I am setting up an application which needs to be scaled. I post messages to Active MQ and read messages from there.
Till now , I have used maximum upto 3 concurrent consumers pointing to a queue( Each consumer operating from a different physical machine ).
I need to know maximum how many consumers I can point to a Queue in Active MQ.
Is there a maximum limit to it ?
I found this link:
http://activemq.apache.org/multiple-consumers-on-a-queue.html
But it does not state anything about Maximum connections / Sessions / consumers. It only says One session per connection.
The JMS specification does not state any limit on the number of consumers. You can add as many consumers as you want for a given Queue or Topic.
The question is how many consumers you really need. Increasing the number of consumers will allow you to do more parallel processing but you will face memory issues. For e.g. If you start thousands of consumers on a single machine it is simply going to start thousands of threads which will consume memory.
Also if you are having multiple consumers for a single Queue it is a good idea to have selectors to filter out messages from the queue so that you can have some control on messages and which listeners should consume them.
Any number of consumer can point to that queue. But only 1 consumer will be able to access the object inside that queue. Once it retrieves the object, that particular consumer will be disconnected and other consumer will get connected to your queue.
You can specify the size of queue in your xml file. You can find it easily in some search engine. I dont remember the tag name exactly.
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´m using activemq(5.14.5) with camel(2.13.4) because I still need java 6.
I have a queue and 15 consumers. The messages sent to them are request reply.
When I start the consumers, the messages are distributed one per consumer as soon as the messages arrive but, after some time, only one consumer receives the messages, the others stay idle and a lot of messages stay pending.
The consumers have this configuration:
concurrentConsumers=15&maxMessagesPerTask=1&destination.consumer.prefetchSize=0&transferException=true
The time spent to process each message can varies a lot because of our business rule so, I don´t know if activemq has some rule that manage slow consumers and redirect to only one that is more "efficient".
The behaviour that I was expecting is that all the messages that arrives, start to process until all the consumers are full, but it is not what is happening.
Anybody knows what is happening?
Following is an image about what is happening:
Your configuration has two eye-catching settings:
maxMessagesPerTask=1
If you did not intend to configure auto-scaling the threadpool, you should remove this setting completely. Is is by default unlimited and it sets how long to keep threads for processing (scaling up/down threadpool).
See also the Spring Docs about this setting
prefetchSize=0
Have you tried setting this to 1 so that every consumer just gets 1 message at a time?
The AMQ docs say about the prefetchSize:
Large prefetch values are recommended for high performance with high message volumes. However, for lower message volumes, where each message takes a long time to process, the prefetch should be set to 1. This ensures that a consumer is only processing one message at a time. Specifying a prefetch limit of zero, however, will cause the consumer to poll for messages, one at a time, instead of the message being pushed to the consumer.
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.
Lets say I have 100 physical servers. Each of them runs a java process. And we have one global rabbitMQ queue. This queue is shared between all 100 processes. In other words I need every of 100 JVMs listen this global queue.
The first question is is it possible to do it?
The second question is if it`s possible, how to dispatch messages from the global queue to 100 nodes and make sure that loading will be distributed proportionally, so we will not get the situation when one node loaded up to 100% and other 99 to 0.0005%.
Each node has minimum and maximum capacity. So ideally, one JVM should ask the queue how many items it has. And then grab needed number of items up to it`s maximum capacity. And every JVM from mentioned 100 should do the same. So how to synchronize the queue? How it will let spring amqp on certain machine know that item is ready for procession? How to balance the loading? Thank you
This sounds like sharding to me. I haven't used it, but there's a plugin for sharding on RabbitMQ now. https://github.com/rabbitmq/rabbitmq-sharding
The first question is is it possible to do it?
Yes, there is no issue.
how to dispatch messages from the global queue to 100 nodes and make
sure that loading will be distributed proportionally, so we will not
get the situation when one node loaded up to 100% and other 99 to
0.0005%.
RabbitMQ does round robin between consumer on the same queue: RabbitMQ will send each message to the next consumer, in sequence. To limit the number of messages sent to a consumer, you can use consumer prefetch and set the QoS according to the maximum capacity of the node. Thus, RabbitMQ will push message to a node as long as the node has not reach its QoS ie. its maximum capacity ie. the number of unacked message is below this threshold.
So the node does not need to know that the queue contains messages, messages will be pushed to the node:
Messages are pushed to the node as long as the QoS threshold is not reach.
Messages are put in a local queue
Once a worker is available, worker takes the message, processes it
Worker ackes the message when it finished the process
Because the message is acked, the current unacked message is below the QoS threshold, loop to 1).
See:
The worker tutorial about round robin and fair dispatching.
How to set wisely the prefetch Some queuing theory: throughput, latency and bandwidth