I'm struggling with proper design of communication between 2 separate akka microservices/bounded contexts in akka cluster.
Lets say we have 2 microservices in cluster per node.
Both microservices are based on akka.
It's clear for me that communication within particular bounded context will be handled by sending messages from actor to actor or from actor on node1 to actor on node2 (if necessary)
Q: but is it OK to use similar communication between separate akka application? e.g. boundedContext1.actor --message--> boundedContext2.actor
or it should be done via much clearer boundaries: In bc1 raise an event - publish to broker and read the event in bc2?
//Edit currently we've implemented a service registry an we're publishing events to service registry via Akka streams.
I think there is no universal answer here.
It is like if your BCs are simple enough you can hold the BCs in one application and even in one project/library with very weak boundaries i.e. just placing them into separate namespaces and providing an API for other BCs.
But if your BCs become more complex, more independent and require its own deployment cycle then it is definitely better to construct more strong boundaries and separate microservices that communicate through message broker.
So, my answer is you should just "feel" the right way according to your particular needs. If you don't "feel" it then follow KISS principle and start with an easier way i.e. using built-in akka communication system. And if in the future your BCs will become more complex then you will have to refactor them. But this decision will be justified and it will not be an unnecessary overhead.
Related
I want to know how can we send a message to a particular actor in cluster without knowing the actual path..
For Example...
I have 5 nodes all have same actorsytem and formed a cluster.
Each node will have 2 actors in it all actors across cluster have unique name.
I have an actor system outside the cluster.which not part of this cluster.
I have to send message from this actor system to cluster actors.
How can i map respective message to respective actor in cluster each time.Without knowing the actual path of the actor.
Without cluster sharding how actors inside cluster will accessed by outside cluster.
There is a direct answer to your question (at the end), but I think you need to go back to the docs and rethink your design. One of the primary reasons for this is that while what you are asking is possible, it goes against all of the best practices and many of the features involved are deprecated or not recommended. The best practice is that clusters should be self-contained and clusters should expose via well defined APIs and not actor internals. To quote from the remoting docs:
When building an Akka application, you would usually not use the Remoting concepts directly, but instead use the more high-level Akka Cluster utilities or technology-agnostic protocols such as HTTP, gRPC etc.
Essentially the docs are tell you that what you are trying to do is a bad idea. If two actors need to be able to discover each other, they should be in the same cluster. If for some reason you can't have them in the same cluster, for maintainability reasons you should expose the actor in the cluster via REST/gRPC or some other well defined API rather than trying to allow direct access to the actors.
Similarly, in the section about cluster client the docs say:
Cluster Client is deprecated in favor of using Akka gRPC. It is not advised to build new applications with Cluster Client, and existing users should migrate.
But, here's an attempt to answer the question directly. To quote the first sentence of the Actor discovery docs, "There are two general ways to obtain Actor references: by creating actors and by discovery using the Receptionist."
Unrelated side note: (That is a little misleading though, because there are some other ways in untyped Actors, and you also can obviously just be given a ActorRef. It's not the the docs are wrong, I'm just taking them a little out of context.).
Anyway, that leads to the direct answer to your question: ClientClusterReceptionist. You can find details on how to use it in the Classic Cluster Client docs. It's going to require some untyped actors, but since you are using deprecated approaches that probably isn't an issue.
But, moreso, you really should rethink this because based on your last question, it seems like you really aren't understanding how either clustering or sharding work.
As #david-ogren said, what you need is cluster client. You can read the documentation here. With cluster client, you still need to know at least one of the cluster node address and either:
The name of the actor you're trying to communicate with, if you're trying to communicate with the actor instance directly, or
A predefined topic to publish to. If you went for this route, you will need to make sure that all of the participating actors inside the cluster subscribes to the topic to receive them.
You can see a working example in this GitHub repository.
I have been learning Akka for few days and I have a simple questions to understand it well. How should be the application architecture created for REST service which using actors? Actors should be:
A simple component (for example Service layer, DAO, controllers, etc)?
An Actor should be a buisness logic element. For example I have a business logic which should be spearate into tasks which are actors?
An Actor = microservice. It is a high level layer. Every microservice in application should work as an separate actor?
I cannot understand it in this way - how should I use actors in correct way? If I create a REST service with layers (controllers, services, DAO and database), how should I separate it as actors in Akka application?
There was a blog (likely this) that reflects my take on Akka Actors pretty well. I don't really use them.
Depends on who you talk with, some people are really into it whereas others may see it as an underlying essential which maybe isn't that useful on an application level.
I use actors for handling state. That's all. Otherwise it's Futures or Akka Streams. I hope you like the blog. If you still have questions after it, please shoot. I have 5+ years of Akka behind me. Happy to help.
I wouldn't recommend building a REST service using raw Akka actors. Actors are better used for encapsulating state and behavior. For example, the loosely-coupled lightweight actors can be used for simulating individual IoT devices (e.g. thermostats), each of which maintains its own internal state (e.g. cool setting) and adjusts/reports its settings via non-blocking message passing.
For REST API/service, you might want to consider using Play which is built on top of Akka, supporting non-blocking I/O, JSON as first-class citizen, Websockets, etc. Here's a basic example of creating a REST service using Play.
On microservice, as noted in the above link:
Building a REST API in Play does not automatically make it a
"microservice" because it does not cover larger scale concerns about
microservices such as ensuring resiliency, consistency, or monitoring.
To incorporate microservice into your REST API, consider the Lagom framework which is built on top of Play/Akka along with the reactive qualities.
I'm looking for a framework/platform to (easily) support cross microservices communication. I was guided to look into Akka with Kafka. Unfortunately I was unable to find specifically this set-up and use case, but also didn't find any specific messages that can say it will not work.
Based on articles/messages here and on other sites I've compiled set of expectation from Akka + Kafka with a few open points.
Could you please review/correct the points below?
Platform for Microservices “cluster” implementation.
Microservices “cluster” is system implemented with set of
Microservices [application]
a. Microservices interact with each other by means of AKKA messaging API. (e.g. tell, ask)
b. There can be multiple instances of Microservice
i. Q: Is mailbox shared between instances (of one microservice=Actor)?
AKKA can be connected to Kafka and use it as transport (Message
Broker), i.e. Kafka API is encapsulated into AKKA API
a. Q: What are the values from Kafka? E.g. performance, throughput, reliability. Why Akka remoting is not enough?
Messaging
a. First, some initialization is needed:
i. e.g. create Actors System, supervisor
ii. There should be some shared pre-configuration between all Microservices (Actors in the cluster). E.g. IP/URI, message types/cases, etc.
b. Worker-Actors can be added/instantiated in run time:
i. Find supervisor by name/URI
ii. Add Work-Actor as child
c. There is ability to broadcast message to all children (e.g. specified by name and wildcards) and wait for responses from all children. Hower it seems it is not "native" for Akka API and code doesn't look as clear enough (samples at stackoverlow: 1, 2)
d. There is no ability to subscribe to certain type of events i.e. define filtering conditions during subscription, in case of filtering is needed – it can be done only inside each actor in onReceive method. So, in case of broadcasting each Actor will receive message and than need to decide whether it is applicable for him or not.
e. Q: what are message tracing/debugging tools/capabilities available in Akka?
Actors execution can be “chained”: E.g. A->B->C, in this case after processing C and B control will be passed back (in case of “ask” method)
AKKA Cluster
a. Provides APIs for app lifecycle mgmt.: start all/start one/stop/scale etc.
Q: how I can support/implement graceful shutdown?
b. It has built-in monitoring capabilities (check app availability,
health etc.)
c. Q: What about Infrastructure Monitoring? Do I need to care about it?
d. Q: Is ConductR is a must or just nice to have?
Service Discovery (e.g. Eureka) is not needed, since we use reactive communications (async. Messages via Message Broker)
a. Q: What about affinity/stickiness? Do I need to care about it? What Akka can offer in this area?
b. Q: How balancing is done?
In addition, if AKKA can really work on underlying Kafka - is there good example? I found only samples with data streaming, but I need just event/message processing.
The Akka doc talks about a variety of seemingly inter-related Akka technologies without distinguishing much between them:
Akka Networking
Akka Remoting
Akka Clustering
The Akka ZeroMQ module
My understanding is that "Akka Networking" is simply a module/lib that gives Akka the ability to speak to remote actor systems over TCP. Akka Remoting is another module/lib (not contained in the core Akka JAR) that gives Akka the use of Gossip protocols. And Akka Clustering is yet another module/lib that then uses these Gossip protocols to allow remote actor systems to cluster together and sharestate changes in a viral/"service discovery"-esque manner. And my understanding of Akka ZeroMQ is that it accomplishes the same thing as Akka Clustering, except using ZeroMQ as the basis of the network connections and protocols (instead of Gossip).
So first, if my understanding of these different modules/libs are incorrect, please begin by correcting me!
Assuming I'm more or less on target here, then my main concern is that I might have Remote Actor System 1 (RAS1) using Akka Clustering (and hence Gossip) trying to communicate with Remote Actor System 2 (RAS2) which uses Akka ZeroMQ. In this case, we're using two completely different clustering technologies and protocols, so does this mean these two remote systems can't speak to each other, or does special care need to be taken so that they are compatible with each other?
Akka Remoting is what allows for one actor to speak to another actor on a different machine. For Akka Remoting to work you need to know the specific IP address (or hostname), ActorSystem name, and Actor path of the actor you want to talk to. The ActorSystem name can be different in the 2 machines.
Akka Clustering takes away the problem of having to know the specific machine you are talking to (via Cluster-aware Routing or via a Receptionist that listens for machines joining or leaving the cluster). Cluster-aware Routing also allows for things like having a minimum of X instances of an actor running on any machine in the cluster. Akka Clustering uses the Gossip protocol to maintain the list of cluster members. A cluster-enabled application must know the address of at least one host which must always be running to be able to join the cluster. There might be 2, 3 or more, but the idea is that at least one of them must always be up. Akka Clustering is built on top of Akka Remoting.
Although I haven't worked with Akka ZeroMQ, I assume it works similarly to Akka AMQP. I see it more as an alternative to Remoting, in the sense that it enables actors on different machines to talk to each other, with the advantage that none of the actors need to know any specifics about any other machines where actors are running. However, as with Remoting, you need to manually create the actors that receive the messages, whereas with Clustering the cluster takes care of it (as long as you've configured your Routers correctly).
Regarding your last question. The easiest way I can think about for a Cluster to talk to Akka ZeroMQ would be to have one (or several?) actors in the Cluster that talk to ZeroMQ actors (i.e., you can actually mix and match). Have an actor inside the Cluster that listens on the queue, and have another one that publishes the message to the queue. Sort of an Adapter pattern.
I am currently working on a Play! project that has the following architecture:
Controllers -> Services (actors) -> Models (Regular case classes)
For each request that comes in, we will issue a call to the service layers like so:
Service ? DoSomething(request, context)
We have a set number of these service actors behind an akka router that are created during app initialization, and is expandable on demand.
And in the service we mostly do modest data manipulation or database calls:
receive = {
case DoSomething(x, y) => {
...
Model.doSometing(...)
sender ! result
}
}
I am having second thoughts on whether we should be using actors for our services or just use Futures only.
We do not have any internal state that needs to be modified in the service actors, whatever message comes in goes to a function and spits out the result. Isn't this the big strength of the actor model?
We are doing a lot of tasks which seem to take a lot away from the actor model
We aren't doing heavy computation and remoting doesn't make sense because most of the work is for the database and roundtriping to a remote actor to make some db call is unnecessary
We do use reactivemongo, so every db call is non blocking. We can make a lot of these calls
It seems to me that removing akka and just use Futures makes our life a lot easier, and we don't really lose anything.
There certainly is no shortage of opinion on the topic of what should and shouldn't be an actor. Like these two posts:
http://noelwelsh.com/programming/2013/03/04/why-i-dont-like-akka-actors/
http://www.chrisstucchio.com/blog/2013/actors_vs_futures.html
I don't think you're going to find an absolute answer to this question other then that it's situational and it's really up to your preferences and your problem. What I can do for you is to offer my opinion that is based on us implementing Akka for about 2 years now.
For me, I like to think of Akka really as a platform. We come for the Actor Model but we stay for all of the other goodness that the platform provides like Clustering/Remoting, FSM, Routing, Circuit Breaker, Throttling and the like. We are trying to build an SOA like architecture with our actors acting as services. We are deploying these services across a cluster, so we are taking advantage of things like Location Transparency and Routing to provide the ability for a service consumer (which itself could be another service) to find and use a service no matter where it is deployed, and in a highly available manner. Akka makes this whole process pretty simple based on the platform tools they offer.
Within our system, we have the concept of what I call Foundation Services. These are really simple services (like basic lookup/management services for a particular entity). These services generally don't call any other services, and in some cases, just perform DB lookups. These services are pooled (router) and don't usually have any state. They are pretty similar to what you are describing some of your services to be like. We then start to build more and more complex services on top of these foundation services. Most of these services are short lived (to avoid asking), sometimes FSM based, that collect data from the foundation services and then crunch and do something as a result. Even though these foundation services are themselves pretty simple, and some would say don't require an actor, I like the flexibility in that when I compose them into a higher level service, I can look them up and they can be anywhere (location transparent) in my cluster with any number of instances available (routing) for using.
So for us, it was really a design decision to baseline around an actor as a sort of micro-like service that is available within our cluster for consumption by any other service, no matter how simple that service is. I like communicating with these services, where ever they are, through a coarse grained interface in an async manner. A lot of those principles are aspects of building out a good SOA. If that's your goal, then I think Akka can be very helpful in achieving that goal. If you are not looking to do something like that, then maybe your are right in questioning your decision to use Akka for your services. Like I said earlier, it's really up to you to figure out what you are trying to do from an architecture perspective and then design your services layer to meet those goals.
I think you're on right tracks.
We do not have any internal state that needs to be modified in the service actors, whatever message comes in goes to a function and spits out the result. Isn't this the big strength of the actor model?
I found Chris Stucchio's blog (referred to by #cmbaxter above) instantly delightful. My case was so simple that architectural considerations were not a valid point. Just Spray routing and lots of database access, like you have. No state. Thus Future. So much simpler code.
Actor should be crated when you need some really long living stuff with modifying state. In other cases there are no any benefits from actors, especially from non-typed ones.
- do pattern matching every time
- control actor's lifecycle
- remember the things which should not be passed between threads
Why do all of this when you may have simple Future?
There are tasks where actors fit very well, but not everywhere
I was wondering the same and what we decide to do was to use Akka for our data access and it works very well, it's very testable (and tested), and very portable.
We created repositories, long living actors, that we bootstrapped in our App : (FYI, we are using slick for our DB Access, but also have a similar design for our MongoDB needs)
val subscriptionRepo = context.actorOf(Props(new SubscriptionRepository(appConfig.db)), "repository-subscription")
Now we are able to send a "Request" Message for data, ex:
case class SubscriptionsRequested(atDate: ZeroMillisDateTime)
that the actor will respond with
case class SubscriptionsFound(users: Seq[UserSubscription])
or Failure(exception)
In our situation (spray apps but also CLI), we wrapped those calls in a short living actor that take the context and complete on reception and closes itself. (You could handle domain specific logic in those actor, have it to extend another actor that manages its lifecycle and exception so you would only have to specify a partial function for your needs and leave the abstract actor to deal with timeouts, common exceptions etc.
We also have situations where we needed more work to be done in the initiating actor, and it very convenient to fire x messages to your repositories and have your actor storing those messages as they arrive, doing something once they are all there, firing back for completion to the sender( for instance) and close itself.
Thanks to this design, we have a very reactive repository living outside our app, completely tested with Akka TestKit and H2, completely DB agnostic, and it's dead easy, to access data from our DBs (and we never do any ASK, only Tell : Tell to repo, tell to sender, complete or x Tells to repos, pattern match on expected results until completion, tell to sender).