Skip to content
Pure Python dependency injector
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
app_build/terraform
tests
twin_sister
.gitignore
LICENSE
README.md
buildspec.yml
requirements.txt
setup.py
upload_package_conditionally.sh

README.md

Twin Sister: Pure Python Dependency Injection

No, I am Zoot's identical twin sister, Dingo.

What is dependency injection and why should I care?

If you write unit tests (and you do write them, right?) you have encountered situations where the unit you are testing depends on some component outside of itself. For example, a unit that retrieves data from an HTTP API depends on an HTTP client. By definition, a unit test does not include systems outside the unit, so does not make real network requests. Instead, it configures the unit to make fake requests using a component with the same interface as the real HTTP client. The mechanism that replaces the real HTTP client with a fake one is a kind of dependency injection.

Dependency injection mechanisms

Most simple: specify initializer arguments

class Knight:

  def __init__(self, *, http_client=None):
    if not http_client:
      http_client = HttpClient()

In the example above, new knight objects will ordinarily construct a real HTTP client for themselves, but the code that creates them has the opportunity to inject an alternative client like this:

fake = FakeHttpClient()
sir_lancelot = Knight(http_client=fake)

This approach has the advantage of being simple and straightforward and can be more than adequate if the problem space is small and well-contained. It begins to break down, however, as the system under test becomes more complex. The initializer must specify each dependency that can be injected and the target bears responsibility for maintaining each injected object and passing it to sub-components as they are created.

In many cases, this approach will force classes to be aware of injected entities that otherwise ought not concern them. For example

class Horse:

  def __init__(self, *, tail=None):
    self.tail = tail or HorseTail()


class Knight:

  def __init__(self, *, tail_for_horse=None):
    self.horse = Horse(tail=tail_for_horse)

The only reason Knight.__init__ has for accepting tail_for_horse is to pass it through to Horse.__init__. This is awkward, aside from its damage to separation of concerns.

Most thorough: subvert the global symbol table

In theory, it would be possible to make all HTTP clients fake by redirecting HttpClient in the global symbol table to FakeHttpClient. This approach has the advantage of not requiring the targeted code to be aware of the injection and is likely to be highly effective if successful. It suffers from major drawbacks, however. The symtable module (sensibly) does not permit write access, so redirection would need to be performed at a lower level which would break compatibility across Python implementations. It's also an extreme hack with potentially serious side effects.

Middle ground: request dependencies explicitly

Twin Sister takes a middle approach. It maintains a registry of symbols that have been injected and then handles requests for dependencies. In this way, only code that requests a dependency explicity is affected by injection:

from twin_sister import dependency

class Horse:

  def __init__(self):
    self.tail = dependency(Tail)()


class Knight:

  def __init__(self):
    self.horse = dependency(Horse)()

dependency returns the injected replacement if one exists. Otherwise, it returns the real thing. In this way, the system will behave sensibly whether injection has occurred or not.

Injecting a dependency with Twin Sister

Installation

python setup.py install

Generic technique to inject any object as a dependency

from twin_sister import dependency, dependency_context

class Knight:

  def __init__(self):
    self.horse = dependency(Horse)()
    self.start_month = dependency(current_month)()
    self.guess = dependency(VELOCITY_OF_SOUTH_AFRICAN_SWALLOW)


with dependency_context as context:
  context.inject(Horse, FakeHorse)
  context.inject(current_month, lambda: 'February')
  context.inject(VELOCITY_OF_SOUTH_AFRICAN_SWALLOW, 42)
  lancelot = Knight()
  lancelot.visit_castle()
  expect(lancelot.strength).to(equal(0))

Injection is effective only inside the dependency context. Inside the context, requests for Horse will return FakeHorse. Outside the context (after the with statement), requests for Horse will return Horse.

Special technique: "classes" that always produce the same object

with dependency_context as context:
  eric_the_horse = FakeHorse()
  context.inject_as_class(Horse, eric_the_horse)
  lancelot = Knight()
  lancelot.visit_castle()
  expect(eric_the_horse.hunger).to(equal(42))

Each time the system under test executes code like this

fresh_horse = dependency(Horse)()

fresh_horse will be the same old eric_the_horse.

Support for xUnit test pattern

Instead of using a context manager, a test can open and close its dependency context explicitly:

from pw_dependency_injector import open_dependency_context


class MyTest(TestCase):

  def setUp(self):
    self.dependencies = open_dependency_context()

  def tearDown(self):
    self.dependencies.close()

  def test_something(self):
    self.dependencies.inject(Horse, FakeHorse)
    outcome = visit_anthrax(spams=37)
    expect(outcome).to(equal('Cardinal Ximinez'))

Support for multi-threaded tests

By default, Twin Sister maintains a separate dependency context for each thread. This allows test cases with different dependency schemes to run in parallel without affecting each other.

However, it also provides a mechanism to attach a dependency context to a running thread:

my_thread = Thread(target=spam)
my_thread.start()

with dependency_context as context:
  context.attach_to_thread(my_thread)
  ...

The usual rules about context scope apply. Even if the thread continues to run, the context will disappear after the with statement ends.

Controlling time

Sometimes it is useful -- or even necessary -- for a test case to control time as its perceived by the system under test. The classic example is a routine that times out after a specified duration has elapsed. Thorough testing should cover both sides of the boundary, but it is usually undesirable or impractical to wait for the duration to elapse. That is where TimeController comes in. It's a self-contained way to inject a fake datetime.datetime:

from expects import expect, be_a, be_none
from twin_sister import TimeController

# Verify that the function times out after 24 hours
time_travel = TimeController(target=some_function_i_want_to_test)
time_travel.start()
time_travel.advance(hours=24)
sleep(0.05)  # Give target a chance to cycle
expect(time_travel.exception_caught).to(be_a(TimeoutError))

# Verify that the function does not time out before 24 hours
time_travel = TimeController(target=some_function_i_want_to_test)
time_travel.start()
time_travel.advance(hours=24 - 0.0001)
sleep(0.05)  # Give target a chance to cycle
expect(time_travel.exception_caught).to(be_none)

The example above checks for the presence or absence of an exception, but it is possible to check any state. For example, let's check the impact of a long-running bound method on its object:

time_travel = TimeController(target=thing.monitor_age)
time_travel.start()
time_travel.advance(days=30)
sleep(0.05)
expect(thing.age_in_days).to(equal(30))
time_travel.advance(days=30)
sleep(0.05)
expect(thing.age_in_days).to(equal(60))

We can also check the return value of the target function:

expected = 42
time_travel = TimeController(target=lambda: expected)
time_travel_start()
time_travel.join()
expect(time_travel.value_returned).to(equal(expected))

There are limitations. The fake datetime affects only .now() and .utcnow() at present. This may change in a future release as needs arise.

You can’t perform that action at this time.