Testing Actor Systems (Scala)

Testing Actor Systems (Scala)

As with any piece of software, automated tests are a very important part of the development cycle. The actor model presents a different view on how units of code are delimited and how they interact, which has an influence on how to perform tests.

Akka comes with a dedicated module akka-testkit for supporting tests at different levels, which fall into two clearly distinct categories:

  • Testing isolated pieces of code without involving the actor model, meaning without multiple threads; this implies completely deterministic behavior concerning the ordering of events and no concurrency concerns and will be called Unit Testing in the following.
  • Testing (multiple) encapsulated actors including multi-threaded scheduling; this implies non-deterministic order of events but shielding from concurrency concerns by the actor model and will be called Integration Testing in the following.

There are of course variations on the granularity of tests in both categories, where unit testing reaches down to white-box tests and integration testing can encompass functional tests of complete actor networks. The important distinction lies in whether concurrency concerns are part of the test or not. The tools offered are described in detail in the following sections.

Note

Be sure to add the module akka-testkit to your dependencies.

Unit Testing with TestActorRef

Testing the business logic inside Actor classes can be divided into two parts: first, each atomic operation must work in isolation, then sequences of incoming events must be processed correctly, even in the presence of some possible variability in the ordering of events. The former is the primary use case for single-threaded unit testing, while the latter can only be verified in integration tests.

Normally, the ActorRef shields the underlying Actor instance from the outside, the only communications channel is the actor’s mailbox. This restriction is an impediment to unit testing, which led to the inception of the TestActorRef. This special type of reference is designed specifically for test purposes and allows access to the actor in two ways: either by obtaining a reference to the underlying actor instance, or by invoking or querying the actor’s behaviour (receive). Each one warrants its own section below.

Obtaining a Reference to an Actor

Having access to the actual Actor object allows application of all traditional unit testing techniques on the contained methods. Obtaining a reference is done like this:

import akka.testkit.TestActorRef

val actorRef = TestActorRef[MyActor]
val actor = actorRef.underlyingActor

Since TestActorRef is generic in the actor type it returns the underlying actor with its proper static type. From this point on you may bring any unit testing tool to bear on your actor as usual.

Expecting Exceptions

Testing that an expected exception is thrown while processing a message sent to the actor under test can be done by using a TestActorRef receive based invocation:

import akka.testkit.TestActorRef

val actorRef = TestActorRef(new Actor {
  def receive = {
    case boom  throw new IllegalArgumentException("boom")
  }
})
intercept[IllegalArgumentException] { actorRef.receive("hello") }

Testing Finite State Machines

If your actor under test is a FSM, you may use the special TestFSMRef which offers all features of a normal TestActorRef and in addition allows access to the internal state:

import akka.testkit.TestFSMRef
import akka.actor.FSM
import akka.util.duration._

val fsm = TestFSMRef(new Actor with FSM[Int, String] {
  startWith(1, "")
  when(1) {
    case Event("go", _)  goto(2) using "go"
  }
  when(2) {
    case Event("back", _)  goto(1) using "back"
  }
})

assert(fsm.stateName == 1)
assert(fsm.stateData == "")
fsm ! "go" // being a TestActorRef, this runs also on the CallingThreadDispatcher
assert(fsm.stateName == 2)
assert(fsm.stateData == "go")

fsm.setState(stateName = 1)
assert(fsm.stateName == 1)

assert(fsm.timerActive_?("test") == false)
fsm.setTimer("test", 12, 10 millis, true)
assert(fsm.timerActive_?("test") == true)
fsm.cancelTimer("test")
assert(fsm.timerActive_?("test") == false)

Due to a limitation in Scala’s type inference, there is only the factory method shown above, so you will probably write code like TestFSMRef(new MyFSM) instead of the hypothetical ActorRef-inspired TestFSMRef[MyFSM]. All methods shown above directly access the FSM state without any synchronization; this is perfectly alright if the CallingThreadDispatcher is used (which is the default for TestFSMRef) and no other threads are involved, but it may lead to surprises if you were to actually exercise timer events, because those are executed on the Scheduler thread.

Testing the Actor’s Behavior

When the dispatcher invokes the processing behavior of an actor on a message, it actually calls apply on the current behavior registered for the actor. This starts out with the return value of the declared receive method, but it may also be changed using become and unbecome in response to external messages. All of this contributes to the overall actor behavior and it does not lend itself to easy testing on the Actor itself. Therefore the TestActorRef offers a different mode of operation to complement the Actor testing: it supports all operations also valid on normal ActorRef. Messages sent to the actor are processed synchronously on the current thread and answers may be sent back as usual. This trick is made possible by the CallingThreadDispatcher described below; this dispatcher is set implicitly for any actor instantiated into a TestActorRef.

import akka.testkit.TestActorRef
import akka.util.duration._
import akka.dispatch.Await
import akka.pattern.ask

val actorRef = TestActorRef(new MyActor)
// hypothetical message stimulating a '42' answer
val result = Await.result((actorRef ? Say42), 5 seconds).asInstanceOf[Int]
result must be(42)

As the TestActorRef is a subclass of LocalActorRef with a few special extras, also aspects like supervision and restarting work properly, but beware that execution is only strictly synchronous as long as all actors involved use the CallingThreadDispatcher. As soon as you add elements which include more sophisticated scheduling you leave the realm of unit testing as you then need to think about asynchronicity again (in most cases the problem will be to wait until the desired effect had a chance to happen).

One more special aspect which is overridden for single-threaded tests is the receiveTimeout, as including that would entail asynchronous queuing of ReceiveTimeout messages, violating the synchronous contract.

Warning

To summarize: TestActorRef overwrites two fields: it sets the dispatcher to CallingThreadDispatcher.global and it sets the receiveTimeout to None.

The Way In-Between

If you want to test the actor behavior, including hotswapping, but without involving a dispatcher and without having the TestActorRef swallow any thrown exceptions, then there is another mode available for you: just use the receive method TestActorRef, which will be forwarded to the underlying actor:

import akka.testkit.TestActorRef
system.eventStream.subscribe(testActor, classOf[UnhandledMessage])
val ref = TestActorRef[MyActor]
ref.receive(Unknown)
expectMsg(1 second, UnhandledMessage(Unknown, system.deadLetters, ref))

The above sample assumes the default behavior for unhandled messages, i.e. that the actor doesn’t swallow all messages and doesn’t override unhandled.

Use Cases

You may of course mix and match both modi operandi of TestActorRef as suits your test needs:

  • one common use case is setting up the actor into a specific internal state before sending the test message
  • another is to verify correct internal state transitions after having sent the test message

Feel free to experiment with the possibilities, and if you find useful patterns, don’t hesitate to let the Akka forums know about them! Who knows, common operations might even be worked into nice DSLs.

Integration Testing with TestKit

When you are reasonably sure that your actor’s business logic is correct, the next step is verifying that it works correctly within its intended environment (if the individual actors are simple enough, possibly because they use the FSM module, this might also be the first step). The definition of the environment depends of course very much on the problem at hand and the level at which you intend to test, ranging for functional/integration tests to full system tests. The minimal setup consists of the test procedure, which provides the desired stimuli, the actor under test, and an actor receiving replies. Bigger systems replace the actor under test with a network of actors, apply stimuli at varying injection points and arrange results to be sent from different emission points, but the basic principle stays the same in that a single procedure drives the test.

The TestKit class contains a collection of tools which makes this common task easy.

import akka.actor.ActorSystem
import akka.actor.Actor
import akka.actor.Props
import akka.testkit.TestKit
import org.scalatest.WordSpec
import org.scalatest.matchers.MustMatchers
import org.scalatest.BeforeAndAfterAll
import akka.testkit.ImplicitSender

object MySpec {
  class EchoActor extends Actor {
    def receive = {
      case x  sender ! x
    }
  }
}

class MySpec(_system: ActorSystem) extends TestKit(_system) with ImplicitSender
  with WordSpec with MustMatchers with BeforeAndAfterAll {

  def this() = this(ActorSystem("MySpec"))

  import MySpec._

  override def afterAll {
    system.shutdown()
  }

  "An Echo actor" must {

    "send back messages unchanged" in {
      val echo = system.actorOf(Props[EchoActor])
      echo ! "hello world"
      expectMsg("hello world")
    }

  }
}

The TestKit contains an actor named testActor which is the entry point for messages to be examined with the various expectMsg... assertions detailed below. When mixing in the trait ImplicitSender this test actor is implicitly used as sender reference when dispatching messages from the test procedure. The testActor may also be passed to other actors as usual, usually subscribing it as notification listener. There is a whole set of examination methods, e.g. receiving all consecutive messages matching certain criteria, receiving a whole sequence of fixed messages or classes, receiving nothing for some time, etc.

Remember to shut down the actor system after the test is finished (also in case of failure) so that all actors—including the test actor—are stopped.

Built-In Assertions

The above mentioned expectMsg is not the only method for formulating assertions concerning received messages. Here is the full list:

  • expectMsg[T](d: Duration, msg: T): T

    The given message object must be received within the specified time; the object will be returned.

  • expectMsgPF[T](d: Duration)(pf: PartialFunction[Any, T]): T

    Within the given time period, a message must be received and the given partial function must be defined for that message; the result from applying the partial function to the received message is returned. The duration may be left unspecified (empty parentheses are required in this case) to use the deadline from the innermost enclosing within block instead.

  • expectMsgClass[T](d: Duration, c: Class[T]): T

    An object which is an instance of the given Class must be received within the allotted time frame; the object will be returned. Note that this does a conformance check; if you need the class to be equal, have a look at expectMsgAllClassOf with a single given class argument.

  • expectMsgType[T: Manifest](d: Duration)

    An object which is an instance of the given type (after erasure) must be received within the allotted time frame; the object will be returned. This method is approximately equivalent to expectMsgClass(manifest[T].erasure).

  • expectMsgAnyOf[T](d: Duration, obj: T*): T

    An object must be received within the given time, and it must be equal ( compared with ==) to at least one of the passed reference objects; the received object will be returned.

  • expectMsgAnyClassOf[T](d: Duration, obj: Class[_ <: T]*): T

    An object must be received within the given time, and it must be an instance of at least one of the supplied Class objects; the received object will be returned.

  • expectMsgAllOf[T](d: Duration, obj: T*): Seq[T]

    A number of objects matching the size of the supplied object array must be received within the given time, and for each of the given objects there must exist at least one among the received ones which equals (compared with ==) it. The full sequence of received objects is returned.

  • expectMsgAllClassOf[T](d: Duration, c: Class[_ <: T]*): Seq[T]

    A number of objects matching the size of the supplied Class array must be received within the given time, and for each of the given classes there must exist at least one among the received objects whose class equals (compared with ==) it (this is not a conformance check). The full sequence of received objects is returned.

  • expectMsgAllConformingOf[T](d: Duration, c: Class[_ <: T]*): Seq[T]

    A number of objects matching the size of the supplied Class array must be received within the given time, and for each of the given classes there must exist at least one among the received objects which is an instance of this class. The full sequence of received objects is returned.

  • expectNoMsg(d: Duration)

    No message must be received within the given time. This also fails if a message has been received before calling this method which has not been removed from the queue using one of the other methods.

  • receiveN(n: Int, d: Duration): Seq[AnyRef]

    n messages must be received within the given time; the received messages are returned.

  • fishForMessage(max: Duration, hint: String)(pf: PartialFunction[Any, Boolean]): Any

    Keep receiving messages as long as the time is not used up and the partial function matches and returns false. Returns the message received for which it returned true or throws an exception, which will include the provided hint for easier debugging.

In addition to message reception assertions there are also methods which help with message flows:

  • receiveOne(d: Duration): AnyRef

    Tries to receive one message for at most the given time interval and returns null in case of failure. If the given Duration is zero, the call is non-blocking (polling mode).

  • receiveWhile[T](max: Duration, idle: Duration, messages: Int)(pf: PartialFunction[Any, T]): Seq[T]

    Collect messages as long as

    • they are matching the given partial function
    • the given time interval is not used up
    • the next message is received within the idle timeout
    • the number of messages has not yet reached the maximum

    All collected messages are returned. The maximum duration defaults to the time remaining in the innermost enclosing within block and the idle duration defaults to infinity (thereby disabling the idle timeout feature). The number of expected messages defaults to Int.MaxValue, which effectively disables this limit.

  • awaitCond(p: => Boolean, max: Duration, interval: Duration)

    Poll the given condition every interval until it returns true or the max duration is used up. The interval defaults to 100 ms and the maximum defaults to the time remaining in the innermost enclosing within block.

  • ignoreMsg(pf: PartialFunction[AnyRef, Boolean])

    ignoreNoMsg

    The internal testActor contains a partial function for ignoring messages: it will only enqueue messages which do not match the function or for which the function returns false. This function can be set and reset using the methods given above; each invocation replaces the previous function, they are not composed.

    This feature is useful e.g. when testing a logging system, where you want to ignore regular messages and are only interested in your specific ones.

Expecting Exceptions

Since an integration test does not allow to the internal processing of the participating actors, verifying expected exceptions cannot be done directly. Instead, use the logging system for this purpose: replacing the normal event handler with the TestEventListener and using an EventFilter allows assertions on log messages, including those which are generated by exceptions:

import akka.testkit.EventFilter
import com.typesafe.config.ConfigFactory

implicit val system = ActorSystem("testsystem", ConfigFactory.parseString("""
  akka.event-handlers = ["akka.testkit.TestEventListener"]
  """))
try {
  val actor = system.actorOf(Props.empty)
  EventFilter[ActorKilledException](occurrences = 1) intercept {
    actor ! Kill
  }
} finally {
  system.shutdown()
}

Timing Assertions

Another important part of functional testing concerns timing: certain events must not happen immediately (like a timer), others need to happen before a deadline. Therefore, all examination methods accept an upper time limit within the positive or negative result must be obtained. Lower time limits need to be checked external to the examination, which is facilitated by a new construct for managing time constraints:

within([min, ]max) {
  ...
}

The block given to within must complete after a Duration which is between min and max, where the former defaults to zero. The deadline calculated by adding the max parameter to the block’s start time is implicitly available within the block to all examination methods, if you do not specify it, is is inherited from the innermost enclosing within block.

It should be noted that if the last message-receiving assertion of the block is expectNoMsg or receiveWhile, the final check of the within is skipped in order to avoid false positives due to wake-up latencies. This means that while individual contained assertions still use the maximum time bound, the overall block may take arbitrarily longer in this case.

import akka.actor.Props
import akka.util.duration._

val worker = system.actorOf(Props[Worker])
within(200 millis) {
  worker ! "some work"
  expectMsg("some result")
  expectNoMsg // will block for the rest of the 200ms
  Thread.sleep(300) // will NOT make this block fail
}

Note

All times are measured using System.nanoTime, meaning that they describe wall time, not CPU time.

Ray Roestenburg has written a great article on using the TestKit: http://roestenburg.agilesquad.com/2011/02/unit-testing-akka-actors-with-testkit_12.html. His full example is also available here.

Accounting for Slow Test Systems

The tight timeouts you use during testing on your lightning-fast notebook will invariably lead to spurious test failures on the heavily loaded Jenkins server (or similar). To account for this situation, all maximum durations are internally scaled by a factor taken from the Configuration, akka.test.timefactor, which defaults to 1.

You can scale other durations with the same factor by using the implicit conversion in akka.testkit package object to add dilated function to Duration.

import akka.util.duration._
import akka.testkit._
10.milliseconds.dilated

Resolving Conflicts with Implicit ActorRef

If you want the sender of messages inside your TestKit-based tests to be the testActor simply mix in ÌmplicitSender into your test.

class MySpec(_system: ActorSystem) extends TestKit(_system) with ImplicitSender
  with WordSpec with MustMatchers with BeforeAndAfterAll {

Using Multiple Probe Actors

When the actors under test are supposed to send various messages to different destinations, it may be difficult distinguishing the message streams arriving at the testActor when using the TestKit as a mixin. Another approach is to use it for creation of simple probe actors to be inserted in the message flows. To make this more powerful and convenient, there is a concrete implementation called TestProbe. The functionality is best explained using a small example:

import akka.testkit.TestProbe
import akka.util.duration._
import akka.actor._
import akka.dispatch.Futures

  class MyDoubleEcho extends Actor {
    var dest1: ActorRef = _
    var dest2: ActorRef = _
    def receive = {
      case (d1: ActorRef, d2: ActorRef) 
        dest1 = d1
        dest2 = d2
      case x 
        dest1 ! x
        dest2 ! x
    }
  }

    val probe1 = TestProbe()
    val probe2 = TestProbe()
    val actor = system.actorOf(Props[MyDoubleEcho])
    actor ! (probe1.ref, probe2.ref)
    actor ! "hello"
    probe1.expectMsg(500 millis, "hello")
    probe2.expectMsg(500 millis, "hello")

Here a the system under test is simulated by MyDoubleEcho, which is supposed to mirror its input to two outputs. Attaching two test probes enables verification of the (simplistic) behavior. Another example would be two actors A and B which collaborate by A sending messages to B. In order to verify this message flow, a TestProbe could be inserted as target of A, using the forwarding capabilities or auto-pilot described below to include a real B in the test setup.

Probes may also be equipped with custom assertions to make your test code even more concise and clear:

case class Update(id: Int, value: String)

val probe = new TestProbe(system) {
  def expectUpdate(x: Int) = {
    expectMsgPF() {
      case Update(id, _) if id == x  true
    }
    sender ! "ACK"
  }
}

You have complete flexibility here in mixing and matching the TestKit facilities with your own checks and choosing an intuitive name for it. In real life your code will probably be a bit more complicated than the example given above; just use the power!

Replying to Messages Received by Probes

The probes keep track of the communications channel for replies, if possible, so they can also reply:

val probe = TestProbe()
val future = probe.ref ? "hello"
probe.expectMsg(0 millis, "hello") // TestActor runs on CallingThreadDispatcher
probe.sender ! "world"
assert(future.isCompleted && future.value == Some(Right("world")))

Forwarding Messages Received by Probes

Given a destination actor dest which in the nominal actor network would receive a message from actor source. If you arrange for the message to be sent to a TestProbe probe instead, you can make assertions concerning volume and timing of the message flow while still keeping the network functioning:

class Source(target: ActorRef) extends Actor {
  def receive = {
    case "start"  target ! "work"
  }
}

class Destination extends Actor {
  def receive = {
    case x  // Do something..
  }
}

  val probe = TestProbe()
  val source = system.actorOf(Props(new Source(probe.ref)))
  val dest = system.actorOf(Props[Destination])
  source ! "start"
  probe.expectMsg("work")
  probe.forward(dest)

The dest actor will receive the same message invocation as if no test probe had intervened.

Auto-Pilot

Receiving messages in a queue for later inspection is nice, but in order to keep a test running and verify traces later you can also install an AutoPilot in the participating test probes (actually in any TestKit) which is invoked before enqueueing to the inspection queue. This code can be used to forward messages, e.g. in a chain A --> Probe --> B, as long as a certain protocol is obeyed.

val probe = TestProbe()
probe.setAutoPilot(new TestActor.AutoPilot {
  def run(sender: ActorRef, msg: Any): Option[TestActor.AutoPilot] =
    msg match {
      case "stop"  None
      case x       testActor.tell(x, sender); Some(this)
    }
})

The run method must return the auto-pilot for the next message, wrapped in an Option; setting it to None terminates the auto-pilot.

Caution about Timing Assertions

The behavior of within blocks when using test probes might be perceived as counter-intuitive: you need to remember that the nicely scoped deadline as described above is local to each probe. Hence, probes do not react to each other’s deadlines or to the deadline set in an enclosing TestKit instance:

class SomeTest extends TestKit(_system: ActorSystem) with ImplicitSender {

  val probe = TestProbe()

  within(100 millis) {
    probe.expectMsg("hallo")  // Will hang forever!
  }
}

This test will hang indefinitely, because the expectMsg call does not see any deadline. Currently, the only option is to use probe.within in the above code to make it work; later versions may include lexically scoped deadlines using implicit arguments.

CallingThreadDispatcher

The CallingThreadDispatcher serves good purposes in unit testing, as described above, but originally it was conceived in order to allow contiguous stack traces to be generated in case of an error. As this special dispatcher runs everything which would normally be queued directly on the current thread, the full history of a message’s processing chain is recorded on the call stack, so long as all intervening actors run on this dispatcher.

How to use it

Just set the dispatcher as you normally would:

import akka.testkit.CallingThreadDispatcher
val ref = system.actorOf(Props[MyActor].withDispatcher(CallingThreadDispatcher.Id))

How it works

When receiving an invocation, the CallingThreadDispatcher checks whether the receiving actor is already active on the current thread. The simplest example for this situation is an actor which sends a message to itself. In this case, processing cannot continue immediately as that would violate the actor model, so the invocation is queued and will be processed when the active invocation on that actor finishes its processing; thus, it will be processed on the calling thread, but simply after the actor finishes its previous work. In the other case, the invocation is simply processed immediately on the current thread. Futures scheduled via this dispatcher are also executed immediately.

This scheme makes the CallingThreadDispatcher work like a general purpose dispatcher for any actors which never block on external events.

In the presence of multiple threads it may happen that two invocations of an actor running on this dispatcher happen on two different threads at the same time. In this case, both will be processed directly on their respective threads, where both compete for the actor’s lock and the loser has to wait. Thus, the actor model is left intact, but the price is loss of concurrency due to limited scheduling. In a sense this is equivalent to traditional mutex style concurrency.

The other remaining difficulty is correct handling of suspend and resume: when an actor is suspended, subsequent invocations will be queued in thread-local queues (the same ones used for queuing in the normal case). The call to resume, however, is done by one specific thread, and all other threads in the system will probably not be executing this specific actor, which leads to the problem that the thread-local queues cannot be emptied by their native threads. Hence, the thread calling resume will collect all currently queued invocations from all threads into its own queue and process them.

Limitations

If an actor’s behavior blocks on a something which would normally be affected by the calling actor after having sent the message, this will obviously dead-lock when using this dispatcher. This is a common scenario in actor tests based on CountDownLatch for synchronization:

val latch = new CountDownLatch(1)
actor ! startWorkAfter(latch)   // actor will call latch.await() before proceeding
doSomeSetupStuff()
latch.countDown()

The example would hang indefinitely within the message processing initiated on the second line and never reach the fourth line, which would unblock it on a normal dispatcher.

Thus, keep in mind that the CallingThreadDispatcher is not a general-purpose replacement for the normal dispatchers. On the other hand it may be quite useful to run your actor network on it for testing, because if it runs without dead-locking chances are very high that it will not dead-lock in production.

Warning

The above sentence is unfortunately not a strong guarantee, because your code might directly or indirectly change its behavior when running on a different dispatcher. If you are looking for a tool to help you debug dead-locks, the CallingThreadDispatcher may help with certain error scenarios, but keep in mind that it has may give false negatives as well as false positives.

Benefits

To summarize, these are the features with the CallingThreadDispatcher has to offer:

  • Deterministic execution of single-threaded tests while retaining nearly full actor semantics
  • Full message processing history leading up to the point of failure in exception stack traces
  • Exclusion of certain classes of dead-lock scenarios

Tracing Actor Invocations

The testing facilities described up to this point were aiming at formulating assertions about a system’s behavior. If a test fails, it is usually your job to find the cause, fix it and verify the test again. This process is supported by debuggers as well as logging, where the Akka toolkit offers the following options:

  • Logging of exceptions thrown within Actor instances

    This is always on; in contrast to the other logging mechanisms, this logs at ERROR level.

  • Logging of message invocations on certain actors

    This is enabled by a setting in the Configuration — namely akka.actor.debug.receive — which enables the loggable statement to be applied to an actor’s receive function:

import akka.event.LoggingReceive
def receive = LoggingReceive {
  case msg  // Do something...
}
.

If the abovementioned setting is not given in the Configuration, this method will pass through the given Receive function unmodified, meaning that there is no runtime cost unless actually enabled.

The logging feature is coupled to this specific local mark-up because enabling it uniformly on all actors is not usually what you need, and it would lead to endless loops if it were applied to EventHandler listeners.

  • Logging of special messages

    Actors handle certain special messages automatically, e.g. Kill, PoisonPill, etc. Tracing of these message invocations is enabled by the setting akka.actor.debug.autoreceive, which enables this on all actors.

  • Logging of the actor lifecycle

    Actor creation, start, restart, monitor start, monitor stop and stop may be traced by enabling the setting akka.actor.debug.lifecycle; this, too, is enabled uniformly on all actors.

All these messages are logged at DEBUG level. To summarize, you can enable full logging of actor activities using this configuration fragment:

akka {
  loglevel = DEBUG
  actor {
    debug {
      receive = on
      autoreceive = on
      lifecycle = on
    }
  }
}

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