I have a huge number of messages coming from CSV files, that then get sent to a rate limited API. I'm using a Queue Channel backed by a database channel message store to make the messages durable while processing. I want to get as close to the rate limit as possible, so I need to be sending messages to the API across multiple threads.
What I had in my head of how it should work is something reads the DB, sees what messages are available, and then delegates each message to one of the threads to be processed in a transaction.
But I haven't been able to do that, what I've had to do is have a transactional poller which has a thread pool of N threads, a fixed rate of say 5 seconds, and a max messages per poll of 10 (something more than what could be processed in 5 seconds) ... which works ok, but has problems when there are not many messages waiting (i.e. if there were 10 messages they would be processed by a single thread) this isn't going to be a problem in practice because we will have 1000's of messages. But it seems conceptually more complex than how I thought it should work.
I might not have explained this very well, but it seems like what might be a common problem when messages come in fast, but go out slower?
Your solution is really correct, but you need to think do not shift messages into an Exectuor since that way you you jump out of the transaction boundaries.
The fact that you have 10 messages processed in the same thread is exactly an implementation details and it looks like this:
AbstractPollingEndpoint.this.taskExecutor.execute(() -> {
int count = 0;
while (AbstractPollingEndpoint.this.initialized
&& (AbstractPollingEndpoint.this.maxMessagesPerPoll <= 0
|| count < AbstractPollingEndpoint.this.maxMessagesPerPoll)) {
try {
if (!Poller.this.pollingTask.call()) {
break;
}
count++;
}
So, we poll messages until maxMessagesPerPoll in the same thread.
To make it really more parallel and still keep transaction do not lose messages you need to consider to use fixedRate:
/**
* Specify whether the periodic interval should be measured between the
* scheduled start times rather than between actual completion times.
* The latter, "fixed delay" behavior, is the default.
*/
public void setFixedRate(boolean fixedRate)
And increase an amount of thread used by the TaskScheduler for the polling.
You can do that declaring a ThreadPoolTaskScheduler bean with the name as IntegrationContextUtils.TASK_SCHEDULER_BEAN_NAME to override a default one with the pool as 10. Or use Global Properties to just override the pool size in that default TaskScheduler: https://docs.spring.io/spring-integration/docs/5.0.6.RELEASE/reference/html/configuration.html#global-properties
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 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.
I am having a scenario where the enable.auto.commit is set to false. For every poll() the records obtained are offloaded to a threadPoolExecutor. And the commitSync() is happening out of the context. But, I doubt if this is the right way to handle as my thread pool may still be processing few message while i commit the messages.
while (true) {
ConsumerRecords < String, NormalizedSyslogMessage > records = consumer.poll(100);
Date startTime = Calendar.getInstance().getTime();
for (ConsumerRecord < String, NormalizedSyslogMessage > record: records) {
NormalizedSyslogMessage normalizedMessage = record.value();
normalizedSyslogMessageList.add(normalizedMessage);
}
Date endTime = Calendar.getInstance().getTime();
long durationInMilliSec = endTime.getTime() - startTime.getTime();
// execute process thread on message size equal to 5000 or timeout > 4000
if (normalizedSyslogMessageList.size() == 5000) {
CorrelationProcessThread correlationProcessThread = applicationContext
.getBean(CorrelationProcessThread.class);
List < NormalizedSyslogMessage > clonedNormalizedSyslogMessages = deepCopy(normalizedSyslogMessageList);
correlationProcessThread.setNormalizedMessage(clonedNormalizedSyslogMessages);
taskExecutor.execute(correlationProcessThread);
normalizedSyslogMessageList.clear();
}
consumer.commitSync();
}
I suppose there are a couple of things to address here.
First is Offsets being out of sync - This is probably caused by either one of the following:
If the number of messages fetched by poll() does not take the size of the normalizedSyslogMessageList to 5000, the commitSync() will still run regardless of whether the current batch of messages has been processed or not.
If however, the size touches 5000 - because the processing is being done in a separate thread, the main consumer thread will never know whether the processing has been completed or not but... The commitSync() would run anyway committing the offsets.
The second part (Which I believe is your actual concern/question) - Whether or not this is the best way to handle this. I would say No because of point number 2 above i.e. the correlationProcessThread is being invoked in a fire-and-forget manner here so you wouldn't know whe the processing of those messages would be completed for you to be able to safely commit the offsets.
Here's a statement from "Kafka's Definitive Guide" -
It is important to remember that commitSync() will commit the latest
offset returned by poll(), so make sure you call commitSync() after
you are done processing all the records in the collection, or you risk
missing messages.
Point number 2 especially will be hard to fix because:
Supplying the consumer reference to the threads in the pool will basically mean multiple threads trying to access one consumer instance (This post makes a mention of this approach and the issues - Mainly, Kafka Consumer NOT being Thread-Safe).
Even if you try and get the status of the processing thread before committing offsets by using the submit() method instead of execute() in the ExecutorService, then you would need to make a blocking get() method call to the correlationProcessThread. So, you may not get a lot of benefit by processing in multiple threads.
Options for fixing this
As I'm not aware of the your context and the exact requirement, I will only be able to suggest conceptual ideas but it might be worth considering:
breaking the consumer instances as per the processing they need to do and carrying out the processing in the same thread or
you could explore the possibility of maintaining the offsets of the messages in a map (as and when they are processed) and then committing those specific offsets (this method)
I hope this helps.
Totally agree with what Lalit has mentioned. Currently i'm going through the same exact situation where my processing are happening in separate threads and consumer & producer in different threads. I've used a ConcurrentHashMap to be shared between producer and consumer threads which updates that a particular offset has been processed or not.
ConcurrentHashMap<OffsetAndMetadata, Boolean>
On the consumer side, a local LinkedHashMap can be used to maintain the order in which the records are consumed from Topic/Partition and do manual commit in the consumer thread itself.
LinkedHashMap<OffsetAndMetadata, TopicPartition>
You can refer to the following link, if your processing thread is maintaining any consumed record order.
Transactions in Kafka
A point to mention in my approach, there will be chance that data will be duplicated in case of any failures.
Want to implement a delayed consumer using the high level consumer api
main idea:
produce messages by key (each msg contains creation timestamp) this makes sure that each partition has ordered messages by produced time.
auto.commit.enable=false (will explicitly commit after each message process)
consume a message
check message timestamp and check if enough time has passed
process message (this operation will never fail)
commit 1 offset
while (it.hasNext()) {
val msg = it.next().message()
//checks timestamp in msg to see delay period exceeded
while (!delayedPeriodPassed(msg)) {
waitSomeTime() //Thread.sleep or something....
}
//certain that the msg was delayed and can now be handled
Try { process(msg) } //the msg process will never fail the consumer
consumer.commitOffsets //commit each msg
}
some concerns about this implementation:
commit each offset might slow ZK down
can consumer.commitOffsets throw an exception? if yes i will consume the same message twice (can solve with idempotent messages)
problem waiting long time without committing the offset, for example delay period is 24 hours, will get next from iterator, sleep for 24 hours, process and commit (ZK session timeout ?)
how can ZK session keep-alive without commit new offsets ? (setting a hive zookeeper.session.timeout.ms can resolve in dead consumer without recognising it)
any other problems im missing?
Thanks!
One way to go about this would be to use a different topic where you push all messages that are to be delayed. If all delayed messages should be processed after the same time delay this will be fairly straight forward:
while(it.hasNext()) {
val message = it.next().message()
if(shouldBeDelayed(message)) {
val delay = 24 hours
val delayTo = getCurrentTime() + delay
putMessageOnDelayedQueue(message, delay, delayTo)
}
else {
process(message)
}
consumer.commitOffset()
}
All regular messages will now be processed as soon as possible while those that need a delay gets put on another topic.
The nice thing is that we know that the message at the head of the delayed topic is the one that should be processed first since its delayTo value will be the smallest. Therefore we can set up another consumer that reads the head message, checks if the timestamp is in the past and if so processes the message and commits the offset. If not it does not commit the offset and instead just sleeps until that time:
while(it.hasNext()) {
val delayedMessage = it.peek().message()
if(delayedMessage.delayTo < getCurrentTime()) {
val readMessage = it.next().message
process(readMessage.originalMessage)
consumer.commitOffset()
} else {
delayProcessingUntil(delayedMessage.delayTo)
}
}
In case there are different delay times you could partition the topic on the delay (e.g. 24 hours, 12 hours, 6 hours). If the delay time is more dynamic than that it becomes a bit more complex. You could solve it by introducing having two delay topics. Read all messages off delay topic A and process all the messages whose delayTo value are in the past. Among the others you just find the one with the closest delayTo and then put them on topic B. Sleep until the closest one should be processed and do it all in reverse, i.e. process messages from topic B and put the once that shouldn't yet be proccessed back on topic A.
To answer your specific questions (some have been addressed in the comments to your question)
Commit each offset might slow ZK down
You could consider switching to storing the offset in Kafka (a feature available from 0.8.2, check out offsets.storage property in consumer config)
Can consumer.commitOffsets throw an exception? if yes, I will consume the same message twice (can solve with idempotent messages)
I believe it can, if it is not able to communicate with the offset storage for instance. Using idempotent messages solves this problem though, as you say.
Problem waiting long time without committing the offset, for example delay period is 24 hours, will get next from iterator, sleep for 24 hours, process and commit (ZK session timeout?)
This won't be a problem with the above outlined solution unless the processing of the message itself takes more than the session timeout.
How can ZK session keep-alive without commit new offsets? (setting a hive zookeeper.session.timeout.ms can resolve in dead consumer without recognizing it)
Again with the above you shouldn't need to set a long session timeout.
Any other problems I'm missing?
There always are ;)
Use Tibco EMS or other JMS Queue's. They have retry delay built in . Kafka may not be the right design choice for what you are doing
I would suggest another route in your cases.
It doesn't make sense to address the waiting time in the main thread of the consumer. This will be an anti-pattern in how the queues are used. Conceptually, you need to process the messages as fastest as possible and keep the queue at a low loading factor.
Instead, I would use a scheduler that will schedule jobs for each message you are need to delay. This way you can process the queue and create asynchronous jobs that will be triggered at predefined points in time.
The downfall of using this technique is that it is sensible to the status of the JVM that holds the scheduled jobs in memory. If that JVM fails, you loose the scheduled jobs and you don't know if the task was or was not executed.
There are scheduler implementations, though that can be configured to run in a cluster environment, thus keeping you safe from JVM crashes.
Take a look at this java scheduling framework: http://www.quartz-scheduler.org/
We had the same issue during one of our tasks. Although, eventually, it was solved without using delayed queues, but when exploring the solution, the best approach we found was to use pause and resume functionality provided by the KafkaConsumer API. This approach and its motivation is perfectly described here: https://medium.com/naukri-engineering/retry-mechanism-and-delay-queues-in-apache-kafka-528a6524f722
Keyed-list on schedule or its redis alternative may be best approaches.
I intend to make a service where in people could submit tasks(specifically transcoding tasks) to the system and they should get serviced soon but at the same time it should not starve anyone else, ie it must be fair. If a person submits 2000 tasks the system should not cater to only him all the time but instead do a round robin or something like that among other people's requests...
Are there any solutions available? I looked at rabbitMQ and other messaging systems but they don't exactly cater to my problem. How are fair task queues implemented?
I would implement like this:
Have a queue listener on a queue which when a message arrives checks the last time a task from the given user was received; if the time < 1 sec put it on queue 1, if time < 10 seconds put on queue 2, if time < 100 seconds put on queue 3, else put on queue 4. You would then have listeners on the 4 queues that would be processing the tasks.
Of course you can change the number of queues and change the times to match the best throughput. Ideally you want your queues to be busy all the time.
I don't think this behavior exists natively but I could see it being implemented with some of RabbitMQ's features.
http://www.rabbitmq.com/blog/2010/08/03/well-ill-let-you-go-basicreject-in-rabbitmq/
That would let you reject messages and requeue them. You would then have to write a utility that can choose to execute or requeue messages based on some identifying property of the message (in this case, the report requester, which is custom to your app). Conceivably you could design the policy entirely around the routing key if it contains the ID of the user you are trying to throttle.
Your policy could be structured using
responding with basic.reject
using {requeue=true}
Hopefully this helps!