Besides the benefits of this model over the shared-memory model, I'm just trying to understand where to apply it for higher levels use-cases.
As to Scala, Actors model fits most of the multi-threaded cases one can think about:
Swing GUI application
Web Applications (see Lift framework)
Application Server in multicore environment:
Batch processing of requests/data
Background tracking tasks
Notifications & Scheduled tasks
Actors model makes design much clearer and greatly simplifies interprocess communication.
OTP Framework : Provides really good framework for network based applications.
Helps in making fault tolerant applications . (process restart using Supervisor's in OTP).
Both Synchronous and Asynchronous modes of communication can be done using gen_server.
Event based callbacks can be used using gen_event.
State machine can be programmed easily using gen_fsm (In case you need to follow some states in your application).
A process crash does not bring the whole application down. Only that particular process crashes.
Functional programming language.
A lot easier to program at binary level.
Garbage collection.
Native compilation option.
Fair amount of good useful modules are available.
Able to make good solid concurrent applications easily.
And lots more.... I really enjoyed working on some applications in erlang , making those in c/c++ would have been very difficult.
Related
I have a small Akka application that passes many messages between its actors and each actor does some calculations on the data it receives. What I want is to profile this application in order to see which parts of the code take up most time and so on.
I tried VisualVM but I cannot really understand what's going on. I added a picture of the profiler output.
My questions are
What for example is this first line and why does it take up so much time? (scala.concurrent.forkjoin.ForkJoinPool.scan())
Can Akka applications because of their asynchronous behaviour be profiled well at all?
Can I see for instance how long one specific actor(-type) works for one specific message(-type) it receives?
Are there other best-practices for profiling Akka applications?
There are packages not profiled by default and it is their time that is accounted in the profile of scala.concurrent.forkjoin.ForkJoinPool.scan(). If all the hidden packages are allowed to be sampled, the true CPU time consumers will be revealed. For example, the following before/after illustrative profiles uncover that threads are put to sleep most of the time by sun.misc.Unsafe.park waiting to be unparked.
Akka applications can be profiled quite well with proper instrumentation and call tracing. Google's prominent Dapper, a Large-Scale Distributed Systems Tracing Infrastructure paper contains detailed explanation of the technique. Twitter created Zipkin based on that. It is open sourced and has an extension for distributed tracing of Akka. Follow its wiki for a good explanation of how to set up a system that allows to
trace call hierarchies inside an actor system;
debug request processing pipelines (you can log to traces, annotate them with custom key-value pairs);
see dependencies between derived requests and their contribution to resulting response time;
find and analyse slowest requests in your system.
There is also a new kid on the block, Kamon. It is a reactive-friendly toolkit for monitoring applications that run on top of the JVM, which is specially enthusiastic to applications built with the Typesafe Reactive Platform. That definitely means yes for Akka and the integration comes in the form of the kamon-akka and kamon-akka-remote modules that bring bytecode instrumentation to gather metrics and perform automatic trace context propagation on your behalf. Explore the documentation starting from Akka Integration Overview to understand what it can and how to achieve that.
Just a couple of days ago TypeSafe announced that TypeSafe console now is free. I don't know what can be better for profiling Scala/Akka applications. Of cause you can try JProfiler for JVM languages, I've used it with Java projects, but it's not free and for Java.
I was thinking about profiling/metrics in code since I also use Akka/Scala a lot for building production applications, but I also eager to hear alternative ways to make sure that application is healthy.
Metrics (like Dropwizard)
Very good tool for collecting metrics in the code, with good documentation and embedded support for Graphite, Ganglia, Logback, etc.
It has verbose tools for collecting in-app statistics like gauges, counter histograms, timings - information to figure out what is the current state of your app, how many actors were created, etc, if they are alive, what the current state is of majority of actors, etc.
Agree, it's a bit different from profiling but helps a lot to find roots of the problem, especially if integrated with some char building tool.
Profilers like (VisualVM, XRebel)
Since I'm a big fun of doing monitoring, it still answers a slightly different question - what are current insights of my application right now?
But there is quite another matter may disturb us - what of my code is slow (or sloppy)?
For that reason, we have VisualVM and another answers to this question - how to profile Akka actors with VisualVM.
Also, I'd suggest trying XRebel profiler that just adds a bit more firepower to process of figuring out what code makes app slower. It's also paid but on my project it saved a lot of time dealing with sloppy code.
New Relic
I'd suggest it for some playground projects since you can get some monitoring/profiling solutions for free, but on more serious projects I'd go for things I highlighted above.
So I hope, that my overview was helpful.
At a couple of places there is state that akka is somehow "real-time". E.g.:
http://doc.akka.io/docs/akka/2.0/intro/what-is-akka.html
Unfortunately I was not able to find a deeper explanation in which way akka is "real-time". So this is the question:
In which way is akka real-time?
I assume akka is not really a real-time computing system in the sense of the following definition, isn't it?: https://en.wikipedia.org/wiki/Real-time_computing
No language built on the JVM can be real-time in the sense that it's guaranteed to react within a certain amount of time unless it is using a JVM that supports real-time extensions (and takes advantage of them). It just isn't technically possible--and Akka is no exception.
However, Akka does provide support for running things quickly and with pretty good timing compared to what is possible. And in the docs, the other definitions of real-time (meaning online, while-running, with-good-average-latency, fast-enough-for-you-not-to-notice-the-delay, etc.) may be used on occasion.
Since akka is a message driven system, the use of real-time relates to one of the definition of the wikipedia article you mention in the domain of data transfer, media processing and enterprise systems, the term is used to mean 'without perceivable delay'.
"real time" here equates to "going with the flow": events/messages are efficiently processed/consumed as they are produced (in opposition to "batch processing").
Akka can be a foundation for a soft real-time system, but not for a hard one, because of the limitations of the JVM. If you scroll a bit down in the Wikipedia article, you will find the section "Criteria for real-time computing", and there is a nice explanation about the different "real-timeness" criteria.
systems that are subject to a "real-time constraint"— e.g. operational
deadlines from event to system response.
en.wikipedia.org/wiki/Real-time_computing
The akka guys might be reffering to features like futures that allow you to add a time constraint on expectations from a computation.
Also the clustering model of akka may be used to mean an online system which is real-time(Abstracted so as to look like its running locally).
My take is that the Akka platform can support a form of real-time constraint by delivering responsive applications through the use of (I'm quoting here):
Asynchronous, non-blocking and highly performant event-driven programming model
Fault tolerance through supervisor hierarchies with “let-it-crash” semantics
Definition of time-out policies in the response delivery
As already said, all these features combined provides a platform with a form of response time guarantee, especially compared to mainstream applications and tools available nowadays on the JVM.
It's still arguable to claim that Akka could be strictly defined as a real-time computing system, as per wikipedia's definition.
For such claims to be proven, you would better refer to the Akka team itself.
After being exposed to scala's Actors and Clojure's Futures, I feel like both languages have excellent support for multi core data processing.
However, I still have not been able to determine the real engineering differences between the concurrency features and pros/cons of the two models. Are these languages complimentary, or opposed in terms of their treatment of concurrent process abstractions?
Secondarily, regarding big data issues, it's not clear wether the scala community continues to support Hadoop explicitly (whereas the clojure community clearly does ). How do Scala developers interface with the hadoop ecosystem?
Some solutions are well solved by agents/actors and some are not. This distinction is not really about languages more than how specific problems fit within general classes of solutions. This is a (very short) comparason of Actors/agents vs. References to try to clarify the point that the tool must fit the concurrency problem.
Actors excel in distributed situation where no data needs to be concurrently modified. If your problem can be expressed purely by passing messages then actors will do the trick. Actors work poorly where they need to modify several related data structures at the same time. The canonical example of this being moving money between bank accounts.
Clojure's refs are a great solution to the problem of many threads needing to modify the same thing at the same time. They excel at shared memory multi-processor systems like today's PCs and Servers. In addition to the Bank account example, Rich Hickey (the author of clojure) uses the example of a baseball game to explain why this is important. If you wanted to use actors to represent a baseball game then before you moved the ball, all the fans would have to send it a message asking it where it was... and if they wanted to watch a player catching the ball things get even more complex.
Clojure has cascalog which makes writing hadoop jobs look a lot like writing clojure.
Actors provide a way of handling the potential interleaving and synchronization control that inevitably comes when trying to get multiple threads to work together. Each actor has a queue of messages that it processes in order one at a time so as to avoid the need to include explicit locks. In this case a Future provides a way of waiting for a response from an actor.
As far as Hadoop is concerned, Twitter just released a library specifically for Hadoop called Scalding but as long as the library is written for the JVM, it should work with either language.
I need to create a simple Chat system like facebook chat and a twitter-like app.
What is the best concurrent program languages in this case ?
Erlang, Haskell, Scala or anything else ?
Thanks ^_^
Chat system like facebook chat
Facebook chat is written in Erlang, http://www.facebook.com/note.php?note_id=14218138919
and a twitter-like app
What aspect? Message delivery? The web frontend? It's all message routing ultimately. Haskell's been used recently for a couple of real time production trading systems, using heavy multicore concurrency. http://www.starling-software.com/misc/icfp-2009-cjs.pdf
More relevant: what's the scale: how many users do you expect to serve concurrently?
Erlang is my choice of drug for something like that. But I would also check out Node.js if you feel more comfortable in JavaScript.
Complete chat application source code using Scala/Lift.
8 minutes and 20 seconds real-time, unedited, webcast writing a chat app from scratch using Scala/Lift.
This is actually a relatively easy problem to solve that can be done by any language with decent threading support including (eg Java, C# and others). The model is fairly simple:
Each client connects to a Web server with an AJAX request that is configured on the server not to timeout;
If it does timeout for any reason the client is configured to issue another AJAX request;
The server endpoint for that AJSX call does a thread wait operation on some monitor waiting for updates;
When the user sends a chat it is sent to the server and any relevant monitors are signaled;
Any threads listening to that monitor are woken up, retrieve any messages waiting for them and return that as the AJAX result to the client;
The client renders those messages and then issues another AJAX request to listen to for messages.
This is the basic framework but isn't the end of the story. Any scalable chat system will support either internal or external federation (meaning the clients can connect to more than one server) but unless you're Google, Facebook or Twitter you're unlikely to have this problem.
If you do you need some kind of message queue/bus for inter-server communication.
This is something you need a heavy duty multithreaded language like Erlang for but it, Haskell and others are of course capable of doing it.
F# for future.
Client/Server chat F#
Concurrent Programming - A Primer
Also take a look at Twisted(Python).
I'd recommend taking a look at Akka (www.akkasource.org)
It's a Scala Actor framework with ALOT of plumbing for making scalable backend applications.
Out of the box it supports:
Actor Supervision
Remote actors
Cluster Membership
Comet over JAX-RS (Project Atmosphere)
HTTP Auth for your services
Distributed storage engine support (Cassandra, MongoDB)
+ a whole lot more
I would go for erlang, it's efficiency in comet-enabled apps is largely proven.
Go with a framework such as nitrogen, where you can start comet requests just as easily as Jquery does ajax.
If it's actually going to be a simple application (and not under very high load), then the answer is to use whichever language you already know that has decent threading. Erlang, Scala, Clojure, Haskell, F#, etc., all do a perfectly good job at things like this--but so do Java and C#. You'll be fine if you pick whichever of these you know and/or like.
If you're using this as an excuse to learn a new generally useful language, I'd pick Scala as a nice blend of highly performant (which Erlang is not in general, though it is fantastic with efficient concurrency), highly functional (which Java and C# are not), highly deployable (due to running on the JVM), and reasonably familiar (assuming you know C-ish languages).
If you're using this as an excuse to practice fault-tolerant concurrency, use Erlang. That's what it was designed for and it does it extremely well.
I'm taking an introductory course (3 months) about real time systems design, but any implementation.
I would like to build something that let me understand better what I'll learn in theory, but since I have never done any real time system I can't estimate how long will take any project. It would be a concept proof project, or something like that, given my available time and knowledge.
Please, could you give me some idea? Thank you in advance.
I programm in TSQL, Delphi and C#, but I'll not have any problem in learning another language.
Suggest you consider exploring the Real-Time Specification for Java (RTSJ). While it is not a traditional environment for constructing real-time software, it is an up-and-coming technology with a lot of interest. Even better, you can witness some of the ongoing debate about what matters and what doesn't in real-time systems.
Sun's JavaRTS is freely available for download, and has some interesting demonstrations available to show deterministic behavior, and show off their RT garbage collector.
In terms of a specific project, I suggest you start simple: 1) Build a work-generator that you can tune to consume a given amount of CPU time; 2) Put this into a framework that can produce a distribution of work-generator tasks (as threads, or as chunks of work executed in a thread) and a mechanism for logging the work produced; 3) Produce charts of the execution time, sojourn time, deadline, slack/overrun of these tasks versus their priority; 4) demonstrate that tasks running in the context of real-time threads (vice timesharing) behave differently.
Bonus points if you can measure the overhead in the scheduler by determining at what supplied load (total CPU time produced by your work generator tasks divided by wall-clock time) your tasks begin missing deadlines.
Try to think of real-time tasks that are time-critical, for instance video-playing, which fails if tasks are not finished (e.g. calculating the next frame) in time.
You can also think of some industrial solutions, but they are probably more difficult to study in your local environment.
You should definitely consider building your system using a hardware development board equipped with a small processor (ARM, PIC, AVR, any one will do). This really helped remove my fear of the low-level when I started developing. You'll have to use C or C++ though.
You will then have two alternatives : either go bare-metal, or use a real-time OS.
Going bare-metal, you can learn :
How to initalize your processor from scratch and most importantly how to use interrupts, which are the fastest way you have to respond to an externel event
How to implement lightweight threads with fast context switching, something every real-time OS implements
In order to ease this a bit, look for a dev kit which comes with lots of documentation and source code. I used Embedded Artists ARM boards and they give you a lot of material.
Going with the RT OS :
You'll fast-track your project, and will be able to learn how to fine-tune a RT OS
You may try your hand at an open-source OS, such as Linux or the BSDs, and learn a lot from the source code
Either choice is good, you will get a really cool hands-on project to show off and hopefully better understand your course material. Good luck!
As most realtime systems are still implemented in C or C++ it may be good to brush up your knowledge of these programming languages. Many realtime systems are also embedded systems, so you might want to play around with a cheap open source one like BeagleBoard (http://beagleboard.org/). This will also give you a chance to learn about cross compiling etc.