Search Results

Search found 908 results on 37 pages for 'optimistic concurrency'.

Page 5/37 | < Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >

• Concurrency pattern of logger in multithreaded application

  - by Dipan Mehta

  The context: We are working on a multi-threaded (Linux-C) application that follows a pipeline model. Each module has a private thread and encapsulated objects which do processing of data; and each stage has a standard form of exchanging data with next unit. The application is free from memory leak and is threadsafe using locks at the point where they exchange data. Total number of threads is about 15- and each thread can have from 1 to 4 objects. Making about 25 - 30 odd objects which all have some critical logging to do. Most discussion I have seen about different levels as in Log4J and it's other translations. The real big questions is about how the overall logging should really happen? One approach is all local logging does fprintf to stderr. The stderr is redirected to some file. This approach is very bad when logs become too big. If all object instantiate their individual loggers - (about 30-40 of them) there will be too many files. And unlike above, one won't have the idea of true order of events. Timestamping is one possibility - but it is still a mess to collate. If there is a single global logger (singleton) pattern - it indirectly blocks so many threads while one is busy putting up logs. This is unacceptable when processing of the threads are heavy. So what should be the ideal way to structure the logging objects? What are some of the best practices in actual large scale applications? I would also love to learn from some of the real designs of large scale applications to get inspirations from!

  Read the article

• concurrency::extent<N> from amp.h

  - by Daniel Moth

  Overview We saw in a previous post how index<N> represents a point in N-dimensional space and in this post we'll see how to define the N-dimensional space itself. With C++ AMP, an N-dimensional space can be specified with the template class extent<N> where you define the size of each dimension. From a look and feel perspective, you'd expect the programmatic interface of a point type and size type to be similar (even though the concepts are different). Indeed, exactly like index<N>, extent<N> is essentially a coordinate vector of N integers ordered from most- to least- significant, BUT each integer represents the size for that dimension (and hence cannot be negative). So, if you read the description of index, you won't be surprised with the below description of extent<N> There is the rank field returning the value of N you passed as the template parameter. You can construct one extent from another (via the copy constructor or the assignment operator), you can construct it by passing an integer array, or via convenience constructor overloads for 1- 2- and 3- dimension extents. Note that the parameterless constructor creates an extent of the specified rank with all bounds initialized to 0. You can access the components of the extent through the subscript operator (passing it an integer). You can perform some arithmetic operations between extent objects through operator overloading, i.e. ==, !=, +=, -=, +, -. There are operator overloads so that you can perform operations between an extent and an integer: -- (pre- and post- decrement), ++ (pre- and post- increment), %=, *=, /=, +=, –= and, finally, there are additional overloads for plus and minus (+,-) between extent<N> and index<N> objects, returning a new extent object as the result. In addition to the usual suspects, extent offers a contains function that tests if an index is within the bounds of the extent (assuming an origin of zero). It also has a size function that returns the total linear size of this extent<N> in units of elements. Example code extent<2> e(3, 4); _ASSERT(e.rank == 2); _ASSERT(e.size() == 3 * 4); e += 3; e[1] += 6; e = e + index<2>(3,-4); _ASSERT(e == extent<2>(9, 9)); _ASSERT( e.contains(index<2>(8, 8))); _ASSERT(!e.contains(index<2>(8, 9))); grid<N> Our upcoming pre-release bits also have a similar type to extent, grid<N>. The way you create a grid is by passing it an extent, e.g. extent<3> e(4,2,6); grid<3> g(e); I am not going to dive deeper into grid, suffice for now to think of grid<N> simply as an alias for the extent<N> object, that you create when you encounter a function that expects a grid object instead of an extent object. Usage The extent class on its own simply defines the size of the N-dimensional space. We'll see in future posts that when you create containers (arrays) and wrappers (array_views) for your data, it is an extent<N> object that you'll need to use to create those (and use an index<N> object to index into them). We'll also see that it is a grid<N> object that you pass to the new parallel_for_each function that I'll cover in the next post. Comments about this post by Daniel Moth welcome at the original blog.

  Read the article

• Unintentional run-in with C# thread concurrency

  - by geekrutherford

  For the first time today we began conducting load testing on a ASP.NET application already in production. Obviously you would normally want to load test prior to releasing to a production environment, but that isn't the point here.   We ran a test which simulated 5 users hitting the application doing the same actions simultaneously. The first few pages visited seemed fine and then things just hung for a while before the test failed. While the test was running I was viewing the performance counters on the server noting that the CPU was consistently pegged at 100% until the testing tool gave up.   Fortunately the application logs all exceptions including those unhandled to the database (thanks to log4net). I checked the log and low and behold the error was:   System.ArgumentException: An item with the same key has already been added. (The rest of the stack trace intentionally omitted)   Since the code was running with debug on the line number where the exception occured was also provided. I began inspecting the code and almost immediately it hit me, the section of code responsible for the exception is trying to initialize a static class. My next question was how is this code being hit multiple times when I have a rudimentary check already in place to prevent this kind of thing (i.e. a check on a public variable of the static class before entering the initializing routine). The answer...the check fails because the value is not set before other threads have already made it through.   Not being one who consistently works with threading I wasn't quite sure how to handle this problem. Fortunately a co-worker recalled having to lock a section of code in the past but couldn't recall exactly how. After a quick search on Google the solution is as follows:   Object objLock = new Object(); lock(objLock) { //logic requiring lock }   The lock statement takes an object and tells the .NET runtime that the current thread has exclusive access while the code within brackets is executing. Once the code completes, the lock is released for another thread to utilize.   In my case, I only need to execute the inner code once to initialize my static class. So within the brackets I have a check on a public variable to prevent it from being initialized again.

  Read the article

• Update n number of records using JPA with Optimistic locking

  - by Jacques René Mesrine

  I have a table with a column called "count" and I want to run a cron job that will trigger a statement that performs a SQL like this: update summaryTable set count=4; Note that there might be concurrent threads reading & changing the value for "count" when this cron job is triggered. The table has a version column to support Optimistic Locking. What's an efficient way to update the count in JPA ?

  Read the article

• jpa @version optimistic locking

  - by cometta

  I loaded same entity record on 2 separate browser window then press submit (hibernate template.merge), version number incremented for both browser window, but never caught any problem with optimistic lock.. so how to test this? my save() look like this hibernatetemplate().merge(..); setJPAObject(null); //reset after save

  Read the article

• How to handle concurrency control in ASP.NET Dynamic Data?

  - by Andrew

  I've been quite impressed with dynamic data and how easy and quick it is to get a simple site up and running. I'm planning on using it for a simple internal HR admin site for registering people's skills/degrees/etc. I've been watching the intro videos at www.asp.net/dynamicdata and one thing they never mention is how to handle concurrency control. It seems that DD does not handle it right out of the box (unless there is some setting I haven't seen) as I manually generated a change conflict exception and the app failed without any user friendly message. Anybody know if DD handles it out of the box? Or do you have to somehow build it into the site?

  Read the article

• How to implement blocking request-reply using Java concurrency primitives?

  - by Uri

  My system consists of a "proxy" class that receives "request" packets, marshals them and sends them over the network to a server, which unmarshals them, processes, and returns some "response packet". My "submit" method on the proxy side should block until a reply is received to the request (packets have ids for identification and referencing purposes) or until a timeout is reached. If I was building this in early versions of Java, I would likely implement in my proxy a collection of "pending messages ids", where I would submit a message, and wait() on the corresponding id (with a timeout). When a reply was received, the handling thread would notify() on the corresponding id. Is there a better way to achieve this using an existing library class, perhaps in java.util.concurrency? If I went with the solution described above, what is the correct way to deal with the potential race condition where a reply arrives before wait() is invoked?

  Read the article

• Formula for PERT - Pessimistic time and optimistic time

  - by karthi

  I found PERT examples include a formula (a + 4m + b)/6 where a= optimistic time , b= pessimistic time and m= actual time to complete the task but how to calculate a and b? please help me as soon as possible

  Read the article

• What are the best settings of the H2 database for high concurrency?

  - by dexter

  There are a lot of settings that can be used in H2 database. AUTO_SERVER, MVCC, LOCK_MODE, FILE_LOCK and MULTI_THREADED. I wonder what combination works best for high concurrency setup e.g. one thread is doing INSERTs and another connection does some UPDATEs and SELECTs? I tried MVCC=TRUE;LOCK_MODE=3lFILE_LOCK=NO but whenever I do some UPDATEs in one connection, the other connection does not see it even though I commit it. By the way the connections are from different processes e.g. separate program.

  Read the article

• Is Akka a good solution for a concurrent pipeline/workflow problem?

  - by herpylderp

  Disclaimer: I am brand new to Akka and the concept of Actors/Event-Driven Architectures in general. I have to implement a fairly complex problem where users can configure a "concurrent pipeline": Pipeline: consists of 1+ Stages; all Stages execute sequentially Stage: consists of 1+ Tasks; all Tasks execute in parallel Task: essentially a Java Runnable As you can see above, a Task is a Runnable that does some unit of work. Tasks are organized into Stages, which execute their Tasks in parallel. Stages are organized into the Pipeline, which executes its Stages sequentially. Hence if a user specifies the following Pipeline: CrossTheRoadSafelyPipeline Stage 1: Look Left Task 1: Turn your head to the left and look for cars Task 2: Listen for cars Stage 2: Look right Task 1: Turn your head to the right and look for cars Task 2: Listen for cars Then, Stage 1 will execute, and then Stage 2 will execute. However, while each Stage is executing, it's individual Tasks are executing in parallel/at the same time. In reality Pipelines will become very complicated, and with hundreds of Stages, dozens of Tasks per Stage (again, executing at the same time). To implement this Pipeline I can only think of several solutions: ESB/Apache Camel Guava Event Bus Java 5 Concurrency Actors/Akka Camel doesn't seem right because its core competency is integration not synchrony and orchestration across worker threads. Guava is great, but this doesn't really feel like a subscriber/publisher-type of problem. And Java 5 Concurrency (ExecutorService, etc.) just feels too low-level and painful. So I ask: is Akka a strong candidate for this type of problem? If so, how? If not, then why, and what is a good candidate?

  Read the article

• In Java Concurrency In Practice by Brian Goetz, why is the Memoizer class not annotated with @ThreadSafe?

  - by dig_dug

  Java Concurrency In Practice by Brian Goetz provides an example of a efficient scalable cache for concurrent use. The final version of the example showing the implementation for class Memoizer (pg 108) shows such a cache. I am wondering why the class is not annotated with @ThreadSafe? The client, class Factorizer, of the cache is properly annotated with @ThreadSafe. The appendix states that if a class is not annotated with either @ThreadSafe or @Immutable that it should be assumed that it isn't thread safe. Memoizer seems thread-safe though. Here is the code for Memoizer: public class Memoizer<A, V> implements Computable<A, V> { private final ConcurrentMap<A, Future<V>> cache = new ConcurrentHashMap<A, Future<V>>(); private final Computable<A, V> c; public Memoizer(Computable<A, V> c) { this.c = c; } public V compute(final A arg) throws InterruptedException { while (true) { Future<V> f = cache.get(arg); if (f == null) { Callable<V> eval = new Callable<V>() { public V call() throws InterruptedException { return c.compute(arg); } }; FutureTask<V> ft = new FutureTask<V>(eval); f = cache.putIfAbsent(arg, ft); if (f == null) { f = ft; ft.run(); } } try { return f.get(); } catch (CancellationException e) { cache.remove(arg, f); } catch (ExecutionException e) { throw launderThrowable(e.getCause()); } } } }

  Read the article

• JPA optimistic lock - setting @Version to entity class cause query to include VERSION as column

  - by masato-san

  I'm using JPA Toplink Essential, Netbean6.8, GlassFish v3 In my Entity class I added @Version annotation to enable optimistic locking at transaction commit however after I added the annotation, my query started including VERSION as column thus throwing SQL exception. None of this is mentioned in any tutorial I've seen so far. What could be wrong? Snippet public class MasatosanTest2 implements Serializable { private static final long serialVersionUID = 1L; @Id @Basic(optional = false) @Column(name = "id") private Integer id; @Column(name = "username") private String username; @Column(name = "note") private String note; //here adding Version @Version int version; query used: SELECT m FROM MasatosanTest2 m Internal Exception: com.microsoft.sqlserver.jdbc.SQLServerException Call: SELECT id, username, note, VERSION FROM MasatosanTest2

  Read the article

• How should I implement simple caches with concurrency on Redis?

  - by solublefish

  Background I have a 2-tier web service - just my app server and an RDBMS. I want to move to a pool of identical app servers behind a load balancer. I currently cache a bunch of objects in-process. I hope to move them to a shared Redis. I have a dozen or so caches of simple, small-sized business objects. For example, I have a set of Foos. Each Foo has a unique FooId and an OwnerId. One "owner" may own multiple Foos. In a traditional RDBMS this is just a table with an index on the PK FooId and one on OwnerId. I'm caching this in one process simply: Dictionary<int,Foo> _cacheFooById; Dictionary<int,HashSet<int>> _indexFooIdsByOwnerId; Reads come straight from here, and writes go here and to the RDBMS. I usually have this invariant: "For a given group [say by OwnerId], the whole group is in cache or none of it is." So when I cache miss on a Foo, I pull that Foo and all the owner's other Foos from the RDBMS. Updates make sure to keep the index up to date and respect the invariant. When an owner calls GetMyFoos I never have to worry that some are cached and some aren't. What I did already The first/simplest answer seems to be to use plain ol' SET and GET with a composite key and json value: SET( "ServiceCache:Foo:" + theFoo.Id, JsonSerialize(theFoo)); I later decided I liked: HSET( "ServiceCache:Foo", theFoo.FooId, JsonSerialize(theFoo)); That lets me get all the values in one cache as HVALS. It also felt right - I'm literally moving hashtables to Redis, so perhaps my top-level items should be hashes. This works to first order. If my high-level code is like: UpdateCache(myFoo); AddToIndex(myFoo); That translates into: HSET ("ServiceCache:Foo", theFoo.FooId, JsonSerialize(theFoo)); var myFoos = JsonDeserialize( HGET ("ServiceCache:FooIndex", theFoo.OwnerId) ); myFoos.Add(theFoo.OwnerId); HSET ("ServiceCache:FooIndex", theFoo.OwnerId, JsonSerialize(myFoos)); However, this is broken in two ways. Two concurrent operations can read/modify/write at the same time. The latter "wins" the final HSET and the former's index update is lost. Another operation could read the index in between the first and second lines. It would miss a Foo that it should find. So how do I index properly? I think I could use a Redis set instead of a json-encoded value for the index. That would solve part of the problem since the "add-to-index-if-not-already-present" would be atomic. I also read about using MULTI as a "transaction" but it doesn't seem like it does what I want. Am I right that I can't really MULTI; HGET; {update}; HSET; EXEC since it doesn't even do the HGET before I issue the EXEC? I also read about using WATCH and MULTI for optimistic concurrency, then retrying on failure. But WATCH only works on top-level keys. So it's back to SET/GET instead of HSET/HGET. And now I need a new index-like-thing to support getting all the values in a given cache. If I understand it right, I can combine all these things to do the job. Something like: while(!succeeded) { WATCH( "ServiceCache:Foo:" + theFoo.FooId ); WATCH( "ServiceCache:FooIndexByOwner:" + theFoo.OwnerId ); WATCH( "ServiceCache:FooIndexAll" ); MULTI(); SET ("ServiceCache:Foo:" + theFoo.FooId, JsonSerialize(theFoo)); SADD ("ServiceCache:FooIndexByOwner:" + theFoo.OwnerId, theFoo.FooId); SADD ("ServiceCache:FooIndexAll", theFoo.FooId); EXEC(); //TODO somehow set succeeded properly } Finally I'd have to translate this pseudocode into real code depending how my client library uses WATCH/MULTI/EXEC; it looks like they need some sort of context to hook them together. All in all this seems like a lot of complexity for what has to be a very common case; I can't help but think there's a better, smarter, Redis-ish way to do things that I'm just not seeing. How do I lock properly? Even if I had no indexes, there's still a (probably rare) race condition. A: HGET - cache miss B: HGET - cache miss A: SELECT B: SELECT A: HSET C: HGET - cache hit C: UPDATE C: HSET B: HSET ** this is stale data that's clobbering C's update. Note that C could just be a really-fast A. Again I think WATCH, MULTI, retry would work, but... ick. I know in some places people use special Redis keys as locks for other objects. Is that a reasonable approach here? Should those be top-level keys like ServiceCache:FooLocks:{Id} or ServiceCache:Locks:Foo:{Id}? Or make a separate hash for them - ServiceCache:Locks with subkeys Foo:{Id}, or ServiceCache:Locks:Foo with subkeys {Id} ? How would I work around abandoned locks, say if a transaction (or a whole server) crashes while "holding" the lock?

  Read the article

• Why is multithreading often preferred for improving performance?

  - by user1849534

  I have a question, it's about why programmers seems to love concurrency and multi-threaded programs in general. I'm considering 2 main approaches here: an async approach basically based on signals, or just an async approach as called by many papers and languages like the new C# 5.0 for example, and a "companion thread" that manages the policy of your pipeline a concurrent approach or multi-threading approach I will just say that I'm thinking about the hardware here and the worst case scenario, and I have tested this 2 paradigms myself, the async paradigm is a winner at the point that I don't get why people 90% of the time talk about multi-threading when they want to speed up things or make a good use of their resources. I have tested multi-threaded programs and async program on an old machine with an Intel quad-core that doesn't offer a memory controller inside the CPU, the memory is managed entirely by the motherboard, well in this case performances are horrible with a multi-threaded application, even a relatively low number of threads like 3-4-5 can be a problem, the application is unresponsive and is just slow and unpleasant. A good async approach is, on the other hand, probably not faster but it's not worst either, my application just waits for the result and doesn't hangs, it's responsive and there is a much better scaling going on. I have also discovered that a context change in the threading world it's not that cheap in real world scenario, it's in fact quite expensive especially when you have more than 2 threads that need to cycle and swap among each other to be computed. On modern CPUs the situation it's not really that different, the memory controller it's integrated but my point is that an x86 CPUs is basically a serial machine and the memory controller works the same way as with the old machine with an external memory controller on the motherboard. The context switch is still a relevant cost in my application and the fact that the memory controller it's integrated or that the newer CPU have more than 2 core it's not bargain for me. For what i have experienced the concurrent approach is good in theory but not that good in practice, with the memory model imposed by the hardware, it's hard to make a good use of this paradigm, also it introduces a lot of issues ranging from the use of my data structures to the join of multiple threads. Also both paradigms do not offer any security abut when the task or the job will be done in a certain point in time, making them really similar from a functional point of view. According to the X86 memory model, why the majority of people suggest to use concurrency with C++ and not just an async approach ? Also why not considering the worst case scenario of a computer where the context switch is probably more expensive than the computation itself ?

  Read the article

• Over Optimistic Daily Productivity

  - by Dan Revell

  I'm a junior developer and have been working since I graduated last summer so coming up to a year now. I have this issue that is starting to get to me. Every night I think back to what I did that day, feel bad that I didn't get as much done as I would have liked and then tick off in my head all the things I'll get done the following day. Come the end of the following day I end haven't gotten through half of what I wanted to. This over optimism that I'm suffering from. Might it be just because I'm relatively new to the profession and aren't aware of how long things will actually take me. The work might be quick to think through in my head but all sorts of time sync's involved can bleed away the hours. If not that then perhaps it's the technology stack that I'm working on. SharePoint isn't the easiest thing to develop for and it's certainly something I came into not knowing a whole lot about. If it's because I'm not yet skilled enough to predict how long things will take me, is this trait of over optimistic predictions universal to the profession? I'd appreciate any input from those experienced with working with younger developers and those that might have suffered from this themselves. [EDIT] Perhaps I worded the question badly. I'm interested in just general day to day work rather than overall project completion estimation.

  Read the article

• EF Forced Concurrency Checks

  - by Imran

  Hi, I have an issue with EF 4.0 that I hope someone can help with. I currently have an entity that I want to update in a last in wins fashion (i.e. ignore concurrency checks and just overwrite whats in the db with what is submitted). It seems Entity Framework not only includes the primary key of the entity in the where clause of the generated sql, but also any foreign key fields. This is annoying as it means that I don't get true last in wins semantics and need to know what value the fk field had before the update or I get a concurrency exception. I am aware that this can be short circuited by including a foreign key field as well as the navigation property on the entity. I would like to avoid this if possible as it's not a very clean solution. I was just wondering if there was any other way to override this behaviour? It seems like more of a bug than a feature. I have no problem with ef doing concurrency checks if I instruct it to do so but not being able to bypass concurrency completely is a bit of a hindrance as there are many valid scenarios where this is not needed

  Read the article

• Groovy concurrency: A better way to aggregate results semantically?

  - by Stefan Kendall

  I need to call a number of methods in parallel and wait for results. Each relies on different resources, so they may return at different times. I need to wait until I receive all results or time out after a certain amount of time. I could just spawn threads with a reference to a shared object via a method call, but is there a better, more groovy way to do this?

  Read the article

• How to leverage concurrency checking with EF 4.0 POCO Self Tracking Entities in a N-Tier scenario?

  - by Mark Lindell

  I'm using VS1010RC with the POCO self tracking T4 templates. In my WCF update service method I am using something similar to the following: using (var context = new MyContext()) { context.MyObjects.ApplyChanges(myObject); context.SaveChanges(); } This works fine until I set ConcurrencyMode=Fixed on the entity and then I get an exception. It appears as if the context does not know about the previous values as the SQL statement is using the changed entities value in the WHERE clause. What is the correct approach when using ConcurrencyMode=Fixed?

  Read the article

• What is a good motivating example for dataflow concurrency?

  - by Alex Miller

  I understand the basics of dataflow programming and have encountered it a bit in Clojure APIs, talks from Jonas Boner, GPars in Groovy, etc. I know it's prevalent in languages like Io (although I have not studied Io). What I am missing is a compelling reason to care about dataflow as a paradigm when building a concurrent program. Why would I use a dataflow model instead of a mutable state+threads+locks model (common in Java, C++, etc) or an actor model (common in Erlang or Scala) or something else? In particular, while I know of library support in the languages above (and Scala and Ruby), I don't know of a single program or library that is a poster child user of this model. Who is using it? Why do they find it better than the other models I mentioned?

  Read the article

• Mysql concurrency: what happens if a locked table is accessed?

  - by PixelSapiens

  the question is rather simple but I couldn't find a precise answer: in a myisam db, what happens if a php file locks a table (with an atomic operation, say an INSERT) and another php file tries to access the same table (reading or writing)? Now, while it is obvious that the second session will not be able to access the table, what exactly happens? Does it return some kind of error? Does is wait in queue until it is able to access it? Thanks in advance!

  Read the article

• scalablity of Scala over Java

  - by Marcus

  I read an article that says Scala handles concurrency better than Java. http://www.theserverside.com/feature/Solving-the-Scalability-Paradox-with-Scala-Clojure-and-Groovy ...the scalability limitation is confined specifically to the Java programming language itself, but it is not a limitation of the Java platform as a whole... The scalability issues with Java aren't a new revelation. In fact, plenty of work has been done to address these very issues, with two of the most successful projects being the programming languages named Scala and Clojure... ...Scala is finding ways around the problematic thread and locking paradigm of the Java language... How is this possible? Doesn't Scala use Java's core libraries which brings all the threading and locking issues from Java to Scala?

  Read the article

• SQLite with two python processes accessing it: one reading, one writing

  - by BBnyc

  I'm developing a small system with two components: one polls data from an internet resource and translates it into sql data to persist it locally; the second one reads that sql data from the local instance and serves it via json and a restful api. I was originally planning to persist the data with postgresql, but because the application will have a very low-volume of data to store and traffic to serve, I thought that was overkill. Is SQLite up to the job? I love the idea of the small footprint and no need to maintain yet another sql server for this one task, but am concerned about concurrency. It seems that with write ahead logging enabled, concurrently reading and writing a SQLite database can happen without locking either process out of the database. Can a single SQLite instance sustain two concurrent processes accessing it, if only one reads and the other writes? I started writing the code but was wondering if this is a misapplication of SQLite.

  Read the article

• Any frameworks or library allow me to run large amount of concurrent jobs schedully?

  - by Yoga

  Are there any high level programming frameworks that allow me to run large amount of concurrent jobs schedully? e.g. I have 100K of urls need to check their uptime every 5 minutes Definitely I can write a program to handle this, but then I need to handle concurrency, queuing, error handling, system throttling, job distribution etc. Will there be a framework that I only focus on a particular job (i.e. the ping task) and the system will take care of the scaling and error handling for me? I am open to any language.

  Read the article

• Performance of concurrent software on multicore processors

  - by Giorgio

  Recently I have often read that, since the trend is to build processors with multiple cores, it will be increasingly important to have programming languages that support concurrent programming in order to better exploit the parallelism offered by these processors. In this respect, certain programming paradigms or models are considered well-suited for writing robust concurrent software: Functional programming languages, e.g. Haskell, Scala, etc. The actor model: Erlang, but also available for Scala / Java (Akka), C++ (Theron, Casablanca, ...), and other programming languages. My questions: What is the state of the art regarding the development of concurrent applications (e.g. using multi-threading) using the above languages / models? Is this area still being explored or are there well-established practices already? Will it be more complex to program applications with a higher level of concurrency, or is it just a matter of learning new paradigms and practices? How does the performance of highly concurrent software compare to the performance of more traditional software when executed on multiple core processors? For example, has anyone implemented a desktop application using C++ / Theron, or Java / Akka? Was there a boost in performance on a multiple core processor due to higher parallelism?

  Read the article

• Can Clojure's thread-based agents handle c10k performance?

  - by elliot42

  I'm writing a c10k-style service and am trying to evaluate Clojure's performance. Can Clojure agents handle this scale of concurrency with its thread-based agents? Other high performance systems seem to be moving towards async-IO/events/greenlets, albeit at a seemingly higher complexity cost. Suppose there are 10,000 clients connected, sending messages that should be appended to 1,000 local files--the Clojure service is trying to write to as many files in parallel as it can, while not letting any two separate requests mangle the same single file by writing at the same time. Clojure agents are extremely elegant conceptually--they would allow separate files to be written independently and asynchronously, while serializing (in the database sense) multiple requests to write to the same file. My understanding is that agents work by starting a thread for each operation (assume we are IO-bound and using send-off)--so in this case is it correct that it would start 1,000+ threads? Can current-day systems handle this number of threads efficiently? Most of them should be IO-bound and sleeping most of the time, but I presume there would still be a context-switching penalty that is theoretically higher than async-IO/event-based systems (e.g. Erlang, Go, node.js). If the Clojure solution can handle the performance, it seems like the most elegant thing to code. However if it can't handle the performance then something like Erlang or Go's lightweight processes might be preferable, since they are designed to have tens of thousands of them spawned at once, and are only moderately more complex to implement. Has anyone approached this problem in Clojure or compared to these other platforms? (Thanks for your thoughts!)

  Read the article

< Previous Page | 1 2 3 4 5 6 7 8 9 10 11 12  | Next Page >