I need to handle a time-consuming and error-prone task (e.g., invoking a SOAP endpoint that will trigger the delivery of an SMS) whenever a given endpoint of my REST API is invoked, but I'd prefer not to make my users wait for that before sending a response back. Spring AMQP is already part of my stack, so I though about leveraging it to establish a "work queue" and have a number of worker processes consuming from the queue and taking care of the "work units". I have, however, the following requirements:
A work unit is guaranteed to be delivered, and delivered to exactly one worker.
Shall a work unit fail to be completed for any reason it must get placed back in the queue so that another worker can pick it up later.
Work units survive server reboots and crashes. This is mandatory because I won't be using a DB of any kind to store them.
I know RabbitMQ and Spring AMQP can be configured in such a way that ensures these three requirements, but I've only ever used it to achieve RPC so I don't know much about anything other than that. Is there any example I might follow? What are some of the pitfalls to watch out for?
While creating queues, rabbitmq gives you two options; transient or durable. Durable messages will be available until you acknowledge them. And messages won't expire if you do not give queue a ttl. For starters you can enable rabbitmq management plugin and play around a little.
But if you really want to guarantee the safety of your messages against hard resets or hardware problems, i guess you need to use a rabbitmq cluster.
Rabbitmq Clustering and you can find high availability subject on the right side of the page.
This guy explaines how to cluster
By the way i like beanstalkd too. You can make it write messages to disk and they will be safe except disk failures.
Related
Currently we're using Hangfire for scheduling and running long lived tasks. We need these tasks to be able to be retried in the event of an ungraceful shutdown, which Hangfire handles for us.
We're looking to try and move to a producer/consumer model and I've built a basic prototype with Masstransit and AWS SQS, but I have some concerns about how to handle the event of a task being processed during an ungraceful shutdown.
I understand that eventually the SQS visibility timeout will expire and the queued item will be picked up for processing again, but setting that timeout isn't trivial as the length of tasks can be quite varied and I'd prefer if the task could immediately resume/retry processing when the application starts up again.
I got reading about Job Consumers and they seemed to be better fitted to this type of scenario, but all the examples I've seen are using RabbitMQ. Wondering if it's possible/appropriate to do this using SQS, or if there's a better approach?
Thank you for taking the time to read this question :)
MassTransit will extend the visibility timeout as long as the consumer is still running.
I believe SQS has an upper-limit of something like 12 hours, but you should look it up and find out.
Job Consumers have significantly greater requirements (sagas, temporary queues, etc.) and SQS is really annoying about not having auto-delete/expiring queues, so I'd stick to a regular consumer if you can swing it.
I've been looking at an SOA using a messaging broker (rabbitmq / rails), however there are still a few niggles I cant get my head around.
If I wanted to run parallel requests as you would using something like Typhoeus with http
a) how in an asynchronous system like this - when you have potentially multiple threads publishing to the same topic exchange do you connect the response message with your request - would you add a unique routing key?
c) what would be the best way initiating and managing multiple parallel calls of this nature in ruby?
Many thanks
In answer to a), yes you use a routing key, or in the parlance of messaging, a correlation identifier.
In answer to c), sorry I haven't a clue about Ruby, but messaging by nature supports parallelism by using queues to manage throughput. I assume that whatever broker you choose would provide the appropriate samples and tooling for your needs.
I would use at sidekiq or rescue for jobs like that. If your system is larger and distributed you can create a module/class which takes your job including key as argument, sends it to rabbitmq, some worker which is subscribed to fan out or channel picks it up and sends the result back as POST to your app (web hook approach).
For simplicity you can also just put some sort of Ajax spinner on your view and poll every 10 seconds or whatever suits you if the result is back. For sure you should have some kind of id for every job. If you have questions about it I could elaborate more. My apps crunch a lot if data in long running tasks with up to 500,000,000 items in rabbit queues.
According to the documentation:
Q: How many times will I receive each message?
Amazon SQS is
engineered to provide “at least once” delivery of all messages in its
queues. Although most of the time each message will be delivered to
your application exactly once, you should design your system so that
processing a message more than once does not create any errors or
inconsistencies.
Is there any good practice to achieve the exactly-once delivery?
I was thinking about using the DynamoDB “Conditional Writes” as distributed locking mechanism but... any better idea?
Some reference to this topic:
At-least-once delivery (Service Behavior)
Exactly-once delivery (Service Behavior)
FIFO queues are now available and provide ordered, exactly once out of the box.
https://aws.amazon.com/sqs/faqs/#fifo-queues
Check your region for availability.
The best solution really depends on exactly how critical it is that you not perform the action suggested in the message more than once. For some actions such as deleting a file or resizing an image it doesn't really matter if it happens twice, so it is fine to do nothing. When it is more critical to not do the work a second time I use an identifier for each message (generated by the sender) and the receiver tracks dups by marking the ids as seen in memchachd. Fine for many things, but probably not if life or money depends on it, especially if there a multiple consumers.
Conditional writes sound like a clever solution, but it has me wondering if perhaps AWS isn't such a great solution for your problem if you need a bullet proof exactly-once solution.
Another alternative for distributed locking is Redis cluster, which can also be provisioned with AWS ElasticCache. Redis supports transactions which guarantee that concurrent calls will get executed in sequence.
One of the advantages of using cache is that you can set expiration timeouts, so if your message processing fails the lock will get timed release.
In this blog post the usage of a low-latency control database like Amazon DynamoDB is also recommended:
https://aws.amazon.com/blogs/compute/new-for-aws-lambda-sqs-fifo-as-an-event-source/
Amazon SQS FIFO queues ensure that the order of processing follows the
message order within a message group. However, it does not guarantee
only once delivery when used as a Lambda trigger. If only once
delivery is important in your serverless application, it’s recommended
to make your function idempotent. You could achieve this by tracking a
unique attribute of the message using a scalable, low-latency control
database like Amazon DynamoDB.
In short - we can put item or update item in dynamodb table with condition expretion attribute_not_exists(for put) or if_not_exists(for update), please check example here
https://stackoverflow.com/a/55110463/9783262
If we get an exception during put/update operations, we have to return from a lambda without further processing, if not get it then process the message (https://aws.amazon.com/premiumsupport/knowledge-center/lambda-function-idempotent/)
The following resources were helpful for me too:
https://ably.com/blog/sqs-fifo-queues-message-ordering-and-exactly-once-processing-guaranteed
https://aws.amazon.com/blogs/aws/introducing-amazon-sns-fifo-first-in-first-out-pub-sub-messaging/
https://youtu.be/8zysQqxgj0I
I found a question that explains how Play Framework's await() mechanism works in 1.2. Essentially if you need to do something that will block for a measurable amount of time (e.g. make a slow external http request), you can suspend your request and free up that worker to work on a different request while it blocks. I am guessing once your blocking operation is finished, your request gets rescheduled for continued processing. This is different than scheduling the work on a background processor and then having the browser poll for completion, I want to block the browser but not the worker process.
Regardless of whether or not my assumptions about Play are true to the letter, is there a technique for doing this in a Rails application? I guess one could consider this a form of long polling, but I didn't find much advice on that subject other than "use node".
I had a similar question about long requests that blocks workers to take other requests. It's a problem with all the web applications. Even Node.js may not be able to solve the problem of consuming too much time on a worker, or could simply run out of memory.
A web application I worked on has a web interface that sends request to Rails REST API, then the Rails controller has to request a Node REST API that runs heavy time consuming task to get some data back. A request from Rails to Node.js could take 2-3 minutes.
We are still trying to find different approaches, but maybe the following could work for you or you can adapt some of the ideas, I would love to get some feedbacks too:
Frontend make a request to Rails API with a generated identifier [A] within the same session. (this identifier helps to identify previous request from the same user session).
Rails API proxies the frontend request and the identifier [A] to the Node.js service
Node.js service add this job to a queue system(e.g. RabbitMQ, or Redis), the message contains the identifier [A]. (Here you should think about based on your own scenario, also assuming a system will consume the queue job and save the results)
If the same request send again, depending on the requirement, you can either kill the current job with the same identifier[A] and schedule/queue the lastest request, or ignore the latest request waiting for the first one to complete, or other decision fits your business requirement.
The Front-end can send interval REST request to check if the data processing with identifier [A] has completed or not, then these requests are lightweight and fast.
Once Node.js completes the job, you can either use the message subscription system or waiting for the next coming check status Request and return the result to the frontend.
You can also use a load balancer, e.g. Amazon load balancer, Haproxy. 37signals has a blog post and video about using Haproxy to off loading some long running requests that does not block shorter ones.
Github uses similar strategy to handle long requests for generating commits/contribution visualisation. They also set a limit of pulling time. If the time is too long, Github display a message saying it's too long and it has been cancelled.
YouTube has a nice message for longer queued tasks: "This is taking longer than expected. Your video has been queued and will be processed as soon as possible."
I think this is just one solution. You can also take a look EventMachine gem, that helps to improve the performance, handler parallel or async request.
Since this kind of problem may involve one or more services. Think about possibility of improving performance between those services(e.g. database, network, message protocol etc..), if caching may help, try out caching frequent requests, or pre-calculate results.
I'm working on a Rails application that periodically needs to perform large numbers of IO-bound operations. These operations can be performed asynchronously. For example, once per day, for each user, the system needs to query Salesforce.com to fetch the user's current list of accounts (companies) that he's tracking. This results in huge numbers (potentially > 100k) of small queries.
Our current approach is to use ActiveMQ with ActiveMessaging. Each of our users is pushed onto a queue as a different message. Then, the consumer pulls the user off the queue, queries Salesforce.com, and processes the results. But this approach gives us horrible performance. Within a single poller process, we can only process a single user at a time. So, the Salesforce.com queries become serialized. Unless we run literally hundreds of poller processes, we can't come anywhere close to saturating the server running poller.
We're looking at EventMachine as an alternative. It has the advantage of allowing us to kickoff large numbers of Salesforce.com queries concurrently within a single EventMachine process. So, we get great parallelism and utilization of our server.
But there are two problems with EventMachine. 1) We lose the reliable message delivery we had with ActiveMQ/ActiveMessaging. 2) We can't easily restart our EventMachine's periodically to lessen the impact of memory growth. For example, with ActiveMessaging, we have a cron job that restarts the poller once per day, and this can be done without worrying about losing any messages. But with EventMachine, if we restart the process, we could literally lose hundreds of messages that were in progress. The only way I can see around this is to build a persistance/reliable delivery layer on top of EventMachine.
Does anyone have a better approach? What's the best way to reliably execute large numbers of asynchronous IO-bound operations?
I maintain ActiveMessaging, and have been thinking about the issues of a multi-threaded poller also, though not perhaps at the same scale you guys are. I'll give you my thoughts here, but am also happy to discuss further o the active messaging list, or via email if you like.
One trick is that the poller is not the only serialized part of this. STOMP subscriptions, if you do client -> ack in order to prevent losing messages on interrupt, will only get sent a new message on a given connection when the prior message has been ack'd. Basically, you can only have one message being worked on at a time per connection.
So to keep using a broker, the trick will be to have many broker connections/subscriptions open at once. The current poller is pretty heavy for this, as it loads up a whole rails env per poller, and one poller is one connection. But there is nothing magical about the current poller, I could imagine writing a poller as an event machine client that is implemented to create new connections to the broker and get many messages at once.
In my own experiments lately, I have been thinking about using Ruby Enterprise Edition and having a master thread that forks many poller worker threads so as to get the benefit of the reduced memory footprint (much like passenger does), but I think the EM trick could work as well.
I am also an admirer of the Resque project, though I do not know that it would be any better at scaling to many workers - I think the workers might be lighter weight.
http://github.com/defunkt/resque
I've used AMQP with RabbitMQ in a way that would work for you. Since ActiveMQ implements AMQP, I imagine you can use it in a similar way. I have not used ActiveMessaging, which although it seems like an awesome package, I suspect may not be appropriate for this use case.
Here's how you could do it, using AMQP:
Have Rails process send a message saying "get info for user i".
The consumer pulls this off the message queue, making sure to specify that the message requires an 'ack' to be permanently removed from the queue. This means that if the message is not acknowledged as processed, it is returned to the queue for another worker eventually.
The worker then spins off the message into the thousands of small requests to SalesForce.
When all of these requests have successfully returned, another callback should be fired to ack the original message and return a "summary message" that has all the info germane to the original request. The key is using a message queue that lets you acknowledge successful processing of a given message, and making sure to do so only when relevant processing is complete.
Another worker pulls that message off the queue and performs whatever synchronous work is appropriate. Since all the latency-inducing bits have already performed, I imagine this should be fine.
If you're using (C)Ruby, try to never combine synchronous and asynchronous stuff in a single process. A process should either do everything via Eventmachine, with no code blocking, or only talk to an Eventmachine process via a message queue.
Also, writing asynchronous code is incredibly useful, but also difficult to write, difficult to test, and bug-prone. Be careful. Investigate using another language or tool if appropriate.
also checkout "cramp" and "beanstalk"
Someone sent me the following link: http://github.com/mperham/evented/tree/master/qanat/. This is a system that's somewhat similar to ActiveMessaging except that it is built on top of EventMachine. It's almost exactly what we need. The only problem is that it seems to only work with Amazon's queue, not ActiveMQ.