Can I combine a BackoffSupervisor with a Router?
The concepts seem very similar, but it seems as though a BackoffSupervisor isn't itself a SupervisionStrategy I can give to the Router.
I can wrap the Props used by a Router in a BackoffSupervisor, so each child in the Router is actually the pair of supervisor/actor. But, this breaks the SmallestMailboxPool, probably among others, because the intermediate supervisor actor's mailbox is always empty. (it just forwards to the actor it supervises)
The goal being, of course, a Router that restarts the children with a backoff.
It isn't really possible implementing back off supervision as a supervision strategy as those needs to decide directly how to deal with failure. This means that if you want to combine that with routing where the routing strategy uses some information about the recipient mailbox you will have to implement your own router taking care of these two concerns.
Note though that the routers that does not inspect mailboxes will work fine routing to BackoffSupervisor-protected actors. (So, no problem if you use RoundRobin, Random, Broadcast, ScatterGatherFirstCompleted, TailChoppingPool or ConsistentHashing).
Related
Suppose we'd have a large number of persistent Person actors, each constructed with an identity and a name argument. What would be the best way to distribute these actors in a cluster, in such a manner that:
new actors are appointed a node by strategy X (round robin, consistent hash, etc.)
a "coordinator" actor contains a mapping from identity to ActorRef
one or more nodes can fail and the affected actors are recovered on other nodes
there is no SPF
I've considered the following, which doesn't seem to solve the problem:
Cluster sharding; all actors are initialised equally and created by coordinator
Cluster aware routing; groups or pools are fixed size and can't be modified dynamically
Sounds like you pretty much exactly are describing Akka cluster sharding and there isn't enough information to see why it would not fit.
The common solution to deal with such a design problem is to have an uninitialized state of the sharded entity where it only accepts an initialize command containing the needed values (so something like CreateUser(id, name)) and when it gets that it toggles to its "normal" behavior.
Another option could be to introduce an intermediate actor that doesn't start the actual actor until it has extracted the name value if you have no means to change the design of your Person actor.
Ofc. you could also drop down to the Akka cluster APIs directly and build something that exactly matches your use case, but handling redistribution on cluster topology change (add, remove nodes etc) is far from trivial to get right.
I think you would also come to the realisation that achieving such a tool that is entirely non-invasive for your entities without the sharding solution being tightly coupled with you business logic is very hard.
I'm currently implementing a system that that receives inbound messages from an external monitoring system. I'm translating these messages into more concise 'events', and I'm using these to alter the state of 'Managed System' objects. Akka Actors seemed like a good use case for encapsulating mutable state in concurrent applications.
The managed systems are identified by a name (99% of the time this is a hostname). Whenever a proper event is received, the system routes the message to the correct actor based on the name property. At first I used to use actorSelection and the complete paths of said actors, but that was very ugly, and I saw several people advise against relying on the fully qualified name of an actor to deliver message.
So I've set up a simple EventBus, which is great as I can now simply do:
eventBus.subscribe(subscriber1, "/managedSystem01")
eventBus.subscribe(subscriber2, "/managedSystem02")
eventBus.publish(MonitoringEvent("/managedSystem01", MonitoringMessage("managedSystem01", "N", "CPU_LOAD_HIGH", True)))
eventBus.publish(MonitoringEvent("/managedSystem02", MonitoringMessage("managedSystem02", "Y", "DISK_USAGE_HIGH", True)))
Of course, I now have the issue that, should I receive and event that concerns a managed system for which I've not spawned an actor yet (this is entirely possibly, it is impossible for me to get an absolute list of managed systems unfortunately), the message will be routed to the dead-letter mailbox.
Ideally I don't want this to happen. When it is unable to address a specific actor, I want to spawn a new one dynamically.
I suppose that, theoretically, I could subscribe to DeadLetter messages but:
That sounds a little 'hacky', since those message are essentially reserved for the system
Is it even possible to recover the original message (in my case, the MonitoringMessage) that is sent to the DeadLetter mailbox?
Alternatively is there a way to check if there are ZERO subscribers to a certain "topic"?
What you describe ("send to Actor by some identifier, if it does not exist buffer until it gets created and then deliver to that newly on-demand created Actor") is implemented in Akka as Cluster Sharding.
While it is designed primarily for sharding load (work) across a cluster, you could use it locally as well, since your requirement is essentially a scaled down (to one node) version of problem that it solves. It takes care of starting new Actors if they don't exist for a given identifier etc, so you'd simply subscribe the shard-region to the events and it'll take care of creating the actors for you.
I know, that if I use a consistent hashing group router, it will always rout to the same registered routees.
So I wrote my application, with a few routees on there own routee-nodes and a public-node with a router, which is reachable by the client.
If the client sends a message it is routed as it should be and it works fine.
Now what I want to do is, add more public-nodes with thier own router that provides the same sending/routing behavior as every other public-node.
What I mean is, that it should not matter if a client sends message XYZ to public-node A, B or C. It should always go to the same routee-node.
At first I thought that akka may provides this behavior by default, because:
I used a group and not a pool router, so everyone knows the same routees
I leared that cluster nodes could be ordered
So I assumed that the routees list is ordered and the different routers map the same keys to the same routees. But testing showed me that I was wrong.
So, is there way in akka to achieve this behavior? Thanks.
As I expected, this behavior shoud be the standart for consistent hashing group routers and it is a bug in the akka-cluster package (current version 2.3.0-RC1)
See this tickt and this google-group post for more details.
I'm new to the Akka framework and I'm building an HTTP server application on top of Netty + Akka.
My idea so far is to create an actor for each type of request. E.g. I would have an actor for a POST to /my-resource and another actor for a GET to /my-resource.
Where I'm confused is how I should go about actor creation? Should I:
Create a new actor for every request (by this I mean for every request should I do a TypedActor.newInstance() of the appropriate actor)? How expensive is it to create a new actor?
Create one instance of each actor on server start up and use that actor instance for every request? I've read that an actor can only process one message at a time, so couldn't this be a bottle neck?
Do something else?
Thanks for any feedback.
Well, you create an Actor for each instance of mutable state that you want to manage.
In your case, that might be just one actor if my-resource is a single object and you want to treat each request serially - that easily ensures that you only return consistent states between modifications.
If (more likely) you manage multiple resources, one actor per resource instance is usually ideal unless you run into many thousands of resources. While you can also run per-request actors, you'll end up with a strange design if you don't think about the state those requests are accessing - e.g. if you just create one Actor per POST request, you'll find yourself worrying how to keep them from concurrently modifying the same resource, which is a clear indication that you've defined your actors wrongly.
I usually have fairly trivial request/reply actors whose main purpose it is to abstract the communication with external systems. Their communication with the "instance" actors is then normally limited to one request/response pair to perform the actual action.
If you are using Akka, you can create an actor per request. Akka is extremely slim on resources and you can create literarily millions of actors on an pretty ordinary JVM heap. Also, they will only consume cpu/stack/threads when they actually do something.
A year ago I made a comparison between the resource consumption of the thread-based and event-based standard actors. And Akka is even better than the event-base.
One of the big points of Akka in my opinion is that it allows you to design your system as "one actor per usage" where earlier actor systems often forced you to do "use only actors for shared services" due to resource overhead.
I would recommend that you go for option 1.
Options 1) or 2) have both their drawbacks. So then, let's use options 3) Routing (Akka 2.0+)
Router is an element which act as a load balancer, routing the requests to other Actors which will perform the task needed.
Akka provides different Router implementations with different logic to route a message (for example SmallestMailboxPool or RoundRobinPool).
Every Router may have several children and its task is to supervise their Mailbox to further decide where to route the received message.
//This will create 5 instances of the actor ExampleActor
//managed and supervised by a RoundRobinRouter
ActorRef roundRobinRouter = getContext().actorOf(
Props.create(ExampleActor.class).withRouter(new RoundRobinRouter(5)),"router");
This procedure is well explained in this blog.
It's quite a reasonable option, but whether it's suitable depends on specifics of your request handling.
Yes, of course it could.
For many cases the best thing to do would be to just have one actor responding to every request (or perhaps one actor per type of request), but the only thing this actor does is to forward the task to another actor (or spawn a Future) which will actually do the job.
For scaling up the serial requests handling, add a master actor (Supervisor) which in turn will delegate to the worker actors (Children) (round-robin fashion).
I am working on my bc thesis project which should be a Minecraft server written in scala and Akka. The server should be easily deployable in the cloud or onto a cluster (not sure whether i use proper terminology...it should run on multiple nodes). I am, however, newbie in akka and i have been wondering how to implement such a thing. The problem i'm trying to figure out right now, is how to share state among actors on different nodes. My first idea was to have an Camel actor that would read tcp stream from minecraft clients and then send it to load balancer which would select a node that would process the request and then send some response to the client via tcp. Lets say i have an AuthenticationService implementing actor that checks whether the credentials provided by user are valid. Every node would have such actor(or perhaps more of them) and all the actors should have exactly same database (or state) of users all the time. My question is, what is the best approach to keep this state? I have came up with some solutions i could think of, but i haven't done anything like this so please point out the faults:
Solution #1: Keep state in a database. This would probably work very well for this authentication example where state is only represented by something like list of username and passwords but it probably wouldn't work in cases where state contains objects that can't be easily broken into integers and strings.
Solution #2: Every time there would be a request to a certain actor that would change it's state, the actor will, after processing the request, broadcast information about the change to all other actors of the same type whom would change their state according to the info send by the original actor. This seems very inefficient and rather clumsy.
Solution #3: Having a certain node serve as sort of a state node, in which there would be actors that represent the state of the entire server. Any other actor, except the actors in such node would have no state and would ask actors in the "state node" everytime they would need some data. This seems also inefficient and kinda fault-nonproof.
So there you have it. Only solution i actually like is the first one, but like i said, it probably works in only very limited subset of problems (when state can be broken into redis structures). Any response from more experienced gurus would be very appriciated.
Regards, Tomas Herman
Solution #1 could possibly be slow. Also, it is a bottleneck and a single point of failure (meaning the application stops working if the node with the database fails). Solution #3 has similar problems.
Solution #2 is less trivial than it seems. First, it is a single point of failure. Second, there are no atomicity or other ordering guarantees (such as regularity) for reads or writes, unless you do a total order broadcast (which is more expensive than a regular broadcast). In fact, most distributed register algorithms will do broadcasts under-the-hood, so, while inefficient, it may be necessary.
From what you've described, you need atomicity for your distributed register. What do I mean by atomicity? Atomicity means that any read or write in a sequence of concurrent reads and writes appears as if it occurs in single point in time.
Informally, in the Solution #2 with a single actor holding a register, this guarantees that if 2 subsequent writes W1 and then W2 to the register occur (meaning 2 broadcasts), then no other actor reading the values from the register will read them in the order different than first W1 and then W2 (it's actually more involved than that). If you go through a couple of examples of subsequent broadcasts where messages arrive to destination at different points in time, you will see that such an ordering property isn't guaranteed at all.
If ordering guarantees or atomicity aren't an issue, some sort of a gossip-based algorithm might do the trick to slowly propagate changes to all the nodes. This probably wouldn't be very helpful in your example.
If you want fully fault-tolerant and atomic, I recommend you to read this book on reliable distributed programming by Rachid Guerraoui and Luís Rodrigues, or the parts related to distributed register abstractions. These algorithms are built on top of a message passing communication layer and maintain a distributed register supporting read and write operations. You can use such an algorithm to store distributed state information. However, they aren't applicable to thousands of nodes or large clusters because they do not scale, typically having complexity polynomial in the number of nodes.
On the other hand, you may not need to have the state of the distributed register replicated across all of the nodes - replicating it across a subset of your nodes (instead of just one node) and accessing those to read or write from it, providing a certain level of fault-tolerance (only if the entire subset of nodes fails, will the register information be lost). You can possibly adapt the algorithms in the book to serve this purpose.