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).
Not that it wouldn't be easy (or fun) enough to write one, it makes sense not to re-invent the wheel so to speak. I've had a look around at various attempts, but I don't seem to have yet come across an implementation that supports these criteria;
Simple queue OSS system with MongoDB persistence;
C# Driver (official) based (so full POCO serialization)
Tailable cursors rather than polling
handles message timeout (GC correctly)
handles consumer failure (ideally crash detecting re-insertion, but timeout with delayed re-insertion is fine) so findAndModify on complete
multiple writers, multiple consumers
threadsafe
Nice to have;
allows for (latest only) message (replace older messages in the Q)
If anyone has nice simple a library like that floating around on GitHub that I've not yet found, please speak up!
There's my little project - a .net message bus implementation that works with MS SQL queues or MongoDB (MongoDB support is a recent addition). Link: http://code.google.com/p/nginn-messagebus/ and http://nginn.org/blog for some examples.
I'm not sure if this is what you're looking for, it's also lacking in documentation and example departments and it doesn't exactly match your specs (polling instead of tailing) - but maybe it's worth giving a try. This is a publish-subscribe message bus, like NServiceBus or MassTransit - not a raw message queue.
PS I'm afraid there are mutually exclusive requirements in your specs: you can't use tailable cursor with concurrent consumers because you lose atomicity. If you want to tail a queue you should use only a single consumer.
I found there is also an Akka actor model, so I am wondering what's the difference between the Akka's Actor and Scala's Actor model?
Well, there isn't. There is just Actor model, and Akka actors and Scala actors are two implementations of that model.
All Actor model says that your concurrency primitives are actors, which can:
receive a message and decide what to do next depending on the content of the message, including:
send messages to any actors they know about
create new actors
and provides certain guarantees, e.g.:
any actor will only handle a single message at a time
messages sent by actor X to actor Y will arrive in the order thay were sent
There is no difference between Scala and Akka actors on this level.
For differences in what they can do, see Different Scala Actor Implementations Overview. The biggest one, for me, is that Akka supports supervisors and ActorRegistry.
There is also a historical answer. The creators of Scala thought there should be an actor framework. Jonas Bonér tried it out, but was not completely satisfied so he started working on a new - which evolved into Akka. However, the Scala people thought it was better than their own - so at Jfokus 2011 they announced that Akka was to become the standard actor framework of Scala. However, that migration will take some time.
This depends a little on what you mean with "model" - you can either refer to the "execution model" or the "programming model" (and perhaps other models as well).
For execution models, there are basically two: thread-based or event-based. The Scala standard actor library contains both. The thread-based uses one thread for each actor, whereas the event-based uses a thread-pool. The former is more intuitive to understand, the latter is more efficient. Akka is built on the event-based model.
For programming model, there is a big difference between the scala standard library and Akka. In the scala standard library, you basically implement the "run" method - and if you want to wait for an incoming message, you get yourself into a waiting state (by calling "receive" or "react"). So, the programming model follow the "thread-metaphor". However, in Akka, the programming metaphor is that you implement a few life-cycle methods - but the "run"-method is written inside the framework. It actually turns out that this programming model works a lot better with the event-based execution model as well.
If you are interested in the different execution models and programming models of scala standard actors I have written a few posts on the issue.
I have quite large files that I need to deal with (500Meg+ zip files).
Are there any non-blocking IO open source implementations for Scala's actors?
If I got your question right, you need non-blocking IO for files. I have bad news for you then.
NIO
Java NIO in Java6 supports only blocking operation when working with files. You may notice this from the fact FileChannel does not implement SelectableChannel interface. (NIO does support non-blocking mode for sockets however)
There is NIO.2 (JSR-203) specification aimed to overcome many current limits of java.io and NIO and to provide support for asynchronous IO on files as well. NIO.2 is to be released with Java 7 as far as I understand.
These are Java library limits, hence you will suffer from them in Scala as well.
Actors
Actors are based on Fork-Join framework of Doug Lea (at least in branch 2.7.x till version 2.7.7). One quote from FJTask class:
There is nothing actually preventing
you from blocking within a FJTask, and
very short waits/blocks are completely
well behaved. But FJTasks are not
designed to support arbitrary
synchronization since there is no way
to suspend and resume individual tasks
once they have begun executing.
FJTasks should also be finite in
duration -- they should not contain
infinite loops. FJTasks that might
need to perform a blocking action, or
hold locks for extended periods, or
loop forever can instead create normal
java Thread objects that will do so.
FJTasks are just not designed to
support these things.
FJ library is enhanced in Scala to provide a unified way permitting an actor to function like a thread or like an event-based task depending on number of working threads and "library activity" (you may find explanation in technical report "Actors that unify Threads and Events" by Philipp Haller and Martin Odersky).
Solution?
But after all if you run blocking code in an actor it behaves just like if it being a thread, so why not use an ordinary Thread for blocking reads and to send events to event-based actors from this thread?
Are you talking about Remote actors? A standard Actor is of course a intra-JVM entity. I'm not aware of an NIO implementation of remote actors I'm afraid.
Hello is that an option for you?
bigdata(R) is a scale-out storage and computing fabric supporting optional transactions, very high concurrency, and very high aggregate IO rates.
http://sourceforge.net/projects/bigdata/
Not that I know of, but you could probably get a lot of mileage out of looking at Naggati, a Scala wrapper around Apache Mina. Mina is a networking library that uses NIO, Naggati translates this into a Scala style of coding.