How to Mock Services?
How to test interactions between services?​
Whenever possible, we should strive to make our functions pure, which makes testing such function easy - you just need to assert on the return value. However in larger applications there is a need for intermediate layers that delegate the work to specialized services.
For example, in a HTTP server the first layer of indirection are so called routes, whose job is to match the request and delegate the processing to downstream layers. Often below there is a second layer of indirection, so called controllers, which consist of several business logic units grouped by their domain. In a RESTful API that would be all operations on a certain model. The controller to perform its job might call on further specialized services for communicating with the database, sending email, logging, et cetera.
If the job of the capability is to call on another capability, how should we test it?
Hidden outputs​
A pure function is such a function which operates only on its inputs and produces only its output. Command-like methods, by definition are impure, as their job is to change state of the collaborating object (performing a side effect). For example:
import scala.concurrent.Future
def processEvent(event: Event): Future[Unit] = Future(println(s"Got $event"))
The signature of this method Event => Future[Unit]
hints us we're dealing with a command. It returns Unit
(well, wrapped in future, but it does
not matter here), you can't do anything useful with Unit
and it does not contain any information. It is the equivalent of returning nothing. It is
also an unreliable return type, as when Scala expects the return type to be Unit
it will discard whatever value it had (for details see
Section 6.26.1 of the Scala Language Specification), which may shadow the fact that the final value produced (and discarded) was
not the one you expected.
Inside the future there may be happening any side effects. It may open a file, print to console, connect to databases. We simply don't know. Let's have a look how this problem would be solved using ZIO's effect system:
import zio._
import zio.console.Console
def processEvent(event: Event): URIO[Console, Unit] =
console.putStrLn(s"Got $event").orDie
With ZIO, we've regained to ability to reason about the effects called. We know that processEvent
can only call on capabilities of Console
, so even though we still have Unit
as the result, we have narrowed the possible effects space to a few.
Note: this is true assuming the programmer disciplines themselves to only perform effects expressed in the type signature. There is no way (at the moment) to enforce this by the compiler. There is some research done in this space, perhaps future programming languages will enable us to further constrain side effects.
However, the same method could be implemented as:
def processEvent2(event: Event): URIO[Console, Unit] =
ZIO.unit
How can we test it did exactly what we expected it to do?
Mocking​
In this sort of situations we need mock implementations of our collaborator service. As Martin Fowler puts it in his excellent article Mocks Aren't Stubs:
Mocks are (...) objects pre-programmed with expectations which form a specification of the calls they are expected to receive.
ZIO Test provides a framework for mocking your modules.
Creating a mock service​
We'll be assuming you've read about modules and layers in the contextual types guide. In the main sources we define the service, a module alias and capability accessors. In test sources we're defining the mock object which extends zio.test.mock.Mock
which holds capability tags and compose layer.
// main sources
import zio.stream.{ ZSink, ZStream }
import zio.test.mock._
type Example = Has[Example.Service]
object Example {
trait Service {
val static : UIO[String]
def zeroArgs : UIO[Int]
def zeroArgsWithParens() : UIO[Long]
def singleArg(arg1: Int) : UIO[String]
def multiArgs(arg1: Int, arg2: Long) : UIO[String]
def multiParamLists(arg1: Int)(arg2: Long) : UIO[String]
def command(arg1: Int) : UIO[Unit]
def overloaded(arg1: Int) : UIO[String]
def overloaded(arg1: Long) : UIO[String]
def function(arg1: Int) : String
def sink(a: Int) : ZSink[Any, String, Int, Int, List[Int]]
def stream(a: Int) : ZStream[Any, String, Int]
}
}
// test sources
object ExampleMock extends Mock[Example] {
object Static extends Effect[Unit, Nothing, String]
object ZeroArgs extends Effect[Unit, Nothing, Int]
object ZeroArgsWithParens extends Effect[Unit, Nothing, Long]
object SingleArg extends Effect[Int, Nothing, String]
object MultiArgs extends Effect[(Int, Long), Nothing, String]
object MultiParamLists extends Effect[(Int, Long), Nothing, String]
object Command extends Effect[Int, Nothing, Unit]
object Overloaded {
object _0 extends Effect[Int, Nothing, String]
object _1 extends Effect[Long, Nothing, String]
}
object Function extends Method[Int, Throwable, String]
object Sink extends Sink[Any, String, Int, Int, List[Int]]
object Stream extends Stream[Any, String, Int]
val compose: URLayer[Has[Proxy], Example] = ???
}
A capability tag is just a value which extends the zio.test.mock.Capability[R <: Has[_], I, E, A]
type constructor, where:
R
is the type of environment the method belongs toI
is the type of methods input argumentsE
is the type of error it can fail withA
is the type of return value it can produce
The Capability
type is not publicly available, instead you have to extend Mock
dependent types Effect
, Method
, Sink
or Stream
.
We model input arguments according to following scheme:
- for zero arguments the type is
Unit
- for one or more arguments, regardless in how many parameter lists, the type is a
TupleN
whereN
is the size of arguments list
Note: we're using tuples to represent multiple argument methods, which follows with a limit to max 22 arguments, as is Scala itself limited.
For overloaded methods we nest a list of numbered objects, each representing subsequent overloads.
Finally we need to define a compose layer that can create our environment from a Proxy
.
A Proxy
holds the mock state and serves predefined responses to calls.
import ExampleMock._
val compose: URLayer[Has[Proxy], Example] =
ZLayer.fromServiceM { proxy =>
withRuntime.map { rts =>
new Example.Service {
val static = proxy(Static)
def zeroArgs = proxy(ZeroArgs)
def zeroArgsWithParens() = proxy(ZeroArgsWithParens)
def singleArg(arg1: Int) = proxy(SingleArg, arg1)
def multiArgs(arg1: Int, arg2: Long) = proxy(MultiArgs, arg1, arg2)
def multiParamLists(arg1: Int)(arg2: Long) = proxy(MultiParamLists, arg1, arg2)
def command(arg1: Int) = proxy(Command, arg1)
def overloaded(arg1: Int) = proxy(Overloaded._0, arg1)
def overloaded(arg1: Long) = proxy(Overloaded._1, arg1)
def function(arg1: Int) = rts.unsafeRunTask(proxy(Function, arg1))
def sink(a: Int) = rts.unsafeRun(proxy(Sink, a).catchAll(error => UIO(ZSink.fail[String, Int](error))))
def stream(a: Int) = rts.unsafeRun(proxy(Stream, a))
}
}
}
Note: The
withRuntime
helper is defined inMock
. It accesses the Runtime viaZIO.runtime
and if you're on JS platform, it will replace the executor to an unyielding one.
A reference to this layer is passed to capability tags so it can be used to automatically build environment for composed expectations on multiple services.
Note: for non-effectful capabilities you need to unsafely run the final effect to satisfy the required interface. For
ZSink
you also need to map the error into a failed sink as demonstrated above.
Complete example​
// main sources
import zio._
import zio.console.Console
import zio.test.mock._
type AccountObserver = Has[AccountObserver.Service]
object AccountObserver {
trait Service {
def processEvent(event: AccountEvent): UIO[Unit]
def runCommand(): UIO[Unit]
}
def processEvent(event: AccountEvent) =
ZIO.accessM[AccountObserver](_.get.processEvent(event))
def runCommand() =
ZIO.accessM[AccountObserver](_.get.runCommand)
val live: ZLayer[Console, Nothing, AccountObserver] =
ZLayer.fromService[Console.Service, Service] { console =>
new Service {
def processEvent(event: AccountEvent): UIO[Unit] =
for {
_ <- console.putStrLn(s"Got $event").orDie
line <- console.getStrLn.orDie
_ <- console.putStrLn(s"You entered: $line").orDie
} yield ()
def runCommand(): UIO[Unit] =
console.putStrLn("Done!").orDie
}
}
}
// test sources
object AccountObserverMock extends Mock[AccountObserver] {
object ProcessEvent extends Effect[AccountEvent, Nothing, Unit]
object RunCommand extends Effect[Unit, Nothing, Unit]
val compose: URLayer[Has[Proxy], AccountObserver] =
ZLayer.fromService { proxy =>
new AccountObserver.Service {
def processEvent(event: AccountEvent) = proxy(ProcessEvent, event)
def runCommand(): UIO[Unit] = proxy(RunCommand)
}
}
}
Note: ZIO provides some useful macros to help you generate repetitive code, see Scrapping the boilerplate with macros.
Provided ZIO services​
For each built-in ZIO service you will find their mockable counterparts in zio.test.mock
package:
MockClock
forzio.clock.Clock
MockConsole
forzio.console.Console
MockRandom
forzio.random.Random
MockSystem
forzio.system.System
Setting up expectations​
To create expectations we use the previously defined capability tags:
import zio.test.Assertion._
import zio.test.mock.Expectation._
import zio.test.mock.MockSystem
val exp01 = ExampleMock.SingleArg( // capability to build an expectation for
equalTo(42), // assertion of the expected input argument
value("bar") // result, that will be returned
)
For methods that take input, the first argument will be an assertion on input, and the second the predefined result.
In the most robust example, the result can be either a successful value or a failure. To construct either we must use
one of following combinators from zio.test.mock.Expectation
companion object:
failure[E](failure: E)
Expectation result failing withE
failureF[I, E](f: I => E)
Maps the input argumentsI
to expectation result failing withE
.failureM[I, E](f: I => IO[E, Nothing])
Effectfully maps the input argumentsI
to expectation result failing withE
.never
Expectation result computing forever.unit
Expectation result succeeding withUnit
.value[A](value: A)
Expectation result succeeding withA
.valueF[I, A](f: I => A)
Maps the input argumentsI
to expectation result succeeding withA
.valueM[I, A](f: I => IO[Nothing, A])
Effectfully maps the input argumentsI
expectation result succeeding withA
.
For methods that take no input, we only define the expected output.
val exp02 = ExampleMock.ZeroArgs(value(42))
For methods that may return Unit
, we may skip the predefined result (it will default to successful value) or use unit
helper.
import zio.test.mock.MockConsole
val exp03 = MockConsole.PutStrLn(equalTo("Welcome to ZIO!"))
val exp04 = MockConsole.PutStrLn(equalTo("Welcome to ZIO!"), unit)
For methods that may return Unit
and take no input we can skip both:
val exp05 = AccountObserverMock.RunCommand()
Finally we're all set and can create ad-hoc mock environments with our services.
import zio.test._
val event = new AccountEvent {}
val app: URIO[AccountObserver, Unit] = AccountObserver.processEvent(event)
val mockEnv: ULayer[Console] = (
MockConsole.PutStrLn(equalTo(s"Got $event"), unit) ++
MockConsole.GetStrLn(value("42")) ++
MockConsole.PutStrLn(equalTo("You entered: 42"))
)
We can combine our expectation to build complex scenarios using combinators defined in zio.test.mock.Expectation
:
andThen
(alias++
) Compose two expectations, producing a new expectation to satisfy both sequentially.and
(alias&&
) Compose two expectations, producing a new expectation to satisfy both in any order.or
(alias||
) Compose two expectations, producing a new expectation to satisfy only one of them.repeated
Repeat expectation within given bounds, produces a new expectation to satisfy itself sequentially given number of times.atLeast
Lower-bounded variant ofrepeated
, produces a new expectation to satisfy itself sequentially at least given number of times.atMost
Upper-bounded variant ofrepeated
, produces a new expectation to satisfy itself sequentially at most given number of times.optional
Alias foratMost(1)
, produces a new expectation to satisfy itself at most once.
Providing mocked environment​
object AccountObserverSpec extends DefaultRunnableSpec {
def spec = suite("processEvent")(
testM("calls putStrLn > getStrLn > putStrLn and returns unit") {
val result = app.provideLayer(mockEnv >>> AccountObserver.live)
assertM(result)(isUnit)
}
)
}
Mocking unused collaborators​
Often the dependency on a collaborator is only in some branches of the code. To test the correct behaviour of branches without depedencies, we still have to provide it to the environment, but we would like to assert it was never called. With the Mock.empty
method you can obtain a ZLayer
with an empty service (no calls expected).
object MaybeConsoleSpec extends DefaultRunnableSpec {
def spec = suite("processEvent")(
testM("expect no call") {
def maybeConsole(invokeConsole: Boolean) =
ZIO.when(invokeConsole)(console.putStrLn("foo"))
val maybeTest1 = maybeConsole(false).provideLayer(MockConsole.empty)
val maybeTest2 = maybeConsole(true).provideLayer(MockConsole.PutStrLn(equalTo("foo")))
assertM(maybeTest1)(isUnit) *> assertM(maybeTest2)(isUnit)
}
)
}
Mocking multiple collaborators​
In some cases we have more than one collaborating service being called. You can create mocks for rich environments and as you enrich the environment by using capability tags from another service, the underlaying mocked layer will be updated.
import zio.console.Console
import zio.random.Random
import zio.test.mock.MockRandom
val combinedEnv: ULayer[Console with Random] = (
MockConsole.PutStrLn(equalTo("What is your name?")) ++
MockConsole.GetStrLn(value("Mike")) ++
MockRandom.NextInt(value(42)) ++
MockConsole.PutStrLn(equalTo("Mike, your lucky number today is 42!"))
)
val combinedApp =
for {
_ <- console.putStrLn("What is your name?")
name <- console.getStrLn.orDie
num <- random.nextInt
_ <- console.putStrLn(s"$name, your lucky number today is $num!")
} yield ()
val result = combinedApp.provideLayer(combinedEnv)
assertM(result)(isUnit)
Polymorphic capabilities​
Mocking polymorphic methods is also supported, but the interface must require zio.Tag
implicit evidence for each type parameter.
// main sources
type PolyExample = Has[PolyExample.Service]
object PolyExample {
trait Service {
def polyInput[I: Tag](input: I): Task[String]
def polyError[E: Tag](input: Int): IO[E, String]
def polyOutput[A: Tag](input: Int): Task[A]
def polyAll[I: Tag, E: Tag, A: Tag](input: I): IO[E, A]
}
}
In the test sources we construct partially applied capability tags by extending Method.Poly
family. The unknown types
must be provided at call site. To produce a final monomorphic Method
tag we must use the of
combinator and pass the
missing types.
// test sources
object PolyExampleMock extends Mock[PolyExample] {
object PolyInput extends Poly.Effect.Input[Throwable, String]
object PolyError extends Poly.Effect.Error[Int, String]
object PolyOutput extends Poly.Effect.Output[Int, Throwable]
object PolyAll extends Poly.Effect.InputErrorOutput
val compose: URLayer[Has[Proxy], PolyExample] =
ZLayer.fromServiceM { proxy =>
withRuntime.map { rts =>
new PolyExample.Service {
def polyInput[I: Tag](input: I) = proxy(PolyInput.of[I], input)
def polyError[E: Tag](input: Int) = proxy(PolyError.of[E], input)
def polyOutput[A: Tag](input: Int) = proxy(PolyOutput.of[A], input)
def polyAll[I: Tag, E: Tag, A: Tag](input: I) = proxy(PolyAll.of[I, E, A], input)
}
}
}
}
Similarly, we use the same of
combinator to refer to concrete monomorphic call in our test suite when building expectations:
import PolyExampleMock._
val exp06 = PolyInput.of[String](equalTo("foo"), value("bar"))
val exp07 = PolyInput.of[Int](equalTo(42), failure(new Exception))
val exp08 = PolyInput.of[Long](equalTo(42L), value("baz"))
val exp09 = PolyAll.of[Int, Throwable, String](equalTo(42), value("foo"))
val exp10 = PolyAll.of[Int, Throwable, String](equalTo(42), failure(new Exception))
More examples​
You can find more examples in the examples
and test-tests
subproject: