcache_stub() # Clear in-context cache before each test. ndb.get_context().clear_cache() def tearDown(self): self.testbed.deactivate() def testEventuallyConsistentGlobalQueryResult(self): class TestModel(ndb.Model): pass user_key = ndb.Key('User', 'ryan') # Put two entities ndb.put_multi([ TestModel(parent=user_key), TestModel(parent=user_key) ]) # Global query doesn't see the data. self.assertEqual(0, TestModel.query().count(3)) # Ancestor query does see the data. self.assertEqual(2, TestModel.query(ancestor=user_key).count(3))
PseudoRandomHRConsistencyPolicy class lets you control the likelihood of a write applying before each global (non-ancestor) query. By setting the probability to 0%, we are instructing the datastore stub to operate with the maximum amount of eventual consistency. Maximum eventual consistency means writes will commit but always fail to apply, so global (non-ancestor) queries will consistently fail to see changes. This is of course not representative of the amount of eventual consistency your application will see when running in production, but for testing purposes, it's very useful to be able to configure the local datastore to behave this way every time. If you use a non-zero probability,
PseudoRandomHRConsistencyPolicy makes a deterministic sequence of consistency decisions so test outcomes are consistent:
def testDeterministicOutcome(self): # 50% chance to apply. self.policy.SetProbability(.5) # Use the pseudo random sequence derived from seed=2. self.policy.SetSeed(2) class TestModel(ndb.Model): pass TestModel().put() self.assertEqual(0, TestModel.query().count(3)) self.assertEqual(0, TestModel.query().count(3)) # Will always be applied before the third query. self.assertEqual(1, TestModel.query().count(3))
The testing APIs are useful for verifying that your application behaves properly in the face of eventual consistency, but please keep in mind that the local High Replication read consistency model is an approximation of the production High Replication read consistency model, not an exact replica. In the local environment, performing a
get() of an
Entity that belongs to an entity group with an unapplied write will always make the results of the unapplied write visible to subsequent global queries. In production this is not the case.
Writing mail tests
You can use the mail service stub to test the mail service. Similar to other services supported by testbed, at first you initialize the stub, then invoke the code which uses the mail API, and finally test whether the correct messages were sent.
import unittest from google.appengine.api import mail from google.appengine.ext import testbed class MailTestCase(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() self.testbed.init_mail_stub() self.mail_stub = self.testbed.get_stub(testbed.MAIL_SERVICE_NAME) def tearDown(self): self.testbed.deactivate() def testMailSent(self): mail.send_mail(firstname.lastname@example.org', subject='This is a test', email@example.com', body='This is a test e-mail') messages = self.mail_stub.get_sent_messages(firstname.lastname@example.org') self.assertEqual(1, len(messages)) self.assertEqual('email@example.com', messages.to)
Writing task queue tests
You can use the taskqueue stub to write tests that use the taskqueue service. Similar to other services supported by testbed, at first you initialize the stub, then invoke the code which uses the taskqueue API, and finally test whether the tasks were properly added to the queue.
import operator import os import unittest from google.appengine.api import taskqueue from google.appengine.ext import deferred from google.appengine.ext import testbed class TaskQueueTestCase(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() # root_path must be set the the location of queue.yaml. # Otherwise, only the 'default' queue will be available. self.testbed.init_taskqueue_stub( root_path=os.path.join(os.path.dirname(__file__), 'resources')) self.taskqueue_stub = self.testbed.get_stub( testbed.TASKQUEUE_SERVICE_NAME) def tearDown(self): self.testbed.deactivate() def testTaskAddedToQueue(self): taskqueue.Task(name='my_task', url='/url/of/my/task/').add() tasks = self.taskqueue_stub.get_filtered_tasks() assert len(tasks) == 1 assert tasks.name == 'my_task'
queue.yaml configuration file
If you want to run tests on code that interacts a non-default queue, you will need to create
and specify a
queue.yaml file for your application to use. Below is an example
For more information on the queue.yaml options available, see task queue configuration.
queue: - name: default rate: 5/s - name: queue-1 rate: 5/s - name: queue-2 rate: 5/s
The location of the
queue.yaml is specified when initializing the stub:
In the sample,
queue.yaml is in the same directory as the tests. If it were in another folder, that path would need to be specified in
The taskqueue stub's
get_filtered_tasks allows you to filter queued tasks. This makes it easier
to write tests that need to verify code that enqueues multiple tasks.
def testFiltering(self): taskqueue.Task(name='task_one', url='/url/of/task/1/').add('queue-1') taskqueue.Task(name='task_two', url='/url/of/task/2/').add('queue-2') # All tasks tasks = self.taskqueue_stub.get_filtered_tasks() assert len(tasks) == 2 # Filter by name tasks = self.taskqueue_stub.get_filtered_tasks(name='task_one') assert len(tasks) == 1 assert tasks.name == 'task_one' # Filter by URL tasks = self.taskqueue_stub.get_filtered_tasks(url='/url/of/task/1/') assert len(tasks) == 1 assert tasks.name == 'task_one' # Filter by queue tasks = self.taskqueue_stub.get_filtered_tasks(queue_names='queue-1') assert len(tasks) == 1 assert tasks.name == 'task_one' # Multiple queues tasks = self.taskqueue_stub.get_filtered_tasks( queue_names=['queue-1', 'queue-2']) assert len(tasks) == 2
Writing deferred task tests
If your application code uses the deferred library you can use the taskqueue stub along with
deferred to verify that deferred functions are queue and executed correctly.
def testTaskAddedByDeferred(self): deferred.defer(operator.add, 1, 2) tasks = self.taskqueue_stub.get_filtered_tasks() assert len(tasks) == 1 result = deferred.run(tasks.payload) assert result == 3
Changing the default environment variables
App Engine services often depend on environment variables. The
testbed.Testbed uses default values for these, but you can set custom values based on your testing needs with the
method of class
For example, let's say you have a test that stores several entities in datastore, all of them linked to the same application ID. Now you want to run the same tests again, but using an application ID different from the one that is linked to the stored entities. To do this, pass the new value into
import os import unittest from google.appengine.ext import testbed class EnvVarsTestCase(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() self.testbed.setup_env( app_id='your-app-id', my_config_setting='example', overwrite=True) def tearDown(self): self.testbed.deactivate() def testEnvVars(self): assert os.environ['APPLICATION_ID'] == 'your-app-id' assert os.environ['MY_CONFIG_SETTING'] == 'example'
Another frequent use for
setup_env is to simulate a user being logged in, either with or without admin privileges, to check if your handlers
operate properly in each case.
import unittest from google.appengine.api import users from google.appengine.ext import testbed class LoginTestCase(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() self.testbed.init_user_stub() def tearDown(self): self.testbed.deactivate() def loginUser(self, firstname.lastname@example.org', id='123', is_admin=False): self.testbed.setup_env( user_email=email, user_id=id, user_is_admin='1' if is_admin else '0', overwrite=True) def testLogin(self): assert not users.get_current_user() self.loginUser() assert users.get_current_user().email() == 'email@example.com' self.loginUser(is_admin=True) assert users.is_current_user_admin()
Now, your test methods can call, for example,
self.loginUser(None, None) to simulate no user being logged in,
self.loginUser('firstname.lastname@example.org', '123') to simulate a non-admin user being logged in,
self.loginUser('email@example.com', '123', is_admin=True) to simulate an admin user being logged in.
Setting up a testing framework
The SDK's testing utilities are not tied to a specific framework. You can run your unit tests with any available App Engine testrunner, for example nose-gae or ferrisnose. You can also write a simple testrunner of your own, or use the one shown below.
The following scripts use Python's unittest module.
You can name the script anything you want. When you run it, provide the path to your Google Cloud SDK or Google App Engine SDK installation and the path to your test modules. The script will discover all tests in the path provided and will print results to the standard error stream. Test files follow the convention of having
test prefixed to their name.
import optparse import os import sys import unittest USAGE = """%prog SDK_PATH TEST_PATH Run unit tests for App Engine apps. SDK_PATH Path to Google Cloud or Google App Engine SDK installation, usually ~/google_cloud_sdk TEST_PATH Path to package containing test modules""" def main(sdk_path, test_path): # If the sdk path points to a google cloud sdk installation # then we should alter it to point to the GAE platform location. if os.path.exists(os.path.join(sdk_path, 'platform/google_appengine')): sys.path.insert(0, os.path.join(sdk_path, 'platform/google_appengine')) else: sys.path.insert(0, sdk_path) # Ensure that the google.appengine.* packages are available # in tests as well as all bundled third-party packages. import dev_appserver dev_appserver.fix_sys_path() # Loading appengine_config from the current project ensures that any # changes to configuration there are available to all tests (e.g. # sys.path modifications, namespaces, etc.) try: import appengine_config (appengine_config) except ImportError: print "Note: unable to import appengine_config." # Discover and run tests. suite = unittest.loader.TestLoader().discover(test_path) unittest.TextTestRunner(verbosity=2).run(suite) if __name__ == '__main__': parser = optparse.OptionParser(USAGE) options, args = parser.parse_args() if len(args) != 2: print 'Error: Exactly 2 arguments required.' parser.print_help() sys.exit(1) SDK_PATH = args TEST_PATH = args main(SDK_PATH, TEST_PATH)