If I wanted to port a Go library that uses Goroutines, would Scala be a good choice because its inbox/akka framework is similar in nature to coroutines?
Nope, they're not. Goroutines are based on the theory of Communicating Sequential Processes, as specified by Tony Hoare in 1978. The idea is that there can be two processes or threads that act independently of one another but share a "channel," which one process/thread puts data into and the other process/thread consumes. The most prominent implementations you'll find are Go's channels and Clojure's core.async, but at this time they are limited to the current runtime and cannot be distributed, even between two runtimes on the same physical box.
CSP evolved to include a static, formal process algebra for proving the existence of deadlocks in code. This is a really nice feature, but neither Goroutines nor core.async currently support it. If and when they do, it will be extremely nice to know before running your code whether or not a deadlock is possible. However, CSP does not support fault tolerance in a meaningful way, so you as the developer have to figure out how to handle failure that can occur on both sides of channels, and such logic ends up getting strewn about all over the application.
Actors, as specified by Carl Hewitt in 1973, involve entities that have their own mailbox. They are asynchronous by nature, and have location transparency that spans runtimes and machines - if you have a reference (Akka) or PID (Erlang) of an actor, you can message it. This is also where some people find fault in Actor-based implementations, in that you have to have a reference to the other actor in order to send it a message, thus coupling the sender and receiver directly. In the CSP model, the channel is shared, and can be shared by multiple producers and consumers. In my experience, this has not been much of an issue. I like the idea of proxy references that mean my code is not littered with implementation details of how to send the message - I just send one, and wherever the actor is located, it receives it. If that node goes down and the actor is reincarnated elsewhere, it's theoretically transparent to me.
Actors have another very nice feature - fault tolerance. By organizing actors into a supervision hierarchy per the OTP specification devised in Erlang, you can build a domain of failure into your application. Just like value classes/DTOs/whatever you want to call them, you can model failure, how it should be handled and at what level of the hierarchy. This is very powerful, as you have very little failure handling capabilities inside of CSP.
Actors are also a concurrency paradigm, where the actor can have mutable state inside of it and a guarantee of no multithreaded access to the state, unless the developer building an actor-based system accidentally introduces it, for example by registering the Actor as a listener for a callback, or going asynchronous inside the actor via Futures.
Shameless plug - I'm writing a new book with the head of the Akka team, Roland Kuhn, called Reactive Design Patterns where we discuss all of this and more. Green threads, CSP, event loops, Iteratees, Reactive Extensions, Actors, Futures/Promises, etc. Expect to see a MEAP on Manning by early next month.
There are two questions here:
Is Scala a good choice to port goroutines?
This is an easy question, since Scala is a general purpose language, which is no worse or better than many others you can choose to "port goroutines".
There are of course many opinions on why Scala is better or worse as a language (e.g. here is mine), but these are just opinions, and don't let them stop you.
Since Scala is general purpose, it "pretty much" comes down to: everything you can do in language X, you can do in Scala. If it sounds too broad.. how about continuations in Java :)
Are Scala actors similar to goroutines?
The only similarity (aside the nitpicking) is they both have to do with concurrency and message passing. But that is where the similarity ends.
Since Jamie's answer gave a good overview of Scala actors, I'll focus more on Goroutines/core.async, but with some actor model intro.
Actors help things to be "worry free distributed"
Where a "worry free" piece is usually associated with terms such as: fault tolerance, resiliency, availability, etc..
Without going into grave details how actors work, in two simple terms actors have to do with:
Locality: each actor has an address/reference that other actors can use to send messages to
Behavior: a function that gets applied/called when the message arrives to an actor
Think "talking processes" where each process has a reference and a function that gets called when a message arrives.
There is much more to it of course (e.g. check out Erlang OTP, or akka docs), but the above two is a good start.
Where it gets interesting with actors is.. implementation. Two big ones, at the moment, are Erlang OTP and Scala AKKA. While they both aim to solve the same thing, there are some differences. Let's look at a couple:
I intentionally do not use lingo such as "referential transparency", "idempotence", etc.. they do no good besides causing confusion, so let's just talk about immutability [a can't change that concept]. Erlang as a language is opinionated, and it leans towards strong immutability, while in Scala it is too easy to make actors that change/mutate their state when a message is received. It is not recommended, but mutability in Scala is right there in front of you, and people do use it.
Another interesting point that Joe Armstrong talks about is the fact that Scala/AKKA is limited by the JVM which just wasn't really designed with "being distributed" in mind, while Erlang VM was. It has to do with many things such as: process isolation, per process vs. the whole VM garbage collection, class loading, process scheduling and others.
The point of the above is not to say that one is better than the other, but it's to show that purity of the actor model as a concept depends on its implementation.
Now to goroutines..
Goroutines help to reason about concurrency sequentially
As other answers already mentioned, goroutines take roots in Communicating Sequential Processes, which is a "formal language for describing patterns of interaction in concurrent systems", which by definition can mean pretty much anything :)
I am going to give examples based on core.async, since I know internals of it better than Goroutines. But core.async was built after the Goroutines/CSP model, so there should not be too many differences conceptually.
The main concurrency primitive in core.async/Goroutine is a channel. Think about a channel as a "queue on rocks". This channel is used to "pass" messages. Any process that would like to "participate in a game" creates or gets a reference to a channel and puts/takes (e.g. sends/receives) messages to/from it.
Free 24 hour Parking
Most of work that is done on channels usually happens inside a "Goroutine" or "go block", which "takes its body and examines it for any channel operations. It will turn the body into a state machine. Upon reaching any blocking operation, the state machine will be 'parked' and the actual thread of control will be released. This approach is similar to that used in C# async. When the blocking operation completes, the code will be resumed (on a thread-pool thread, or the sole thread in a JS VM)" (source).
It is a lot easier to convey with a visual. Here is what a blocking IO execution looks like:
You can see that threads mostly spend time waiting for work. Here is the same work but done via "Goroutine"/"go block" approach:
Here 2 threads did all the work, that 4 threads did in a blocking approach, while taking the same amount of time.
The kicker in above description is: "threads are parked" when they have no work, which means, their state gets "offloaded" to a state machine, and the actual live JVM thread is free to do other work (source for a great visual)
note: in core.async, channel can be used outside of "go block"s, which will be backed by a JVM thread without parking ability: e.g. if it blocks, it blocks the real thread.
Power of a Go Channel
Another huge thing in "Goroutines"/"go blocks" is operations that can be performed on a channel. For example, a timeout channel can be created, which will close in X milliseconds. Or select/alt! function that, when used in conjunction with many channels, works like a "are you ready" polling mechanism across different channels. Think about it as a socket selector in non blocking IO. Here is an example of using timeout channel and alt! together:
(defn race [q]
(searching [:.yahoo :.google :.bing])
(let [t (timeout timeout-ms)
start (now)]
(go
(alt!
(GET (str "/yahoo?q=" q)) ([v] (winner :.yahoo v (took start)))
(GET (str "/bing?q=" q)) ([v] (winner :.bing v (took start)))
(GET (str "/google?q=" q)) ([v] (winner :.google v (took start)))
t ([v] (show-timeout timeout-ms))))))
This code snippet is taken from wracer, where it sends the same request to all three: Yahoo, Bing and Google, and returns a result from the fastest one, or times out (returns a timeout message) if none returned within a given time. Clojure may not be your first language, but you can't disagree on how sequential this implementation of concurrency looks and feels.
You can also merge/fan-in/fan-out data from/to many channels, map/reduce/filter/... channels data and more. Channels are also first class citizens: you can pass a channel to a channel..
Go UI Go!
Since core.async "go blocks" has this ability to "park" execution state, and have a very sequential "look and feel" when dealing with concurrency, how about JavaScript? There is no concurrency in JavaScript, since there is only one thread, right? And the way concurrency is mimicked is via 1024 callbacks.
But it does not have to be this way. The above example from wracer is in fact written in ClojureScript that compiles down to JavaScript. Yes, it will work on the server with many threads and/or in a browser: the code can stay the same.
Goroutines vs. core.async
Again, a couple of implementation differences [there are more] to underline the fact that theoretical concept is not exactly one to one in practice:
In Go, a channel is typed, in core.async it is not: e.g. in core.async you can put messages of any type on the same channel.
In Go, you can put mutable things on a channel. It is not recommended, but you can. In core.async, by Clojure design, all data structures are immutable, hence data inside channels feels a lot safer for its wellbeing.
So what's the verdict?
I hope the above shed some light on differences between the actor model and CSP.
Not to cause a flame war, but to give you yet another perspective of let's say Rich Hickey:
"I remain unenthusiastic about actors. They still couple the producer with the consumer. Yes, one can emulate or implement certain kinds of queues with actors (and, notably, people often do), but since any actor mechanism already incorporates a queue, it seems evident that queues are more primitive. It should be noted that Clojure's mechanisms for concurrent use of state remain viable, and channels are oriented towards the flow aspects of a system."(source)
However, in practice, Whatsapp is based on Erlang OTP, and it seemed to sell pretty well.
Another interesting quote is from Rob Pike:
"Buffered sends are not confirmed to the sender and can take arbitrarily long. Buffered channels and goroutines are very close to the actor model.
The real difference between the actor model and Go is that channels are first-class citizens. Also important: they are indirect, like file descriptors rather than file names, permitting styles of concurrency that are not as easily expressed in the actor model. There are also cases in which the reverse is true; I am not making a value judgement. In theory the models are equivalent."(source)
Moving some of my comments to an answer. It was getting too long :D (Not to take away from jamie and tolitius's posts; they're both very useful answers.)
It isn't quite true that you could do the exact same things that you do with goroutines in Akka. Go channels are often used as synchronization points. You cannot reproduce that directly in Akka. In Akka, post-sync processing has to be moved into a separate handler ("strewn" in jamie's words :D). I'd say the design patterns are different. You can kick off a goroutine with a chan, do some stuff, and then <- to wait for it to finish before moving on. Akka has a less-powerful form of this with ask, but ask isn't really the Akka way IMO.
Chans are also typed, while mailboxes are not. That's a big deal IMO, and it's pretty shocking for a Scala-based system. I understand that become is hard to implement with typed messages, but maybe that indicates that become isn't very Scala-like. I could say that about Akka generally. It often feels like its own thing that happens to run on Scala. Goroutines are a key reason Go exists.
Don't get me wrong; I like the actor model a lot, and I generally like Akka and find it pleasant to work in. I also generally like Go (I find Scala beautiful, while I find Go merely useful; but it is quite useful).
But fault tolerance is really the point of Akka IMO. You happen to get concurrency with that. Concurrency is the heart of goroutines. Fault-tolerance is a separate thing in Go, delegated to defer and recover, which can be used to implement quite a bit of fault tolerance. Akka's fault tolerance is more formal and feature-rich, but it can also be a bit more complicated.
All said, despite having some passing similarities, Akka is not a superset of Go, and they have significant divergence in features. Akka and Go are quite different in how they encourage you to approach problems, and things that are easy in one, are awkward, impractical, or at least non-idiomatic in the other. And that's the key differentiators in any system.
So bringing it back to your actual question: I would strongly recommend rethinking the Go interface before bringing it to Scala or Akka (which are also quite different things IMO). Make sure you're doing it the way your target environment means to do things. A straight port of a complicated Go library is likely to not fit in well with either environment.
These are all great and thorough answers. But for a simple way to look at it, here is my view. Goroutines are a simple abstraction of Actors. Actors are just a more specific use-case of Goroutines.
You could implement Actors using Goroutines by creating the Goroutine aside a Channel. By deciding that the channel is 'owned' by that Goroutine you're saying that only that Goroutine will consume from it. Your Goroutine simply runs an inbox-message-matching loop on that Channel. You can then simply pass the Channel around as the 'address' of your "Actor" (Goroutine).
But as Goroutines are an abstraction, a more general design than actors, Goroutines can be used for far more tasks and designs than Actors.
A trade-off though, is that since Actors are a more specific case, implementations of actors like Erlang can optimize them better (rail recursion on the inbox loop) and can provide other built-in features more easily (multi process and machine actors).
can we say that in Actor Model, the addressable entity is the Actor, the recipient of message. whereas in Go channels, the addressable entity is the channel, the pipe in which message flows.
in Go channel, you send message to the channel, and any number of recipients can be listening, and one of them will receive the message.
in Actor only one actor to whose actor-ref you send the message, will receive the message.
In a 1978 Paper by Hoare we have an idea called Communicating Sequential Processes. This is used by Go, Occam, and in Clojure in core.async.
Is it possible to use CSP as an alternative to the Actor Model in Scala? (I'm seeing JCSP but I'm wondering if this is the only option, if it is mature, and if anyone uses it).
EDIT - I'm also seeing Communicating Scala Objects as an alternative to JCSP in Scala. But those of these seem to be tied to real threads - which seems to miss one of the benefits of CSP, being to get away from the memory resource cost of keeping large numbers of threads always active.
You should consult this document, but in general there are a few differences:
Channels are anonymous while actors have identities
In CSP, you use channels to transmit messages, but actors can directly contact each other.
In CSP communication is done in the form of rendezvous (i.e., it is synchronous). Actors support asynchronous message passing.
And yes, it is possible to use CSP as an alternative to the Actor model if these differences are acceptable in your position. I don't have any experience with JCSP but I wouldn't recommend using that specific library (the reason is as I see there aren't any activity in the project since 2011).
I am starting to learn Scala and functional programming. I was reading the book !Programming scala: Tackle Multi-Core Complexity on the Java Virtual Machine". Upon the first chapter I've seen the word Event-Driven concurrency and Actor model. Before I continue reading this book I want to have an idea about Event-Driven concurrency or Actor Model.
What is Event-Driven concurrency, and how is it related to Actor Model?
An Event Driven programming model involves registering code to be run when a given event fires. An example is, instead of calling a method that returns some data from a database:
val user = db.getUser(1)
println(user.name)
You could instead register a callback to be run when the data is ready:
db.getUser(1, u => println(u.name))
In the first example, no concurrency was happening; The current thread would block until db.getUser(1) returned data from the database. In the second example db.getUser would return immediately and carry on executing the next code in the program. In parallel to this, the callback u => println(u.name) will be executed at some point in the future.
Some people prefer the second approach as it doesn't mean memory hungry Threads are needlessly sat around waiting for slow I/O to return.
The Actor Model is an example of how Event-Driven concepts can be used to help the programmer easily write concurrent programs.
From a super high level, Actors are objects that define a series of Event Driven message handlers that get fired when the Actor receives messages. In Akka, each instance of an Actor is single Threaded, however when many of these Actors are put together they create a system with concurrency.
For example, Actor A could send messages to Actor B and C in parallel. Actor B and C could fire messages back to Actor A. Actor A would have message handlers to receive these messages and behave as desired.
To learn more about the Actor model I would recommend reading the Akka documentation. It is really well written: http://doc.akka.io/docs/akka/2.1.4/
There is also lot's of good documentation around the web about Event Driven Concurrency that us much more detailed than what I've written here. http://berb.github.io/diploma-thesis/original/055_events.html
Theon's answer provides a good modern overview. I'd like to add some historical perspective.
Tony Hoare and Robert Milner both developed mathematical algebra for analysing concurrent systems (Communicating Sequential Processes, CSP, and Communicating Concurrent Systems, CCS). Both of these look like heavy mathematics to most of us but the practical application is relatively straightforward. CSP led directly to the Occam programming language amongst others, with Go being the newest example. CCS led to Pi calculus and the mobility of communicating channel ends, a feature that is part of Go and was added to Occam in the last decade or so.
CSP models concurrency purely by considering automomous entities ('processes', v.lightweight things like green threads) interacting simply by event exchange. The medium for passing events is along channels. Processes may have to deal with several inputs or outputs and they do this by selecting the event that is ready first. The events usually carry data from the sender to the receiver.
A principle feature of the CSP model is that a pair of processes engage in communication only when both are ready - in practical terms this leads to what is usually called 'synchronous' communication. However, the actual implementations (Go, Occam, Akka) allow channels to be buffered (the normal state in Akka) so that the lock-step exchange of events is often actually decoupled instead.
So in summary, an event-driven CSP-based system is really a data-flow network of processes connected by channels.
Besides the CSP interpretation of event-driven, there have been others. An important example is the 'event-wheel' approach, once popular for modelling concurrent systems whilst actually having a single processing thread. Such systems handle events by putting them into a processing queue and dealing with them due course, usually via a callback. Java Swing's event processing engine is a good example. There were others, e.g. for time-based simulation engines. One might think of the Javascript / NodeJS model as fitting into this category as well.
So in summary, an event-wheel was a way to express concurrency but without parallelism.
The irony of this is that the two approaches I've described above are both described as event driven but what they mean by event driven is different in each case. In one case, hardware-like entities are wired together; in the other, almost all actions are executed by callbacks. The CSP approach claims to be scalable because it's fully composable; it's naturally adept at parallel execution also. If there are any reasons to favour one over the other, these are probably it.
To understand the answer to this you have to look at event concurrency from the OS layer up. First you start with threads which are the smallest section of code that can be run by the OS and eventually deal with I/O, timing and other kinds of events.
The OS groups threads into a process in which they share the same memory, protection and security permissions. Above that layer you have user programs which typically make I/O requests that are handled by user libraries.
The I/O libraries handle these requests in one of two ways. Unix-like systems use a "reactor" model in which the library registers I/O handlers for all the different types of I/O and events in the system. These handlers are activated when I/O is ready on a specific device. Windows-like systems use an I/O completion model in which I/O requests are made and a callback is triggered when the request is complete.
Both of these models require a significant amount of overhead to manage overall program state if you were to use them directly. However some programming tasks (web apps / services) lend themselves to a seemingly more direct implementation if you use an event model directly, but you still need to manage all of that program state. In order to track program logic across dispatches of several related events you have to manually track state and pass it around to the callbacks. This tracking structure is usually called a state context or baton. As you might imagine passing batons around all over the place to numerous seemingly unrelated handlers makes for some extremely hard to read and spaghetti-like code. It's also a pain to write and debug -- especially when you're trying to handle the synchronization of various concurrent paths of execution. You start getting into Futures and then the code becomes really difficult to read.
One well-known event processing library is call libuv. It's a portable event loop that integrates Unix's reactor model with Windows' completion model into a single model usually called a "proactor". Its the event handler that drives NodeJS.
Which brings us to communicating sequential processes.
https://en.wikipedia.org/wiki/Communicating_sequential_processes
Rather than writing asynchronous I/O dispatch and synchronization code using one or more concurrency models (and their often competing conventions), we flip the problem on its head. We use a "coroutine" which looks like normal sequential code.
A simple example is a coroutine that receives a single byte over an event channel from another coroutine that sends a single byte. This effectively synchronizes I/O producer and consumer because the writer/sender has to wait for a reader/receiver and vice-versa. While either process is waiting they explicitly yield execution to other processes. When a coroutine yields, its scoped program state is saved on a stack frame thus saving you from the confusion of managing multi-layered baton state in an event loop.
Using applications built on these event channels we can construct arbitrary, reusable, concurrent logic and the algorithms no longer look like spaghetti code. In pure CSP systems if you write to a channel and there is no reader, you will be blocked. The channel endpoints are known via handles internally to the program.
Actor systems are different in a couple of ways. First, the endpoints are the actor threads and they are named and known external to the mainline program. The second difference is that sends and receives on these channels are buffered. In other words if you send a message to an actor and there isn't one listening or its busy you aren't blocked until one reads from their input channel. Other differences exist like one actor can publish to two different actors concurrently.
As you might guess Actor systems can easily be built from CSP systems. There are other details like waiting for specific event patterns and selecting from them, but that's the basics.
I hope that clarifies things a bit.
Other constructs can be built from these ideas. Various programming systems (Go, Erlang, etc) include CSP implementations within them. Operating systems like Inferno and Node9 use CSPs and Channels as the basis of their distributed computing model.
Go: https://en.wikipedia.org/wiki/Go_(programming_language)
Erlang: https://en.wikipedia.org/wiki/Erlang_(programming_language)
Inferno: https://en.wikipedia.org/wiki/Inferno_(operating_system)
Node9: https://github.com/jvburnes/node9
I have prior experience in writing both event and poll based embedded systems (for tiny MCU's with no preemptive OS).
In an event based system, tasks usually receives events (messages) on a queue and handles them in turn.
In a polled based system, tasks polls status with a certain interval and responds to change.
Which architecture do you prefer? Can both co-exist?
UPDATE: POINTS MADE
POLL BASED
- Tight coupling related to timing aspects (#Lundin)
* Can co-exist alongside event system using queues (#embedded.kyle)
* Fine for smaller programs (#Lundin)
EVENT BASED
+ More flexible system in the long run (#embedded.kyle)
- RTOS edition adds complexity (#Lundin)
* Small programs = state-machine controlled (#Lundin)
* Can be implemented using queues and a "super-loop" (inside controller/main) (#embedded.kyle)
* Only true "events" are hw interrupts ones (#Lundin)
RELATED QUESTIONS
* Looking for a comparison of different scheduling algorithms for a Finite State Machine (#embedded.kyle)
RELATED INFO
* "Prefer Using Active Objects Instead of Naked Threads" (#Miro)
http://www.drdobbs.com/parallel/prefer-using-active-objects-instead-of-n/225700095
* "Use Threads Correctly = Isolation + Asynchronous Messages" (#Miro)
http://www.drdobbs.com/parallel/use-threads-correctly-isolation-asynch/215900465
There is really no such thing as "event-driven" on a bare bone MCU platform, despite what the buzzword-spitters are trying to tell you. The only kind of true events you can receive are hardware interrupts.
Depending on the nature of the application and its real time requirements, interrupts may or may not be suitable. Generally, it is far easier to achieve deterministic real time with a polling system. However, systems relying solely on polling are very hard to maintain, because you get tight coupling between all timing aspects.
Suppose you try to start up a LCD, which is slow. Instead of polling some timer repeatedly while burning CPU cycles in an empty loop, you would perhaps decide to receive some data over a bus in the meantime. And then you want to print the data received on the LCD. Such a design has created a tight coupling between the LCD startup time and the serial bus, and another tight coupling between the serial bus and the printing of data. From an object-oriented point-of-view these things are not related to each other at all. If you were to speed up the serial bus at some point in the future, then suddenly you could encounter LCD printing bugs, because it has not finished starting up when you try to print on it.
In a small program, it is perfectly fine to use polling like in the above example. But if the program has potential of growing, polling will make it very complex and the tight coupling will ultimately lead to many strange and fatal bugs.
On the other hand, multi-threading and RTOS adds quite a lot of extra complexity which in turn can lead to bugs as well. Where to draw the line isn't simple to determine.
Out of personal experience I'd say that any program smaller than 20-30k LOC will not benefit from scheduling and multitasking, beyond simple state machines. If the program gets larger than that, I'd consider a multitasking RTOS.
Also, low-end MCUs (8- and 16-bitters) are far from suitable to run an OS. If you find that you need an OS to handle complexity on a 8- or 16-bit platform, you probably picked the wrong MCU to begin with. I'd be sceptical against any attempts to introduce an OS on anything smaller than a 32-bitter.
Actually, event-driven programming and threads can be combined and the resulting pattern is widely known as "active objects" or "actors".
Active objects (actors) are encapsulated, event-driven state machines, which communicate with one another asynchronously by posting events to each other. Active objects process all events in their own thread of execution (at least conceptually, if a cooperative scheduler is used), so they avoid by design most concurrency hazards.
Actors and active objects are all the rage (again) in the general-purpose computing (you can search for Erlang, Scala, Akka). Herb Sutter has written a couple of good articles that explain the "active object" pattern: "Prefer Using Active Objects Instead of Naked Threads" (http://www.drdobbs.com/parallel/prefer-using-active-objects-instead-of-n/225700095) and "Use Threads Correctly = Isolation + Asynchronous Messages" (http://www.drdobbs.com/parallel/use-threads-correctly-isolation-asynch/215900465)
Here is what Herb says in the first of these articles:
"Using raw threads directly is trouble for a number of reasons ...
Active objects dramatically improve our ability to reason about our thread's code and operation by giving us higher-level abstractions and idioms that raise the semantic level of our program and let us express our intent more directly. As with all good patterns, we also get better vocabulary to talk about our design. Note that active objects aren't a novelty: UML and various libraries have provided support for active classes"
So, all this is really not new. But what's perhaps less known, especially in the embedded systems community, is that active objects are not only fully applicable to the embedded systems, but they are actually a perfect match for embedded and they are lighter than a traditional RTOS.
I've been using the event-driven active objects for over a decade now and have created the QP family of active object frameworks for embedded systems (see http://www.state-machine.com/). I would never go back to the polling "superloop" or the raw RTOS.
I prefer whichever architecture is best suited to the application at hand.
Both can co-exist in a multilevel queue architecture. One queue works on a poll basis running in the main loop. While another, most likely tasked with higher priority events, works by using interrupt based preemption.
See my answer to this SO question for a more detailed explanation and comparison of the different scheduling algorithms.
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.