I'm currently writing a small wrapper for NamedPipeServerStream/NamedPipeClientStream that is fully Event based as oppose to using AsyncCallbacks.
I expose sync and async methods for pretty much everything possible (connecting/waiting for connection, writing, etc) so if a consumer wanted to, for example, start a server instance and send a message when a client connects he could either go full sync route and do something like ...
var server = new NamedPipeServer("helloWorld");
server.StartAndWait();
server.Write("Welcome!");
or the async way like...
var server = new NamedPipeServer("helloWorld);
server.ClientConnected += x => x.WriteAsync("Welcome!");
server.Start(); //Start() returns immediately
However I'm struggling with finding a good way to do the same for reading messages. Currently when a message is read I fire a MessageAvailable event and pass the message in as one of the arguments.
I just can't come up with a proper way of implementing synchronous reads.
What I've considered is the following:
Having a GetNextMessage() sync method that gets the message. Internally, this could be handled in two different ways:
I could keep an IEnumerable<Message> with all of the not yet consumed messages. So as soon as the other side sends a message, I'd read it from the stream and store it in memory so they can be later consumed by GetNextMessage(). The advantage is that it frees up the stream pretty much as soon as the message is written, so it doesn't block the other side from sending other messages. The disadvantage is that I have absolutely no control over how many messages I'll be holding or the size of them. My IEnumerable<Message> might end up having 10GB worth of non-consumed messages, and there's nothing I can do about it since I can't force the consumer to retrieve messages.
I could take the view that I only ever store one message in an internal buffer, and only ever start reading again once that one was consumed via GetNextMessage(). If I do that though, the other side would be prevented from writing other messages until the previous one was consumed. To be more exact, the other side would be able to write until the stream is full. Which could be either multiple small complete messages or a single incomplete message. In the case of an incomplete single message I think this is a worse approach because in between part 1 of the message being sent and subsequent parts, the other end might end up disconnecting and the whole message will be lost.
To make things harder, in either of the approaches above there's always the chance that the consumer is using events for receiving messages (remember the event contains the message received) and therefore has no need for GetNextMessage(). I'd either need to stop sending the message in the event altogether, or find a way of not pushing the event to the internal buffer if the message is consumed via the event. And while I can easily tell whether there is an event handler or not, there's no way of knowing if the message is actually being handled there (i.e., consider a class implementing this one and listens to that event, yet does nothing with it). The only real approach I can see here is to remove the message from the event, force consumers to always call GetNextMessage(), but am open to other ideas.
There's also another problem with either of the approaches, which is the fact that I can't control the order in which the messages are sent if WriteAsync() is used (or Write() is used from different threads).
Can anyone think of a better way of tackling this problem?
I'd suggest the following approach. Create interface:
public interface ISubscription : IDisposable {
Message NextMessage(TimeSpan? timeout);
}
public class Message {
}
And then implement like that:
public class NamedPipeServer {
public void StartAndWait() {
}
public ISubscription StartAndSubscribe() {
// prevent race condition before Start and subscribing to MessageAvailable
var subscription = new Subscription(this);
StartAndWait();
return subscription;
}
public ISubscription Subscribe() {
// if user wants to subscribe and some point after start - why not
return new Subscription(this);
}
public event Action<Message> MessageAvailable;
private class Subscription : ISubscription {
// buffer
private readonly BlockingCollection<Message> _queue = new BlockingCollection<Message>(
new ConcurrentQueue<Message>());
private readonly NamedPipeServer _server;
public Subscription(NamedPipeServer server) {
// subscribe to event
_server = server;
_server.MessageAvailable += OnMessageAvailable;
}
public Message NextMessage(TimeSpan? timeout) {
// this is blocking call
if (timeout == null)
return _queue.Take();
else {
Message tmp;
if (_queue.TryTake(out tmp, timeout.Value))
return tmp;
return null;
}
}
private void OnMessageAvailable(Message msg) {
// add to buffer
_queue.Add(msg);
}
public void Dispose() {
// clean up
_server.MessageAvailable -= OnMessageAvailable;
_queue.CompleteAdding();
_queue.Dispose();
}
}
}
Client then either calls Subscribe or StartAndSubscribe.
var sub = server.StartAndSubscribe();
var message = sub.NextMessage();
var messageOrNull = sub.NextMessage(TimeSpan.FromSeconds(1));
sub.Dispose();
That way if no one subscribes - you buffer no messages. And if someone subscribes and then does not consume - it's their problem, not yours, because buffering happens in subscription they now own. You can also limit size of _queue blocking collection, then adding to it will block if limit is reached, blocking your MessageAvailable event, but I won't recommend doing that.
Related
I have pretty naive code :
public async Task Produce(string topic, object message, MessageHeader messageHeaders)
{
try
{
var producerClient = _EventHubProducerClientFactory.Get(topic);
var eventData = CreateEventData(message, messageHeaders);
messageHeaders.Times?.Add(DateTime.Now);
await producerClient.SendAsync(new EventData[] { eventData });
messageHeaders.Times?.Add(DateTime.Now);
//.....
Log.Info($"Milliseconds spent: {(messageHeaders.Times[1]- messageHeaders.Times[0]).TotalMilliseconds});
}
}
private EventData CreateEventData(object message, MessageHeader messageHeaders)
{
var eventData = new EventData(Encoding.UTF8.GetBytes(JsonConvert.SerializeObject(message)));
eventData.Properties.Add("CorrelationId", messageHeaders.CorrelationId);
if (messageHeaders.DateTime != null)
eventData.Properties.Add("DateTime", messageHeaders.DateTime?.ToString("s"));
if (messageHeaders.Version != null)
eventData.Properties.Add("Version", messageHeaders.Version);
return eventData;
}
in logs I had values for almost 1 second (~ 800 milliseconds)
What could be a reason for such long execution time?
The EventHubProducerClient opens connections to the Event Hubs service lazily, waiting until the first time an operation requires it. In your snippet, the call to SendAsync triggers an AMQP connection to be created, an AMQP link to be created, and authentication to be performed.
Unless the client is closed, most future calls won't incur that overhead as the connection and link are persistent. Most being an important distinction in that statement, as the client may need to reconnect in the face of a network error, when activity is low and the connection idles out, or if the Event Hubs service terminates the connection/link.
As Serkant mentions, if you're looking to understand timings, you'd probably be best served using a library like Benchmark.NET that works ove a large number of iterations to derive statistically meaningful results.
You are measuring the first 'Send'. That will incur some overhead that other Sends won't. So, always do warm up first like send single event and then measure the next one.
Another important thing. It is not right to measure just single 'Send' call. Measure bunch of calls instead and calculate latency percentile. That should provide a better figure for your tests.
I have some code that uses the Service Bus Event Data, and I suspect that I need to use the offset property as, currently, my program is (or seems to be) re-running the same Event Hub data over and over again.
My code is as follows:
public class EventHubListener : IEventProcessor
{
private static EventHubClient _eventHubClient;
private const string EhConnectionStringNoPath = "Endpoint=...";
private const string EhConnectionString = EhConnectionStringNoPath + ";...";
private const string EhEntityPath = "...";
public void Start()
{
_eventHubClient = EventHubClient.CreateFromConnectionString(EhConnectionString);
EventHubConsumerGroup defaultConsumerGroup = _eventHubClient.GetDefaultConsumerGroup();
EventHubDescription eventHub = NamespaceManager.CreateFromConnectionString(EhConnectionStringNoPath).GetEventHub(EhEntityPath);
foreach (string partitionId in eventHub.PartitionIds)
{
defaultConsumerGroup.RegisterProcessor<EventHubListener>(new Lease
{
PartitionId = partitionId
}, new EventProcessorCheckpointManager());
Console.WriteLine("Processing : " + partitionId);
}
}
public Task ProcessEventsAsync(PartitionContext context, IEnumerable<EventData> messages)
{
foreach (EventData eventData in messages)
{
string bytes = Encoding.UTF8.GetString(eventData.GetBytes());
MyData data = JsonConvert.DeserializeObject<MyData>(bytes);
As I get the same messages over and over again, I suspect that I need to do something like this:
string bytes = Encoding.UTF8.GetString(eventData.GetBytes(), eventData.Offset, eventData.SerializedSizeInBytes - eventData.Offset);
However, Offset is a string, even though it seems to be a numeric value ("12345" for example). The documentation on context.CheckPointAsync() made it seem like that might be the answer; however, issuing that at the end of the loop seems to make no difference.
So, I have a two part question:
What is offset? Is it what I think it is (i.e. a numeric marker to a point in the stream) and, if so, why is it a string?
Why would I be getting the same messages over again? As I understand Event Hubs, although they guarantee at least once, once a Checkpoint has been issues, I shouldn't be getting the same messages back.
EDIT:
After a while of messing about, I've come up with something that avoids this problem; however, I certainly wouldn't claim it's a solution:
var filteredMessages =
messages.Where(a => a.EnqueuedTimeUtc >= _startDate)
.OrderBy(a => a.EnqueuedTimeUtc);
Using the EventProcessorHost seemed to actually make the problem worse; that is, not only were historical events being replayed, but they seemed to be replayed in a random order.
EDIT:
I came across this excellent article by #Mikhail, which does seem to address my exact issue. However; and presumably the root of my problem (or one of them, assuming this is correct, then I'm unsure why using the EventProcessorHost doesn't just work out of the box as #Mikhail said himself in the comments). However, the ServiceBus version of ICheckpointManager only has a single interface method:
namespace Microsoft.ServiceBus.Messaging
{
public interface ICheckpointManager
{
Task CheckpointAsync(Lease lease, string offset, long sequenceNumber);
}
}
Your title should be event hub, rather than service bus. For your question:
Although event hub has similar design as Kafka, but one big difference is that you should manage offsets by yourself. Event hub broker has completely no idea about your consumer group's offset.
So event hub sdk provide some help class to store offset in storage account, but you still need to call checkpoint manually after processing the message.
What is offset? Is it what I think it is (i.e. a numeric marker to a point in the stream) and, if so, why is it a string?
The offset is the pointer within a stream. The offset of an event changes as events gets removed from your Event Hub when the Message Retention policy has elapsed. So a message that was once at offset 10, maybe at offset 0 several days later because older messages were dropped from the stream. This has a good diagram: Event Hubs: Stream Offsets.
Why would I be getting the same messages over again? As I understand Event Hubs, although they guarantee at least once, once a Checkpoint has been issues, I shouldn't be getting the same messages back.
You may be getting the same messages again if you are using the low-level EventReceiver offset since messages expire from the Event Hub when the Message Retention policy elapses (ie. Default is 1 day). Sequence number is a better field to leverage because it does not change.
When checkpointing succeeds, it tells us the last event that was successfully processed, so you shouldn't be getting the same event back because when the client starts, it'll create a stream to a position in the event stream after that event. You can file an issue on GitHub.
EventProcessorHost is helpful as it tries to balance the processing of partitions between the number of instances running. (ie. Consider a 6 partition Event Hub. If you have 2 EventProcessorHosts connected to the same Event Hub reading with the same consumer group, they'll end up balancing the processing of those partitions with 3 each.) It also reconnects when there are transient failures like network loss.
It supports checkpointing to durable storage like Azure Storage Blob. Here is a sample: Process Events using an EventProcessorClient
We currently have a NServiceBus 5 system, which contains two recurring Sagas. Since they act as dispatcher to periodically pull multiple sorts of data from an external system, we're using the Timeouts to trigger this: We created a generic and empty class called ExecuteTask, which is used by the Saga to handle the timeout.
public class ScheduleSaga1 : Saga<SchedulerSagaData>,
IAmStartedByMessages<StartScheduleSaga1>,
IHandleMessages<StopSchedulingSaga>,
IHandleTimeouts<ExecuteTask>
And the other Saga is almost identically defined:
public class ScheduleSaga2: Saga<SchedulerSagaData>,
IAmStartedByMessages<StartScheduleSaga2>,
IHandleMessages<StopSchedulingSaga>,
IHandleTimeouts<ExecuteTask>
The timeout is handled equally in both Sagas:
public void Handle(StartScheduleSaga1 message)
{
if (_schedulingService.IsDisabled())
{
_logger.Info($"Task '{message.TaskName}' is disabled!");
}
else
{
Debugger.DoDebug($"Scheduling '{message.TaskName}' started!");
Data.TaskName = message.TaskName;
// Check to avoid that if the saga is already started, don't initiate any more tasks
// as those timeout messages will arrive when the specified time is up.
if (!Data.IsTaskAlreadyScheduled)
{
// Setup a timeout for the specified interval for the task to be executed.
Data.IsTaskAlreadyScheduled = true;
// Send the first Message Immediately!
SendMessage();
// Set the timeout
var timeout = _schedulingService.GetTimeout();
RequestTimeout<ExecuteTask>(timeout);
}
}
}
public void Timeout(ExecuteTask state)
{
if (_schedulingService.IsDisabled())
{
_logger.Info($"Task '{Data.TaskName}' is disabled!");
}
else
{
SendMessage();
// Action that gets executed when the specified time is up
var timeout = _schedulingService.GetTimeout();
Debugger.DoDebug($"Request timeout for Task '{Data.TaskName}' set to {timeout}!");
RequestTimeout<ExecuteTask>(timeout);
}
}
private void SendMessage()
{
// Send the Message to the bus so that the handler can handle it
Bus.Send(EndpointConfig.EndpointName, Activator.CreateInstance(typeof(PullData1Request)));
}
Now the problem: Since both Sagas are requesting Timeouts for ExecuteTask, it gets dispatched to both Sagas!
Even worse, it seems like the stateful Data in the Sagas gets messed up, since both Sagas are sending both message.
Therefore, it seems like the Timeouts are getting sent to all the Saga Instances which are requesting it.
But looking at the example https://docs.particular.net/samples/saga/simple/ there is no special logic regarding multiple Saga instances and their state.
Is my assumption correct? If this is the case, what are the best practices to have multiple Sagas requesting and receiving Timeouts?
The only reason I can think of when this is happening is that they share the same identifier to uniquely identify the saga instance.
Both ScheduleSaga1 and ScheduleSaga2 are using the same SchedulerSagaData for storing state. NServiceBus sees an incoming message and tries to retrieve the state, based on the unique identifier in the incoming message. If both StartScheduleSaga1 and StartScheduleSaga2 come in with identifier 1 for example, NServiceBus will search for saga state in the table SchedulerSagaData with unique identifier 1.
Both ScheduleSaga1 and ScheduleSaga2 will then share the same row!!!
Timeouts are based on SagaId in the TimeoutEntity table. Because both sagas share the same SagaId, it's logical they are both executed once the timeout arrives.
At the minimum you should not reuse the identifier to schedule tasks. It's probably better to not share the same class for storing saga state. Also easier to debug.
I'm using RabbitMQ in C# with the EasyNetQ library. I'm using a pub/sub pattern here. I still have a few issues that I hope anyone can help me with:
When there's an error while consuming a message, it's automatically moved to an error queue. How can I implement retries (so that it's placed back on the originating queue, and when it fails to process X times, it's moved to a dead letter queue)?
As far as I can see there's always 1 error queue that's used to dump messages from all other queues. How can I have 1 error queue per type, so that each queue has its own associated error queue?
How can I easily retry messages that are in an error queue? I tried Hosepipe, but it justs republishes the messages to the error queue instead of the originating queue. I don't really like this option either because I don't want to be fiddling around in a console. Preferably I'd just program against the error queue.
Anyone?
The problem you are running into with EasyNetQ/RabbitMQ is that it's much more "raw" when compared to other messaging services like SQS or Azure Service Bus/Queues, but I'll do my best to point you in the right direction.
Question 1.
This will be on you to do. The simplest way is that you can No-Ack a message in RabbitMQ/EasyNetQ, and it will be placed at the head of the queue for you to retry. This is not really advisable because it will be retried almost immediately (With no time delay), and will also block other messages from being processed (If you have a single subscriber with a prefetch count of 1).
I've seen other implementations of using a "MessageEnvelope". So a wrapper class that when a message fails, you increment a retry variable on the MessageEnvelope and redeliver the message back onto the queue. YOU would have to do this and write the wrapping code around your message handlers, it would not be a function of EasyNetQ.
Using the above, I've also seen people use envelopes, but allow the message to be dead lettered. Once it's on the dead letter queue, there is another application/worker reading items from the dead letter queue.
All of these approaches above have a small issue in that there isn't really any nice way to have a logarithmic/exponential/any sort of increasing delay in processing the message. You can "hold" the message in code for some time before returning it to the queue, but it's not a nice way around.
Out of all of these options, your own custom application reading the dead letter queue and deciding whether to reroute the message based on an envelope that contains the retry count is probably the best way.
Question 2.
You can specify a dead letter exchange per queue using the advanced API. (https://github.com/EasyNetQ/EasyNetQ/wiki/The-Advanced-API#declaring-queues). However this means you will have to use the advanced API pretty much everywhere as using the simple IBus implementation of subscribe/publish looks for queues that are named based on both the message type and subscriber name. Using a custom declare of queue means you are going to be handling the naming of your queues yourself, which means when you subscribe, you will need to know the name of what you want etc. No more auto subscribing for you!
Question 3
An Error Queue/Dead Letter Queue is just another queue. You can listen to this queue and do what you need to do with it. But there is not really any out of the box solution that sounds like it would fit your needs.
I've implemented exactly what you describe. Here are some tips based on my experience and related to each of your questions.
Q1 (how to retry X times):
For this, you can use IMessage.Body.BasicProperties.Headers. When you consume a message off an error queue, just add a header with a name that you choose. Look for this header on each message that comes into the error queue and increment it. This will give you a running retry count.
It's very important that you have a strategy for what to do when a message exceeds the retry limit of X. You don't want to lose that message. In my case, I write the message to disk at that point. It gives you lots of helpful debugging information to come back to later, because EasyNetQ automatically wraps your originating message with error info. It also has the original message so that you can, if you like, manually (or maybe automated, through some batch re-processing code) requeue the message later in some controlled way.
You can look at the code in the Hosepipe utility to see a good way of doing this. In fact, if you follow the pattern you see there then you can even use Hosepipe later to requeue the messages if you need to.
Q2 (how to create an error queue per originating queue):
You can use the EasyNetQ Advanced Bus to do this cleanly. Use IBus.Advanced.Container.Resolve<IConventions> to get at the conventions interface. Then you can set the conventions for the error queue naming with conventions.ErrorExchangeNamingConvention and conventions.ErrorQueueNamingConvention. In my case I set the convention to be based on the name of the originating queue so that I get a queue/queue_error pair of queues every time I create a queue.
Q3 (how to process messages in the error queues):
You can declare a consumer for the error queue the same way you do any other queue. Again, the AdvancedBus lets you do this cleanly by specifying that the type coming off of the queue is EasyNetQ.SystemMessage.Error. So, IAdvancedBus.Consume<EasyNetQ.SystemMessage.Error>() will get you there. Retrying simply means republishing to the original exchange (paying attention to the retry count you put in the header (see my answer to Q1, above), and information in the Error message that you consumed off the error queue can help you find the target for republishing.
I know this is an old post but - just in case it helps someone else - here is my self-answered question (I needed to ask it because existing help was not enough) that explains how I implemented retrying failed messages on their original queues. The following should answer your question #1 and #3. For #2, you may have to use the Advanced API, which I haven't used (and I think it defeats the purpose of EasyNetQ; one might as well use RabbitMQ client directly). Also consider implementing IConsumerErrorStrategy, though.
1) Since there can be multiple consumers of a message and all may not need to retry a msg, I have a Dictionary<consumerId, RetryInfo> in the body of the message, as EasyNetQ does not (out of the box) support complex types in message headers.
public interface IMessageType
{
int MsgTypeId { get; }
Dictionary<string, TryInfo> MsgTryInfo {get; set;}
}
2) I have implemented a class RetryEnabledErrorMessageSerializer : IErrorMessageSerializer that just updates the TryCount and other information every time it is called by the framework. I attach this custom serializer to the framework on a per-consumer basis via the IoC support provided by EasyNetQ.
public class RetryEnabledErrorMessageSerializer<T> : IErrorMessageSerializer where T : class, IMessageType
{
public string Serialize(byte[] messageBody)
{
string stringifiedMsgBody = Encoding.UTF8.GetString(messageBody);
var objectifiedMsgBody = JObject.Parse(stringifiedMsgBody);
// Add/update RetryInformation into objectifiedMsgBody here
// I have a dictionary that saves <key:consumerId, val: TryInfoObj>
return JsonConvert.SerializeObject(objectifiedMsgBody);
}
}
And in my EasyNetQ wrapper class:
public void SetupMessageBroker(string givenSubscriptionId, bool enableRetry = false)
{
if (enableRetry)
{
_defaultBus = RabbitHutch.CreateBus(currentConnString,
serviceRegister => serviceRegister.Register<IErrorMessageSerializer>(serviceProvider => new RetryEnabledErrorMessageSerializer<IMessageType>(givenSubscriptionId))
);
}
else // EasyNetQ's DefaultErrorMessageSerializer will wrap error messages
{
_defaultBus = RabbitHutch.CreateBus(currentConnString);
}
}
public bool SubscribeAsync<T>(Func<T, Task> eventHandler, string subscriptionId)
{
IMsgHandler<T> currMsgHandler = new MsgHandler<T>(eventHandler, subscriptionId);
// Using the msgHandler allows to add a mediator between EasyNetQ and the actual callback function
// The mediator can transmit the retried msg or choose to ignore it
return _defaultBus.SubscribeAsync<T>(subscriptionId, currMsgHandler.InvokeMsgCallbackFunc).Queue != null;
}
3) Once the message is added to the default error queue, you can have a simple console app/windows service that periodically republishes existing error messages on their original queues. Something like:
var client = new ManagementClient(AppConfig.BaseAddress, AppConfig.RabbitUsername, AppConfig.RabbitPassword);
var vhost = client.GetVhostAsync("/").Result;
var aliveRes = client.IsAliveAsync(vhost).Result;
var errQueue = client.GetQueueAsync(Constants.EasyNetQErrorQueueName, vhost).Result;
var crit = new GetMessagesCriteria(long.MaxValue, Ackmodes.ack_requeue_false);
var errMsgs = client.GetMessagesFromQueueAsync(errQueue, crit).Result;
foreach (var errMsg in errMsgs)
{
var innerMsg = JsonConvert.DeserializeObject<Error>(errMsg.Payload);
var pubInfo = new PublishInfo(innerMsg.RoutingKey, innerMsg.Message);
pubInfo.Properties.Add("type", innerMsg.BasicProperties.Type);
pubInfo.Properties.Add("correlation_id", innerMsg.BasicProperties.CorrelationId);
pubInfo.Properties.Add("delivery_mode", innerMsg.BasicProperties.DeliveryMode);
var pubRes = client.PublishAsync(client.GetExchangeAsync(innerMsg.Exchange, vhost).Result, pubInfo).Result;
}
4) I have a MessageHandler class that contains a callback func. Whenever a message is delivered to the consumer, it goes to the MessageHandler, which decides if the message try is valid and calls the actual callback if so. If try is not valid (maxRetriesExceeded/the consumer does not need to retry anyway), I ignore the message. You can choose to Dead Letter the message in this case.
public interface IMsgHandler<T> where T: class, IMessageType
{
Task InvokeMsgCallbackFunc(T msg);
Func<T, Task> MsgCallbackFunc { get; set; }
bool IsTryValid(T msg, string refSubscriptionId); // Calls callback only
// if Retry is valid
}
Here is the mediator function in MsgHandler that invokes the callback:
public async Task InvokeMsgCallbackFunc(T msg)
{
if (IsTryValid(msg, CurrSubscriptionId))
{
await this.MsgCallbackFunc(msg);
}
else
{
// Do whatever you want
}
}
Here, I have implemented a Nuget package (EasyDeadLetter) for this purpose, which can be easily implemented with the minimum changes in any project.
All you need to do is follow the four steps :
First of all, Decorate your class object with QeueuAttribute
[Queue(“Product.Report”, ExchangeName = “Product.Report”)]
public class ProductReport { }
The second step is to define your dead-letter queue with the same QueueAttribute and also inherit the dead-letter object from the Main object class.
[Queue(“Product.Report.DeadLetter”, ExchangeName =
“Product.Report.DeadLetter”)]
public class ProductReportDeadLetter : ProductReport { }
Now, it’s time to decorate your main queue object with the EasyDeadLetter attribute and set the type of dead-letter queue.
[EasyDeadLetter(DeadLetterType =
typeof(ProductReportDeadLetter))]
[Queue(“Product.Report”, ExchangeName = “Product.Report”)]
public class ProductReport { }
In the final step, you need to register EasyDeadLetterStrategy as the default error handler (IConsumerErrorStrategy).
services.AddSingleton<IBus>
(RabbitHutch.CreateBus(“connectionString”,
serviceRegister =>
{
serviceRegister.Register<IConsumerErrorStrategy,
EasyDeadLetterStrategy>();
}));
That’s all. from now on any failed message will be moved to the related dead-letter queue.
See more detail here :
GitHub Repository
NuGet Package
I have a network equipment to which I connect once using sockets, and the connection is maintained open all the time until application closes.
Now I have a class in C# that encapsulates the communication. There is a method SendMessage to the equipment. I need to use Socket.ReceiveAsync to get the response.
Let's say there are 3 methods: 1. GetEqValA(), GetEqValB(), GetEqValC() that call SendMessage with a specific message for the equipment.
I have created only one instance of socket Event args like that:
_completeArgs = new SocketAsyncEventArgs();
_completeArgs.SetBuffer(buffer, 0, buffer.Length);
_completeArgs.UserToken = _mySocket;
_completeArgs.RemoteEndPoint = _mySocket.RemoteEndPoint;
_completeArgs.Completed += new EventHandler<SocketAsyncEventArgs>(DataAvailable);
_mySocket.ReceiveAsync(_completeArgs);
Now, the DataAvailable method has something similar to the code below:
for (int i = 0; i < e.BytesTransferred; i++)
{
_tcpData.Add(e.Buffer[i]);
}
if (_tcpData.Count == _expectedTcpDataCount)
{
_expectedTcpDataCount = -1;
ProcessData();
// I don't want to put here, because it will wait for data until
// someone sends a message and the equipment responds with data
//_mySocket.ReceiveAsync(e);
}
else
{
_mySocket.ReceiveAsync(e);
}
Now, the 3 methods from above can be called by anyone, even different threads. I do have a lock mechanism for that.
My problem is that if I reuse _completeArgs in SendMessage for the next message to send, I get an exception that this eventArgs object is already in use by an asynchronous operation, whereas if I do the same(but not directly, by taking the SocketAsyncEventArgs e parameter from DataAvailable) in DataAvailable, no problem occurs.
_mySocket.ReceiveAsync(_completeArgs);
_mySocket.Send(pMessage);
The idea is that I don't want to call ReceiveAsync all the time, even if I know that nothing will come in there, but I want to call ReceiveAsync before sending any message to the device, because I know that I will get something.
The exception appears at method GetEqValC(), if I call them one after another in the sequence A,B,C.
What I don't understand, can you help me? Can I don what I want to do?
I use .NET 3.5.
P.S. Summary: I need to keep the connection alive, but read something from it only when I know for sure I must have something in there. Only one call at a time will be. One send, followed by one receive!