If I sends the message to a task inside my service and returns from the RabbitMQ “Received” callback I get a new callback immediately. This is not intended since my service then will work as a new queue and makes spawning of a new worker more less useless since the first service has dequeued all messages.
I can see in the python examples, that the callback acks the message when it’s done but the C# doesn’t. Can this ack bed used for blocking for new messages until the current message is processed.
Also finds blocking the “Received” event function with a semaphore until processing is done very very hacky.
You need to set consumer prefetch to 1: https://www.rabbitmq.com/consumer-prefetch.html
Ack the message when you're done with it, and you'll get the next one.
NOTE: the RabbitMQ team monitors the rabbitmq-users mailing list and only sometimes answers questions on StackOverflow.
Related
I am new to Kafka and looking for a way to know if the message is ready for consumption to the consumer before calling consume method.
I am doing the POC on integrating C# with Kafka, previously I did that for RabbitMQ which has a method "MessageCount", but for Kafka, I cannot find any.
Actually Kafka has an infinite loop, in which it calls the poll() function to get eventual new records from a partition.
The configuration : max.poll.intervall.ms, specifies the interval of time after which, if the poll() function is not called, the consumer is considered dead and a rebalance is operated.
So to answer your question, Kafka always calls the poll() function to check if a message is available to be consummed. However, there some consumer configurations that allow to wait for a minimum size of messages before consumming the message:
fetch.min.bytes : you will wait untill you have x bytes of messages to consume them
fetch.max.wait.ms : set how much time you are gonna wait for the fetch.min.bytes to be gathered
In theory, if you can view if messages exist you are already using processes to connect to kafka. So you might as well just do a try catch with consume with the same performance.
I'm using RabbitMQ to deliver messages to worker processes (using the official C# client). I have been running simple tests during the implementation, and all has been going swimmingly until now.
I ran a test where I queued messages for a worker process that was not listening (no connection). Once I had queued several hundred messages, I started that process. It created its IModel, declared its queue (which already existed), and began consuming messages (with BasicConsume). This went great. This process, as it processed messages, created messages for other queues. There were processes already listening to these queues (with BasicConsume), and so the messages were immediately delivered to those clients (or so the server thought...). The messages are never processed.
The server definitely believes that the messages have been delivered (the messages are all in the "unacked" bucket, not the "ready" bucket), but
IBasicConsumer.HandleBasicDeliver never got called on the client. I have tried several different techniques (using a Subscription, using QueueingBasicConsumer as well as my own custom consumer), and the outcome is exactly the same. I'm at a complete loss. If I close the connection (there is only one connection here), then the messages immediately move from the "unacked" bucket to the "ready" bucket".
Why doesn't the client get notified when messages are delivered?
Looking into the code, ModelBase.Close() calls ConsumerDispatcher.Shutdown() (ModelBase.cs line 301), and from there, it calls workService.StopWork() (ConcurrentConsumerDispatcher.cs line 27). It seems to me (by a cursory view of the code) that this stops ALL work in the connection's ConsumerWorkService. Instead, should ConcurrentConsumerDispatcher.Shutdown() be calling workService.StopWork(this) on line 27?
It's a bug in the RabbitMQ client, and a fix has already been merged in.
It should be available in the next nightly build, on 4/18/2015.
If your BasicConsume defines noAck = false, after you Dequeues a message needs to run the next code:channel.BasicAck(result.DeliveryTag, false);
If your BasicConsume defines noAck = true, after you Dequeues a message it's removed from the server automatically.
We have pub/sub application that involves an external client subscribing to a Web Role publisher via an Azure Service Bus Topic. Our current billing cycle indicates we've sent/received >25K messages, while our dashboard indicates we've sent <100. We're investigating our implementation and checking our assumptions in order to understand the disparity.
As part of our investigation we've gathered wireshark captures of client<=>service bus traffic on the client machine. We've noticed a regular pattern of communication that we haven't seen documented and would like to better understand. The following exchange occurs once every 50s when there is otherwise no activity on the bus:
The client pushes ~200B to the service bus.
10s later, the service bus pushes ~800B to the client. The client registers the receipt of an empty message (determined via breakpoint.)
The client immediately responds by pushing ~1000B to the service bus.
Some relevant information:
This occurs when our web role is not actively pushing data to the service bus.
Upon receiving a legit message from the Web Role, the pattern described above will not occur again until a full 50s has passed.
Both client and server connect to sb://namespace.servicebus.windows.net via TCP.
Our application messages are <64 KB
Questions
What is responsible for the regular, 3-packet message exchange we're seeing? Is it some sort of keep-alive?
Do each of the 3 packets count as a separately billable message?
Is this behavior configurable or otherwise documented?
EDIT:
This is the code the receives the messages:
private void Listen()
{
_subscriptionClient.ReceiveAsync().ContinueWith(MessageReceived);
}
private void MessageReceived(Task<BrokeredMessage> task)
{
if (task.Status != TaskStatus.Faulted && task.Result != null)
{
task.Result.CompleteAsync();
// Do some things...
}
Listen();
}
I think what you are seeing is the Receive call in the background. Behind the scenes the Receive calls are all using long polling. Which means they call out to the Service Bus endpoint and ask for a message. The Service Bus service gets that request and if it has a message it will return it immediately. If it doesn't have a message it will hold the connection open for a time period in case a message arrives. If a message arrives within that time frame it will be returned to the client. If a message is not available by the end of the time frame a response is sent to the client indicating that no message was there (aka, your null BrokeredMessage). If you call Receive with no overloads (like you've done here) it will immediately make another request. This loop continues to happend until a message is received.
Thus, what you are seeing are the number of times the client requests a message but there isn't one there. The long polling makes it nicer than what the Windows Azure Storage Queues have because they will just immediately return a null result if there is no message. For both technologies it is common to implement an exponential back off for requests. There are lots of examples out there of how to do this. This cuts back on how often you need to go check the queue and can reduce your transaction count.
To answer your questions:
Yes, this is normal expected behaviour.
No, this is only one transaction. For Service Bus you get charged a transaction each time you put a message on a queue and each time a message is requested (which can be a little opaque given that Recieve makes calls multiple times in the background). Note that the docs point out that you get charged for each idle transaction (meaning a null result from a Receive call).
Again, you can implement a back off methodology so that you aren't hitting the queue so often. Another suggestion I've recently heard was if you have a queue that isn't seeing a lot of traffic you could also check the queue depth to see if it was > 0 before entering the loop for processing and if you get no messages back from a receive call you could go back to watching the queue depth. I've not tried that and it is possible that you could get throttled if you did the queue depth check too often I'd think.
If these are your production numbers then your subscription isn't really processing a lot of messages. It would likely be a really good idea to have a back off policy to a time that is acceptable to wait before it is processed. Like, if it is okay that a message sits for more than 10 minutes then create a back off approach that will eventually just be checking for a message every 10 minutes, then when it gets one process it and immediately check again.
Oh, there is a Receive overload that takes a timeout, but I'm not 100% that is a server timeout or a local timeout. If it is local then it could still be making the calls every X seconds to the service. I think this is based on the OperationTimeout value set on the Messaging Factory Settings when creating the SubscriptionClient. You'd have to test that.
I've got a server side protocol that controls a telephony system, I've already implemented a client library that communicates with it which is in production now, however there are some problems with the system I have at the moment, so I am considering re-writing it.
My client library is currently written in Java but I am thinking of re-writing it in both C# and Java to allow for different clients to have access to the same back end.
The messages start with a keyword have a number of bytes of meta data and then some data. The messages are always terminated by an end of message character.
Communication is duplex between the client and the server usually taking the form of a request from the Client which provokes several responses from the server, but can be notifications.
The messages are marked as being on of:
C: Command
P: Pending (server is still handling the request)
D: Data data as a response to
R: Response
B: Busy (Server is too busy to handle response at the moment)
N: Notification
My current architecture has each message being parsed and a thread spawned to handle it, however I'm finding that some of the Notifications are processed out of order which is causing me some trouble as they have to be handled in the same order they arrive.
The duplex messages tend to take the following message format:
Client -> Server: Command
Server -> Client: Pending (Optional)
Server -> Client: Data (optional)
Server -> Client: Response (2nd entry in message data denotes whether this is an error or not)
I've been using the protocol for over a year and I've never seen the a busy message but that doesn't mean they don't happen.
The server can also send notifications to the client, and there are a few Response messages that are auto triggered by events on the server so they are sent without a corresponding Command being issued.
Some Notification Messages will arrive as part of sequence of messages, which are related for example:
NotificationName M00001
NotificationName M00001
NotificationName M00000
The string M0000X means that either there is more data to come or that this is the end of the messages.
At present the tcp client is fairly dumb it just spawns a thread that notifies an event on a subscriber that the message has been received, the event is specific to the message keyword and the type of message (So data,Responses and Notifications are handled separately) this works fairly effectively for Data and response messages, but falls over with the notification messages as they seem to arrive in rapid sequence and a race condition sometimes seems to cause the Message end to be processed before the ones that have the data are processed, leading to lost message data.
Given this really badly written description of how the system works how would you go about writing the client side transport code?
The meta data does not have a message number, and I have not control over the underlying protocol as it's provided by a vendor.
The requirement that messages must be processed in the order in which they're received almost forces a producer/consumer design, where the listener gets requests from the client, parses them, and then places the parsed request into a queue. A separate thread (the consumer) takes each message from the queue in order, processes it, and sends a response to the client.
Alternately, the consumer could put the result into a queue so that another thread (perhaps the listener thread?) can send the result to the client. In that case you'd have two producer/consumer relationships:
Listener -> event queue -> processing thread -> output queue -> output thread
In .NET, this kind of thing is pretty easy to implement using BlockingCollection to handle the queues. I don't know if there is something similar in Java.
The possibility of a multi-message request complicates things a little bit, as it seems like the listener will have to buffer messages until the last part of the request comes in before placing the entire thing into the queue.
To me, the beauty of the producer/consumer design is that it forces a hard separation between different parts of the program, making each much easier to debug and minimizing the possibility of shared state causing problems. The only slightly complicated part here is that you'll have to include the connection (socket or whatever) as part of the message that gets shared in the queues so that the output thread knows where to send the response.
It's not clear to me if you have to process all messages in the order they're received or if you just need to process messages for any particular client in the proper order. For example, if you have:
Client 1 message A
Client 1 message B
Client 2 message A
Is it okay to process the first message from Client 2 before you process the second message from Client 1? If so, then you can increase throughput by using what is logically multiple queues--one per client. Your "consumer" then becomes multiple threads. You just have to make sure that only one message per client is being processed at any time.
I would have one thread per client which does the parsing and processing. That way the processing would be in the order it is sent/arrives.
As you have stated, the tasks cannot be perform in parallel safely. performing the parsing and processing in different threads is likely to add as much overhead as you might save.
If your processing is relatively simple and doesn't depend on external systems, a single thread should be able to handle 1K to 20K messages per second.
Is there any other issues you would want to fix?
I can recommend only for Java-based solution.
I would use some already mature transport framework. By "some" I mean the only one I have worked with until now -- Apache MINA. However, it works and it's very flexible.
Regarding processing messages out-of-order -- for messages which must be produced in the order they were received you could build queues and put such messages into queues.
To limit number of queues, you could instantiate, say, 4 queues, and route incoming message to particular queue depending on the last 2 bits (indeces 0-3) of the hash of the ordering part of the message (for example, on the client_id contained in the message).
If you have more concrete questions, I can update my answer appropriately.
I'm using ActiveMQ in a .Net program and I'm flooded with message-events.
In short when I get a queue-event 'onMessage(IMessage receivedMsg)' I put the message into an internal queue out of which X threads do their thing.
At first I had: 'AcknowledgementMode.AutoAcknowledge' when creating the session so I'm guessing that all the messages in the queue got sucked down and put into the memory queue (which is risky since with a crash, everything is lost).
So then I used: 'AcknowledgementMode.ClientAcknowledge' when creating the session, and when a worker was ready with the message it calls the 'commit()' method on the message. However, still all the messages get sucked down from the queue.
How can I configure it that ONLY an X amount of messages are being processed or are in an internal queue, and that not everything is being 'downloaded' right away?
Are you on .NET 4.0? You could use a BlockingCollection . Set it to the maximum amount it may contain. As soon as a thread tries to put in an excess element, the Add operation will block until the collection falls below the threshold again.
Maybe that would do it for throttling?
There is also an API for throttling in the Rx framework, but I do not know how it is implemented. If you implement your Queue source as Observable, this API would become available for you, but I don't know if this hits your needs.
You can set the client prefetch to control how many messages the client will be sent. When the Session is in Auto Ack, the client will only ack a message once its been delivered to your app via the onMessage callback or through a synchronous receive. By default the client will prefetch 1000 messages from the broker, if the client goes down these messages would be redelivered to another client it this was a Queue, otherwise for a topic they are just discarded as a topic is a broadcast based channel. If you set the prefetch to one then you client would only be sent one message from the sever, then each time your onMessage callback completes a new message would be dispatched as the client would ack that message, that is if the session is in Auto Ack mode.
Refer to the NMS configuration page for all the options:
http://activemq.apache.org/nms/configuring.html
Regards
Tim.
FuseSource.com