The Live trait provides access to the "live" environment from within the
mock environment for effects such as printing test results to the console or
timing out tests where it is necessary to access the real environment.
The Live trait provides access to the "live" environment from within the
mock environment for effects such as printing test results to the console or
timing out tests where it is necessary to access the real environment.
The easiest way to access the "live" environment is to use the live method
with an effect that would otherwise access the mock environment.
import zio.clock import zio.test.mock._ val realTime = live(clock.nanoTime)
The withLive method can be used to apply a transformation to an effect
with the live environment while ensuring that the effect itself still runs
with the mock environment, for example to time out a test. Both of these
methods are re-exported in the mock package for easy availability.
MockClock makes it easy to deterministically and efficiently test effects
involving the passage of time.
MockClock makes it easy to deterministically and efficiently test effects
involving the passage of time.
Instead of waiting for actual time to pass, sleep and methods implemented
in terms of it schedule effects to take place at a given clock time. Users
can adjust the clock time using the adjust and setTime methods, and all
effects scheduled to take place on or before that time will automically be
run.
For example, here is how we can test ZIO.timeout using MockClock:
import zio.ZIO import zio.duration._ import zio.test.mock.MockClock for { fiber <- ZIO.sleep(5.minutes).timeout(1.minute).fork _ <- MockClock.adjust(1.minute) result <- fiber.join } yield result == None
Note how we forked the fiber that sleep was invoked on. Calls to sleep
and methods derived from it will semantically block until the time is
set to on or after the time they are scheduled to run. If we didn't fork the
fiber on which we called sleep we would never get to set the the time on the
line below. Thus, a useful pattern when using MockClock is to fork the
effect being tested, then adjust the clock to the desired time, and finally
verify that the expected effects have been performed.
Sleep and related combinators schedule events to occur at a specified
duration in the future relative to the current fiber time (e.g. 10 seconds
from the current fiber time). The fiber time is backed by a FiberRef and
is incremented for the duration each fiber is sleeping. Child fibers inherit
the fiber time of their parent so methods that rely on repeated sleep
calls work as you would expect.
For example, here is how we can test an effect that recurs with a fixed delay:
import zio.Queue import zio.duration._ import zio.test.mock.MockClock for { q <- Queue.unbounded[Unit] _ <- (q.offer(()).delay(60.minutes)).forever.fork a <- q.poll.map(_.isEmpty) _ <- MockClock.adjust(60.minutes) b <- q.take.as(true) c <- q.poll.map(_.isEmpty) _ <- MockClock.adjust(60.minutes) d <- q.take.as(true) e <- q.poll.map(_.isEmpty) } yield a && b && c && d && e
Here we verify that no effect is performed before the recurrence period, that an effect is performed after the recurrence period, and that the effect is performed exactly once. The key thing to note here is that after each recurrence the next recurrence is scheduled to occur at the appropriate time in the future, so when we adjust the clock by 60 minutes exactly one value is placed in the queue, and when we adjust the clock by another 60 minutes exactly one more value is placed in the queue.
MockConsole provides a testable interface for programs interacting with
the console by modeling input and output as reading from and writing to
intput and output buffers maintained by MockConsole and backed by a Ref.
MockConsole provides a testable interface for programs interacting with
the console by modeling input and output as reading from and writing to
intput and output buffers maintained by MockConsole and backed by a Ref.
All calls to putStr and putStrLn using the MockConsole will write the
string to the output buffer and all calls to getStrLn will take a string
from the input buffer. No actual printing or reading from the console will
occur. MockConsole has several methods to access and manipulate the
content of these buffers including feedLines to feed strings to the input
buffer that will then be returned by calls to getStrLn, output to get
the content of the output buffer from calls to putStr and putStrLn, and
clearInput and clearOutput to clear the respective buffers.
Together, these functions make it easy to test programs interacting with the console.
import zio.console._ import zio.test.mock._ import zio.ZIO val sayHello = for { name <- getStrLn _ <- putStrLn("Hello, " + name + "!") } yield () for { _ <- MockConsole.feedLines("John", "Jane", "Sally") _ <- ZIO.collectAll(List.fill(3)(sayHello)) result <- MockConsole.output } yield result == Vector("Hello, John!\n", "Hello, Jane!\n", "Hello, Sally!\n")
MockRandom allows for deterministically testing effects involving
randomness.
MockRandom allows for deterministically testing effects involving
randomness.
MockRandom operates in two modes. In the first mode, MockRandom is a
purely functional pseudo-random number generator. It will generate
pseudo-random values just like scala.util.Random except that no internal
state is mutated. Instead, methods like nextInt describe state transitions
from one random state to another that are automatically composed together
through methods like flatMap. The random seed can be set using setSeed
and MockRandom is guaranteed to return the same sequence of values for any
given seed. This is useful for deterministically generating a sequence of
pseudo-random values and powers the property based testing functionality in
ZIO Test.
In the second mode, MockRandom maintains an internal buffer of values that
can be "fed" with methods such as feedInts and then when random values of
that type are generated they will first be taken from the buffer. This is
useful for verifying that functions produce the expected output for a given
sequence of "random" inputs.
import zio.random._ import zio.test.mock.MockRandom for { _ <- MockRandom.feedInts(4, 5, 2) x <- random.nextInt(6) y <- random.nextInt(6) z <- random.nextInt(6) } yield x + y + z == 11
MockRandom will automatically take values from the buffer if a value of
the appropriate type is available and otherwise generate a pseudo-random
value, so there is nothing you need to do to switch between the two modes.
Just generate random values as you normally would to get pseudo-random
values, or feed in values of your own to get those values back. You can also
use methods like clearInts to clear the buffer of values of a given type
so you can fill the buffer with new values or go back to pseuedo-random
number generation.
MockSystem supports deterministic testing of effects involving system
properties.
MockSystem supports deterministic testing of effects involving system
properties. Internally, MockSystem maintains mappings of environment
variables and system properties that can be set and accessed. No actual
environment variables or system properties will be accessed or set as a
result of these actions.
import zio.system import zio.test.mock._ for { _ <- MockSystem.putProperty("java.vm.name", "VM") result <- system.property("java.vm.name") } yield result == Some("VM")
Provides an effect with the "real" environment as opposed to the mock environment.
Provides an effect with the "real" environment as opposed to the mock environment. This is useful for performing effects such as timing out tests, accessing the real time, or printing to the real console.
A managed version of the MockEnvironment containing testable versions of
all the standard ZIO environmental effects.
Transforms this effect with the specified function.
Transforms this effect with the specified function. The mock environment will be provided to this effect, but the live environment will be provided to the transformation function. This can be useful for applying transformations to an effect that require access to the "real" environment while ensuring that the effect itself uses the mock environment.
withLive(test)(_.timeout(duration))
The
mockpackage contains testable versions of all the standard ZIO environment types through the MockClock, MockConsole, MockSystem, and MockRandom modules. See the documentation on the individual modules for more detail about using each of them.If you are using ZIO Test and extending
DefaultRunnableSpecaMockEnvironmentcontaining all of them will be automatically provided to each of your tests. Otherwise, the easiest way to use the mocking functionality in ZIO Test is by providing theMockEnvironmentto your program.import zio.test.mock._ myProgram.provideManaged(mockEnvironmentManaged)Then all environmental effects, such as printing to the console or generating random numbers, will be implemented by the
MockEnvironmentand will be fully testable. When you do need to access the "live" environment, for example to print debugging information to the close, just use thelivecombinator along with the effect as your normally would.If you are only interested in one of the mocking modules for your application, you can also access them a la carte through the
makemethod on each module. Each mock module requires some data on initialization. Default data is included for each asDefaultData.import zio.test.mock._ myProgram.provideM(MockConsole.make(MockConsole.DefaultData))Finally, you can create a
Mockobject that implements the mock interface directly using themakeMockmethod. This can be useful when you want to access some mocking functionality without using the environment type.This can also be useful when you are creating a more complex environment to provide the implementation for mock services that you mix in.