Search Results

Search found 191 results on 8 pages for 'pagelet producer'.

Page 3/8 | < Previous Page | 1 2 3 4 5 6 7 8  | Next Page >

  • In .NET, what thread will Events be handled in?

    - by Ben
    I have attempted to implement a producer/consumer pattern in c#. I have a consumer thread that monitors a shared queue, and a producer thread that places items onto the shared queue. The producer thread is subscribed to receive data...that is, it has an event handler, and just sits around and waits for an OnData event to fire (the data is being sent from a 3rd party api). When it gets the data, it sticks it on the queue so the consumer can handle it. When the OnData event does fire in the producer, I had expected it to be handled by my producer thread. But that doesn't seem to be what is happening. The OnData event seems as if it's being handled on a new thread instead! Is this how .net always works...events are handled on their own thread? Can I control what thread will handle events when they're raised? What if hundreds of events are raised near-simultaneously...would each have its own thread?

    Read the article

  • Are there any tools to optimize the number of consumer and producer threads on a JMS queue?

    - by lindelof
    I'm working on an application that is distributed over two JBoss instances and that produces/consumes JMS messages on several JMS queues. When we configured the application we had to determine which threading model we would use, in particular the number of producing and consuming threads per queue. We have done this in a rather ad-hoc fashion but after reading the most recent columns by Herb Sutter in Dr Dobbs (in particular this one) I would like to size our threads in a more rigorous manner. Are there any methods/tools to measure the throughput of JMS queues (in particular JBoss Messaging queues) as a function of the number of producing/consuming threads?

    Read the article

  • Hosting an NServiceBus subscriber in the same application as the producer.

    - by Jack Ryan
    Is it possible to use NServiceBus to publish and consume messages in the same application, specifically a web application? In the future we will almost certainly need to maintain a separate long running service to process messages generated by this application, and this is why we are hoping to use NServiceBus from the start, but right now it would be nice to just start up the consumer and the publisher when the web application starts. This will make testing and deployment far easier for us. I presume I will need to reference the NServiceBus.Host.exe and start up the process in the global.asax, but need help on what exactly I need to call to do this.

    Read the article

  • How do I create two mutual producer/consumers with internal state in Haskell?

    - by Keith
    I've got an agent that takes in states and returns actions, while keeping an internal representation of the utility of state/action pairs. I've also got an environment that takes in actions and returns state/reward pairs. I need to be able to set the agent up with a start state and then continuously go from agent -(action)- environment -(state, reward)- agent -(action)-... However, the internal states (which need to be updated every iteration) need to stay private (that is, within the agent or the environment). This means that I can't simply call environment as a function within the agent using state and action as arguments. I'm somewhat of a Haskell noobie, so I'm not even sure if this is possible.

    Read the article

  • RabbitMQ as a proxy between a data store and a producer ?

    - by hyperboreean
    I have some code that produces lots of data that should be stored in the database. The problem is that the database can't keep with the data that it gets produced. So I am wondering whether some kind of queuing mechanism would help in this situation - I am thinking in particular at RabiitMQ and whether is feasible to have the data stored in its queues until some consumer gets the data out of it and pushes it to the database. Also, I am not particular interested whether that data made it to the database or not because pretty soon, the same data will be updated.

    Read the article

  • Thread locking issue with FileHelpers between calling engine.ReadNext() method and readign engine.Li

    - by Rad
    I use producer/consumer pattern with FileHelpers library to import data from one file (which can be huge) using multiple threads. Each thread is supposed to import a chunk of that file and I would like to use LineNumber property of the FileHelperAsyncEngine instance that is reading the file as primary key for imported rows. FileHelperAsyncEngine internally has an IEnumerator IEnumerable.GetEnumerator(); which is iterated over using engine.ReadNext() method. That internally sets LineNumber property (which seems is not thread safe). Consumers will have Producers assiciated with them that will supply DataTables to Consumers which will consume them via SqlBulkLoad class which will use IDataReader implementation which will iterate over a collection of DataTables which are internal to a Consumer instance. Each instance of will have one SqlBulkCopy instance associate with it. I have thread locking issue. Below is how I create multiple Producer threads. I start each thread afterwords. Produce method on a producer instance will be called determining which chunk of input file will be processed. It seems that engine.LineNumber is not thread safe and I doesn't import a proper LineNumber in the database. It seems that by the time engine.LineNumber is read some other thread called engine.ReadNext() and changed engine.LineNumber property. I don't want to lock the loop that is supposed to process a chunk of input file because I loose parallelism. How to reorganize the code to solve this threading issue? Thanks Rad for (int i = 0; i < numberOfProducerThreads; i++) DataConsumer consumer = dataConsumers[i]; //create a new producer DataProducer producer = new DataProducer(); //consumer has already being created consumer.Subscribe(producer); FileHelperAsyncEngine orderDetailEngine = new FileHelperAsyncEngine(recordType); orderDetailEngine.Options.RecordCondition.Condition = RecordCondition.ExcludeIfBegins; orderDetailEngine.Options.RecordCondition.Selector = STR_ORDR; int skipLines = i * numberOfBufferTablesToProcess * DataBuffer.MaxBufferRowCount; Thread newThread = new Thread(() => { producer.Produce(consumer, inputFilePath, lineNumberFieldName, dict, orderDetailEngine, skipLines, numberOfBufferTablesToProcess); consumer.SetEndOfData(producer); }); producerThreads.Add(newThread); thread.Start();} public void Produce(DataConsumer consumer, string inputFilePath, string lineNumberFieldName, Dictionary<string, object> dict, FileHelperAsyncEngine engine, int skipLines, int numberOfBufferTablesToProcess) { lock (this) { engine.Options.IgnoreFirstLines = skipLines; engine.BeginReadFile(inputFilePath); } int rowCount = 1; DataTable buffer = consumer.BufferDataTable; while (engine.ReadNext() != null) { lock (this) { dict[lineNumberFieldName] = engine.LineNumber; buffer.Rows.Add(ObjectFieldsDataRowMapper.MapObjectFieldsToDataRow(engine.LastRecord, dict, buffer)); if (rowCount % DataBuffer.MaxBufferRowCount == 0) { consumer.AddBufferDataTable(buffer); buffer = consumer.BufferDataTable; } if (rowCount % (numberOfBufferTablesToProcess * DataBuffer.MaxBufferRowCount) == 0) { break; } rowCount++; } } if (buffer.Rows.Count > 0) { consumer.AddBufferDataTable(buffer); } engine.Close(); }

    Read the article

  • What are alternatives to Win32 PulseEvent() function?

    - by Bill
    The documentation for the Win32 API PulseEvent() function (kernel32.dll) states that this function is “… unreliable and should not be used by new applications. Instead, use condition variables”. However, condition variables cannot be used across process boundaries like (named) events can. I have a scenario that is cross-process, cross-runtime (native and managed code) in which a single producer occasionally has something interesting to make known to zero or more consumers. Right now, a well-known named event is used (and set to signaled state) by the producer using this PulseEvent function when it needs to make something known. Zero or more consumers wait on that event (WaitForSingleObject()) and perform an action in response. There is no need for two-way communication in my scenario, and the producer does not need to know if the event has any listeners, nor does it need to know if the event was successfully acted upon. On the other hand, I do not want any consumers to ever miss any events. In other words, the system needs to be perfectly reliable – but the producer does not need to know if that is the case or not. The scenario can be thought of as a “clock ticker” – i.e., the producer provides a semi-regular signal for zero or more consumers to count. And all consumers must have the correct count over any given period of time. No polling by consumers is allowed (performance reasons). The ticker is just a few milliseconds (20 or so, but not perfectly regular). Raymen Chen (The Old New Thing) has a blog post pointing out the “fundamentally flawed” nature of the PulseEvent() function, but I do not see an alternative for my scenario from Chen or the posted comments. Can anyone please suggest one? Please keep in mind that the IPC signal must cross process boundries on the machine, not simply threads. And the solution needs to have high performance in that consumers must be able to act within 10ms of each event.

    Read the article

  • In C#, what thread will Events be handled in?

    - by Ben
    Hi, I have attempted to implement a producer/consumer pattern in c#. I have a consumer thread that monitors a shared queue, and a producer thread that places items onto the shared queue. The producer thread is subscribed to receive data...that is, it has an event handler, and just sits around and waits for an OnData event to fire (the data is being sent from a 3rd party api). When it gets the data, it sticks it on the queue so the consumer can handle it. When the OnData event does fire in the producer, I had expected it to be handled by my producer thread. But that doesn't seem to be what is happening. The OnData event seems as if it's being handled on a new thread instead! Is this how .net always works...events are handled on their own thread? Can I control what thread will handle events when they're raised? What if hundreds of events are raised near-simultaneously...would each have its own thread? Thank in advance! Ben

    Read the article

  • .Net Thread Synchronization

    - by user209293
    Hello, I am planning to use Auto reset Event Handle for Inter Thread communication. EventWaitHandle handle = new EventWaitHandle(false, EventResetMode.AutoReset); My producer thread code look like below produceSomething(); handle.Set(); In the consumer thread, I have to download data for every one minute or when prodcuer is called Set method try { while(true) { handle.WaitOne(60000, false); doSomething(); - downloads data from internet. takes lot of time to complete it. } } catch(ThreadAbortException) { cleanup(); } My question is if consumer thread is running doSomething funtion and producer calls set function, what would be state of Auto reset event object? My requreiment is as soon as producer calls set method i have to downlaod fresh data from intenet . If doSomething function is running, when Producer calls set method, i have to interrupt it and call again. Any help is appreciated. Regards Raju

    Read the article

  • Design pattern for an automated mechanical test bench

    - by JJS
    Background I have a test fixture with a number of communication/data acquisition devices on it that is used as an end of line test for a product. Because of all the various sensors used in the bench and the need to run the test procedure in near real-time, I'm having a hard time structuring the program to be more friendly to modify later on. For example, a National Instruments USB data acquisition device is used to control an analog output (load) and monitor an analog input (current), a digital scale with a serial data interface measures position, an air pressure gauge with a different serial data interface, and the product is interfaced through a proprietary DLL that handles its own serial communication. The hard part The "real-time" aspect of the program is my biggest tripping point. For example, I need to time how long the product needs to go from position 0 to position 10,000 to the tenth of a second. While it's traveling, I need to ramp up an output of the NI DAQ when it reaches position 6,000 and ramp it down when it reaches position 8,000. This sort of control looks easy from browsing NI's LabVIEW docs but I'm stuck with C# for now. All external communication is done by polling which makes for lots of annoying loops. I've slapped together a loose Producer Consumer model where the Producer thread loops through reading the sensors and sets the outputs. The Consumer thread executes functions containing timed loops that poll the Producer for current data and execute movement commands as required. The UI thread polls both threads for updating some gauges indicating current test progress. Unsure where to start Is there a more appropriate pattern for this type of application? Are there any good resources for writing control loops in software (non-LabVIEW) that interface with external sensors and whatnot?

    Read the article

  • request response with activemq - always send double response.

    - by Chris Valley
    Hi, I'm new at activeMq. I tried to create a simple request response like this. public Listener(string destination) { // set factory ConnectionFactory factory = new ConnectionFactory(URL); IConnection connection; try { connection = factory.CreateConnection(); connection.Start(); ISession session = connection.CreateSession(); // create consumer for designated destination IMessageConsumer consumer = session.CreateConsumer(new Apache.NMS.ActiveMQ.Commands.ActiveMQQueue(destination)); consumer.Listener += new MessageListener(consumer_Listener); Console.ReadLine(); } catch (Exception ex) { Console.WriteLine(ex.ToString()); throw new Exception("Exception in Listening ", ex); } } The OnMessage static void consumer_Listener(IMessage message) { IConnectionFactory factory = new ConnectionFactory("tcp://localhost:61616/"); using (IConnection connection = factory.CreateConnection()) { //Create the Session using (ISession session = connection.CreateSession()) { //Create the Producer for the topic/queue // IMessageProducer prod = session.CreateProducer(new Apache.NMS.ActiveMQ.Commands.ActiveMQTempQueue(message.NMSDestination)); IMessageProducer producer = session.CreateProducer(message.NMSDestination); // Create Response // IMessage response = session.CreateMessage(); ITextMessage response = producer.CreateTextMessage("Replied from VS2010 Test"); //response.NMSReplyTo = new Apache.NMS.ActiveMQ.Commands.ActiveMQQueue("testQ1"); response.NMSCorrelationID = message.NMSCorrelationID; if (message.NMSReplyTo != null) { producer.Send(message.NMSReplyTo, response); Console.WriteLine("Receive: " + ((ITextMessage)message).NMSCorrelationID); Console.WriteLine("Received from : " + message.NMSDestination.ToString()); Console.WriteLine("----------------------------------------------------"); } } } } Every time i tried to send a request to the listener, the response always send repeatedly. The first response will have NMSReplyTo properties while the other not. My workaround to stop this situation by cheking the NMSReplyTo properties if (message.NMSReplyTo != null) { producer.Send(message.NMSReplyTo, response); Console.WriteLine("Receive: " + ((ITextMessage)message).NMSCorrelationID); Console.WriteLine("Received from : " + message.NMSDestination.ToString()); Console.WriteLine("----------------------------------------------------"); } In my understanding, this happened because there was a circular send response in the listener to the same Queue. Could you guys help me how to fix this? Many Thanks, Chris

    Read the article

  • Critical Threads Optimization

    - by Rafael Vanoni
    Background One of the more common issues we've been seeing in the field is the growing difficulty in optimizing performance of multi-threaded applications. A good portion of this difficulty is due to the increasing complexity of modern processors that present various degrees of sharing relationships between hardware components. Take any current CMT processor and you'll find any number of CPUs sharing execution pipelines, floating point units, caches, etc. Consequently, applying the traditional recipe of one software thread for each CPU will have varying degrees of success, according to the layout of the underlying hardware. On top of this increasing complexity we've also seen processors with features that aim at dynamically resourcing software threads according to their utilization. Intel's Turbo Boost allows processors to increase their operating frequency if there is enough thermal headroom available and the processor isn't fully utilized. More recently, the SPARC T4 processor introduced dynamic threading, allowing each core to dynamically allocate more resources to its active CPUs. Both cases are in essence recognizing that current processors will be running a wide mix of workloads, some will be designed for throughput, others for low latency. The hardware is providing mechanisms to dynamically resource threads according to their runtime behavior. We're very aware of these challenges in Solaris, and have been working to provide the best out of box performance while providing mechanisms to further optimize applications when necessary. The Critical Threads Optimzation was introduced in Solaris 10 8/11 and Solaris 11 as one such mechanism that allows customers to both address issues caused by contention over shared hardware resources and explicitly take advantage of features such as T4's dynamic threading. What it is The basic idea is to allow performance critical threads to execute with more exclusive access to hardware resources. For example, when deploying an application that implements a producer/consumer model, it'll likely be advantageous to give the producer more exclusive access to the hardware instead of having it competing for resources with all the consumers. In the case of a T4 based system, we may want to have a producer running by itself on a single core and create one consumer for each of the remaining CPUs. With the Critical Threads Optimization we're extending the semantics of scheduling priorities (which thread should run first) to include priority over shared resources (which thread should have more "space"). Now the scheduler will not only run higher priority threads first: it will also provide them with more exclusive access to hardware resources if they are available. How does it work ? Using the previous example in Solaris 11, all you'd have to do would be to place the producer in the Fixed Priority (FX) scheduling class at priority 60, or in the Real Time (RT) class at any priority and Solaris will try to give it more "hardware space". On both Solaris 10 8/11 and Solaris 11 this can be achieved through the existing priocntl(1,2) and priocntlset(2) interfaces. If your application already assigns these priorities to performance critical threads, there's no additional step you need to take. One important aspect of this optimization is that it requires some level of idleness in the system, either as a result of sizing the application before hand or through periods of transient idleness during runtime. If the system is fully committed, the scheduler will put all the available CPUs to work.Best practices If you're an application developer, we encourage you to look into assigning the right priorities for the different threads in your application. Solaris provides different scheduling classes (Time Share, Interactive, Fair Share, Fixed Priority and Real Time) that offer different policies and behaviors. It is not always simple to figure out which set of threads are critical to the performance of a workload, and it may not always be feasible to take advantage of this optimization, but we believe that this can be correctly (and safely) done during development. Overall, the out of box performance in Solaris should meet your workload's requirements. If you are looking into that extra bit of performance, then the Critical Threads Optimization may be what you're looking for.

    Read the article

  • Why does ActiveMQ hold messages that should be deleted from Topic?

    - by rauch
    I use ActiveMQ as Notification System(Pub/Sub model). On server: if any changes of data occur, Server send this updated data (File) to Topic using BlobMessages. There are few Clients, that subscribe on this Topic and get updated File if it exsist in Topic. The problem is that all of BlobMessages, that were sent to Topic, are hold by ActiveMQ all time. this.producer = new ProducerTool.Builder("tcp://localhost:61616?jms.blobTransferPolicy.uploadUrl=http://localhost:8161/fileserver/", "ServerProdTopic").topic(true) .transacted(false).durable(false).timeToLive(10000L).build(); this.consumer = new ConsumerTool.Builder("tcp://localhost:61616", "ServerConsTopic").topic(true) .transacted(false).durable(false).build(); consumer.setMessageListener(this); The File is sent: connection = createConnection(); session = createSession(connection); producer = createProducer(session); BlobMessage blobMsg = ((ActiveMQSession) session).createBlobMessage(resource); blobMsg.setStringProperty("sourceName", resource.getName()); producer.send(blobMsg); if (transacted) { System.out.println("Producer Committing..."); session.commit(); } Where createProducer is: protected Connection createConnection() throws JMSException, Exception { ActiveMQConnectionFactory connectionFactory = new ActiveMQConnectionFactory(url); //connectionFactory.getBlobTransferPolicy().setUploadUrl("http://localhost:8161/fileserver/"); Connection connection = connectionFactory.createConnection(); connection.start(); ((ActiveMQConnection) connection).setCopyMessageOnSend(false); return connection; } All, that could be useful I set as need: Session.AUTO_ACKNOWLEDGE; Non-Durable Subscription; TimeToLive = 9000; JMSDeliveryMode = Non-Persistent; What I have at runtime: in ActiveMQ directory: ~/apache-activemq-5.3.0/webapps/fileserver/ tere are all File, that where delivered and not delivered to Subscribers. Why? Sometimes Server send Big Files about 1 GB....And even this Files are hold at that directory, Even after stopping Subscribers(Clients), Publisher(Server) and ActiveMQ Broker.

    Read the article

  • nHibernate query looking for the related object's related object

    - by code-zoop
    I have an nHibernate querie issue that looks quite straight forward, but I can't seem to get my head around it! I am creating some simple example classes to illustrate my problem: public class Car { public int Id { get; set; } public IList<Interior> InteriorParts { get; set; } } public class Interior { public int Id { get; set; } public InteriorProducer Producer { get; set; } } public class InteriorProducer { public int Id { get; set; } } Now to the query: I have the id of the InteriorProducer, but need to get a list of Cars where the interior have been produced by the interior producer. So in a simple, pseudo SQL, it looks something like this: select cars where car.InteriorParts.Producer.Id = Id I have a really hard time getting my head around this to create an nHibernate query. Any Ideas? Thanks

    Read the article

  • Using a backwards relation (i.e FOO_set) for ModelChoiceField in Django

    - by Bwmat
    I have a model called Movie, which has a ManyToManyField called director to a model called Person, and I'm trying to create a form with ModelChoiceField like so: class MovieSearchForm(forms.Form): producer = forms.ModelChoiceField(label='Produced by', queryset=movies.models.Person.producer_set, required=False) but this seems to be failing to compile (I'm getting a ViewDoesNotExist exception for the view that uses the form, but it goes away if I just replace the queryset with all the person objects), I'm guessing because '.producer_set' is being evaluated too 'early'. How can I get this work? here are the relevant parts of the movie/person classes: class Person(models.Model): name = models.CharField(max_length=100) class Movie(models.Model): ... producer = models.ForeignKey(Person, related_name="producers") director = models.ForeignKey(Person, related_name="directors") What I'm trying to do is get ever Person who is used in the producer field of some Movie.

    Read the article

  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

    Read the article

  • Use Case Diagrams - should I create a diagram just for a view business rule?

    - by Periback
    I'm modeling a UCD where I have two actors ( a content producer and a developer).. the content producer is going to create and specify details of a storyboard functionality, and the other actor (developer) will only be able to view this storyboard ( he'll log in the application and read the storyboard to start developing what it says, outside the application..) I'm working on the specification of this storyboard functionality and I'd like to know it would be like a best-practice if I describe something like " actor- developer", "UCD - read scenes of storyboard" . This is the specification of an application I developed for my thesis and they asked me to add some specification...

    Read the article

  • ActiveMQ 5.2.0 + REST + HTTP POST = java.lang.OutOfMemoryError

    - by Bruce Loth
    First off, I am a newbie when it comes to JMS & ActiveMQ. I have been looking into a messaging solution to serve as middleware for a message producer that will insert XML messages into a queue via HTTP POST. The producer is an existing system written in C++ that cannot be modified (so Java and the C++ API are out). Using the "demo" examples and some trial and error, I have cobbled together a working example of what I want to do (on a windows box). The web.xml I configured in a test directory under "webapps" specifies that the HTTP POST messages received from the producer are to be handled by the MessageServlet. I added a line for the text app in "activemq.xml" ('ow' is the test app dir): I created a test script to "insert" messages into the queue which works well. The problem I am running into is that it as I continue to insert messages via REST/HTTP POST, the memory consumption and thread count used by ActiveMQ continues to rise (It happens when I have timely consumers as well as slow or non-existent consumers). When memory consumption gets around 250MB's and the thread count exceeds 5000 (as shown in windows task manager), ActiveMQ crashes and I see this in the log: Exception in thread "ActiveMQ Transport Initiator: vm://localhost#3564" java.lang.OutOfMemoryError: unable to create new native thread It is as if Jetty is spawning a new thread to handle each HTTP POST and the thread never dies. I did look at this page: http://activemq.apache.org/javalangoutofmemory.html and tried but that didn't fix the problem (although I didn't fully understand the implications of the change either). Does anyone have any ideas? Thanks! Bruce Loth PS - I included the "test message producer" python script below for what it is worth. I created batches of 100 messages and continued to run the script manually from the command line while watching the memory consumption and thread count of ActiveMQ in task manager. def foo(): import httplib, urllib body = "<?xml version='1.0' encoding='UTF-8'?>\n \ <ROOT>\n \ [snip: xml deleted to save space] </ROOT>" headers = {"content-type": "text/xml", "content-length": str(len(body))} conn = httplib.HTTPConnection("127.0.0.1:8161") conn.request("POST", "/ow/message/RDRCP_Inbox?type=queue", body, headers) response = conn.getresponse() print response.status, response.reason data = response.read() conn.close() ## end method definition ## Begin test code count = 0; while(count < 100): # Test with batches of 100 msgs count += 1 foo()

    Read the article

  • Multithreading, when to yield versus sleep

    - by aaa
    hello. To clarify terminology, yield is when thread gives up its time slice. My platform of interest is POSIX threads, but I think question is general. Suppose I have consumer/producer pattern. If I want to throttle either consumer or producer, which is better to use, sleep or yield? I am mostly interested in efficiency of using either function. Thanks

    Read the article

  • What are common uses of condition variables in C++?

    - by jasonline
    I'm trying to learn about condition variables. I would like to know what are the common situations where condition variables are used. One example is in a blocking queue, where two threads access the queue - the producer thread pushes an item into the queue, while the consumer thread pops an item from the queue. If the queue is empty, the consumer thread is waiting until a signal is sent by the producer thread. What are other design situations where you need a condition variable to be used?

    Read the article

  • Fork two processes and kill the second when the first is done

    - by User1
    The title says it all. I want to create a bash script that will launch two processes and kill the second process when the first is done. Here's an example: #fork first process producer& #fork second process consumer& #wait for producer to finish ... #kill the consumer ... I have a feeling this can get ugly but has a very simple solution. Please help me fill in the blanks.

    Read the article

  • Question regarding xsd

    - by Hima
    I have an application which reads the data from the database, creates an object out of the data, marshalls it into an xml and enqueue the xml to a queue which is producer. The xml is dequeued from the queue by a consumer. I need to use xsds at two different places. For database access while reading the data from the database and For interaction between producer and consumer Can the same xsd be used in both the cases? Or do I need to use different xsds?

    Read the article

  • C#: System.Collections.Concurrent.ConcurrentQueue vs. Queue

    - by James Michael Hare
    I love new toys, so of course when .NET 4.0 came out I felt like the proverbial kid in the candy store!  Now, some people get all excited about the IDE and it’s new features or about changes to WPF and Silver Light and yes, those are all very fine and grand.  But me, I get all excited about things that tend to affect my life on the backside of development.  That’s why when I heard there were going to be concurrent container implementations in the latest version of .NET I was salivating like Pavlov’s dog at the dinner bell. They seem so simple, really, that one could easily overlook them.  Essentially they are implementations of containers (many that mirror the generic collections, others are new) that have either been optimized with very efficient, limited, or no locking but are still completely thread safe -- and I just had to see what kind of an improvement that would translate into. Since part of my job as a solutions architect here where I work is to help design, develop, and maintain the systems that process tons of requests each second, the thought of extremely efficient thread-safe containers was extremely appealing.  Of course, they also rolled out a whole parallel development framework which I won’t get into in this post but will cover bits and pieces of as time goes by. This time, I was mainly curious as to how well these new concurrent containers would perform compared to areas in our code where we manually synchronize them using lock or some other mechanism.  So I set about to run a processing test with a series of producers and consumers that would be either processing a traditional System.Collections.Generic.Queue or a System.Collection.Concurrent.ConcurrentQueue. Now, I wanted to keep the code as common as possible to make sure that the only variance was the container, so I created a test Producer and a test Consumer.  The test Producer takes an Action<string> delegate which is responsible for taking a string and placing it on whichever queue we’re testing in a thread-safe manner: 1: internal class Producer 2: { 3: public int Iterations { get; set; } 4: public Action<string> ProduceDelegate { get; set; } 5: 6: public void Produce() 7: { 8: for (int i = 0; i < Iterations; i++) 9: { 10: ProduceDelegate(“Hello”); 11: } 12: } 13: } Then likewise, I created a consumer that took a Func<string> that would read from whichever queue we’re testing and return either the string if data exists or null if not.  Then, if the item doesn’t exist, it will do a 10 ms wait before testing again.  Once all the producers are done and join the main thread, a flag will be set in each of the consumers to tell them once the queue is empty they can shut down since no other data is coming: 1: internal class Consumer 2: { 3: public Func<string> ConsumeDelegate { get; set; } 4: public bool HaltWhenEmpty { get; set; } 5: 6: public void Consume() 7: { 8: bool processing = true; 9: 10: while (processing) 11: { 12: string result = ConsumeDelegate(); 13: 14: if(result == null) 15: { 16: if (HaltWhenEmpty) 17: { 18: processing = false; 19: } 20: else 21: { 22: Thread.Sleep(TimeSpan.FromMilliseconds(10)); 23: } 24: } 25: else 26: { 27: DoWork(); // do something non-trivial so consumers lag behind a bit 28: } 29: } 30: } 31: } Okay, now that we’ve done that, we can launch threads of varying numbers using lambdas for each different method of production/consumption.  First let's look at the lambdas for a typical System.Collections.Generics.Queue with locking: 1: // lambda for putting to typical Queue with locking... 2: var productionDelegate = s => 3: { 4: lock (_mutex) 5: { 6: _mutexQueue.Enqueue(s); 7: } 8: }; 9:  10: // and lambda for typical getting from Queue with locking... 11: var consumptionDelegate = () => 12: { 13: lock (_mutex) 14: { 15: if (_mutexQueue.Count > 0) 16: { 17: return _mutexQueue.Dequeue(); 18: } 19: } 20: return null; 21: }; Nothing new or interesting here.  Just typical locks on an internal object instance.  Now let's look at using a ConcurrentQueue from the System.Collections.Concurrent library: 1: // lambda for putting to a ConcurrentQueue, notice it needs no locking! 2: var productionDelegate = s => 3: { 4: _concurrentQueue.Enqueue(s); 5: }; 6:  7: // lambda for getting from a ConcurrentQueue, once again, no locking required. 8: var consumptionDelegate = () => 9: { 10: string s; 11: return _concurrentQueue.TryDequeue(out s) ? s : null; 12: }; So I pass each of these lambdas and the number of producer and consumers threads to launch and take a look at the timing results.  Basically I’m timing from the time all threads start and begin producing/consuming to the time that all threads rejoin.  I won't bore you with the test code, basically it just launches code that creates the producers and consumers and launches them in their own threads, then waits for them all to rejoin.  The following are the timings from the start of all threads to the Join() on all threads completing.  The producers create 10,000,000 items evenly between themselves and then when all producers are done they trigger the consumers to stop once the queue is empty. These are the results in milliseconds from the ordinary Queue with locking: 1: Consumers Producers 1 2 3 Time (ms) 2: ---------- ---------- ------ ------ ------ --------- 3: 1 1 4284 5153 4226 4554.33 4: 10 10 4044 3831 5010 4295.00 5: 100 100 5497 5378 5612 5495.67 6: 1000 1000 24234 25409 27160 25601.00 And the following are the results in milliseconds from the ConcurrentQueue with no locking necessary: 1: Consumers Producers 1 2 3 Time (ms) 2: ---------- ---------- ------ ------ ------ --------- 3: 1 1 3647 3643 3718 3669.33 4: 10 10 2311 2136 2142 2196.33 5: 100 100 2480 2416 2190 2362.00 6: 1000 1000 7289 6897 7061 7082.33 Note that even though obviously 2000 threads is quite extreme, the concurrent queue actually scales really well, whereas the traditional queue with simple locking scales much more poorly. I love the new concurrent collections, they look so much simpler without littering your code with the locking logic, and they perform much better.  All in all, a great new toy to add to your arsenal of multi-threaded processing!

    Read the article

  • What's the best Communication Pattern for EJB3-based applications?

    - by Hank
    I'm starting a JEE project that needs to be strongly scalable. So far, the concept was: several Message Driven Beans, responsible for different parts of the architecture each MDB has a Session Bean injected, handling the business logic a couple of Entity Beans, providing access to the persistence layer communication between the different parts of the architecture via Request/Reply concept via JMS messages: MDB receives msg containing activity request uses its session bean to execute necessary business logic returns response object in msg to original requester The idea was that by de-coupling parts of the architecture from each other via the message bus, there is no limit to the scalability. Simply start more components - as long as they are connected to the same bus, we can grow and grow. Unfortunately, we're having massive problems with the request-reply concept. Transaction Mgmt seems to be in our way plenty. It seams that session beans are not supposed to consume messages?! Reading http://blogs.sun.com/fkieviet/entry/request_reply_from_an_ejb and http://forums.sun.com/message.jspa?messageID=10338789, I get the feeling that people actually recommend against the request/reply concept for EJBs. If that is the case, how do you communicate between your EJBs? (Remember, scalability is what I'm after) Details of my current setup: MDB 1 'TestController', uses (local) SLSB 1 'TestService' for business logic TestController.onMessage() makes TestService send a message to queue XYZ and requests a reply TestService uses Bean Managed Transactions TestService establishes a connection & session to the JMS broker via a joint connection factory upon initialization (@PostConstruct) TestService commits the transaction after sending, then begins another transaction and waits 10 sec for the response Message gets to MDB 2 'LocationController', which uses (local) SLSB 2 'LocationService' for business logic LocationController.onMessage() makes LocationService send a message back to the requested JMSReplyTo queue Same BMT concept, same @PostConstruct concept all use the same connection factory to access the broker Problem: The first message gets send (by SLSB 1) and received (by MDB 2) ok. The sending of the returning message (by SLSB 2) is fine as well. However, SLSB 1 never receives anything - it just times out. I tried without the messageSelector, no change, still no receiving message. Is it not ok to consume message by a session bean? SLSB 1 - TestService.java @Resource(name = "jms/mvs.MVSControllerFactory") private javax.jms.ConnectionFactory connectionFactory; @PostConstruct public void initialize() { try { jmsConnection = connectionFactory.createConnection(); session = jmsConnection.createSession(false, Session.AUTO_ACKNOWLEDGE); System.out.println("Connection to JMS Provider established"); } catch (Exception e) { } } public Serializable sendMessageWithResponse(Destination reqDest, Destination respDest, Serializable request) { Serializable response = null; try { utx.begin(); Random rand = new Random(); String correlationId = rand.nextLong() + "-" + (new Date()).getTime(); // prepare the sending message object ObjectMessage reqMsg = session.createObjectMessage(); reqMsg.setObject(request); reqMsg.setJMSReplyTo(respDest); reqMsg.setJMSCorrelationID(correlationId); // prepare the publishers and subscribers MessageProducer producer = session.createProducer(reqDest); // send the message producer.send(reqMsg); System.out.println("Request Message has been sent!"); utx.commit(); // need to start second transaction, otherwise the first msg never gets sent utx.begin(); MessageConsumer consumer = session.createConsumer(respDest, "JMSCorrelationID = '" + correlationId + "'"); jmsConnection.start(); ObjectMessage respMsg = (ObjectMessage) consumer.receive(10000L); utx.commit(); if (respMsg != null) { response = respMsg.getObject(); System.out.println("Response Message has been received!"); } else { // timeout waiting for response System.out.println("Timeout waiting for response!"); } } catch (Exception e) { } return response; } SLSB 2 - LocationService.Java (only the reply method, rest is same as above) public boolean reply(Message origMsg, Serializable o) { boolean rc = false; try { // check if we have necessary correlationID and replyTo destination if (!origMsg.getJMSCorrelationID().equals("") && (origMsg.getJMSReplyTo() != null)) { // prepare the payload utx.begin(); ObjectMessage msg = session.createObjectMessage(); msg.setObject(o); // make it a response msg.setJMSCorrelationID(origMsg.getJMSCorrelationID()); Destination dest = origMsg.getJMSReplyTo(); // send it MessageProducer producer = session.createProducer(dest); producer.send(msg); producer.close(); System.out.println("Reply Message has been sent"); utx.commit(); rc = true; } } catch (Exception e) {} return rc; } sun-resources.xml <admin-object-resource enabled="true" jndi-name="jms/mvs.LocationControllerRequest" res-type="javax.jms.Queue" res-adapter="jmsra"> <property name="Name" value="mvs.LocationControllerRequestQueue"/> </admin-object-resource> <admin-object-resource enabled="true" jndi-name="jms/mvs.LocationControllerResponse" res-type="javax.jms.Queue" res-adapter="jmsra"> <property name="Name" value="mvs.LocationControllerResponseQueue"/> </admin-object-resource> <connector-connection-pool name="jms/mvs.MVSControllerFactoryPool" connection-definition-name="javax.jms.QueueConnectionFactory" resource-adapter-name="jmsra"/> <connector-resource enabled="true" jndi-name="jms/mvs.MVSControllerFactory" pool-name="jms/mvs.MVSControllerFactoryPool" />

    Read the article

  • Does this use of Monitor.Wait/Pulse have a race condition?

    - by jw
    I have a simple producer/consumer scenario, where there is only ever a single item being produced/consumed. Also, the producer waits for the worker thread to finish before continuing. I realize that kind of obviates the whole point of multithreading, but please just assume it really needs to be this way (: This code doesn't compile, but I hope you get the idea: // m_data is initially null // This could be called by any number of producer threads simultaneously void SetData(object foo) { lock(x) // Line A { assert(m_data == null); m_data = foo; Monitor.Pulse(x) // Line B while(m_data != null) Monitor.Wait(x) // Line C } } // This is only ever called by a single worker thread void UseData() { lock(x) // Line D { while(m_data == null) Monitor.Wait(x) // Line E // here, do something with m_data m_data = null; Monitor.Pulse(x) // Line F } } Here is the situation that I am not sure about: Suppose many threads call SetData() with different inputs. Only one of them will get inside the lock, and the rest will be blocked on Line A. Suppose the one that got inside the lock sets m_data and makes its way to Line C. Question: Could the Wait() on Line C allow another thread at Line A to obtain the lock and overwrite m_data before the worker thread even gets to it? Supposing that doesn't happen, and the worker thread processes the original m_data, and eventually makes its way to Line F, what happens when that Pulse() goes off? Will only the thread waiting on Line C be able to get the lock? Or will it be competing with all the other threads waiting on Line A as well? Essentially, I want to know if Pulse()/Wait() communicate with each other specially "under the hood" or if they are on the same level with lock(). The solution to these problems, if they exist, is obvious of course - just surround SetData() with another lock - say, lock(y). I'm just curious if it's even an issue to begin with.

    Read the article

< Previous Page | 1 2 3 4 5 6 7 8  | Next Page >