I want to use Pulsar as a message queue using shared consumers and the Java client. For the moment being, there are no strict ordering requirements, and also no partitions. The tasks triggered by the messages usually take up to 2 seconds. Is there any clear preference which of the following two methods of splitting the work between threads in a single application instance should be picked:
1 consumer with receive queue size 100 and 10 threads in a threadpool calling consumer.receive() in a loop.
10 consumers with receive queue size 10 each, using the MessageListener interface and running the task inside the original MessageListener.receive() call.
The best answer should be - just measure it :) Saying that, the first approach should be more efficient since no broker communication overhead is involved.
Related
I have 2 #JmsListener instances on 1 queue, and I want to take a fixed number of messages from the queue and then hold the rest in pending for some time for bulk processing. I have added the condition to check the number of pending message, but due to 2 listeners it is failing. Also, I have to add this condition only inside #JmsListener.
Please suggest how to add the logic of taking fixed messages from queue and holding the rest in pending for achieving throttling.
I don't believe you will be able to use Spring's #JmsListener to do what you want because you simply don't have the control of the consumer which you need to fetch multiple messages and then process them all at once. A listener only gets one message at time and it is invoked as messages arrive so you have no control over when and how you fetch the messages in contrast to a normal JMS MessageConsumer which you can use to manually invoke receive() as many times as you like.
Also, ActiveMQ will do its best to treat each consumer fairly and therefore distribute the same amount of messages to each. Generally speaking, it is bad for one consumer to get all (or most) the messages as it can starve the other consumers and waste resources. That said, you could potentially use consumer priority if you really needed some consumers to get more messages than others.
I am new to Apache Kafka and I am trying to configure Apache Kafka that it receives messages from the producer as much as possible but it only sends to the consumer configured number of messages per specific time.
In other words How to configure Apache Kafka to send only "50 messages for example" per "30 seconds"
to the consumer regardless of the number of the messages, and in the next 30 seconds it takes another 50 messages from the cashed messages and so on.
If you have control over the consumer
You could use max.poll.records property to limit max number of records per poll() method call. And then you only need to ensure that poll() is called once in 30 seconds.
In general you can take a look at all available configuration properties here.
If you cannot control consumer
Then the only option for you is to write messages as per your demand - write at most 50 messages in 30 seconds. There are no configuration options available. Only your application logic can achieve that.
updated - how to control ensure call to poll
The simplest way is to:
while (true) {
consumer.poll()
// .. do your stuff
Thread.sleep(30000);
}
You can make things more complex with measuring time for processing (i.e. starting after poll call up to Thread.sleep() to not wait more then 30 seconds at all.
The problem that producer really doesn't send messages to the consumer. There is that persistent Kafka topic in between where producer places its messages. And it really doesn't care if there is any consumer on the other side. Same from the consumer perspective: it just subscribers for data from the topic and doesn't care if there is some producer on the other side. So, thinking about a back-pressure from the consumer down to producer where there is a messaging middle ware is wrong direction.
On the other hand it is not clear how those consumed messages may impact your third party service. The point is that Kafka consumer is single-threaded per partition. So, all the messages from one partition is going to be (must) processed one by one in the same thread. This way you cannot send more than one messages to your service: the next one can be sent only when the previous has been replied. So, think about it: how it is even possible in your consumer application to excess rate limit?
However if you have enough partitions and high concurrency on the consumer side, so you really may end up with several requests to your service in parallel from different threads. For this purpose I would suggest to take a look into a Rate Limiter pattern. This library provides a good implementation: https://resilience4j.readme.io/docs/ratelimiter. It is much better to keep messages in the topic then try to limit producer somehow.
To conclude: even if the consumer side is not your project, it is better to discuss with that team how to improve their consumer. You did your part well: the producer sends messages to Kafka topic. What else you can do over here?
Interesting use case and not sure why you need it, but two possible solutions: 1. To protect the cluster, you could use quotas, not for amount of messages but for bandwidth throughput: https://kafka.apache.org/documentation/#design_quotas . 2. If you need an exact amount of messages per time frame, you could put a buffering service (rate limiter) in between where you consume and pause, publishing messages to the consumed topic. Rate limiter could consume next 50 then pause until minute passes. This will increase space used on your cluster because of duplicated messages. You also need to be careful of how to pause the consumer, hearbeats need to be sent else you will rebalance your consumer continuously, ie you can't just sleep till next minute. This is obviously if you can't control the end consumer.
I have 1 thread putting Requests to Queue and Another Cron Job (thread) would run every 15 minutes and has to take all requests from queue and start processing on it and also empty the queue.
How can I manage this synchronization and make sure no requests are lost in system.
I have thought of using Linked Queue for the same.
Other suggestion are welcome.
I am new to Java so asking this naive question.
In java.util.concurrent package you have a whole bunch of queues to your disposal, however, I don't believe that there's one particular queue just for the scenario you described above.
I would recommend just pick one of the Blocking queues, and in parallel just run a job that every 15 minutes will drain all items in your queue.
Suppose you have multiple producers and one consumer which wants to receive persistent messages from all publishers available.
Producers work with different speed. Let's say that system A produces 10 requests/sec and system B 1 request/sec. So if you use the only queue you will process 10 messages from A then 1 message from B.
But what if you want to balance load and process one message from A then one message from B etc.? Consuming from multiple queues is not a good option because we can't use wildcard binding in this case.
Update:
Queue per producer seems as the best approach. Producers don't know their speed which changes constantly. Having one queue per consumer I can subscribe to one topic and receive messages from all publishers available. But having a queue per producer I need to code the logic by myself:
Get all available queues through management plugin (AMQP doesn't allow to list queues).
Filter by queue name.
Implement round robin strategy.
Implement notification mechanism to subscribe to new publishers that can appear at any moment.
Remove unnecessary queue when publisher had disappeared and client read all messages.
Well, it seems pretty easy but I thought that broker could provide all of this functionality without any coding. In case with one queue I just create one persistent queue, bind it to a topic exchange then start any number of publishers that send messages to the topic. This option works almost out of the box.
I know I'm late for the party, but still.
In Azure Service Bus terms it's called "partitioning" and it's based on the partition key. The best part is in Azure SB the receiving client is not aware of the partitioning, it simply subscribes to the single queue.
In RabbitMQ there is a X-Consistent-Hashing plugin ("rabbitmq_consistent_hash_exchange") but unfortunately it's not that convenient. The consumers must be explicitly configured to consume from specific queues. If you have ten queues then you need to setup your consumers so that all ten are covered.
Another two options:
Random Exchange Type
Sharding Plugin
Bear in mind that with the Sharding Plugin even though it creates "one logical queue to consume" you'll have to have as many subscribers as there are virtual queues, otherwise some of the queues will be left unconsumed.
You can use the Priority Queue Support and associate a priority according to the producer speed. With the caveat that the priority must be set with caution (for example, if the consumer speed is below the system B, the consumer will only consume messages from B) and producers must be aware of their producing speed.
Another option to consider is creating 3 types of queues according to the producing speed: HIGH, MEDIUM, LOW. The three queues are binded to the exchange with the binding key set according to the producing speed. It could be done using.
Consumer will consume messages from these 3 queues using a round robin strategy. With the caveat that producers must be aware of their producing speed.
But the best option may be a queue per producer especially if producers speed is not stable and cannot be categorized. Thus, producers do not need to know their producing speed.
I have to write heavy load system, with pretty easy task to do. So i decided to split this tasks into multiple workers in different locations (or clouds). To communicate i want to use rabbitmq queue.
In my system there will be two kinds of software nodes: schedulers and workers. Schedulers will take user input from queue_input, split it into smaller task and put this smaller task into workers_queue. Workers reads this queue and 'do the thing'. I used round-robbin load balancing here - and all works pretty well, as long, as some worker crashed. Then i loose information about task completion (it's not allowed to do single operation twice, each task contains a pack of 50 iterations of doing worker-code with diffirent data).
I consider something like technical_queue - another channel to scheduler-worker communication, and I wonder, how to design it in a good way. I used tutorials from rabbitmq page, so my worker thread looks like :
while(true) {
message = consume(QUEUE,...);
handle(message); //do 50 simple tasks in loop for data in message
}
How can i handle second queue? Another thread we some while(true) {} loop?, or is there a better sollution to this? Maybe should I reuse existing queue with topic exchange? (but i wanted to have independent way of communication, while handling the task, which may take some time.
You should probably take a look at spring-amqp (doc). I hate to tell you to add a layer but that spring library takes care of the threading issues and management of threads with its SimpleMessageListenerContainer. Each container goes to a queue and you can specify # of threads (ie workers) per queue.
Alternatively you can make your own using an ExecutorService but you will probably end up rewriting what SimpleMessageListenerContainer does. Also you just could execute (via OS or batch scripts) more processes and that will add more consumers to each queue.
As far as queue topology is concerned it is entirely dependent on business logic/concerns and generally less on performance needs. More often you had more queues for business reasons and more workers for performance reasons but if a queue gets backed up with the same type of message considering giving that type of message its own queue. What your describing sounds like two queues with multiple consumer on your worker queue.
Other than the threading issue and queue topology I'm not entirely sure what else you are asking.
I would recommend you create a second queue consumer
consumer1 -> queue_process
consumer2 -> queue_process
Both consumers should make listening to the same queue.
Greetings I hope will help