I'm working on a web application written in Scala, using the Play! framework and Akka. The code is organized basically like this: Play controllers send messages to Akka actors. The actors, in turn, talk to a persistence layer, that abstracts database access. A typical example of usage of these components in the application:
class OrderController(orderActor: ActorRef) extends Controller {
def showOrders(customerId: Long) = {
implicit request => Async {
val futureOrders = orderActor ? FindOrdersByCustomerId(id)
// Handle the result, showing the orders list to the user or showing an error message.
}
}
}
object OrderActor extends Actor {
def receive = {
case FindOrdersByCustomerId(id) =>
sender ! OrderRepository.findByCustomerId(id)
case InsertOrder(order) =>
sender ! OrderRepository.insert(order)
//Trigger some notification, like sending an email. Maybe calling another actor.
}
}
object OrderRepository {
def findByCustomerId(id: Long): Try[List[Order]] = ???
def insert(order: Order): Try[Long] = ???
}
As you can see, this is the basic CRUD pattern, much like what you would see in other languages and frameworks. A query gets passed down to the layers below and, when the application gets a result from the database, that result comes back up until it reaches the UI. The only relevant difference is the use of actors and asynchronous calls.
Now, I'm very new to the concept of actors, so I don't quite get it yet. But, from what I've read, this is not how actors are supposed to be used. Observe, though, that in some cases (e.g. sending an email when an order is inserted) we do need true asynchronous message passing.
So, my question is: is it a good idea to use actors in this way? What are the alternatives for writing CRUD applications in Scala, taking advantage of Futures and the other concurrency capabilities of Akka?
Although actor based concurrency doesn't fit with transactional operations out of the box but that doesn't stop you from using actors that way if you play nicely with the persistence layer. If you can guarantee that the insert ( write ) is atomic then you can safely have a pool of actors doing it for you. Normally databases have a thread safe read so find should also work as expected. Apart from that if the insert is not threadsafe, you can have one single WriteActor dedicated simply for write operations and the sequential processing of messages will ensure atomicity for you.
One thing to be aware of is that an actor process one message at a time, which would be rather limiting in this case. You can use a pool of actors by using routers.
Your example define a blocking api of the repository, which might be the only thing you can do, depending on your database driver. If possible you should go for an async api there also, i.e. returning Futures. In the actor you would then instead pipe the result of the Future to the sender.
Related
In Lagom, what do you do when a command handler must perform some asynchronous operations? For example:
override def behavior = Actions().onCommand[MyCommand, Done] {
case (cmd, ctx, state) =>
// some complex code that performs asynchronous operations
// (for example, querying other persistent entities or the read-side
// by making calls that return Future[...] and composing those),
// as summarized in a placeholder below:
val events: Future[Seq[Event]] = ???
events map {
xs => ctx.thenPersistAll(xs: _*) { () => ctx.reply(Done) }
}
}
The problem with the code like that is that the compiler expects the command handler to return Persist, not Future[Persist].
Is this done on purpose, to make sure that the events are persisted in the correct order (that is, the events generated by a prior command must be saved before the events generated by a later command)? But can't that be handled by proper management of the event offsets, so that the journal always orders them correctly, regardless of when they are actually saved?
And what does one do in situations like this, when the command handling is complex enough to require making asynchronous calls from the command handler?
There is a similar question on the mailing list with an answer from James.
https://groups.google.com/forum/?utm_medium=email&utm_source=footer#!topic/lagom-framework/Z6lynjNTqgE
In short, your entity in a CQRS application is a consistency boundary and should only depend on data that it's immediately available inside it, not outside (no call to external services).
What you are probably looking for a what it's called Command Enrichment. You receive an request, collect some data from external services and build a command containing everything you need to send to your Entity.
You certainly should not query the read-side to make business decisions inside your write-side entity. You also should not make business decisions on data coming from other entities.
Your entity should be able to make all the decision because it is the consistency boundary of your model.
What I've been doing in these cases is to pass the PersistentEntityRef to the asynchronous operation, so that it can issue commands to the entity and its those command-handlers (not the one that spawned the async computation) that then persist the events.
Just bear in mind that none of this is atomic, so you have to think about what happens if the asynchronous operation fails midway through issuing the commands or if some commands succeed and some fail, etc. Presumably you'll want some retry mechanism for systemic failures. If you build your command-handlers to be idempotent, it will help you deal with the duplicates.
I'm fairly new to both Scala and Akka and I'm trying to figure out how you would create a proper domain model, which also is an Actor.
Let's imagine we have a simple business case where you can open a new Bank Account. Let's say that one of the rules is that you can only create one bank account per last name (not realistic, but just for the sake of simplicity). My first approach, without applying any business rules, would look something like this:
object Main {
def main(args: Array[String]): Unit = {
implicit val system = ActorSystem("accout")
implicit val materializer = ActorMaterializer()
implicit val executionContext = system.dispatcher
val account = system.actorOf(Props[Account])
account ! CreateAccount("Doe")
}
}
case class CreateAccount(lastName: String)
class Account extends Actor {
var lastName: String = null
override def receive: Receive = {
case createAccount: CreateAccount =>
this.lastName = lastName
}
}
Eventually you would persist this data somewhere. However, when adding the rule that there can only be one Bank Account per last name, a query to some data storage needs to be done. Let's say we put that logic inside a repository and the repository eventually returns an Account, we get to the problem where Account isn't an Actor anymore, since the repository won't be able to create Actors.
This is definitely a wrong implementation and not how Actors should be used. My question is, what are ways to solve these kind of problems? I am aware that my knowledge of Akka is not on a decent level yet, so it might be a weird/stupid formulated question.
This might be a long answer and I am sorry there isn't a TLDR version. :)
Ok, so you want to "Actorize" your domain model? Bad idea. Domain models are not necessarily actors. Sometimes they are but often they are not. It would be an anti-pattern to deploy one actor per domain model because if you do that you are simply offloading the method calling to message calling but losing all of the single threaded paradigm of the method calling. You cannot guarantee the timing of the messages hitting your actor and programming based upon ASK patterns is a good way to introduce a system that is not scalable, eventually you have too many threads and too many futures and cant proceed further, the system bogs and chokes. So what does that mean for your particular problem?
First you have to stop thinking of the domain model as a single thing and definitely stop using POJO entities. I entirely agree with Martin Fowler when he discusses the anemic domain model. In a well built actor system there will often be three domain models. One is the persisted model which has entities that model your database. The second is the immutable model. This is the model that the actors use to communicate with each other. All the entities are immutable from the bottom up, all collections unmodifiable, all objects only have getters, all constructors copy the collections to new immutable collections. The immutable model means your actors never have to copy anything, they just pass around references to data. Lastly you will have the API model, this is usually the set of entities that model the JSON for the clients to consume. The API model is there to insulate the back end from client code changes and vice versa, its the contract between the systems.
To create your actors stop thinking about your persistent model and what you will do with it but instead start thinking of the use cases. What does your system have to do? Model your actors based on the use cases and that will change the implementation of the actors and their deployment strategies.
For example, consider a server that delivers inventory information to users including current stock levels, reviews by users and so on for products by a single vendor. The users hammer this information and it changes quickly as stock levels change. This information is likely stored in half a dozen different tables. We don't model an actor for each table but rather a single actor to serve this use case. In this case this information is accessed by a large group of people in heavy load environment. So we are best creating an actor to aggregate all of this data and replicating the actor to each node and whenever the data changes we inform all replicants on all nodes of the changes. This means the user getting the overview doesn't even touch the database. They hit the actors, get the immutable model, convert that to the API model and then return the data.
On the other hand if a user wants to change the stock levels, we need to make sure that two users don't do it concurrently yet large DB transactions slows down the system massively. So instead we pick one node that will hold the stock management actor for that vendor and we cluster shard the actor. Any requests are routed to that actor and handled serially. The company user logs in and notes the receipt of a delivery of 20 new items. The message goes from whatever node they hit to the node holding the actor for that vendor, the vendor then makes the appropriate database changes and the broadcasts the change which is picked up by all the replicated inventory view actors to change their data.
Now this is simplistic because you have to deal with lost messages (read the articles on why reliable messaging is not necessary). However once you start to go down that road you soon realize that simply making your domain model an actor system is an anti-pattern and there are better ways to do things.
Anyway that is my 2 cents :)
General Design
Actors should generally be simple dispatchers to business logic and contain as little functionality as possible. Think of Actors as similar to a Future; when you want concurrency in scala you don't extend the Future class, you just use Future functionality around your existing logic.
Limiting your Actors to bare-bones responsibility has several advantages:
Testing the code can be done without having to construct ActorSystems, probes, ActorRefs, etc...
The business logic can easily be transplanted to other asynchronous libraries, e.g. Futures and akka streams.
It's easier to create a "proper domain model" with plain old classes and functions than it is with Actors.
Placing business logic in Actors naturally emphasizes a more object oriented code/system design rather than a functional approach (we picked scala for a reason).
Business Logic (No Akka)
Here we will setup all of the domain specific logic without using any akka related "stuff".
object BusinessLogicDomain {
type FirstName = String
type LastName = String
type Balance = Double
val defaultBalance : Balance = 0.0
case class Account(firstName : FirstName,
lastName : LastName,
balance : Balance = defaultBalance)
Lets model your account directory as a HashMap:
type AccountDirectory = HashMap[LastName, Account]
val emptyDirectory : AccountDirectory = HashMap.empty[LastName, Account]
We can now create a function that matches your requirements for distinct account per last name:
val addAccount : (AccountDirectory, Account) => AccountDirectory =
(accountDirectory, account) =>
if(accountDirectory contains account.lastName)
accountDirectory
else
accountDirectory + (account.lastName -> account)
}//end object BusinessLogicDomain
Repository (Akka)
Now that the unpolluted business code is complete, and isolated, we can add the concurrency layer on top of the foundational logic.
We can use the become functionality of Actors to store the state and respond to requests:
import BusinessLogicDomain.{Account, AccountDirectory, emptyDirectory, addAccount}
case object QueryAccountDirectory
class RepoActor(accountDirectory : AccountDirectory = emptyDirectory) extends Actor {
val statefulReceive : AccountDirectory => Receive =
currentDirectory => {
case account : Account =>
context become statefulReceive(addAccount(currentDirectory, account))
case QueryAccountDirectory =>
sender ! currentDirectory
}
override def receive : Receive = statefulReceive(accountDirectory)
}
When it comes to creating a REST web service with 60+ API on akka http. How can I choose whether I should go with akka streams or akka actors?
In his post, Jos shows two ways to create an API on akka http but he doesn't show when I should select one over the other.
This is a difficult question. Obviously, both approaches work. So to a certain degree it is a matter of taste/familiarity. So everything following now is just my personal opinion.
When possible, I prefer using akka-stream due to its more high-level nature and type safety. But whether this is a viable approach depends very much on the task of the REST API.
Akka-stream
If your REST API is a service that e.g. answers questions based on external data (e.g. a currency exchange rate API), it is preferable to implement it using akka-stream.
Another example where akka-stream would be preferable would be some kind of database frontend where the task of the REST API is to parse query parameters, translate them into a DB query, execute the query and translate the result according to the content-type requested by the user. In both cases, the data flow maps easily to akka-stream primitives.
Actors
An example where using actors would be preferable might be if your API allows querying and updating a number of persistent actors on a cluster. In that case either a pure actor-based solution or a mixed solution (parsing query parameters and translating results using akka-stream, do the rest using actors) might be preferable.
Another example where an actor-based solution might be preferable would be if you have a REST API for long-running requests (e.g. websockets), and want to deploy the processing pipeline of the REST API itself on a cluster. I don't think something like this is currently possible at all using akka-stream.
Summary
So to summarize: look at the data flow of each API and see if it maps cleanly to the primitives offered by akka-stream. If this is the case, implement it using akka-stream. Otherwise, implement using actors or a mixed solution.
Don't Forget Futures!
One addendum I would make to Rudiger Klaehn's fine answer is to also consider the use case of a Future. The composability of Futures and resource management of ExecutionContext make Futures ideal for many, if not most, situations.
There is an excellent blog post describing when Futures are a better choice than Actors. Further, the back-pressure provided by Streams comes with some pretty hefty overhead.
Just because you're down the rabbit hole using akka-http does not mean all concurrency within your request handler has to be confined to Actors or Streams.
Route
Route inherently accomodates Futures in the type definition:
type Route = (RequestContext) ⇒ Future[RouteResult]
Therefore you can bake a Future directly into your Route using only functions and Futures, no Directives:
val requestHandler : RequestContext => HttpResponse = ???
val route : Route =
(requestContext) => Future(requestHandler(requestContext)) map RouteResult.Complete
onComplete Directive
The onComplete Directive allows you to "unwrap" a Future within your Route:
val route =
get {
val future : Future[HttpResponse] = ???
onComplete(future) {
case Success(httpResponse) => complete(httpResponse)
case Failure(exception) => complete(InternalServerError -> exception.toString)
}
}
I am designing a backend using CQRS + Event sourcing, using Akka + Scala. I am not sure about how to handle a growing state. For instance, I will have a growing list of users. To my understanding, each user will be created following a UserCreated event, such events will be replayed by the PersistentActor, and the users will be stored in a collection. Something like:
class UsersActor extends PersistentActor {
override def persistenceId = ....
private case class UsersState(users: List[User])
private var state = UsersState()
....
}
Obviously such state would eventually grow too big to be held in memory by this actor, so I guess I'm doing something wrong.
I found this example project: the idea seems that each user should be held by a different actor, and loaded (from the event history) as needed.
What is the right way to do this? Thanks a lot.
The answer is: each aggregate/entity (in my example, each User) gets its own actor, which embeds the state for that particular entity and that one only.
Newbie in Scala here.
I'm doing a simple project in Scala with a simple web service, so I don't want to use a full blown db.
All my application is immutable. But I don't know how to do the "data" part.
My web service has GET and some POST and PUT. So I'd like my data to be:
- stored in memory
- thread safe (so many concurrent PUT won't mess it
- immmutable? (I'm new to the concept, but I guess is not possible)
I thought of an object like:
object UserContainer {
var users: List[User] = initializeUsers()
def editUser(...) = ...
def addUser(...) = ...
}
But it's not thread safe, neither immutable. I found that I can use "Actors" or "synchronize" for this.
How should I approach?
The primary concurrency abstraction provided by Actors is to provide threadsafe access to mutable state in a asynchronous and non-blocking fashion. Looks like that is your use case.
Some options for building build web services you should look at Spray.io or akka-http - both of which are based on Akka actors. However, creating actors (esp. using Akka) needs an ActorSystem which some argue is heavyweight. Additionally, actors (as of today) are not typed, so you don't get the type safety of Scala.