I'm just curious what it means in the documentation when it's talking about monitoring a FIFO?
I'd kind of like to be able to monitor a stream of logs for particular recurring errors and take an action on that. This sounds like FIFO, but I'm not sure.
Monit is referring to a "First In, First Out" queue:
a method for organizing and manipulating a data buffer, where the
oldest (first) entry, or 'head' of the queue, is processed first.
See FIFO for more information
Related
I'm needing to execute a process in the future, let's say 20min, based on some event happening, but I may need to cancel that scheduled process depending on different factors. Or , i may need to restart the timer on the job, depending on another event....etc. You get the idea. All different permutations of this. Does anyone know of a good technology for this need? Maybe quartz(does quartz suck? does it do all these things?), maybe activemq, maybe some other job scheduling technology?
Thanks!
-Ron
ActiveMQ's scheduler is a good fit for this. The pattern can go something like:
Kick off a process (get some identifier)
Send a message to the ActiveMQ scheduler to fire in x time period
Message Consumer receives the timer message, pulls the identifier to check on the status
If process is done.. continue and finish up
If process needs more wait time, send another timer message to ActiveMQ
Everything is asynchronous, and code required is very minimal. The big advantage of using ActiveMQ is you can have multiple consumers listening for the scheduled message to provide for high availability.
In some exceptional situations I need somehow to tell consumer on receiving point that some messages shouldn’t be processed. Otherwise two systems will become out-of-sync (we deal with some outdates external systems, and if, for example, connection is dropped we have to discard all queued operations in scope of that connection).
Take a risk and resolve problem messages manually? Compensation actions (that could be tough to support in my case)? Anything else?
There are a few ways:
You can set a time-to-live when sending a message: await endpoint.Send(myMessage, c => c.TimeToLive = TimeSpan.FromHours(1));, but this will apply to all messages that are sent (or published) like this. I would consider this, after looking at your requirements. This is technical, but it is a proper messaging pattern.
Make TTL and generation timestamp properties of your message itself and let the consumer decide if the message is still worth processing. This is more business and, probably, the most correct way.
Combine tech and business - keep the timestamp and TTL in message headers so they don't pollute your message contracts, and filter them out using a custom middleware. In this case, you need to be careful to log such drops so you won't be left wonder why messages disappear now and then.
Almost any unreliable integration can be monitored using sagas, with timeouts. For example, we use a saga to integrate with Twilio. Since we have no ability to open a webhook for them, we poll after some interval to check the message status. You can start a saga when you get a message and schedule a message to check if the processing is still waiting. As discussed in comments, you can either use the "human intervention required" way to fix the issue or let the saga decide to drop the message.
A similar way could be to use a lookup table, where you put the list of messages that aren't relevant for processing. Such a table would be similar to the list of sagas. It seems that this way would also require scheduling. Both here, and for the saga, I'd recommend using a separate receive endpoint (a queue) for the DropIt message, with only one consumer. It would prevent DropIt messages from getting stuck behind the integration messages that are waiting to be processed (and some should be already dropped)
Use RMQ management API to remove messages from the queue. This is the worst method, I won't recommend it.
From what I understand, you're building a system that sends messages to 3rd party systems. In other words, systems you don't control. It has an API but compensating actions aren't always possible, because the API doesn't provide it or because actions are performed inside the 3rd party system that can't be compensated or rolled back?
If possible try to solve this via sagas. Make sure the saga executes the different steps (the sending of messages) in the right order. So that messages that cannot be compensated are sent last. This way message that can be compensated if they fail, will be compensated by the saga. The ones that cannot be compensated should be sent last, when you're as sure as possible that they don't have to be compensated. Because that last message is the last step in synchronizing all systems.
All in all this is one of the problems with distributed systems, keeping everything in sync. Compensating actions is the way to deal with this. If compensating actions aren't possible, you're in a very difficult situation. Try to see if the business can help by becoming more flexible and accepting that you need to compensate things, where they'll tell you it's not possible.
In some exceptional situations I need somehow to tell consumer on receiving point that some messages shouldn’t be processed.
Can't you revert this into:
Tell the consumer that an earlier message can be processed.
This way you can easily turn this in a state machine (like a saga) that acts on two messages. If the 2nd message never arrives then you can discard the 1st after a while or do something else.
The strategy here is to halt/wait until certain that no actions need to be reverted.
We want to develop an application based on micro services architecture.
To communicate between various services asynchronously, we plan to use message queues(like RabbitMQ,ActiveMQ,JMS etc.,) . Is there any approach other than message queue is available to achieve inter process communication?
Thanks.
You should use Queues to handle the tasks that needs not to be completed in real time.
Append the tasks in queue and when there is a room, processor will take tasks from queue and will handle & will remove from queue.
Example :
Assuming your application deals with images, users are uploading so many images. Upload the tasks in a queue to compress the images. And when processor is free it will compress the queued images.
When you want to write some kind of logs of your system, give it to the queue and one process will take logs from queue and write that to disk. So the main process will not waste its time for the I/O operations.
Suggestion :
If you want the real time responses, you should not use the queue. You need to ping the queue constantly to read the incomings, and that is bad practice. And there is no guarantee that queue will handle your tasks immediately.
So the solutions are :
Redis cache - You can put your messages into cache and other process will read that message. Redis is "In memory data-structure". It is very fast and easy to use. Too much libraries and good resources available on the Internet, as it is open source. Read more about Redis. But here you also need to keep check whether there is some kind of message available and if available read from it, process and give response. But to read from Redis, is not very much costlier. With redis, you do not need to worry about memory management, it is well managed by open source community.
Using Sockets. Socket is very much faster, you can make this lightweight(if you want) as it is event based. One process will ping on port and other process will listen and give response. But you need to manage memory. If the buffered memory gets full, you can not put more messages here. If there are so many users producing messages, you need to manage to whom to you want to respond.
So it depends upon your requirement, like do you want to read messages constantly?, do you want to make one to one communication or many to one communication?
We're currently using RabbitMQ, where a continuously super-fast producer is paired with a consumer limited by a limited resource (e.g. slow-ish MySQL inserts).
We don't like declaring a queue with x-max-length, since all messages will be dropped or dead-lettered once the limit is reached, and we don't want to loose messages.
Adding more consumers is easy, but they'll all be limited by the one shared resource, so that won't work. The problem still remains: How to slow down the producer?
Sure, we could put a flow control flag in Redis, memcached, MySQL or something else that the producer reads as pointed out in an answer to a similar question, or perhaps better, the producer could periodically test for queue length and throttle itself, but these seem like hacks to me.
I'm mostly questioning whether I have a fundamental misunderstanding. I had expected this to be a common scenario, and so I'm wondering:
What is best practice for throttling producers? How is this done with RabbitMQ? Or do you do this in a completely different way?
Background
Assume the producer actually knows how to slow himself down with the right input. E.g. a hardware sensor or hardware random number generator, that can generate as many events as needed.
In our particular real case, we have an API that users can use to add messages. Instead of devouring and discarding messages, we'd like to apply back-pressure by having our API return an error if the queue is "full", so the caller/user knows to back-off, or have the API block until the consumer catches up. We don't control our user, so regardless of how fast the consumer is, I can create a producer that is faster.
I was hoping for something like the API for a TCP socket, where a write() can block and where a select() can be used to determine if a handle is writable. So either having the RabbitMQ API block or have it return an error if the queue is full.
For the x-max-length property, you said you don't want messages to be dropped or dead-lettered. I see there was an update in adding some more capabilities for this. As I see it is specified in the documentation:
"Use the overflow setting to configure queue overflow behaviour. If overflow is set to reject-publish, the most recently published messages will be discarded. In addition, if publisher confirms are enabled, the publisher will be informed of the reject via a basic.nack message"
So as I understand it, you can use queue limit to reject the new messages from publishers thus pushing some backpressure to the upstream.
I don't think that this is in any way rabbitmq specific. Basically you have a scenario, where there are two systems of different processing capabilities, and this mismatch will either pose a risk of overflowing the queue (whatever it would be), or even in case of a constant mismatch between producer and consumer, simply create more and more time-distance between event creation and its handling.
I used to deal with this kind of scenarios, and unfortunately there is no magic bullet. You either have to speed up even handling (better hardware, more suited software?) or throttle the event creation (which has nothing to do with MQ really).
Now, I would ask you what's the goal and how the events are produced. Are the events are produced constantly, with either unlimitted or just very high rate (for example readings from sensors - the more, the better), or are they created in batches/spikes (for example: user requests in specific time periods, batch loads from CRM system). I assume that the goal is to process everything cause you mention you don't want to loose any queued message.
If the output is constant, then some limiter (either internal counter, if the producer is the only producer, or external queue length checks if queue can be filled with some other system) is definitely in place.
IF eventsInTimePeriod/timePeriod > estimatedConsumerBandwidth
THEN LowerRate()
ELSE RiseRate()
In real world scenarios we used to simply limit the output manually to the estimated values and there were some alerts set for queue length, time from queue entry to queue leaving etc. Where such limiters were omitted (by mistake mostly) we used to find later some tasks that were supposed to be handled in few hours, that were waiting for three months for their turn.
I'm afraid it's hard to answer to "How to slow down the producer?" if we know nothing about it, but some ideas are: aforementioned rate check or maybe a blocking AddMessage method:
AddMessage(message)
WHILE(getQueueLength() > maxAllowedQueueLength)
spin(1000); // or sleep or whatever
mqAdapter.AddMessage(message)
I'd say it all depends on specific of the producer application and in general your architecture.
I am using RabbitMQ to have worker processes encode video files. I would like to know when all of the files are complete - that is, when all of the worker processes have finished.
The only way I can think to do this is by using a database. When a video finishes encoding:
UPDATE videos SET status = 'complete' WHERE filename = 'foo.wmv'
-- etc etc etc as each worker finishes --
And then to check whether or not all of the videos have been encoded:
SELECT count(*) FROM videos WHERE status != 'complete'
But if I'm going to do this, then I feel like I am losing the benefit of RabbitMQ as a mechanism for multiple distributed worker processes, since I still have to manually maintain a database queue.
Is there a standard mechanism for RabbitMQ dependencies? That is, a way to say "wait for these 5 tasks to finish, and once they are done, then kick off a new task?"
I don't want to have a parent process add these tasks to a queue and then "wait" for each of them to return a "completed" status. Then I have to maintain a separate process for each group of videos, at which point I've lost the advantage of decoupled worker processes as compared to a single ThreadPool concept.
Am I asking for something which is impossible? Or, are there standard widely-adopted solutions to manage the overall state of tasks in a queue that I have missed?
Edit: after searching, I found this similar question: Getting result of a long running task with RabbitMQ
Are there any particular thoughts that people have about this?
Use a "response" queue. I don't know any specifics about RabbitMQ, so this is general:
Have your parent process send out requests and keep track of how many it sent
Make the parent process also wait on a specific response queue (that the children know about)
Whenever a child finishes something (or can't finish for some reason), send a message to the response queue
Whenever numSent == numResponded, you're done
Something to keep in mind is a timeout -- What happens if a child process dies? You have to do slightly more work, but basically:
With every sent message, include some sort of ID, and add that ID and the current time to a hash table.
For every response, remove that ID from the hash table
Periodically walk the hash table and remove anything that has timed out
This is called the Request Reply Pattern.
Based on Brendan's extremely helpful answer, which should be accepted, I knocked up this quick diagram which be helpful to some.
I have implemented a workflow where the workflow state machine is implemented as a series of queues. A worker receives a message on one queue, processes the work, and then publishes the same message onto another queue. Then another type of worker process picks up that message, etc.
In your case, it sounds like you need to implement one of the patterns from Enterprise Integration Patterns (that is a free online book) and have a simple worker that collects messages until a set of work is done, and then processes a single message to a queue representing the next step in the workflow.