Akka helps you build reliable applications which make use of multiple processor cores in one machine (“scaling up”) or distributed across a computer network (“scaling out”). The key abstraction to make this work is that all interactions between your code units—actors—happen via message passing, which is why the precise semantics of how messages are passed between actors deserve their own chapter.
In order to give some context to the discussion below, consider an application which spans multiple network hosts. The basic mechanism for communication is the same whether sending to an actor on the local JVM or to a remote actor, but of course there will be observable differences in the latency of delivery (possibly also depending on the bandwidth of the network link and the message size) and the reliability. In case of a remote message send there are obviously more steps involved which means that more can go wrong. Another aspect is that local sending will just pass a reference to the message inside the same JVM, without any restrictions on the underlying object which is sent, whereas a remote transport will place a limit on the message size.
Writing your actors such that every interaction could possibly be remote is the safe, pessimistic bet. It means to only rely on those properties which are always guaranteed and which are discussed in detail below. This has of course some overhead in the actor’s implementation. If you are willing to sacrifice full location transparency—for example in case of a group of closely collaborating actors—you can place them always on the same JVM and enjoy stricter guarantees on message delivery. The details of this trade-off are discussed further below.
As a supplementary part we give a few pointers at how to build stronger reliability on top of the built-in ones. The chapter closes by discussing the role of the “Dead Letter Office”.
These are the rules for message sends (i.e. the
! method, which also underlies the
- at-most-once delivery, i.e. no guaranteed delivery
- message ordering per sender–receiver pair
The first rule is typically found also in other actor implementations while the second is specific to Akka.
When it comes to describing the semantics of a delivery mechanism, there are three basic categories:
- at-most-once delivery means that for each message handed to the mechanism, that message is delivered zero or one times; in more casual terms it means that messages may be lost.
- at-least-once delivery means that for each message handed to the mechanism potentially multiple attempts are made at delivering it, such that at least one succeeds; again, in more casual terms this means that messages may be duplicated but not lost.
- exactly-once delivery means that for each message handed to the mechanism exactly one delivery is made to the recipient; the message can neither be lost nor duplicated.
The first one is the cheapest—highest performance, least implementation overhead—because it can be done in a fire-and-forget fashion without keeping state at the sending end or in the transport mechanism. The second one requires retries to counter transport losses, which means keeping state at the sending end and having an acknowledgement mechanism at the receiving end. The third is most expensive—and has consequently worst performance—because in addition to the second it requires state to be kept at the receiving end in order to filter out duplicate deliveries.
At the core of the problem lies the question what exactly this guarantee shall mean:
- The message is sent out on the network?
- The message is received by the other host?
- The message is put into the target actor’s mailbox?
- The message is starting to be processed by the target actor?
- The message is processed successfully by the target actor?
Each one of these have different challenges and costs, and it is obvious that there are conditions under which any message passing library would be unable to comply; think for example about configurable mailbox types and how a bounded mailbox would interact with the third point, or even what it would mean to decide upon the “successfully” part of point five.
Along those same lines goes the reasoning in Nobody Needs Reliable Messaging. The only meaningful way for a sender to know whether an interaction was successful is by receiving a business-level acknowledgement message, which is not something Akka could make up on its own (neither are we writing a “do what I mean” framework nor would you want us to).
Akka embraces distributed computing and makes the fallibility of communication explicit through message passing, therefore it does not try to lie and emulate a leaky abstraction. This is a model that has been used with great success in Erlang and requires the users to design their applications around it. You can read more about this approach in the Erlang documentation (section 10.9 and 10.10), Akka follows it closely.
Another angle on this issue is that by providing only basic guarantees those use cases which do not need stronger reliability do not pay the cost of their implementation; it is always possible to add stronger reliability on top of basic ones, but it is not possible to retro-actively remove reliability in order to gain more performance.
The rule more specifically is that for a given pair of actors, messages sent directly from the first to the second will not be received out-of-order. The word directly emphasizes that this guarantee only applies when sending with the tell operator to the final destination, not when employing mediators or other message dissemination features (unless stated otherwise).
The guarantee is illustrated in the following:
This means that:
M1is delivered it must be delivered before
M2is delivered it must be delivered before
M4is delivered it must be delivered before
M5is delivered it must be delivered before
A2can see messages from
A1interleaved with messages from
- Since there is no guaranteed delivery, any of the messages may be dropped, i.e. not arrive at
It is important to note that Akka’s guarantee applies to the order in which messages are enqueued into the recipient’s mailbox. If the mailbox implementation does not respect FIFO order (e.g. a
PriorityMailbox), then the order of processing by the actor can deviate from the enqueueing order.
Please note that this rule is not transitive:
Athen sends message
M2in any order
Causal transitive ordering would imply that
M2 is never received before
M1 at actor
C (though any of them might be lost). This ordering can be violated due to different message delivery latencies when
C reside on different network hosts, see more below.
Actor creation is treated as a message sent from the parent to the child, with the same semantics as discussed above. Sending a message to an actor in a way which could be reordered with this initial creation message means that the message might not arrive because the actor does not exist yet. An example where the message might arrive too early would be to create a remote-deployed actor R1, send its reference to another remote actor R2 and have R2 send a message to R1. An example of well-defined ordering is a parent which creates an actor and immediately sends a message to it.
Please note, that the ordering guarantees discussed above only hold for user messages between actors. Failure of a child of an actor is communicated by special system messages that are not ordered relative to ordinary user messages. In particular:
Mto its parent
Child actor fails with failure
Pmight receive the two events either in order
The reason for this is that internal system messages has their own mailboxes therefore the ordering of enqueue calls of a user and system message cannot guarantee the ordering of their dequeue times.
Relying on the stronger reliability in this section is not recommended since it will bind your application to local-only deployment: an application may have to be designed differently (as opposed to just employing some message exchange patterns local to some actors) in order to be fit for running on a cluster of machines. Our credo is “design once, deploy any way you wish”, and to achieve this you should only rely on The General Rules.
The Akka test suite relies on not losing messages in the local context (and for non-error condition tests also for remote deployment), meaning that we actually do apply the best effort to keep our tests stable. A local
tell operation can however fail for the same reasons as a normal method call can on the JVM:
In addition, local sends can fail in Akka-specific ways:
- if the mailbox does not accept the message (e.g. full BoundedMailbox)
- if the receiving actor fails while processing the message or is already terminated
While the first is clearly a matter of configuration the second deserves some thought: the sender of a message does not get feedback if there was an exception while processing, that notification goes to the supervisor instead. This is in general not distinguishable from a lost message for an outside observer.
Assuming strict FIFO mailboxes the aforementioned caveat of non-transitivity of the message ordering guarantee is eliminated under certain conditions. As you will note, these are quite subtle as it stands, and it is even possible that future performance optimizations will invalidate this whole paragraph. The possibly non-exhaustive list of counter-indications is:
- Before receiving the first reply from a top-level actor, there is a lock which protects an internal interim queue, and this lock is not fair; the implication is that enqueue requests from different senders which arrive during the actor’s construction (figuratively, the details are more involved) may be reordered depending on low-level thread scheduling. Since completely fair locks do not exist on the JVM this is unfixable.
- The same mechanism is used during the construction of a Router, more precisely the routed ActorRef, hence the same problem exists for actors deployed with Routers.
- As mentioned above, the problem occurs anywhere a lock is involved during enqueueing, which may also apply to custom mailboxes.
This list has been compiled carefully, but other problematic scenarios may have escaped our analysis.
The rule that for a given pair of actors, messages sent directly from the first to the second will not be received out-of-order holds for messages sent over the network with the TCP based Akka remote transport protocol.
As explained in the previous section local message sends obey transitive causal ordering under certain conditions. This ordering can be violated due to different message delivery latencies. For example:
Aon node-1 sends message
Aon node-1 then sends message
Bon node-2 forwards message
M2in any order
It might take longer time for
M1 to “travel” to node-3 than it takes for
M2 to “travel” to node-3 via node-2.
Based on a small and consistent tool set in Akka’s core, Akka also provides powerful, higher-level abstractions on top it.
As discussed above a straight-forward answer to the requirement of reliable delivery is an explicit ACK–RETRY protocol. In its simplest form this requires
- a way to identify individual messages to correlate message with acknowledgement
- a retry mechanism which will resend messages if not acknowledged in time
- a way for the receiver to detect and discard duplicates
The third becomes necessary by virtue of the acknowledgements not being guaranteed to arrive either. An ACK-RETRY protocol with business-level acknowledgements is supported by At-Least-Once Delivery of the Akka Persistence module. Duplicates can be detected by tracking the identifiers of messages sent via At-Least-Once Delivery. Another way of implementing the third part would be to make processing the messages idempotent on the level of the business logic.
Event sourcing (and sharding) is what makes large websites scale to billions of users, and the idea is quite simple: when a component (think actor) processes a command it will generate a list of events representing the effect of the command. These events are stored in addition to being applied to the component’s state. The nice thing about this scheme is that events only ever are appended to the storage, nothing is ever mutated; this enables perfect replication and scaling of consumers of this event stream (i.e. other components may consume the event stream as a means to replicate the component’s state on a different continent or to react to changes). If the component’s state is lost—due to a machine failure or by being pushed out of a cache—it can easily be reconstructed by replaying the event stream (usually employing snapshots to speed up the process). Event sourcing is supported by Akka Persistence.
By implementing a custom mailbox type it is possible to retry message processing at the receiving actor’s end in order to handle temporary failures. This pattern is mostly useful in the local communication context where delivery guarantees are otherwise sufficient to fulfill the application’s requirements.
Please note that the caveats for The Rules for In-JVM (Local) Message Sends do apply.
Messages which cannot be delivered (and for which this can be ascertained) will be delivered to a synthetic actor called
/deadLetters. This delivery happens on a best-effort basis; it may fail even within the local JVM (e.g. during actor termination). Messages sent via unreliable network transports will be lost without turning up as dead letters.
The main use of this facility is for debugging, especially if an actor send does not arrive consistently (where usually inspecting the dead letters will tell you that the sender or recipient was set wrong somewhere along the way). In order to be useful for this purpose it is good practice to avoid sending to deadLetters where possible, i.e. run your application with a suitable dead letter logger (see more below) from time to time and clean up the log output. This exercise—like all else—requires judicious application of common sense: it may well be that avoiding to send to a terminated actor complicates the sender’s code more than is gained in debug output clarity.
The dead letter service follows the same rules with respect to delivery guarantees as all other message sends, hence it cannot be used to implement guaranteed delivery.
An actor can subscribe to class
akka.actor.DeadLetter on the event stream, see Event Stream for how to do that. The subscribed actor will then receive all dead letters published in the (local) system from that point onwards. Dead letters are not propagated over the network, if you want to collect them in one place you will have to subscribe one actor per network node and forward them manually. Also consider that dead letters are generated at that node which can determine that a send operation is failed, which for a remote send can be the local system (if no network connection can be established) or the remote one (if the actor you are sending to does not exist at that point in time).
Every time an actor does not terminate by its own decision, there is a chance that some messages which it sends to itself are lost. There is one which happens quite easily in complex shutdown scenarios that is usually benign: seeing a
akka.dispatch.Terminate message dropped means that two termination requests were given, but of course only one can succeed. In the same vein, you might see
akka.actor.Terminated messages from children while stopping a hierarchy of actors turning up in dead letters if the parent is still watching the child when the parent terminates.