Search Results

Search found 2447 results on 98 pages for 'apprentice queue'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 25/98 | < Previous Page | 21 22 23 24 25 26 27 28 29 30 31 32  | Next Page >

  • /etc/hosts modification does not properly working. What to do?

    - by Curious Apprentice
    I have added these lines to the hosts file: 127.0.0.1 localhost adobe.com 127.0.0.1 adobe.com It works perfectly on windows. But in Ubuntu 11.10 when I try to access it using Firefox the website is opening. Google chrome though supporting /etc/hosts configuration. Google chrome is displaying : "It works! This is the default web page for this server. The web server software is running but no content has been added, yet."

    Read the article

  • Is it a good idea to create seperate root, home, swap prior to installing Ubuntu or just Installing Ubuntu on a Single partition is a Good Choice?

    - by Curious Apprentice
    I wish to go for dual boot installation with already installed windows 7. Now, should I choose " Install along Side of Windows 7 " or go to advanced and make separate partitions for home, swap ,root etc ? What are the advantages of doing it ? There are similar topics on askubuntu.com. But here I want a complete answer. Edit : What is / and /root ? How i can allocate maximum space for software installation ? (70% for software and 30 % for home)

    Read the article

  • Solving embarassingly parallel problems using Python multiprocessing

    - by gotgenes
    How does one use multiprocessing to tackle embarrassingly parallel problems? Embarassingly parallel problems typically consist of three basic parts: Read input data (from a file, database, tcp connection, etc.). Run calculations on the input data, where each calculation is independent of any other calculation. Write results of calculations (to a file, database, tcp connection, etc.). We can parallelize the program in two dimensions: Part 2 can run on multiple cores, since each calculation is independent; order of processing doesn't matter. Each part can run independently. Part 1 can place data on an input queue, part 2 can pull data off the input queue and put results onto an output queue, and part 3 can pull results off the output queue and write them out. This seems a most basic pattern in concurrent programming, but I am still lost in trying to solve it, so let's write a canonical example to illustrate how this is done using multiprocessing. Here is the example problem: Given a CSV file with rows of integers as input, compute their sums. Separate the problem into three parts, which can all run in parallel: Process the input file into raw data (lists/iterables of integers) Calculate the sums of the data, in parallel Output the sums Below is traditional, single-process bound Python program which solves these three tasks: #!/usr/bin/env python # -*- coding: UTF-8 -*- # basicsums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file. """ import csv import optparse import sys def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) return cli_parser def parse_input_csv(csvfile): """Parses the input CSV and yields tuples with the index of the row as the first element, and the integers of the row as the second element. The index is zero-index based. :Parameters: - `csvfile`: a `csv.reader` instance """ for i, row in enumerate(csvfile): row = [int(entry) for entry in row] yield i, row def sum_rows(rows): """Yields a tuple with the index of each input list of integers as the first element, and the sum of the list of integers as the second element. The index is zero-index based. :Parameters: - `rows`: an iterable of tuples, with the index of the original row as the first element, and a list of integers as the second element """ for i, row in rows: yield i, sum(row) def write_results(csvfile, results): """Writes a series of results to an outfile, where the first column is the index of the original row of data, and the second column is the result of the calculation. The index is zero-index based. :Parameters: - `csvfile`: a `csv.writer` instance to which to write results - `results`: an iterable of tuples, with the index (zero-based) of the original row as the first element, and the calculated result from that row as the second element """ for result_row in results: csvfile.writerow(result_row) def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # gets an iterable of rows that's not yet evaluated input_rows = parse_input_csv(in_csvfile) # sends the rows iterable to sum_rows() for results iterable, but # still not evaluated result_rows = sum_rows(input_rows) # finally evaluation takes place as a chain in write_results() write_results(out_csvfile, result_rows) infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) Let's take this program and rewrite it to use multiprocessing to parallelize the three parts outlined above. Below is a skeleton of this new, parallelized program, that needs to be fleshed out to address the parts in the comments: #!/usr/bin/env python # -*- coding: UTF-8 -*- # multiproc_sums.py """A program that reads integer values from a CSV file and writes out their sums to another CSV file, using multiple processes if desired. """ import csv import multiprocessing import optparse import sys NUM_PROCS = multiprocessing.cpu_count() def make_cli_parser(): """Make the command line interface parser.""" usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV", __doc__, """ ARGUMENTS: INPUT_CSV: an input CSV file with rows of numbers OUTPUT_CSV: an output file that will contain the sums\ """]) cli_parser = optparse.OptionParser(usage) cli_parser.add_option('-n', '--numprocs', type='int', default=NUM_PROCS, help="Number of processes to launch [DEFAULT: %default]") return cli_parser def main(argv): cli_parser = make_cli_parser() opts, args = cli_parser.parse_args(argv) if len(args) != 2: cli_parser.error("Please provide an input file and output file.") infile = open(args[0]) in_csvfile = csv.reader(infile) outfile = open(args[1], 'w') out_csvfile = csv.writer(outfile) # Parse the input file and add the parsed data to a queue for # processing, possibly chunking to decrease communication between # processes. # Process the parsed data as soon as any (chunks) appear on the # queue, using as many processes as allotted by the user # (opts.numprocs); place results on a queue for output. # # Terminate processes when the parser stops putting data in the # input queue. # Write the results to disk as soon as they appear on the output # queue. # Ensure all child processes have terminated. # Clean up files. infile.close() outfile.close() if __name__ == '__main__': main(sys.argv[1:]) These pieces of code, as well as another piece of code that can generate example CSV files for testing purposes, can be found on github. I would appreciate any insight here as to how you concurrency gurus would approach this problem. Here are some questions I had when thinking about this problem. Bonus points for addressing any/all: Should I have child processes for reading in the data and placing it into the queue, or can the main process do this without blocking until all input is read? Likewise, should I have a child process for writing the results out from the processed queue, or can the main process do this without having to wait for all the results? Should I use a processes pool for the sum operations? If yes, what method do I call on the pool to get it to start processing the results coming into the input queue, without blocking the input and output processes, too? apply_async()? map_async()? imap()? imap_unordered()? Suppose we didn't need to siphon off the input and output queues as data entered them, but could wait until all input was parsed and all results were calculated (e.g., because we know all the input and output will fit in system memory). Should we change the algorithm in any way (e.g., not run any processes concurrently with I/O)?

    Read the article

  • TcpListener is queuing connections faster than I can clear them

    - by Matthew Brindley
    As I understand it, TcpListener will queue connections once you call Start(). Each time you call AcceptTcpClient (or BeginAcceptTcpClient), it will dequeue one item from the queue. If we load test our TcpListener app by sending 1,000 connections to it at once, the queue builds far faster than we can clear it, leading (eventually) to timeouts from the client because it didn't get a response because its connection was still in the queue. However, the server doesn't appear to be under much pressure, our app isn't consuming much CPU time and the other monitored resources on the machine aren't breaking a sweat. It feels like we're not running efficiently enough right now. We're calling BeginAcceptTcpListener and then immediately handing over to a ThreadPool thread to actually do the work, then calling BeginAcceptTcpClient again. The work involved doesn't seem to put any pressure on the machine, it's basically just a 3 second sleep followed by a dictionary lookup and then a 100 byte write to the TcpClient's stream. Here's the TcpListener code we're using: // Thread signal. private static ManualResetEvent tcpClientConnected = new ManualResetEvent(false); public void DoBeginAcceptTcpClient(TcpListener listener) { // Set the event to nonsignaled state. tcpClientConnected.Reset(); listener.BeginAcceptTcpClient( new AsyncCallback(DoAcceptTcpClientCallback), listener); // Wait for signal tcpClientConnected.WaitOne(); } public void DoAcceptTcpClientCallback(IAsyncResult ar) { // Get the listener that handles the client request, and the TcpClient TcpListener listener = (TcpListener)ar.AsyncState; TcpClient client = listener.EndAcceptTcpClient(ar); if (inProduction) ThreadPool.QueueUserWorkItem(state => HandleTcpRequest(client, serverCertificate)); // With SSL else ThreadPool.QueueUserWorkItem(state => HandleTcpRequest(client)); // Without SSL // Signal the calling thread to continue. tcpClientConnected.Set(); } public void Start() { currentHandledRequests = 0; tcpListener = new TcpListener(IPAddress.Any, 10000); try { tcpListener.Start(); while (true) DoBeginAcceptTcpClient(tcpListener); } catch (SocketException) { // The TcpListener is shutting down, exit gracefully CheckBuffer(); return; } } I'm assuming the answer will be related to using Sockets instead of TcpListener, or at least using TcpListener.AcceptSocket, but I wondered how we'd go about doing that? One idea we had was to call AcceptTcpClient and immediately Enqueue the TcpClient into one of multiple Queue<TcpClient> objects. That way, we could poll those queues on separate threads (one queue per thread), without running into monitors that might block the thread while waiting for other Dequeue operations. Each queue thread could then use ThreadPool.QueueUserWorkItem to have the work done in a ThreadPool thread and then move onto dequeuing the next TcpClient in its queue. Would you recommend this approach, or is our problem that we're using TcpListener and no amount of rapid dequeueing is going to fix that?

    Read the article

  • [MQ] How to check which point is cause of problem

    - by Fuangwith S.
    Hello everybody, I use MQ for send/receive message between my system and other system. Sometime I found that no response message in response queue, yet other system have already put response message into response queue (check from log). So, how to check which point is cause of problem, how to prove message is not arrive to my response queue. In addition, when message arrive my queue it will be written to log file. Thanks

    Read the article

  • How to check which point is cause of problem with MQ?

    - by Fuangwith S.
    I use MQ for send/receive message between my system and other system. Sometime I found that no response message in response queue, yet other system have already put response message into response queue (check from log). So, how to check which point is cause of problem, how to prove message is not arrive to my response queue. In addition, when message arrive my queue it will be written to log file.

    Read the article

  • How can I connect to MSMQ over a workgroup?

    - by cyclotis04
    I'm writing a simple console client-server app using MSMQ. I'm attempting to run it over the workgroup we have set up. They run just fine when run on the same computer, but I can't get them to connect over the network. I've tried adding Direct=, OS:, and a bunch of combinations of other prefaces, but I'm running out of ideas, and obviously don't know the right way to do it. My queue's don't have GUIDs, which is also slightly confusing. Whenever I attempt to connect to a remote machine, I get an invalid queue name message. What do I have to do to make this work? Server: class Program { static string _queue = @"\Private$\qim"; static MessageQueue _mq; static readonly object _mqLock = new object(); static void Main(string[] args) { _queue = Dns.GetHostName() + _queue; lock (_mqLock) { if (!MessageQueue.Exists(_queue)) _mq = MessageQueue.Create(_queue); else _mq = new MessageQueue(_queue); } Console.Write("Starting server at {0}:\n\n", _mq.Path); _mq.Formatter = new BinaryMessageFormatter(); _mq.BeginReceive(new TimeSpan(0, 1, 0), new object(), OnReceive); while (Console.ReadKey().Key != ConsoleKey.Escape) { } _mq.Close(); } static void OnReceive(IAsyncResult result) { Message msg; lock (_mqLock) { try { msg = _mq.EndReceive(result); Console.Write(msg.Body); } catch (Exception ex) { Console.Write("\n" + ex.Message + "\n"); } } _mq.BeginReceive(new TimeSpan(0, 1, 0), new object(), OnReceive); } } Client: class Program { static MessageQueue _mq; static void Main(string[] args) { string queue; while (_mq == null) { Console.Write("Enter the queue name:\n"); queue = Console.ReadLine(); //queue += @"\Private$\qim"; try { if (MessageQueue.Exists(queue)) _mq = new MessageQueue(queue); } catch (Exception ex) { Console.Write("\n" + ex.Message + "\n"); _mq = null; } } Console.Write("Connected. Begin typing.\n\n"); _mq.Formatter = new BinaryMessageFormatter(); ConsoleKeyInfo key = new ConsoleKeyInfo(); while (key.Key != ConsoleKey.Escape) { key = Console.ReadKey(); _mq.Send(key.KeyChar.ToString()); } } }

    Read the article

  • Seg Fault with malloc'd pointers

    - by anon
    I'm making a thread class to use as a wrapper for pthreads. I have a Queue class to use as a queue, but I'm having trouble with it. It seems to allocate and fill the queue struct fine, but when I try to get the data from it, it Seg. faults. http://pastebin.com/Bquqzxt0 (the printf's are for debugging, both throw seg faults) edit: the queue is stored in a dynamically allocated "struct queueset" array as a pointer to the data and an index for the data

    Read the article

  • Audio decoding delay when changing the audio language

    - by mahendiran.b
    My gstreamer Pipeline is like this Approach1 --------------input-selector->Queue->AduioParser->AudioSink | Souphttpsrc->tsdemux-->| | --------------- Queue->videoParser->videoSink In this approach 1, there is a delay in audio decoding when I toggle between various audio language. Approach2 ------ input-selector-> Queue->AduioParser->AudioSink | Souphttpsrc->tsdemux---multiqueue>| | ------- Queue->videoParser->VideoSink But there is no delay is observed in approach2. Can anyone please explain the reason behind this ? what is the specialty of multiqueue here?

    Read the article

  • Windows Azure Service Bus Splitter and Aggregator

    - by Alan Smith
    This article will cover basic implementations of the Splitter and Aggregator patterns using the Windows Azure Service Bus. The content will be included in the next release of the “Windows Azure Service Bus Developer Guide”, along with some other patterns I am working on. I’ve taken the pattern descriptions from the book “Enterprise Integration Patterns” by Gregor Hohpe. I bought a copy of the book in 2004, and recently dusted it off when I started to look at implementing the patterns on the Windows Azure Service Bus. Gregor has also presented an session in 2011 “Enterprise Integration Patterns: Past, Present and Future” which is well worth a look. I’ll be covering more patterns in the coming weeks, I’m currently working on Wire-Tap and Scatter-Gather. There will no doubt be a section on implementing these patterns in my “SOA, Connectivity and Integration using the Windows Azure Service Bus” course. There are a number of scenarios where a message needs to be divided into a number of sub messages, and also where a number of sub messages need to be combined to form one message. The splitter and aggregator patterns provide a definition of how this can be achieved. This section will focus on the implementation of basic splitter and aggregator patens using the Windows Azure Service Bus direct programming model. In BizTalk Server receive pipelines are typically used to implement the splitter patterns, with sequential convoy orchestrations often used to aggregate messages. In the current release of the Service Bus, there is no functionality in the direct programming model that implements these patterns, so it is up to the developer to implement them in the applications that send and receive messages. Splitter A message splitter takes a message and spits the message into a number of sub messages. As there are different scenarios for how a message can be split into sub messages, message splitters are implemented using different algorithms. The Enterprise Integration Patterns book describes the splatter pattern as follows: How can we process a message if it contains multiple elements, each of which may have to be processed in a different way? Use a Splitter to break out the composite message into a series of individual messages, each containing data related to one item. The Enterprise Integration Patterns website provides a description of the Splitter pattern here. In some scenarios a batch message could be split into the sub messages that are contained in the batch. The splitting of a message could be based on the message type of sub-message, or the trading partner that the sub message is to be sent to. Aggregator An aggregator takes a stream or related messages and combines them together to form one message. The Enterprise Integration Patterns book describes the aggregator pattern as follows: How do we combine the results of individual, but related messages so that they can be processed as a whole? Use a stateful filter, an Aggregator, to collect and store individual messages until a complete set of related messages has been received. Then, the Aggregator publishes a single message distilled from the individual messages. The Enterprise Integration Patterns website provides a description of the Aggregator pattern here. A common example of the need for an aggregator is in scenarios where a stream of messages needs to be combined into a daily batch to be sent to a legacy line-of-business application. The BizTalk Server EDI functionality provides support for batching messages in this way using a sequential convoy orchestration. Scenario The scenario for this implementation of the splitter and aggregator patterns is the sending and receiving of large messages using a Service Bus queue. In the current release, the Windows Azure Service Bus currently supports a maximum message size of 256 KB, with a maximum header size of 64 KB. This leaves a safe maximum body size of 192 KB. The BrokeredMessage class will support messages larger than 256 KB; in fact the Size property is of type long, implying that very large messages may be supported at some point in the future. The 256 KB size restriction is set in the service bus components that are deployed in the Windows Azure data centers. One of the ways of working around this size restriction is to split large messages into a sequence of smaller sub messages in the sending application, send them via a queue, and then reassemble them in the receiving application. This scenario will be used to demonstrate the pattern implementations. Implementation The splitter and aggregator will be used to provide functionality to send and receive large messages over the Windows Azure Service Bus. In order to make the implementations generic and reusable they will be implemented as a class library. The splitter will be implemented in the LargeMessageSender class and the aggregator in the LargeMessageReceiver class. A class diagram showing the two classes is shown below. Implementing the Splitter The splitter will take a large brokered message, and split the messages into a sequence of smaller sub-messages that can be transmitted over the service bus messaging entities. The LargeMessageSender class provides a Send method that takes a large brokered message as a parameter. The implementation of the class is shown below; console output has been added to provide details of the splitting operation. public class LargeMessageSender {     private static int SubMessageBodySize = 192 * 1024;     private QueueClient m_QueueClient;       public LargeMessageSender(QueueClient queueClient)     {         m_QueueClient = queueClient;     }       public void Send(BrokeredMessage message)     {         // Calculate the number of sub messages required.         long messageBodySize = message.Size;         int nrSubMessages = (int)(messageBodySize / SubMessageBodySize);         if (messageBodySize % SubMessageBodySize != 0)         {             nrSubMessages++;         }           // Create a unique session Id.         string sessionId = Guid.NewGuid().ToString();         Console.WriteLine("Message session Id: " + sessionId);         Console.Write("Sending {0} sub-messages", nrSubMessages);           Stream bodyStream = message.GetBody<Stream>();         for (int streamOffest = 0; streamOffest < messageBodySize;             streamOffest += SubMessageBodySize)         {                                     // Get the stream chunk from the large message             long arraySize = (messageBodySize - streamOffest) > SubMessageBodySize                 ? SubMessageBodySize : messageBodySize - streamOffest;             byte[] subMessageBytes = new byte[arraySize];             int result = bodyStream.Read(subMessageBytes, 0, (int)arraySize);             MemoryStream subMessageStream = new MemoryStream(subMessageBytes);               // Create a new message             BrokeredMessage subMessage = new BrokeredMessage(subMessageStream, true);             subMessage.SessionId = sessionId;               // Send the message             m_QueueClient.Send(subMessage);             Console.Write(".");         }         Console.WriteLine("Done!");     }} The LargeMessageSender class is initialized with a QueueClient that is created by the sending application. When the large message is sent, the number of sub messages is calculated based on the size of the body of the large message. A unique session Id is created to allow the sub messages to be sent as a message session, this session Id will be used for correlation in the aggregator. A for loop in then used to create the sequence of sub messages by creating chunks of data from the stream of the large message. The sub messages are then sent to the queue using the QueueClient. As sessions are used to correlate the messages, the queue used for message exchange must be created with the RequiresSession property set to true. Implementing the Aggregator The aggregator will receive the sub messages in the message session that was created by the splitter, and combine them to form a single, large message. The aggregator is implemented in the LargeMessageReceiver class, with a Receive method that returns a BrokeredMessage. The implementation of the class is shown below; console output has been added to provide details of the splitting operation.   public class LargeMessageReceiver {     private QueueClient m_QueueClient;       public LargeMessageReceiver(QueueClient queueClient)     {         m_QueueClient = queueClient;     }       public BrokeredMessage Receive()     {         // Create a memory stream to store the large message body.         MemoryStream largeMessageStream = new MemoryStream();           // Accept a message session from the queue.         MessageSession session = m_QueueClient.AcceptMessageSession();         Console.WriteLine("Message session Id: " + session.SessionId);         Console.Write("Receiving sub messages");           while (true)         {             // Receive a sub message             BrokeredMessage subMessage = session.Receive(TimeSpan.FromSeconds(5));               if (subMessage != null)             {                 // Copy the sub message body to the large message stream.                 Stream subMessageStream = subMessage.GetBody<Stream>();                 subMessageStream.CopyTo(largeMessageStream);                   // Mark the message as complete.                 subMessage.Complete();                 Console.Write(".");             }             else             {                 // The last message in the sequence is our completeness criteria.                 Console.WriteLine("Done!");                 break;             }         }                     // Create an aggregated message from the large message stream.         BrokeredMessage largeMessage = new BrokeredMessage(largeMessageStream, true);         return largeMessage;     } }   The LargeMessageReceiver initialized using a QueueClient that is created by the receiving application. The receive method creates a memory stream that will be used to aggregate the large message body. The AcceptMessageSession method on the QueueClient is then called, which will wait for the first message in a message session to become available on the queue. As the AcceptMessageSession can throw a timeout exception if no message is available on the queue after 60 seconds, a real-world implementation should handle this accordingly. Once the message session as accepted, the sub messages in the session are received, and their message body streams copied to the memory stream. Once all the messages have been received, the memory stream is used to create a large message, that is then returned to the receiving application. Testing the Implementation The splitter and aggregator are tested by creating a message sender and message receiver application. The payload for the large message will be one of the webcast video files from http://www.cloudcasts.net/, the file size is 9,697 KB, well over the 256 KB threshold imposed by the Service Bus. As the splitter and aggregator are implemented in a separate class library, the code used in the sender and receiver console is fairly basic. The implementation of the main method of the sending application is shown below.   static void Main(string[] args) {     // Create a token provider with the relevant credentials.     TokenProvider credentials =         TokenProvider.CreateSharedSecretTokenProvider         (AccountDetails.Name, AccountDetails.Key);       // Create a URI for the serivce bus.     Uri serviceBusUri = ServiceBusEnvironment.CreateServiceUri         ("sb", AccountDetails.Namespace, string.Empty);       // Create the MessagingFactory     MessagingFactory factory = MessagingFactory.Create(serviceBusUri, credentials);       // Use the MessagingFactory to create a queue client     QueueClient queueClient = factory.CreateQueueClient(AccountDetails.QueueName);       // Open the input file.     FileStream fileStream = new FileStream(AccountDetails.TestFile, FileMode.Open);       // Create a BrokeredMessage for the file.     BrokeredMessage largeMessage = new BrokeredMessage(fileStream, true);       Console.WriteLine("Sending: " + AccountDetails.TestFile);     Console.WriteLine("Message body size: " + largeMessage.Size);     Console.WriteLine();         // Send the message with a LargeMessageSender     LargeMessageSender sender = new LargeMessageSender(queueClient);     sender.Send(largeMessage);       // Close the messaging facory.     factory.Close();  } The implementation of the main method of the receiving application is shown below. static void Main(string[] args) {       // Create a token provider with the relevant credentials.     TokenProvider credentials =         TokenProvider.CreateSharedSecretTokenProvider         (AccountDetails.Name, AccountDetails.Key);       // Create a URI for the serivce bus.     Uri serviceBusUri = ServiceBusEnvironment.CreateServiceUri         ("sb", AccountDetails.Namespace, string.Empty);       // Create the MessagingFactory     MessagingFactory factory = MessagingFactory.Create(serviceBusUri, credentials);       // Use the MessagingFactory to create a queue client     QueueClient queueClient = factory.CreateQueueClient(AccountDetails.QueueName);       // Create a LargeMessageReceiver and receive the message.     LargeMessageReceiver receiver = new LargeMessageReceiver(queueClient);     BrokeredMessage largeMessage = receiver.Receive();       Console.WriteLine("Received message");     Console.WriteLine("Message body size: " + largeMessage.Size);       string testFile = AccountDetails.TestFile.Replace(@"\In\", @"\Out\");     Console.WriteLine("Saving file: " + testFile);       // Save the message body as a file.     Stream largeMessageStream = largeMessage.GetBody<Stream>();     largeMessageStream.Seek(0, SeekOrigin.Begin);     FileStream fileOut = new FileStream(testFile, FileMode.Create);     largeMessageStream.CopyTo(fileOut);     fileOut.Close();       Console.WriteLine("Done!"); } In order to test the application, the sending application is executed, which will use the LargeMessageSender class to split the message and place it on the queue. The output of the sender console is shown below. The console shows that the body size of the large message was 9,929,365 bytes, and the message was sent as a sequence of 51 sub messages. When the receiving application is executed the results are shown below. The console application shows that the aggregator has received the 51 messages from the message sequence that was creating in the sending application. The messages have been aggregated to form a massage with a body of 9,929,365 bytes, which is the same as the original large message. The message body is then saved as a file. Improvements to the Implementation The splitter and aggregator patterns in this implementation were created in order to show the usage of the patterns in a demo, which they do quite well. When implementing these patterns in a real-world scenario there are a number of improvements that could be made to the design. Copying Message Header Properties When sending a large message using these classes, it would be great if the message header properties in the message that was received were copied from the message that was sent. The sending application may well add information to the message context that will be required in the receiving application. When the sub messages are created in the splitter, the header properties in the first message could be set to the values in the original large message. The aggregator could then used the values from this first sub message to set the properties in the message header of the large message during the aggregation process. Using Asynchronous Methods The current implementation uses the synchronous send and receive methods of the QueueClient class. It would be much more performant to use the asynchronous methods, however doing so may well affect the sequence in which the sub messages are enqueued, which would require the implementation of a resequencer in the aggregator to restore the correct message sequence. Handling Exceptions In order to keep the code readable no exception handling was added to the implementations. In a real-world scenario exceptions should be handled accordingly.

    Read the article

  • Should vendors have an express queue for people who have a clue? What passes for support today?

    - by Greg Low
    It's good to see some airports that have queues for people that travel frequently and know what they're doing. But I'm left thinking that IT vendors need to have something similar. Bigpond (part of Telstra) in Australia have recently introduced new 42MB/sec modems on their 3G network. It's actually just a pair of 21MB/sec modems linked together but the idea is cute. Around most of the country, they work pretty well. In the middle of the CBD in Melbourne however, at present they just don't work. Having...(read more)

    Read the article

  • Why is Postfix trying to connect to other machines SMTP port 25?

    - by TryTryAgain
    Jul 5 11:09:25 relay postfix/smtp[3084]: connect to ab.xyz.com[10.41.0.101]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3087]: connect to ab.xyz.com[10.41.0.247]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3088]: connect to ab.xyz.com[10.41.0.101]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3084]: connect to ab.xyz.com[10.41.0.247]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3087]: connect to ab.xyz.com[10.41.0.110]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3088]: connect to ab.xyz.com[10.41.0.110]:25: Connection refused Jul 5 11:09:25 relay postfix/smtp[3084]: connect to ab.xyz.com[10.41.0.102]:25: Connection refused Jul 5 11:09:30 relay postfix/smtp[3085]: connect to ab.xyz.com[10.41.0.102]:25: Connection refused Jul 5 11:09:30 relay postfix/smtp[3086]: connect to ab.xyz.com[10.41.0.247]:25: Connection refused Jul 5 11:09:30 relay postfix/smtp[3086]: connect to ab.xyz.com[10.41.0.102]:25: Connection refused Jul 5 11:09:55 relay postfix/smtp[3087]: connect to ab.xyz.com[10.40.40.130]:25: Connection timed out Jul 5 11:09:55 relay postfix/smtp[3084]: connect to ab.xyz.com[10.40.40.130]:25: Connection timed out Jul 5 11:09:55 relay postfix/smtp[3088]: connect to ab.xyz.com[10.40.40.130]:25: Connection timed out Jul 5 11:09:55 relay postfix/smtp[3087]: connect to ab.xyz.com[10.41.0.135]:25: Connection refused Jul 5 11:09:55 relay postfix/smtp[3084]: connect to ab.xyz.com[10.41.0.110]:25: Connection refused Jul 5 11:09:55 relay postfix/smtp[3088]: connect to ab.xyz.com[10.41.0.247]:25: Connection refused Is this a DNS thing, doubtful as I've changed from our local DNS to Google's..still Postfix will occasionally try and connect to ab.xyz.com from a variety of addresses that may or may not have port 25 open and act as mail servers to begin with. Why is Postfix attempting to connect to other machines as seen in the log? Mail is being sent properly, other than that, it appears all is good. Occasionally I'll also see: relay postfix/error[3090]: 3F1AB42132: to=, relay=none, delay=32754, delays=32724/30/0/0, dsn=4.4.1, status=deferred (delivery temporarily suspended: connect to ab.xyz.com[10.41.0.102]:25: Connection refused) I have Postfix setup with very little restrictions: mynetworks = 127.0.0.0/8, 10.0.0.0/8 only. Like I said it appears all mail is getting passed through, but I hate seeing errors and it is confusing me as to why it would be attempting to connect to other machines as seen in the log. Some Output of cat /var/log/mail.log|grep 3F1AB42132 Jul 5 02:04:01 relay postfix/smtpd[1653]: 3F1AB42132: client=unknown[10.41.0.109] Jul 5 02:04:01 relay postfix/cleanup[1655]: 3F1AB42132: message-id= Jul 5 02:04:01 relay postfix/qmgr[1588]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 02:04:31 relay postfix/smtp[1634]: 3F1AB42132: to=, relay=none, delay=30, delays=0.02/0/30/0, dsn=4.4.1, status=deferred (connect to ab.xyz.com[10.41.0.110]:25: Connection refused) Jul 5 02:13:58 relay postfix/qmgr[1588]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 02:14:28 relay postfix/smtp[1681]: 3F1AB42132: to=, relay=none, delay=628, delays=598/0.01/30/0, dsn=4.4.1, status=deferred (connect to ab.xyz.com[10.41.0.247]:25: Connection refused) Jul 5 02:28:58 relay postfix/qmgr[1588]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 02:29:28 relay postfix/smtp[1684]: 3F1AB42132: to=, relay=none, delay=1527, delays=1497/0/30/0, dsn=4.4.1, status=deferred (connect to ab.xyz.com[10.41.0.135]:25: Connection refused) Jul 5 02:58:58 relay postfix/qmgr[1588]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 02:59:28 relay postfix/smtp[1739]: 3F1AB42132: to=, relay=none, delay=3327, delays=3297/0/30/0, dsn=4.4.1, status=deferred (connect to ab.xyz.com[10.40.40.130]:25: Connection timed out) Jul 5 03:58:58 relay postfix/qmgr[1588]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 03:59:28 relay postfix/smtp[1839]: 3F1AB42132: to=, relay=none, delay=6928, delays=6897/0.03/30/0, dsn=4.4.1, status=deferred (connect to ab.xyz.com[10.41.0.101]:25: Connection refused) Jul 5 04:11:03 relay postfix/qmgr[2039]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 04:11:33 relay postfix/error[2093]: 3F1AB42132: to=, relay=none, delay=7653, delays=7622/30/0/0, dsn=4.4.1, status=deferred (delivery temporarily suspended: connect to ab.xyz.com[10.41.0.101]:25: Connection refused) Jul 5 05:21:03 relay postfix/qmgr[2039]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 05:21:33 relay postfix/error[2217]: 3F1AB42132: to=, relay=none, delay=11853, delays=11822/30/0/0, dsn=4.4.1, status=deferred (delivery temporarily suspended: connect to ab.xyz.com[10.41.0.101]:25: Connection refused) Jul 5 06:29:25 relay postfix/qmgr[2420]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 06:29:55 relay postfix/error[2428]: 3F1AB42132: to=, relay=none, delay=15954, delays=15924/30/0/0.08, dsn=4.4.1, status=deferred (delivery temporarily suspended: connect to ab.xyz.com[10.41.0.101]:25: Connection refused) Jul 5 07:39:24 relay postfix/qmgr[2885]: 3F1AB42132: from=, size=3404, nrcpt=1 (queue active) Jul 5 07:39:54 relay postfix/error[2936]: 3F1AB42132: to=, relay=none, delay=20153, delays=20123/30/0/0, dsn=4.4.1, status=deferred (delivery temporarily suspended: connect to ab.xyz.com[10.40.40.130]:25: Connection timed out)

    Read the article

  • Java client listening to WebSphere MQ Server?

    - by user595234
    I need to write a Java client listening to WebSphere MQ Server. Message is put into a queue in the server. I developed this code, but am not sure it is correct or not. If correct, then how can I test it? This is a standalone Java project, no application server support. Which jars I should put into classpath? I have the MQ settings, where I should put into my codes? Standard JMS can skip these settings? confusing .... import javax.jms.Destination; import javax.jms.Message; import javax.jms.MessageConsumer; import javax.jms.MessageListener; import javax.jms.Queue; import javax.jms.QueueConnection; import javax.jms.QueueConnectionFactory; import javax.jms.QueueReceiver; import javax.jms.QueueSession; import javax.jms.Session; import javax.naming.Context; import javax.naming.InitialContext; import javax.naming.NamingException; public class Main { Context jndiContext = null; QueueConnectionFactory queueConnectionFactory = null; QueueConnection queueConnection = null; QueueSession queueSession = null; Queue controlQueue = null; QueueReceiver queueReceiver = null; private String queueSubject = ""; private void start() { try { queueConnection.start(); queueSession = queueConnection.createQueueSession(false, Session.AUTO_ACKNOWLEDGE); Destination destination = queueSession.createQueue(queueSubject); MessageConsumer consumer = queueSession.createConsumer(destination); consumer.setMessageListener(new MyListener()); } catch (Exception e) { e.printStackTrace(); } } private void close() { try { queueSession.close(); queueConnection.close(); } catch (Exception e) { e.printStackTrace(); } } private void init() { try { jndiContext = new InitialContext(); queueConnectionFactory = (QueueConnectionFactory) this.jndiLookup("QueueConnectionFactory"); queueConnection = queueConnectionFactory.createQueueConnection(); queueConnection.start(); } catch (Exception e) { System.err.println("Could not create JNDI API " + "context: " + e.toString()); System.exit(1); } } private class MyListener implements MessageListener { @Override public void onMessage(Message message) { System.out.println("get message:" + message); } } private Object jndiLookup(String name) throws NamingException { Object obj = null; if (jndiContext == null) { try { jndiContext = new InitialContext(); } catch (NamingException e) { System.err.println("Could not create JNDI API " + "context: " + e.toString()); throw e; } } try { obj = jndiContext.lookup(name); } catch (NamingException e) { System.err.println("JNDI API lookup failed: " + e.toString()); throw e; } return obj; } public Main() { } public static void main(String[] args) { new Main(); } } MQ Queue setting <queue-manager> <name>AAA</name> <port>1423</port> <hostname>ddd</hostname> <clientChannel>EEE.CLIENTS.00</clientChannel> <securityClass>PKIJCExit</securityClass> <transportType>1</transportType> <targetClientMatching>1</targetClientMatching> </queue-manager> <queues> <queue-details id="queue-1"> <name>GGGG.NY.00</name> <transacted>false</transacted> <acknowledgeMode>1</acknowledgeMode> <targetClient>1</targetClient> </queue-details> </queues>

    Read the article

  • Forcing an External Activation with Service Broker

    - by Davide Mauri
    In these last days I’ve been working quite a lot with Service Broker, a technology I’m really happy to work with, since it can give a lot of satisfaction. The scale-out solution one can easily build is simply astonishing. I’m helping a company to build a very scalable and – yet almost inexpensive – invoicing system that has to be able to scale out using commodity hardware. To offload the work from the main server to satellite “compute nodes” (yes, I’ve borrowed this term from PDW) we’re using Service Broker and the External Activator application available in the SQL Server Feature Pack. For those who are not used to work with SSB, the External Activation is a feature that allows you to intercept the arrival of a message in a queue right from your application code. http://msdn.microsoft.com/en-us/library/ms171617.aspx (Look for “Event-Based Activation”) In order to make life even more easier, Microsoft released the External Activation application that saves you even from writing even this code. http://blogs.msdn.com/b/sql_service_broker/archive/tags/external+activator/ The External Activator application can be configured to execute your own application so that each time a message – an invoice in my case – arrives in the target queue, the invoking application is executed and the invoice is calculated. The very nice feature of External Activator is that it can automatically execute as many configured application in order to process as many messages as your system can handle.  This also a lot of create a scale-out solution, leaving to the developer only a fraction of the problems that usually came with asynchronous programming. Developers are also shielded from Service Broker since everything can be encapsulated in Stored Procedures, so that – for them – developing such scale-out asynchronous solution is not much more complex than just executing a bunch of Stored Procedures. Now, if everything works correctly, you don’t have to bother of anything else. You put messages in the queue and your application, invoked by the External Activator, process them. But what happen if for some reason your application fails to process the messages. For examples, it crashes? The message is safe in the queue so you just need to process it again. But your application is invoked by the External Activator application, so now the question is, how do you wake up that app? Service Broker will engage the activation process only if certain conditions are met: http://msdn.microsoft.com/en-us/library/ms171601.aspx But how we can invoke the activation process manually, without having to wait for another message to arrive (the arrival of a new message is a condition that can fire the activation process)? The “trick” is to do manually with the activation process does: sending a system message to a queue in charge of handling External Activation messages: declare @conversationHandle uniqueidentifier; declare @n xml = N' <EVENT_INSTANCE>   <EventType>QUEUE_ACTIVATION</EventType>   <PostTime>' + CONVERT(CHAR(24),GETDATE(),126) + '</PostTime>   <SPID>' + CAST(@@SPID AS VARCHAR(9)) + '</SPID>   <ServerName>[your_server_name]</ServerName>   <LoginName>[your_login_name]</LoginName>   <UserName>[your_user_name]</UserName>   <DatabaseName>[your_database_name]</DatabaseName>   <SchemaName>[your_queue_schema_name]</SchemaName>   <ObjectName>[your_queue_name]</ObjectName>   <ObjectType>QUEUE</ObjectType> </EVENT_INSTANCE>' begin dialog conversation     @conversationHandle from service        [<your_initiator_service_name>] to service          '<your_event_notification_service>' on contract         [http://schemas.microsoft.com/SQL/Notifications/PostEventNotification] with     encryption = off,     lifetime = 6000 ; send on conversation     @conversationHandle message type     [http://schemas.microsoft.com/SQL/Notifications/EventNotification] (@n) ;     end conversation @conversationHandle; That’s it! Put the code in a Stored Procedure and you can add to your application a button that says “Force Queue Processing” (or something similar) in order to start the activation process whenever you need it (which should not occur too frequently but it may happen). PS I know that the “fire-and-forget” (ending the conversation without waiting for an answer) technique is not a best practice, but in this case I don’t see how it can hurts so I decided to stay very close to the KISS principle []

    Read the article

  • Audio recording error kAudioQueueErr_CannotStart on iPhone OS 3.0

    - by Jeremy Borden
    I'm working on a couple different iphone apps that both record and play sounds concurrently. Think multitrack mixing... play one sound a save it then listen to that sound while recording the next sound to another file. My mechanism for this has been to start up two different audio queues, one for recording, and one for playing. This was working A-OK until the release of OS 3.0... Since then, however, the following happens: If I start the recording queue first, it supposedly starts fine, but the call to AudioQueueStart for the playback queue returns kAudioQueueErr_CannotStart. If I start the playback queue first, it also supposedly starts fine, but the call to AudioQueueStart for the record queue returns the same error, kAudioQueueErr_CannotStart. Anyone have any luck debugging this error? Seems like maybe the two queues are stomping on each other's memory or something? The official description is: "The audio queue has encountered a problem and cannot start." Not super helpful... Jeremy

    Read the article

  • Azure Table Storage data Consistency

    - by SeeR
    Let say I have Table in Azure Table Storage public class MyTable { public string PK {get; set;} public string RowPK {get; set;} public double Amount {get; set;} } And message in Azure Queue which says Add 10 to Amount. Now let say one worker role Takes this message from queue Takes row from table Amount += 10 Updates Row in Table And Fails After a while message is available in queue again. So next worker role: Takes this message from queue Takes row from table Amount += 10 Updates Row in Table Removes message from queue Those actions results in Amount += 20 instead of Amount += 10. How can I avoid such situations?

    Read the article

  • AWS: setting up auto-scale for EC2 instances

    - by Elton Stoneman
    Originally posted on: http://geekswithblogs.net/EltonStoneman/archive/2013/10/16/aws-setting-up-auto-scale-for-ec2-instances.aspxWith Amazon Web Services, there’s no direct equivalent to Azure Worker Roles – no Elastic Beanstalk-style application for .NET background workers. But you can get the auto-scale part by configuring an auto-scaling group for your EC2 instance. This is a step-by-step guide, that shows you how to create the auto-scaling configuration, which for EC2 you need to do with the command line, and then link your scaling policies to CloudWatch alarms in the Web console. I’m using queue size as my metric for CloudWatch,  which is a good fit if your background workers are pulling messages from a queue and processing them.  If the queue is getting too big, the “high” alarm will fire and spin up a new instance to share the workload. If the queue is draining down, the “low” alarm will fire and shut down one of the instances. To start with, you need to manually set up your app in an EC2 VM, for a background worker that would mean hosting your code in a Windows Service (I always use Topshelf). If you’re dual-running Azure and AWS, then you can isolate your logic in one library, with a generic entry point that has Start() and Stop()  functions, so your Worker Role and Windows Service are essentially using the same code. When you have your instance set up with the Windows Service running automatically, and you’ve tested it starts up and works properly from a reboot, shut the machine down and take an image of the VM, using Create Image (EBS AMI) from the Web Console: When that completes, you’ll have your own AMI which you can use to spin up new instances, and you’re ready to create your auto-scaling group. You need to dip into the command-line tools for this, so follow this guide to set up the AWS autoscale command line tool. Now we’re ready to go. 1. Create a launch configuration This launch configuration tells AWS what to do when a new instance needs to be spun up. You create it with the as-create-launch-config command, which looks like this: as-create-launch-config sc-xyz-launcher # name of the launch config --image-id ami-7b9e9f12 # id of the AMI you extracted from your VM --region eu-west-1 # which region the new instance gets created in --instance-type t1.micro # size of the instance to create --group quicklaunch-1 #security group for the new instance 2. Create an auto-scaling group The auto-scaling group links to the launch config, and defines the overall configuration of the collection of instances: as-create-auto-scaling-group sc-xyz-asg # auto-scaling group name --region eu-west-1 # region to create in --launch-configuration sc-xyz-launcher # name of the launch config to invoke for new instances --min-size 1 # minimum number of nodes in the group --max-size 5 # maximum number of nodes in the group --default-cooldown 300 # period to wait (in seconds) after each scaling event, before checking if another scaling event is required --availability-zones eu-west-1a eu-west-1b eu-west-1c # which availability zones you want your instances to be allocated in – multiple entries means EC@ will use any of them 3. Create a scale-up policy The policy dictates what will happen in response to a scaling event being triggered from a “high” alarm being breached. It links to the auto-scaling group; this sample results in one additional node being spun up: as-put-scaling-policy scale-up-policy # policy name -g sc-psod-woker-asg # auto-scaling group the policy works with --adjustment 1 # size of the adjustment --region eu-west-1 # region --type ChangeInCapacity # type of adjustment, this specifies a fixed number of nodes, but you can use PercentChangeInCapacity to make an adjustment relative to the current number of nodes, e.g. increasing by 50% 4. Create a scale-down policy The policy dictates what will happen in response to a scaling event being triggered from a “low” alarm being breached. It links to the auto-scaling group; this sample results in one node from the group being taken offline: as-put-scaling-policy scale-down-policy -g sc-psod-woker-asg "--adjustment=-1" # in Windows, use double-quotes to surround a negative adjustment value –-type ChangeInCapacity --region eu-west-1 5. Create a “high” CloudWatch alarm We’re done with the command line now. In the Web Console, open up the CloudWatch view and create a new alarm. This alarm will monitor your metrics and invoke the scale-up policy from your auto-scaling group, when the group is working too hard. Configure your metric – this example will fire the alarm if there are more than 10 messages in my queue for over a minute: Then link the alarm to the scale-up policy in your group: 6. Create a “low” CloudWatch alarm The opposite of step 4, this alarm will trigger when the instances in your group don’t have enough work to do (e.g fewer than 2 messages in the queue for 1 minute), and will invoke the scale-down policy. And that’s it. You don’t need your original VM as the auto-scale group has a minimum number of nodes connected. You can test out the scaling by flexing your CloudWatch metric – in this example, filling up a queue from a  stub publisher – and watching AWS create new nodes as required, then stopping the publisher and watch AWS kill off the spare nodes.

    Read the article

  • Forcing an External Activation with Service Broker

    - by Davide Mauri
    In these last days I’ve been working quite a lot with Service Broker, a technology I’m really happy to work with, since it can give a lot of satisfaction. The scale-out solution one can easily build is simply astonishing. I’m helping a company to build a very scalable and – yet almost inexpensive – invoicing system that has to be able to scale out using commodity hardware. To offload the work from the main server to satellite “compute nodes” (yes, I’ve borrowed this term from PDW) we’re using Service Broker and the External Activator application available in the SQL Server Feature Pack. For those who are not used to work with SSB, the External Activation is a feature that allows you to intercept the arrival of a message in a queue right from your application code. http://msdn.microsoft.com/en-us/library/ms171617.aspx (Look for “Event-Based Activation”) In order to make life even more easier, Microsoft released the External Activation application that saves you even from writing even this code. http://blogs.msdn.com/b/sql_service_broker/archive/tags/external+activator/ The External Activator application can be configured to execute your own application so that each time a message – an invoice in my case – arrives in the target queue, the invoking application is executed and the invoice is calculated. The very nice feature of External Activator is that it can automatically execute as many configured application in order to process as many messages as your system can handle.  This also a lot of create a scale-out solution, leaving to the developer only a fraction of the problems that usually came with asynchronous programming. Developers are also shielded from Service Broker since everything can be encapsulated in Stored Procedures, so that – for them – developing such scale-out asynchronous solution is not much more complex than just executing a bunch of Stored Procedures. Now, if everything works correctly, you don’t have to bother of anything else. You put messages in the queue and your application, invoked by the External Activator, process them. But what happen if for some reason your application fails to process the messages. For examples, it crashes? The message is safe in the queue so you just need to process it again. But your application is invoked by the External Activator application, so now the question is, how do you wake up that app? Service Broker will engage the activation process only if certain conditions are met: http://msdn.microsoft.com/en-us/library/ms171601.aspx But how we can invoke the activation process manually, without having to wait for another message to arrive (the arrival of a new message is a condition that can fire the activation process)? The “trick” is to do manually with the activation process does: sending a system message to a queue in charge of handling External Activation messages: declare @conversationHandle uniqueidentifier; declare @n xml = N' <EVENT_INSTANCE>   <EventType>QUEUE_ACTIVATION</EventType>   <PostTime>' + CONVERT(CHAR(24),GETDATE(),126) + '</PostTime>   <SPID>' + CAST(@@SPID AS VARCHAR(9)) + '</SPID>   <ServerName>[your_server_name]</ServerName>   <LoginName>[your_login_name]</LoginName>   <UserName>[your_user_name]</UserName>   <DatabaseName>[your_database_name]</DatabaseName>   <SchemaName>[your_queue_schema_name]</SchemaName>   <ObjectName>[your_queue_name]</ObjectName>   <ObjectType>QUEUE</ObjectType> </EVENT_INSTANCE>' begin dialog conversation     @conversationHandle from service        [<your_initiator_service_name>] to service          '<your_event_notification_service>' on contract         [http://schemas.microsoft.com/SQL/Notifications/PostEventNotification] with     encryption = off,     lifetime = 6000 ; send on conversation     @conversationHandle message type     [http://schemas.microsoft.com/SQL/Notifications/EventNotification] (@n) ;     end conversation @conversationHandle; That’s it! Put the code in a Stored Procedure and you can add to your application a button that says “Force Queue Processing” (or something similar) in order to start the activation process whenever you need it (which should not occur too frequently but it may happen). PS I know that the “fire-and-forget” (ending the conversation without waiting for an answer) technique is not a best practice, but in this case I don’t see how it can hurts so I decided to stay very close to the KISS principle []

    Read the article

  • Removing the contents of a Chan or MVar in a single discrete step

    - by Bill
    I'm writing a discrete simulation where request values from multiple threads accumulate in a centralized queue. Every n milliseconds, a manager wakes up to process requests. When the manager wakes up, it should retrieve all of the contents of the central queue in a single discrete step. While processing these, any client threads attempting to submit to the queue should block. When processing completes, the queue reopens and the manager goes back to sleep. What's the best way to do this? The retry behavior of STM isn't really what I want. If I use a Chan or MVar, there's no way to prevent clients from enqueuing additional requests during processing. One approach is to use an MVar as a mutex on a Chan holding the queue. Are there other ways to do this?

    Read the article

  • Wait for tasks to get completed in threadpool.

    - by Alien01
    Hello I have created a thread pool in C++ which stores all tasks in a queue. Thread pool start n number of threads which takes tasks from queue , process each task and then delete tasks from queue. Now , I want to wait till all tasks get completed. Checking for empty queue for completion of tasks may not work as , task can be given to each thread and queue can be emptied but still the tasks can in processing mode. I am not getting idea how to wait for all the tasks completion.This is a design problem. Any suggestions?

    Read the article

  • Rails: Design Pattern to Store Order of Relations

    - by ChrisInCambo
    Hi, I have four models: Customer, QueueRed, QueueBlue, QueueGreen. The Queue models have a one to many relationship with customers A customer must always be in a queue A customer can only be in one queue at a time A customer can change queues We must be able to find out the customers current position in their respective queue In an object model the queues would just have an array property containing customers, but ActiveRecord doesn't have arrays. In a DB I would probably create some extra tables just to handle the order of the stories in the queue. My question is what it the best way to model the relationship in ActiveRecord? Obviously there are many ways this could be done, but what is the best or the most in line with how ActiveRecord should be used? Cheers, Chris

    Read the article

  • WCF/MSMQ Transport Security with Certificates

    - by user104295
    Hi there, my goal is to secure the communication between MSMQ Queue Managers – I don’t want unknown clients sending messages to my MSMQ server. I have spent many hours now trying to get Transport security working for the net.msmq binding in WCF, where MSMQ is in Workgroup mode and the client and server do not have Active Directory… so I’m using certificates. I have created a new X.509 certificate, called Kristan and put it into the “Trusted people” store on the server and into the My store of Current User of the client. The error I’m getting is: An error occurred while sending to the queue: Unrecognized error -1072824272 (0xc00e0030).Ensure that MSMQ is installed and running. If you are sending to a local queue, ensure the queue exists with the required access mode and authorization. Using smartsniff, I see that there’s no attempted connection with the remote MSMQ, however, it’s an error probably coming from the local queue manager. The stack trace is: at System.ServiceModel.Channels.MsmqOutputChannel.OnSend(Message message, TimeSpan timeout) at System.ServiceModel.Channels.OutputChannel.Send(Message message, TimeSpan timeout) at System.ServiceModel.Dispatcher.OutputChannelBinder.Send(Message message, TimeSpan timeout) at System.ServiceModel.Channels.ServiceChannel.Call(String action, Boolean oneway, ProxyOperationRuntime operation, Object[] ins, Object[] outs, TimeSpan timeout) at System.ServiceModel.Channels.ServiceChannelProxy.InvokeService(IMethodCallMessage methodCall, ProxyOperationRuntime operation) at System.ServiceModel.Channels.ServiceChannelProxy.Invoke(IMessage message) The code:- EndpointAddress endpointAddress = new EndpointAddress(new Uri(endPointAddress)); NetMsmqBinding clientBinding = new NetMsmqBinding(); clientBinding.Security.Mode = NetMsmqSecurityMode.Transport; clientBinding.Security.Transport.MsmqAuthenticationMode = MsmqAuthenticationMode.Certificate; clientBinding.Security.Transport.MsmqProtectionLevel = System.Net.Security.ProtectionLevel.Sign; clientBinding.ExactlyOnce = false; clientBinding.UseActiveDirectory = false; // start new var channelFactory = new ChannelFactory<IAsyncImportApi>(clientBinding, endpointAddress); channelFactory.Credentials.ClientCertificate.SetCertificate("CN=Kristan", StoreLocation.CurrentUser, StoreName.My); The queue is flagged as ‘Authenticated’ on the server. I have checked the effect of this and if I turn off all security in the client send, then I get ‘Signature is invalid’ – which is understandable and shows that it’s definitely looking for a sig. Are there are special ports that I need to check are open for cert-based msmq auth? thanks Kris

    Read the article

  • Message driven bean not responding until client method is complete

    - by poijoi
    Hi, I have a MDB deployed on Jboss 4.2.2 and a client on the same server that produces messages and expects a reply from the MDB via a temporary queue created before the message is sent. When I run the client, I see that it creates the message, puts it in the queue and waits for the reply (no problem so far) ... but when I check in the logs I see that the timeout is reached and no response is received. When the timeout occurs and the client's method is complete the MDB starts processing the message that should have been processed the moment the client put it in the queue. As a consequence of this timing issue, when the MDB tries to reply to the temp queue, it fails since the client is already gone. If I run the same client from a remote server, I have no problem... The MDB picks up the message from the queue right away and the client receives its response right after the processing is complete. I'm using container managed transactions. I suspect it has something to do with that... I think the client's "send message/receive reply" might be all be considered a transaction before it commits to put the message in the queue... but I'm not sure if this is correct. If this is the case, why did I not see the same behavior from the remote client? is client managed transaction the default setting and that's what my remote server was using? Any idea how to fix this? Thanks in advance! PJ

    Read the article

  • ReaderWriterLockSlim and Pulse/Wait

    - by Jono
    Is there an equivalent of Monitor.Pulse and Monitor.Wait that I can use in conjunction with a ReaderWriterLockSlim? I have a class where I've encapsulated multi-threaded access to an underlying queue. To enqueue something, I acquire a lock that protects the underlying queue (and a couple of other objects) then add the item and Monitor.Pulse the locked object to signal that something was added to the queue. public void Enqueue(ITask task) { lock (mutex) { underlying.Enqueue(task); Monitor.Pulse(mutex); } } On the other end of the queue, I have a single background thread that continuously processes messages as they arrive on the queue. It uses Monitor.Wait when there are no items in the queue, to avoid unnecessary polling. (I consider this to be good design, but any flames (within reason) are welcome if they help me learn otherwise.) private void DequeueForProcessing(object state) { while (true) { ITask task; lock (mutex) { while (underlying.Count == 0) { Monitor.Wait(mutex); } task = underlying.Dequeue(); } Process(task); } } As more operations are added to this class (requiring read-only access to the lock protected underlying), someone suggested using ReaderWriterLockSlim. I've never used the class before, and assuming it can offer some performance benefit, I'm not against it, but only if I can keep the Pulse/Wait design.

    Read the article

  • Manhattan Heuristic function for A-star (A*)

    - by Shawn Mclean
    I found this algorithm here. I have a problem, I cant seem to understand how to set up and pass my heuristic function. static public Path<TNode> AStar<TNode>(TNode start, TNode destination, Func<TNode, TNode, double> distance, Func<TNode, double> estimate) where TNode : IHasNeighbours<TNode> { var closed = new HashSet<TNode>(); var queue = new PriorityQueue<double, Path<TNode>>(); queue.Enqueue(0, new Path<TNode>(start)); while (!queue.IsEmpty) { var path = queue.Dequeue(); if (closed.Contains(path.LastStep)) continue; if (path.LastStep.Equals(destination)) return path; closed.Add(path.LastStep); foreach (TNode n in path.LastStep.Neighbours) { double d = distance(path.LastStep, n); var newPath = path.AddStep(n, d); queue.Enqueue(newPath.TotalCost + estimate(n), newPath); } } return null; } As you can see, it accepts 2 functions, a distance and a estimate function. Using the Manhattan Heuristic Distance function, I need to take 2 parameters. Do I need to modify his source and change it to accepting 2 parameters of TNode so I can pass a Manhattan estimate to it? This means the 4th param will look like this: Func<TNode, TNode, double> estimate) where TNode : IHasNeighbours<TNode> and change the estimate function to: queue.Enqueue(newPath.TotalCost + estimate(n, path.LastStep), newPath); My Manhattan function is: private float manhattanHeuristic(Vector3 newNode, Vector3 end) { return (Math.Abs(newNode.X - end.X) + Math.Abs(newNode.Y - end.Y)); }

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 21 22 23 24 25 26 27 28 29 30 31 32  | Next Page >