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  • .net 4.0 concurrent queue dictionary

    - by freddy smith
    I would like to use the new concurrent collections in .NET 4.0 to solve the following problem. The basic data structure I want to have is a producer consumer queue, there will be a single consumer and multiple producers. There are items of type A,B,C,D,E that will be added to this queue. Items of type A,B,C are added to the queue in the normal manner and processed in order. However items of type D or E can only exist in the queue zero or once. If one of these is to be added and there already exists another of the same type that has not yet been processed then this should update that other one in-place in the queue. The queue position would not change (i.e. would not go to the back of the queue) after the update. Which .NET 4.0 classes would be best for this?

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  • UI message passing programming paradigm

    - by Ronald Wildenberg
    I recently (about two months ago) read an article that explained some user interface paradigm that I can't remember the name of and I also can't find the article anymore. The paradigm allows for decoupling the user interface and backend through message passing (via some queueing implementation). So each user action results in a message being pased to the backend. The user interface is then updated to inform the user that his request is being processed. The assumption is that a user interface is stale by definition. When you read data from some store into memory, it is stale because another transaction may be updating the same data already. If you assume this, it makes no sense to try to represent the 'current' database state in the user interface (so the delay introduced by passing messages to a backend doesn't matter). If I remember correctly, the article also mentioned a read-optimized data store for rendering the user interface. The article assumed a high-traffic web application. A primary reason for using a message queue communicating with the backend is performance: returning control to the user as soon as possible. Updating backend stores is handled by another process and eventually these changes also become visible to the user. I hope I have explained accurately enough what I'm looking for. If someone can provide some pointers to what I'm looking for, thanks very much in advance.

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  • Custom Task Queue in App Engine?

    - by demos
    I have created a new task queue and defined it in queue.yaml I am not sure how to start adding tasks to this queue? with the default queue it is simple taskqueue.add(...) how do we do it for a custom queue?

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  • Scaling-out Your Services by Message Bus based WCF Transport Extension &ndash; Part 1 &ndash; Background

    - by Shaun
    Cloud computing gives us more flexibility on the computing resource, we can provision and deploy an application or service with multiple instances over multiple machines. With the increment of the service instances, how to balance the incoming message and workload would become a new challenge. Currently there are two approaches we can use to pass the incoming messages to the service instances, I would like call them dispatcher mode and pulling mode.   Dispatcher Mode The dispatcher mode introduces a role which takes the responsible to find the best service instance to process the request. The image below describes the sharp of this mode. There are four clients communicate with the service through the underlying transportation. For example, if we are using HTTP the clients might be connecting to the same service URL. On the server side there’s a dispatcher listening on this URL and try to retrieve all messages. When a message came in, the dispatcher will find a proper service instance to process it. There are three mechanism to find the instance: Round-robin: Dispatcher will always send the message to the next instance. For example, if the dispatcher sent the message to instance 2, then the next message will be sent to instance 3, regardless if instance 3 is busy or not at that moment. Random: Dispatcher will find a service instance randomly, and same as the round-robin mode it regardless if the instance is busy or not. Sticky: Dispatcher will send all related messages to the same service instance. This approach always being used if the service methods are state-ful or session-ful. But as you can see, all of these approaches are not really load balanced. The clients will send messages at any time, and each message might take different process duration on the server side. This means in some cases, some of the service instances are very busy while others are almost idle. For example, if we were using round-robin mode, it could be happened that most of the simple task messages were passed to instance 1 while the complex ones were sent to instance 3, even though instance 1 should be idle. This brings some problem in our architecture. The first one is that, the response to the clients might be longer than it should be. As it’s shown in the figure above, message 6 and 9 can be processed by instance 1 or instance 2, but in reality they were dispatched to the busy instance 3 since the dispatcher and round-robin mode. Secondly, if there are many requests came from the clients in a very short period, service instances might be filled by tons of pending tasks and some instances might be crashed. Third, if we are using some cloud platform to host our service instances, for example the Windows Azure, the computing resource is billed by service deployment period instead of the actual CPU usage. This means if any service instance is idle it is wasting our money! Last one, the dispatcher would be the bottleneck of our system since all incoming messages must be routed by the dispatcher. If we are using HTTP or TCP as the transport, the dispatcher would be a network load balance. If we wants more capacity, we have to scale-up, or buy a hardware load balance which is very expensive, as well as scaling-out the service instances. Pulling Mode Pulling mode doesn’t need a dispatcher to route the messages. All service instances are listening to the same transport and try to retrieve the next proper message to process if they are idle. Since there is no dispatcher in pulling mode, it requires some features on the transportation. The transportation must support multiple client connection and server listening. HTTP and TCP doesn’t allow multiple clients are listening on the same address and port, so it cannot be used in pulling mode directly. All messages in the transportation must be FIFO, which means the old message must be received before the new one. Message selection would be a plus on the transportation. This means both service and client can specify some selection criteria and just receive some specified kinds of messages. This feature is not mandatory but would be very useful when implementing the request reply and duplex WCF channel modes. Otherwise we must have a memory dictionary to store the reply messages. I will explain more about this in the following articles. Message bus, or the message queue would be best candidate as the transportation when using the pulling mode. First, it allows multiple application to listen on the same queue, and it’s FIFO. Some of the message bus also support the message selection, such as TIBCO EMS, RabbitMQ. Some others provide in memory dictionary which can store the reply messages, for example the Redis. The principle of pulling mode is to let the service instances self-managed. This means each instance will try to retrieve the next pending incoming message if they finished the current task. This gives us more benefit and can solve the problems we met with in the dispatcher mode. The incoming message will be received to the best instance to process, which means this will be very balanced. And it will not happen that some instances are busy while other are idle, since the idle one will retrieve more tasks to make them busy. Since all instances are try their best to be busy we can use less instances than dispatcher mode, which more cost effective. Since there’s no dispatcher in the system, there is no bottleneck. When we introduced more service instances, in dispatcher mode we have to change something to let the dispatcher know the new instances. But in pulling mode since all service instance are self-managed, there no extra change at all. If there are many incoming messages, since the message bus can queue them in the transportation, service instances would not be crashed. All above are the benefits using the pulling mode, but it will introduce some problem as well. The process tracking and debugging become more difficult. Since the service instances are self-managed, we cannot know which instance will process the message. So we need more information to support debug and track. Real-time response may not be supported. All service instances will process the next message after the current one has done, if we have some real-time request this may not be a good solution. Compare with the Pros and Cons above, the pulling mode would a better solution for the distributed system architecture. Because what we need more is the scalability, cost-effect and the self-management.   WCF and WCF Transport Extensibility Windows Communication Foundation (WCF) is a framework for building service-oriented applications. In the .NET world WCF is the best way to implement the service. In this series I’m going to demonstrate how to implement the pulling mode on top of a message bus by extending the WCF. I don’t want to deep into every related field in WCF but will highlight its transport extensibility. When we implemented an RPC foundation there are many aspects we need to deal with, for example the message encoding, encryption, authentication and message sending and receiving. In WCF, each aspect is represented by a channel. A message will be passed through all necessary channels and finally send to the underlying transportation. And on the other side the message will be received from the transport and though the same channels until the business logic. This mode is called “Channel Stack” in WCF, and the last channel in the channel stack must always be a transport channel, which takes the responsible for sending and receiving the messages. As we are going to implement the WCF over message bus and implement the pulling mode scaling-out solution, we need to create our own transport channel so that the client and service can exchange messages over our bus. Before we deep into the transport channel, let’s have a look on the message exchange patterns that WCF defines. Message exchange pattern (MEP) defines how client and service exchange the messages over the transportation. WCF defines 3 basic MEPs which are datagram, Request-Reply and Duplex. Datagram: Also known as one-way, or fire-forgot mode. The message sent from the client to the service, and no need any reply from the service. The client doesn’t care about the message result at all. Request-Reply: Very common used pattern. The client send the request message to the service and wait until the reply message comes from the service. Duplex: The client sent message to the service, when the service processing the message it can callback to the client. When callback the service would be like a client while the client would be like a service. In WCF, each MEP represent some channels associated. MEP Channels Datagram IInputChannel, IOutputChannel Request-Reply IRequestChannel, IReplyChannel Duplex IDuplexChannel And the channels are created by ChannelListener on the server side, and ChannelFactory on the client side. The ChannelListener and ChannelFactory are created by the TransportBindingElement. The TransportBindingElement is created by the Binding, which can be defined as a new binding or from a custom binding. For more information about the transport channel mode, please refer to the MSDN document. The figure below shows the transport channel objects when using the request-reply MEP. And this is the datagram MEP. And this is the duplex MEP. After investigated the WCF transport architecture, channel mode and MEP, we finally identified what we should do to extend our message bus based transport layer. They are: Binding: (Optional) Defines the channel elements in the channel stack and added our transport binding element at the bottom of the stack. But we can use the build-in CustomBinding as well. TransportBindingElement: Defines which MEP is supported in our transport and create the related ChannelListener and ChannelFactory. This also defines the scheme of the endpoint if using this transport. ChannelListener: Create the server side channel based on the MEP it’s. We can have one ChannelListener to create channels for all supported MEPs, or we can have ChannelListener for each MEP. In this series I will use the second approach. ChannelFactory: Create the client side channel based on the MEP it’s. We can have one ChannelFactory to create channels for all supported MEPs, or we can have ChannelFactory for each MEP. In this series I will use the second approach. Channels: Based on the MEPs we want to support, we need to implement the channels accordingly. For example, if we want our transport support Request-Reply mode we should implement IRequestChannel and IReplyChannel. In this series I will implement all 3 MEPs listed above one by one. Scaffold: In order to make our transport extension works we also need to implement some scaffold stuff. For example we need some classes to send and receive message though out message bus. We also need some codes to read and write the WCF message, etc.. These are not necessary but would be very useful in our example.   Message Bus There is only one thing remained before we can begin to implement our scaling-out support WCF transport, which is the message bus. As I mentioned above, the message bus must have some features to fulfill all the WCF MEPs. In my company we will be using TIBCO EMS, which is an enterprise message bus product. And I have said before we can use any message bus production if it’s satisfied with our requests. Here I would like to introduce an interface to separate the message bus from the WCF. This allows us to implement the bus operations by any kinds bus we are going to use. The interface would be like this. 1: public interface IBus : IDisposable 2: { 3: string SendRequest(string message, bool fromClient, string from, string to = null); 4:  5: void SendReply(string message, bool fromClient, string replyTo); 6:  7: BusMessage Receive(bool fromClient, string replyTo); 8: } There are only three methods for the bus interface. Let me explain one by one. The SendRequest method takes the responsible for sending the request message into the bus. The parameters description are: message: The WCF message content. fromClient: Indicates if this message was came from the client. from: The channel ID that this message was sent from. The channel ID will be generated when any kinds of channel was created, which will be explained in the following articles. to: The channel ID that this message should be received. In Request-Reply and Duplex MEP this is necessary since the reply message must be received by the channel which sent the related request message. The SendReply method takes the responsible for sending the reply message. It’s very similar as the previous one but no “from” parameter. This is because it’s no need to reply a reply message again in any MEPs. The Receive method takes the responsible for waiting for a incoming message, includes the request message and specified reply message. It returned a BusMessage object, which contains some information about the channel information. The code of the BusMessage class is 1: public class BusMessage 2: { 3: public string MessageID { get; private set; } 4: public string From { get; private set; } 5: public string ReplyTo { get; private set; } 6: public string Content { get; private set; } 7:  8: public BusMessage(string messageId, string fromChannelId, string replyToChannelId, string content) 9: { 10: MessageID = messageId; 11: From = fromChannelId; 12: ReplyTo = replyToChannelId; 13: Content = content; 14: } 15: } Now let’s implement a message bus based on the IBus interface. Since I don’t want you to buy and install the TIBCO EMS or any other message bus products, I will implement an in process memory bus. This bus is only for test and sample purpose. It can only be used if the service and client are in the same process. Very straightforward. 1: public class InProcMessageBus : IBus 2: { 3: private readonly ConcurrentDictionary<Guid, InProcMessageEntity> _queue; 4: private readonly object _lock; 5:  6: public InProcMessageBus() 7: { 8: _queue = new ConcurrentDictionary<Guid, InProcMessageEntity>(); 9: _lock = new object(); 10: } 11:  12: public string SendRequest(string message, bool fromClient, string from, string to = null) 13: { 14: var entity = new InProcMessageEntity(message, fromClient, from, to); 15: _queue.TryAdd(entity.ID, entity); 16: return entity.ID.ToString(); 17: } 18:  19: public void SendReply(string message, bool fromClient, string replyTo) 20: { 21: var entity = new InProcMessageEntity(message, fromClient, null, replyTo); 22: _queue.TryAdd(entity.ID, entity); 23: } 24:  25: public BusMessage Receive(bool fromClient, string replyTo) 26: { 27: InProcMessageEntity e = null; 28: while (true) 29: { 30: lock (_lock) 31: { 32: var entity = _queue 33: .Where(kvp => kvp.Value.FromClient == fromClient && (kvp.Value.To == replyTo || string.IsNullOrWhiteSpace(kvp.Value.To))) 34: .FirstOrDefault(); 35: if (entity.Key != Guid.Empty && entity.Value != null) 36: { 37: _queue.TryRemove(entity.Key, out e); 38: } 39: } 40: if (e == null) 41: { 42: Thread.Sleep(100); 43: } 44: else 45: { 46: return new BusMessage(e.ID.ToString(), e.From, e.To, e.Content); 47: } 48: } 49: } 50:  51: public void Dispose() 52: { 53: } 54: } The InProcMessageBus stores the messages in the objects of InProcMessageEntity, which can take some extra information beside the WCF message itself. 1: public class InProcMessageEntity 2: { 3: public Guid ID { get; set; } 4: public string Content { get; set; } 5: public bool FromClient { get; set; } 6: public string From { get; set; } 7: public string To { get; set; } 8:  9: public InProcMessageEntity() 10: : this(string.Empty, false, string.Empty, string.Empty) 11: { 12: } 13:  14: public InProcMessageEntity(string content, bool fromClient, string from, string to) 15: { 16: ID = Guid.NewGuid(); 17: Content = content; 18: FromClient = fromClient; 19: From = from; 20: To = to; 21: } 22: }   Summary OK, now I have all necessary stuff ready. The next step would be implementing our WCF message bus transport extension. In this post I described two scaling-out approaches on the service side especially if we are using the cloud platform: dispatcher mode and pulling mode. And I compared the Pros and Cons of them. Then I introduced the WCF channel stack, channel mode and the transport extension part, and identified what we should do to create our own WCF transport extension, to let our WCF services using pulling mode based on a message bus. And finally I provided some classes that need to be used in the future posts that working against an in process memory message bus, for the demonstration purpose only. In the next post I will begin to implement the transport extension step by step.   Hope this helps, Shaun All documents and related graphics, codes are provided "AS IS" without warranty of any kind. Copyright © Shaun Ziyan Xu. This work is licensed under the Creative Commons License.

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  • Mac OS X Server (10.5) mail trapped in queue

    - by Meltemi
    We've got mail accumulating in our Leopard Server's queue and not sure exactly why. This machine has required little maintenance over the years so I'm hoping someone here spot the obvious and save us some time. Let me know what other information would be helfull. Server appears to be functioning normally except for "clogged" queue and the following error associated with each "trapped" message: Looking at messages in the queue each one states something like this: Message ID: 4213C3B8B3F Date: October 27, 2009 11:33:27 AM Size: 1824 Sender: [email protected] Recipient(s) & Status: ---------------------- [email protected]: connect to 127.0.0.1[127.0.0.1]: Connection refused Under SettingsRelay we have checked Accept SMTP relays only from these hosts and networks: 127.0.0.0/8 10.0.1.0/24 The mail in queue is addressed to users whose accounts are on this server. Mail.app on the client appears to be functioning normally and checking checking mail on the server. We did add a virtual domain some time ago but all that was working fine for some time... This just started happening recently...any ideas? Edit: toggling the filter services on and off seems to have fixed this except for 2 remaining queued messages that show "mail transport unavailable" as an error!?!

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  • Mail queue directory stuck in IIS SMTP server

    - by Loftx
    Hi there, We have an IIS SMTP server which sends out a largish number of mails (4000 or so) in batches overnight, and recently we've seen mails get "stuck" in the queue directory. Normally restarting the SMTP service seems to fix this, but it's happened a few times so I'm looking for more information. We sent out around 12,000 emails last night in 3 batches of roughly 4000. Around 10 hours later there are still 2000 or so in the queue directory which don't seem to be leaving the queue. Any new mails which appear in the queue are picked up almost immediately and sent to their destination, but these 2000 or so don't seem to move. Looking at the date modified on the emails some match up with the time they were sent, but around 1000 of them have modified dates stretching up to now. e.g. there was one mail with a date in the message headers of 5:30 this morning, but it's date modified is 11:50 and there are 3 other messages with a date modified of 11:50, then 5 with 11:49, 2 with 11:45 stretching back for a few hours and all with actual message headers far earlier. The logs for the server look like this 11:54:52 127.0.0.1 EHLO - 250 11:54:52 127.0.0.1 MAIL - 250 11:54:52 127.0.0.1 RCPT - 250 11:54:52 127.0.0.1 DATA - 250 11:54:52 127.0.0.1 QUIT - 240 11:54:53 85.115.62.190 - - 0 11:54:53 85.115.62.190 EHLO - 0 11:54:53 85.115.62.190 - - 0 11:54:53 85.115.62.190 MAIL - 0 11:54:53 85.115.62.190 - - 0 11:54:53 85.115.62.190 RCPT - 0 11:54:53 85.115.62.190 - - 0 11:54:53 85.115.62.190 DATA - 0 11:54:53 85.115.62.190 - - 0 11:54:54 85.115.62.190 - - 0 11:54:54 85.115.62.190 QUIT - 0 11:54:54 85.115.62.190 - - 0 All codes are either 250 or 240 or 0. I believe 250 and 240 indicate success, but I don't know what all the 0s are. Could someone with more experience of mail server troubleshooting give me a hand or tell me what to try next. Thanks, Tom

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  • JMS Step 1 - How to Create a Simple JMS Queue in Weblogic Server 11g

    - by John-Brown.Evans
    JMS Step 1 - How to Create a Simple JMS Queue in Weblogic Server 11g ol{margin:0;padding:0} .c5{vertical-align:top;width:156pt;border-style:solid;border-color:#000000;border-width:1pt;padding:0pt 2pt 0pt 2pt} .c7{list-style-type:disc;margin:0;padding:0} .c4{background-color:#ffffff} .c14{color:#1155cc;text-decoration:underline} .c6{height:11pt;text-align:center} .c13{color:inherit;text-decoration:inherit} .c3{padding-left:0pt;margin-left:36pt} .c0{border-collapse:collapse} .c12{text-align:center} .c1{direction:ltr} .c8{background-color:#f3f3f3} .c2{line-height:1.0} .c11{font-style:italic} .c10{height:11pt} .c9{font-weight:bold} .title{padding-top:24pt;line-height:1.15;text-align:left;color:#000000;font-size:36pt;font-family:"Arial";font-weight:bold;padding-bottom:6pt}.subtitle{padding-top:18pt;line-height:1.15;text-align:left;color:#666666;font-style:italic;font-size:24pt;font-family:"Georgia";padding-bottom:4pt} li{color:#000000;font-size:10pt;font-family:"Arial"} p{color:#000000;font-size:10pt;margin:0;font-family:"Arial"} h1{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-size:18pt;font-family:"Arial";font-weight:normal;padding-bottom:0pt} h2{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-size:14pt;font-family:"Arial";font-weight:normal;padding-bottom:0pt} h3{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-size:12pt;font-family:"Arial";font-weight:normal;padding-bottom:0pt} h4{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-style:italic;font-size:11pt;font-family:"Arial";padding-bottom:0pt} h5{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-size:10pt;font-family:"Arial";font-weight:normal;padding-bottom:0pt} h6{padding-top:0pt;line-height:1.15;text-align:left;color:#666;font-style:italic;font-size:10pt;font-family:"Arial";padding-bottom:0pt} This example shows the steps to create a simple JMS queue in WebLogic Server 11g for testing purposes. For example, to use with the two sample programs QueueSend.java and QueueReceive.java which will be shown in later examples. Additional, detailed information on JMS can be found in the following Oracle documentation: Oracle® Fusion Middleware Configuring and Managing JMS for Oracle WebLogic Server 11g Release 1 (10.3.6) Part Number E13738-06 http://docs.oracle.com/cd/E23943_01/web.1111/e13738/toc.htm 1. Introduction and Definitions A JMS queue in Weblogic Server is associated with a number of additional resources: JMS Server A JMS server acts as a management container for resources within JMS modules. Some of its responsibilities include the maintenance of persistence and state of messages and subscribers. A JMS server is required in order to create a JMS module. JMS Module A JMS module is a definition which contains JMS resources such as queues and topics. A JMS module is required in order to create a JMS queue. Subdeployment JMS modules are targeted to one or more WLS instances or a cluster. Resources within a JMS module, such as queues and topics are also targeted to a JMS server or WLS server instances. A subdeployment is a grouping of targets. It is also known as advanced targeting. Connection Factory A connection factory is a resource that enables JMS clients to create connections to JMS destinations. JMS Queue A JMS queue (as opposed to a JMS topic) is a point-to-point destination type. A message is written to a specific queue or received from a specific queue. The objects used in this example are: Object Name Type JNDI Name TestJMSServer JMS Server TestJMSModule JMS Module TestSubDeployment Subdeployment TestConnectionFactory Connection Factory jms/TestConnectionFactory TestJMSQueue JMS Queue jms/TestJMSQueue 2. Configuration Steps The following steps are done in the WebLogic Server Console, beginning with the left-hand navigation menu. 2.1 Create a JMS Server Services > Messaging > JMS Servers Select New Name: TestJMSServer Persistent Store: (none) Target: soa_server1  (or choose an available server) Finish The JMS server should now be visible in the list with Health OK. 2.2 Create a JMS Module Services > Messaging > JMS Modules Select New Name: TestJMSModule Leave the other options empty Targets: soa_server1  (or choose the same one as the JMS server)Press Next Leave “Would you like to add resources to this JMS system module” unchecked and  press Finish . 2.3 Create a SubDeployment A subdeployment is not necessary for the JMS queue to work, but it allows you to easily target subcomponents of the JMS module to a single target or group of targets. We will use the subdeployment in this example to target the following connection factory and JMS queue to the JMS server we created earlier. Services > Messaging > JMS Modules Select TestJMSModule Select the Subdeployments  tab and New Subdeployment Name: TestSubdeployment Press Next Here you can select the target(s) for the subdeployment. You can choose either Servers (i.e. WebLogic managed servers, such as the soa_server1) or JMS Servers such as the JMS Server created earlier. As the purpose of our subdeployment in this example is to target a specific JMS server, we will choose the JMS Server option. Select the TestJMSServer created earlier Press Finish 2.4  Create a Connection Factory Services > Messaging > JMS Modules Select TestJMSModule  and press New Select Connection Factory  and Next Name: TestConnectionFactory JNDI Name: jms/TestConnectionFactory Leave the other values at default On the Targets page, select the Advanced Targeting  button and select TestSubdeployment Press Finish The connection factory should be listed on the following page with TestSubdeployment and TestJMSServer as the target. 2.5 Create a JMS Queue Services > Messaging > JMS Modules Select TestJMSModule  and press New Select Queue and Next Name: TestJMSQueueJNDI Name: jms/TestJMSQueueTemplate: NonePress Next Subdeployments: TestSubdeployment Finish The TestJMSQueue should be listed on the following page with TestSubdeployment and TestJMSServer. Confirm the resources for the TestJMSModule. Using the Domain Structure tree, navigate to soa_domain > Services > Messaging > JMS Modules then select TestJMSModule You should see the following resources The JMS queue is now complete and can be accessed using the JNDI names jms/TestConnectionFactory andjms/TestJMSQueue. In the following blog post in this series, I will show you how to write a message to this queue, using the WebLogic sample Java program QueueSend.java.

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  • Clojure agents consuming from a queue

    - by erikcw
    I'm trying to figure out the best way to use agents to consume items from a Message Queue (Amazon SQS). Right now I have a function (process-queue-item) that grabs an items from the queue, and processes it. I want to process these items concurrently, but I can't wrap my head around how to control the agents. Basically I want to keep all of the agents busy as much as possible without pulling to many items from the Queue and developing a backlog (I'll have this running on a couple of machines, so items need to be left in the queue until they are really needed). Can anyone give me some pointers on improving my implementation? (def active-agents (ref 0)) (defn process-queue-item [_] (dosync (alter active-agents inc)) ;retrieve item from Message Queue (Amazon SQS) and process (dosync (alter active-agents dec))) (defn -main [] (def agents (for [x (range 20)] (agent x))) (loop [loop-count 0] (if (< @active-agents 20) (doseq [agent agents] (if (agent-errors agent) (clear-agent-errors agent)) ;should skip this agent until later if it is still busy processing (not sure how) (send-off agent process-queue-item))) ;(apply await-for (* 10 1000) agents) (Thread/sleep 10000) (logging/info (str "ACTIVE AGENTS " @active-agents)) (if (> 10 loop-count) (do (logging/info (str "done, let's cleanup " count)) (doseq [agent agents] (if (agent-errors agent) (clear-agent-errors agent))) (apply await agents) (shutdown-agents)) (recur (inc count)))))

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  • Task queue java

    - by user268515
    Hi i'm new to Task queue java API i tried a simple Example for it. My idea is to redirect the queue file to a servlet and to print some statement in the servlet.But it doesn't work. i mapped web.xml and used default queue I didnt get any Error but the file is not redirected to servlet . this is the codee i followed taskq.java public class taskq extends HttpServlet { public void doGet(HttpServletRequest req, HttpServletResponse resp)throwsIOException { Queue queue = QueueFactory.getDefaultQueue(); System.out.println("taskqueue"); queue.add(url("/worker")); } worker.java public class worker extends HttpServlet { private static final long serialVersionUID = 1L; public String s; public void doGet(HttpServletRequest req, HttpServletResponse resp)throws IOException { String s="crimsom"; System.out.println(s); } } Please Help me on this issue. Regards Sharun.

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  • Pointers to structures

    - by blacktooth
    typedef struct queue { int q[max]; int qhead; int qrear; } queue; void init_queue(queue *QUEUE) { QUEUE.qhead = 0; QUEUE.qrear = -1; } void enqueue(queue *QUEUE,int data) { QUEUE.qrear++; QUEUE.q[QUEUE.qrear] = data; } int process_queue(queue *QUEUE) { if(QUEUE.qhead > QUEUE.qrear) return -1; else return QUEUE.q[QUEUE.qhead++]; } I am implementing queues using arrays just to keep it simple. Wats the error with the above code?

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  • Operator Overloading for Queue C++

    - by Josh
    I was trying to use the overload operator method to copy the entries of one queue into another, but I am going wrong with my function. I don't know how else to access the values of the queue "original" any other way than what I have below: struct Node { int item; Node* next; }; class Queue { public: [...] //Extra code here void operator = (const Queue &original); protected: Node *front, *end; } void Queue::operator=(const Queue &original) { //THIS IS WHERE IM GOING WRONG while(original.front->next != NULL) { front->item = original.front->item; front->next = new Node; front = front->next; original.front = original.front->next; } }

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  • JDownloader: lost my queue

    - by Fuxi
    hi, unfortunately my jDownloader crashed and my queue is empty. i've googled already and unzipped the database.zip into the config dir - but didn't help. any ideas how to get my queue back? thx

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  • Program crash on deque from queue

    - by SwedishGit
    My first question asked here, so please excuse if I fail to include something... I'm working on a homework project, which basically consists of creating a "Jukebox" (importing/exporting albums from txt files, creating and "playing" a playlist, etc.). I've become stuck on one point: When "playing" the playlist, which consists of a self-made Queue, a copy of it is made from which songs are dequeued and printed out with a time delay. This appears to run fine on the first run through the program, but if the "play" option is chosen again (with the same playlist, created from a different menu option), it crashes before managing to print the first song. It also crashes if creating a new playlist, but then it manages to print some songs (seem to depend on the number of songs in the first/new playlists...) before crashing. With printouts I've been able to track the crashing down to being on the "item = n-data" call in the deque function... but can't get my head around why this would crash. Below is the code I think should be relevant... let me know if there are other parts that would help if I include. Edit: The Debug Error shown on crash is: R6010 abort() has been called The method to play from the playlist: void Jukebox::playList() { if(songList.getNodes() > 0) { Queue tmpList(songList); Song tmpSong; while(tmpList.deque(tmpSong)) { clock_t temp; temp = clock () + 2 * CLOCKS_PER_SEC ; while (clock() < temp) {} } } else cout << "There are no songs in the playlist!" << endl; } Queue: // Queue.h - Projekt-uppgift // Håkan Sjölin 2014-05-31 //----------------------------------------------------------------------------- #ifndef queue_h #define queue_h #include "Song.h" using namespace std; typedef Song Item; class Node; class Queue { private: Node *first; Node *last; int nodes; public: Queue():first(nullptr),last(nullptr),nodes(0){}; ~Queue(); void enque(Item item); bool deque(Item &item); int getNodes() const { return nodes; } void empty(); }; #endif // Queue.cpp - Projekt-uppgift // Håkan Sjölin 2014-05-31 //----------------------------------------------------------------------------- #include "queue.h" using namespace std; class Node { public: Node *next; Item data; Node (Node *n, Item newData) : next(n), data(newData) {} }; //------------------------------------------------------------------------------ // Funktionsdefinitioner för klassen Queue //------------------------------------------------------------------------------ //------------------------------------------------------------------------------ // Destruktor //------------------------------------------------------------------------------ Queue::~Queue() { while(first!=0) { Node *tmp = first; first = first->next; delete tmp; } } //------------------------------------------------------------------------------ // Lägg till data sist i kön //------------------------------------------------------------------------------ void Queue::enque(Item item) { Node *pNew = new Node(0,item); if(getNodes() < 1) first = pNew; else last->next = pNew; last = pNew; nodes++; } //------------------------------------------------------------------------------ // Ta bort data först i kön //------------------------------------------------------------------------------ bool Queue::deque(Item &item) { if(getNodes() < 1) return false; //cout << "deque: test2" << endl; Node *n = first; //cout << "deque: test3" << endl; //cout << "item = " << item << endl; //cout << "first = " << first << endl; //cout << "n->data = " << n->data << endl; item = n->data; //cout << "deque: test4" << endl; first = first->next; //delete n; nodes--; if(getNodes() < 1) // Kön BLEV tom last = nullptr; return true; } //------------------------------------------------------------------------------ // Töm kön //------------------------------------------------------------------------------ void Queue::empty() { while (getNodes() > 0) { Item item; deque(item); } } //------------------------------------------------------------------------------ Song: // Song.h - Projekt-uppgift // Håkan Sjölin 2014-05-15 //----------------------------------------------------------------------------- #ifndef song_h #define song_h #include "Time.h" #include <string> #include <iostream> using namespace std; class Song { private: string title; string artist; Time length; public: Song(); Song(string pTitle, string pArtist, Time pLength); // Setfunktioner void setTitle(string pTitle); void setArtist(string pArtist); void setLength(Time pLength); // Getfunktioner string getTitle() const { return title;} string getArtist() const { return artist;} Time getLength() const { return length;} }; ostream &operator<<(ostream &os, const Song &song); istream &operator>>(istream &is, Song &song); #endif // Song.cpp - Projekt-uppgift // Håkan Sjölin 2014-05-15 //----------------------------------------------------------------------------- #include "Song.h" #include "Constants.h" #include <iostream> //------------------------------------------------------------------------------ // Definiering av Songs medlemsfunktioner //------------------------------------------------------------------------------ // Fövald konstruktor //------------------------------------------------------------------------------ Song::Song() { } //------------------------------------------------------------------------------ // Initieringskonstruktor //------------------------------------------------------------------------------ Song::Song(string pTitle, string pArtist, Time pLength) { title = pTitle; artist = pArtist; length = pLength; } //------------------------------------------------------------------------------ // Setfunktioner //------------------------------------------------------------------------------ //------------------------------------------------------------------------------ // setTitle // Ange titel //------------------------------------------------------------------------------ void Song::setTitle(string pTitle) { title = pTitle; } //------------------------------------------------------------------------------ // setArtist // Ange artist //------------------------------------------------------------------------------ void Song::setArtist(string pArtist) { artist = pArtist; } //------------------------------------------------------------------------------ // setTitle // Ange titel //------------------------------------------------------------------------------ void Song::setLength(Time pLength) { length = pLength; } //--------------------------------------------------------------------------- // Överlagring av utskriftsoperatorn //--------------------------------------------------------------------------- ostream &operator<<(ostream &os, const Song &song) { os << song.getTitle() << DELIM << song.getArtist() << DELIM << song.getLength(); return os; } //--------------------------------------------------------------------------- // Överlagring av inmatningsoperatorn //--------------------------------------------------------------------------- istream &operator>>(istream &is, Song &song) { string tmpString; Time tmpLength; getline(is, tmpString, DELIM); song.setTitle(tmpString); getline(is, tmpString, DELIM); song.setArtist(tmpString); is >> tmpLength; is.get(); song.setLength(tmpLength); return is; } //--------------------------------------------------------------------------- Album: // Album.h - Projekt-uppgift // Håkan Sjölin 2014-05-17 //----------------------------------------------------------------------------- #ifndef album_h #define album_h #include "Song.h" #include <string> #include <vector> #include <iostream> using namespace std; class Album { private: string name; vector<Song> songs; public: Album(); Album(string pNameTitle, vector<Song> pSongs); // Setfunktioner void setName(string pName); // Getfunktioner string getName() const { return name;} vector<Song> getSongs() const { return songs;} int getNumberOfSongs() const { return songs.size();} Time getTotalTime() const; void addSong(Song pSong); bool operator<(const Album &album) const; }; ostream &operator<<(ostream &os, const Album &album); istream &operator>>(istream &is, Album &album); #endif // Album.cpp - Projekt-uppgift // Håkan Sjölin 2014-05-17 //----------------------------------------------------------------------------- #include "Album.h" #include "Constants.h" #include <iostream> #include <string> //------------------------------------------------------------------------------ // Definiering av Albums medlemsfunktioner //------------------------------------------------------------------------------ // Fövald konstruktor //------------------------------------------------------------------------------ Album::Album() { } //------------------------------------------------------------------------------ // Initieringskonstruktor //------------------------------------------------------------------------------ Album::Album(string pName, vector<Song> pSongs) { name = pName; songs = pSongs; } //------------------------------------------------------------------------------ // Setfunktioner //------------------------------------------------------------------------------ //------------------------------------------------------------------------------ // setName // Ange namn //------------------------------------------------------------------------------ void Album::setName(string pName) { name = pName; } //------------------------------------------------------------------------------ // addSong // Lägg till song //------------------------------------------------------------------------------ void Album::addSong(Song pSong) { songs.push_back(pSong); } //------------------------------------------------------------------------------ // getTotalTime // Returnera total speltid //------------------------------------------------------------------------------ Time Album::getTotalTime() const { Time tTime(0,0,0); for(Song s : songs) { tTime = tTime + s.getLength(); } return tTime; } //--------------------------------------------------------------------------- // Mindre än //--------------------------------------------------------------------------- bool Album::operator<(const Album &album) const { return getTotalTime() < album.getTotalTime(); } //--------------------------------------------------------------------------- // Överlagring av utskriftsoperatorn //--------------------------------------------------------------------------- ostream &operator<<(ostream &os, const Album &album) { os << album.getName() << endl; os << album.getNumberOfSongs() << endl; for (size_t i = 0; i < album.getSongs().size(); i++) os << album.getSongs().at(i) << endl; return os; } //--------------------------------------------------------------------------- // Överlagring av inmatningsoperatorn //--------------------------------------------------------------------------- istream &operator>>(istream &is, Album &album) { string tmpString; int tmpNumberOfSongs; Song tmpSong; getline(is, tmpString); album.setName(tmpString); is >> tmpNumberOfSongs; is.get(); for (int i = 0; i < tmpNumberOfSongs; i++) { is >> tmpSong; album.addSong(tmpSong); } return is; } //--------------------------------------------------------------------------- Time: // Time.h - Projekt-uppgift // Håkan Sjölin 2014-05-15 //----------------------------------------------------------------------------- #ifndef time_h #define time_h #include <iostream> using namespace std; class Time { private: int hours; int minutes; int seconds; public: Time(); Time(int pHour, int pMinute, int pSecond); // Setfunktioner void setHour(int pHour); void setMinute(int pMinute); void setSecond(int pSecond); // Getfunktioner int getHour() const { return hours;} int getMinute() const { return minutes;} int getSecond() const { return seconds;} Time operator+(const Time &time) const; bool operator==(const Time &time) const; bool operator<(const Time &time) const; }; ostream &operator<<(ostream &os, const Time &time); istream &operator>>(istream &is, Time &Time); #endif // Time.cpp - Projekt-uppgift // Håkan Sjölin 2014-05-15 //----------------------------------------------------------------------------- #include "Time.h" #include <iostream> //------------------------------------------------------------------------------ // Definiering av Times medlemsfunktioner //------------------------------------------------------------------------------ // Fövald konstruktor //------------------------------------------------------------------------------ Time::Time() { } //------------------------------------------------------------------------------ // Initieringskonstruktor //------------------------------------------------------------------------------ Time::Time(int pHour, int pMinute, int pSecond) { setHour(pHour); setMinute(pMinute); setSecond(pSecond); } //------------------------------------------------------------------------------ // Setfunktioner //------------------------------------------------------------------------------ //------------------------------------------------------------------------------ // setHour // Ange timme //------------------------------------------------------------------------------ void Time::setHour(int pHour) { if(pHour>-1) hours = pHour; else hours = 0; } //------------------------------------------------------------------------------ // setMinute // Ange minut //------------------------------------------------------------------------------ void Time::setMinute(int pMinute) { if(pMinute < 60 && pMinute > -1) { minutes = pMinute; } else minutes = 0; } //------------------------------------------------------------------------------ // setSecond // Ange sekund //------------------------------------------------------------------------------ void Time::setSecond(int pSecond) { if(pSecond < 60 && pSecond > -1) { seconds = pSecond; } else seconds = 0; } //--------------------------------------------------------------------------- // Överlagring av utskriftsoperatorn //--------------------------------------------------------------------------- ostream &operator<<(ostream &os, const Time &time) { os << time.getHour()*3600+time.getMinute()*60+time.getSecond(); return os; } //--------------------------------------------------------------------------- // Överlagring av inmatningsoperatorn //--------------------------------------------------------------------------- istream &operator>>(istream &is, Time &time) { int tmp; is >> tmp; time.setSecond(tmp%60); time.setMinute((tmp/60)%60); time.setHour(tmp/3600); return is; } //--------------------------------------------------------------------------- // Likhet //-------------------------------------------------------------------------- bool Time::operator==(const Time &time) const { return hours == time.getHour() && minutes == time.getMinute() && seconds == time.getSecond(); } //--------------------------------------------------------------------------- // Mindre än //--------------------------------------------------------------------------- bool Time::operator<(const Time &time) const { if(hours == time.getHour()) { if(minutes == time.getMinute()) { return seconds < time.getSecond(); } else { return minutes < time.getMinute(); } } else { return hours < time.getHour(); } } //--------------------------------------------------------------------------- // Addition //--------------------------------------------------------------------------- Time Time::operator+(const Time &time) const { return Time(hours+time.getHour() + (minutes+time.getMinute() + (seconds+time.getSecond())/60)/60, (minutes+time.getMinute() + (seconds+time.getSecond())/60)%60, (seconds+time.getSecond())%60); } //--------------------------------------------------------------------------- Thanks in advance for any help! Edit2: Didn't think of including the more detailed crash info (as it didn't show in the crash pop-up, so to say). Anyway, here it is: Output: 'Jukebox.exe' (Win32): Loaded 'C:\Users\Håkan\Documents\Studier - IT\Objektbaserad programmering i C++\Inlämningsuppgifter\Projekt\Jukebox\Debug\Jukebox.exe'. Symbols loaded. 'Jukebox.exe' (Win32): Loaded 'C:\Windows\SysWOW64\ntdll.dll'. Cannot find or open the PDB file. 'Jukebox.exe' (Win32): Loaded 'C:\Windows\SysWOW64\kernel32.dll'. Cannot find or open the PDB file. 'Jukebox.exe' (Win32): Loaded 'C:\Windows\SysWOW64\KernelBase.dll'. Cannot find or open the PDB file. 'Jukebox.exe' (Win32): Loaded 'C:\Windows\SysWOW64\msvcp110d.dll'. Symbols loaded. 'Jukebox.exe' (Win32): Loaded 'C:\Windows\SysWOW64\msvcr110d.dll'. Symbols loaded. The thread 0xe50 has exited with code 0 (0x0). Unhandled exception at 0x0083630C in Jukebox.exe: 0xC0000005: Access violation reading location 0x0000003C. Call stack: > Jukebox.exe!Song::getLength() Line 27 C++ Jukebox.exe!operator<<(std::basic_ostream<char,std::char_traits<char> > & os, const Song & song) Line 59 C++ Jukebox.exe!Queue::deque(Song & item) Line 55 C++ Jukebox.exe!Jukebox::playList() Line 493 C++ Jukebox.exe!Jukebox::play() Line 385 C++ Jukebox.exe!Jukebox::run() Line 536 C++ Jukebox.exe!main() Line 547 C++ Jukebox.exe!__tmainCRTStartup() Line 536 C Jukebox.exe!mainCRTStartup() Line 377 C kernel32.dll!754d86e3() Unknown [Frames below may be incorrect and/or missing, no symbols loaded for kernel32.dll] ntdll.dll!7748bf39() Unknown ntdll.dll!7748bf0c() Unknown

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  • Priority queue with dynamic item priorities.

    - by sean
    I need to implement a priority queue where the priority of an item in the queue can change and the queue adjusts itself so that items are always removed in the correct order. I have some ideas of how I could implement this but I'm sure this is quite a common data structure so I'm hoping I can use an implementation by someone smarter than me as a base. Can anyone tell me the name of this type of priority queue so I know what to search for or, even better, point me to an implementation?

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  • 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 --

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  • Message Queue or Scheduler

    - by Walter White
    Hi all, I am currently using Quartz Scheduler for asynchronous tasks such as sending an email when an exception occurs, sending an email from the web interface, or periodically analyzing traffic. Should I use a message queue for sending an email? Is it any more efficient or correct to do it that way? The scheduler approach works just fine. If I use a queue and the email failed to send, is it possible for the queue to retry sending the email at a later time? The queue approach looks simpler than the scheduler for tasks that need to happen immediately, but for scheduler tasks, the scheduler still, unless there is more to the queue than I am aware of. I have not yet used JMS, so this is what I have read. Walter

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  • 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.

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  • Multiple Producers Single Consumer Queue

    - by Talguy
    I am new to multithreading and have designed a program that receives data from two microcontroller measuring various temperatures (Ambient and Water) and draws the data to the screen. Right now the program is singly threaded and its performance SUCKS A BIG ONE. I get basic design approaches with multithreading but not well enough to create a thread to do a task but what I don't get is how to get threads to perform seperate task and place the data into a shared data pool. I figured that I need to make a queue that has one consumer and multiple producers (would like to use std::queue). I have seen some code on the gtkmm threading docs that show a single Con/Pro queue and they would lock the queue object produce data and signal the sleeping thread that it is finished then the producer would sleep. For what I need would I need to sleep a thread, would there be data conflicts if i didn't sleep any of the threads, and would sleeping a thread cause a data signifcant data delay (I need realtime data to be drawn 30 frames a sec) How would I go about coding such a queue using the gtkmm/glibmm library.

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  • c++ stl priority queue insert bad_alloc exception

    - by bsg
    Hi, I am working on a query processor that reads in long lists of document id's from memory and looks for matching id's. When it finds one, it creates a DOC struct containing the docid (an int) and the document's rank (a double) and pushes it on to a priority queue. My problem is that when the word(s) searched for has a long list, when I try to push the DOC on to the queue, I get the following exception: Unhandled exception at 0x7c812afb in QueryProcessor.exe: Microsoft C++ exception: std::bad_alloc at memory location 0x0012ee88.. When the word has a short list, it works fine. I tried pushing DOC's onto the queue in several places in my code, and they all work until a certain line; after that, I get the above error. I am completely at a loss as to what is wrong because the longest list read in is less than 1 MB and I free all memory that I allocate. Why should there suddenly be a bad_alloc exception when I try to push a DOC onto a queue that has a capacity to hold it (I used a vector with enough space reserved as the underlying data structure for the priority queue)? I know that questions like this are almost impossible to answer without seeing all the code, but it's too long to post here. I'm putting as much as I can and am anxiously hoping that someone can give me an answer, because I am at my wits' end. The NextGEQ function is too long to put here, but it reads a list of compressed blocks of docids block by block. That is, if it sees that the lastdocid in the block (in a separate list) is larger than the docid passed in, it decompresses the block and searches until it finds the right one. If it sees that it was already decompressed, it just searches. Below, when I call the function the first time, it decompresses a block and finds the docid; the push onto the queue after that works. The second time, it doesn't even need to decompress; that is, no new memory is allocated, but after that time, pushing on to the queue gives a bad_alloc error. struct DOC{ long int docid; long double rank; public: DOC() { docid = 0; rank = 0.0; } DOC(int num, double ranking) { docid = num; rank = ranking; } bool operator>( const DOC & d ) const { return rank > d.rank; } bool operator<( const DOC & d ) const { return rank < d.rank; } }; struct listnode{ int* metapointer; int* blockpointer; int docposition; int frequency; int numberdocs; int* iquery; listnode* nextnode; }; void QUERYMANAGER::SubmitQuery(char *query){ vector<DOC> docvec; docvec.reserve(20); DOC doct; //create a priority queue to use as a min-heap to store the documents and rankings; //although the priority queue uses the heap as its underlying data structure, //I found it easier to use the STL priority queue implementation priority_queue<DOC, vector<DOC>,std::greater<DOC>> q(docvec.begin(), docvec.end()); q.push(doct); //do some processing here; startlist is a pointer to a listnode struct that starts the //linked list cout << "Opening lists:" << endl; //point the linked list start pointer to the node returned by the OpenList method startlist = &OpenList(value); listnode* minpointer; q.push(doct); //more processing here; else{ //start by finding the first docid in the shortest list int i = 0; q.push(doct); num = NextGEQ(0, *startlist); q.push(doct); while(num != -1) cout << "finding nextGEQ from shortest list" << endl; q.push(doct); //the is where the problem starts - every previous q.push(doct) works; the one after //NextGEQ(num +1, *startlist) gives the bad_alloc error num = NextGEQ(num + 1, *startlist); q.push(doct); //if you didn't break out of the loop; i.e., all lists contain a matching docid, //calculate the document's rank; if it's one of the top 20, create a struct //containing the docid and the rank and add it to the priority queue if(!loop) { cout << "found match" << endl; if(num < 0) { cout << "reached end of list" << endl; //reached the end of the shortest list; close the list CloseList(startlist); break; } rank = calculateRanking(table, num); try{ //if the heap is not full, create a DOC struct with the docid and //rank and add it to the heap if(q.size() < 20) { doc.docid = num; doc.rank = rank; q.push(doct); q.push(doc); } } catch (exception& e) { cout << e.what() << endl; } } } Thank you very much, bsg.

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  • Creating a blocking Queue<T> in .NET?

    - by spoon16
    I have a scenario where I have multiple threads adding to a queue and multiple threads reading from the same queue. If the queue reaches a specific size all threads that are filling the queue will be blocked on add until an item is removed from the queue. The solution below is what I am using right now and my question is: How can this be improved? Is there an object that already enables this behavior in the BCL that I should be using? internal class BlockingCollection<T> : CollectionBase, IEnumerable { //todo: might be worth changing this into a proper QUEUE private AutoResetEvent _FullEvent = new AutoResetEvent(false); internal T this[int i] { get { return (T) List[i]; } } private int _MaxSize; internal int MaxSize { get { return _MaxSize; } set { _MaxSize = value; checkSize(); } } internal BlockingCollection(int maxSize) { MaxSize = maxSize; } internal void Add(T item) { Trace.WriteLine(string.Format("BlockingCollection add waiting: {0}", Thread.CurrentThread.ManagedThreadId)); _FullEvent.WaitOne(); List.Add(item); Trace.WriteLine(string.Format("BlockingCollection item added: {0}", Thread.CurrentThread.ManagedThreadId)); checkSize(); } internal void Remove(T item) { lock (List) { List.Remove(item); } Trace.WriteLine(string.Format("BlockingCollection item removed: {0}", Thread.CurrentThread.ManagedThreadId)); } protected override void OnRemoveComplete(int index, object value) { checkSize(); base.OnRemoveComplete(index, value); } internal new IEnumerator GetEnumerator() { return List.GetEnumerator(); } private void checkSize() { if (Count < MaxSize) { Trace.WriteLine(string.Format("BlockingCollection FullEvent set: {0}", Thread.CurrentThread.ManagedThreadId)); _FullEvent.Set(); } else { Trace.WriteLine(string.Format("BlockingCollection FullEvent reset: {0}", Thread.CurrentThread.ManagedThreadId)); _FullEvent.Reset(); } } }

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  • problem with implementing a simple work queue

    - by John Deerikio
    Hi all, I am having troubles with implementing a simple work queue. Doing some analysis, I am facing a subtle problem. The work queue is backed by a regular linked list. The code looks like this (simplified): 0. while (true) 1. while (enabled == true) 2. acquire lock on the list and get the next action to be executed (blocking operation) (store it in a local variable) 3. execute the action (outside the lock on the list on previous line) 4. get lock on this work queue 5. wait until this work queue has been notified (triggered when setEnabled(true) has been callled) The setEnabled(e) operation looks like this (simplified): enabled = e if (enabled == true) acquire lock on this work queue and do notify() Although this works, there is a condition in which a deadlock occurs. It happens in the following rare situation: while an action is being executed, setEnabled(false) is called just before step (4) is entered, setEnabled(true) is called now step (5) keeps waiting forever, because this work queue has already been notified How do I solve this? I have been looking at this for some time, but I cannot come up with a solution. Please note I am fairly new to thread synchronization. Thanks a lot.

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  • Best implementation of Java Queue?

    - by Georges Oates Larsen
    I am working (In java) on a recursive image processing algorithm that recursively traverses the pixels of the image, outward from a center point. Unfortunately... That causes stack overflows, so I have decided to switch to a Queue-based algorithm. Now, this is all fine and dandy -- But considering the fact that its queue will be analyzing THOUSANDS of pixels in a very short amount of time, while constantly popping and pushing, WITHOUT maintaining a predictable state (It could be anywhere between length 100, and 20000); The queue implementation needs to have significantly fast popping and pushing abilities. A linked list seems attractive due to its ability to push elements unto its self without rearranging anything else in the list, but in order for it to be fast enough, it would need easy access to both its head, AND its tail (or second-to-last node if it were not doubly-linked). Sadly, though I cannot find any information related to the underlying implementation of linked lists in Java, so it's hard to say if a linked list is really the way to go... This brings me to my question... What would be the best implementation of the Queue interface in Java for what I intend to do? (I do not wish to edit or even access anything other than the head and tail of the queue -- I do not wish to do any sort of rearranging, or anything. On the flip side, I DO intend to do a lot of pushing and popping, and the queue will be changing size quite a bit, so preallocating would be inefficient)

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  • Using the Queue class in Python 2.6

    - by voipme
    Let's assume I'm stuck using Python 2.6, and can't upgrade (even if that would help). I've written a program that uses the Queue class. My producer is a simple directory listing. My consumer threads pull a file from the queue, and do stuff with it. If the file has already been processed, I skip it. The processed list is generated before all of the threads are started, so it isn't empty. Here's some pseudo-code. import Queue, sys, threading processed = [] def consumer(): while True: file = dirlist.get(block=True) if file in processed: print "Ignoring %s" % file else: # do stuff here dirlist.task_done() dirlist = Queue.Queue() for f in os.listdir("/some/dir"): dirlist.put(f) max_threads = 8 for i in range(max_threads): thr = Thread(target=consumer) thr.start() dirlist.join() The strange behavior I'm getting is that if a thread encounters a file that's already been processed, the thread stalls out and waits until the entire program ends. I've done a little bit of testing, and the first 7 threads (assuming 8 is the max) stop, while the 8th thread keeps processing, one file at a time. But, by doing that, I'm losing the entire reason for threading the application. Am I doing something wrong, or is this the expected behavior of the Queue/threading classes in Python 2.6?

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