Handling resource-intensive tasks with work queues in RabbitMQ

All computationally intensive work at The Grid; such as image analysing, and image processing are off-loaded as jobs in RabbitMQ. Instead of having a web server waiting for a result immediately, it is free to keep processing other requests.

All jobs that are added to the queue are consumed by Heroku workers running with MsgFlo. This article focus on how to use work queues in RabbitMQ, how The Grid uses RabbitMQ and how you can automatically scale on Heroku with the help of GuvScale.

Background

The Grid wants to make the art of building attractive, functional websites as easy as keeping up a social network profile; you get an AI website that design itself with help of small inputs from you. The Grid produces websites from user-provided content. You are able to set up a webpage within a few minutes by selecting color schemes and uploading your own content. The Grid currently serve thousands of sites. It’s understandable that they are doing a lot of computationally heavy work server-side.

I had the opportunity to chat with Jon Nordby and Henri Bergius from flowhub.io about building The Grid.

When we were building The Grid, we went with an architecture heavily reliant on microservices communicating using message queues. RabbitMQ was a natural choice for CPU intensive work.

Microservices communicating via RabbitMQ

Communication between microservices in The Grid is done via RabbitMQ, which results in one microservice consuming data from a queue and publishing the outcome to another queue. As a result, a chain of microservices processes the requests.

All computationally intensive work is created as jobs in an AMQP/RabbitMQ message queue, which are consumed by Heroku workers. It includes jobs like:

  • Image analytics (for understanding the content)
  • Constraint solving (for page layout)
  • Image processing (optimization and filtering to achieve a particular look).

RabbitMQ work queues are used to distribute time-consuming tasks among multiple workers. The main idea behind Work Queues (also called task queues) is to avoid doing a resource-intensive task immediately and having to wait for it to complete. A task can be schedule to be done later. The task is encapsulated as a message and sent to the queue. A worker process running in the background pop the task and execute the job. By running many workers, the tasks will be shared between them.

Work queues in RabbitMQ

Rate limiting

Some of the microservices also deals with external APIs, allowing them to handle service failures and API rate limiting in a robust manner. If the rate limit was reached for a 3rd party service, messages were simply added to a queue, delayed and handled once the restrictions were removed.

RabbitMQ Acknowledgement

If one of your microservices crashe, you probably would like your tasks to be delivered to another worker or held in the queue until the worker is ready to receive the request again. In order to make sure a message is never lost, RabbitMQ support message acknowledgments. An acknowledgement can be sent back from the worker to tell the message queue that a particular message have been received and processed and that the message queue is free to delete it.

If a worker dies without sending an acknowledgement, the message queue will understand that a message wasn't processed fully and will redeliver it to the queue. That way you can be sure that no message is lost.

RabbitMQ Dead lettering

RabbitMQ can also be configured to route any failed operations into a dead-letter queue for further inspection. This gives you a full record of any failing operations. The queue can grow, and preserve the messages while the code to handle them is being fixed.

Heroku - Platform as a service (PaaS)

The Grid runs on Heroku; For those who are not familiar with Heroku, it’s a platform as a service (PaaS) that enables developers to build, run, and operate applications entirely in the cloud. That means you do not have to worry about infrastructure; you can simply focus on your application.

CloudAMQP was a simple choice - it was available with a click of a button as add-on in Heroku.

Using Heroku is an easy way to get started with message queueing and RabbitMQ. Step-by-step guides for deploying your first app can be found here. It’s easy to deploy and even easier to scale if needed.

GuvScale - Automatic scaling of Heroku workers

In the RabbitMQ documentation, one can read: If all the workers are busy, your queue can fill up. You will want to keep an eye on that, and maybe add more workers, or have some other strategy.

The Grid scaled up and down manually in the beginning, during day, and during night. With Heroku it’s easy to change the number of workers with one command, but on the other hand, it's expensive to start a lot of workers if you don’t need them. Heroku bills workers used by the second. This scaling however, still left them personally with the decision on how much compute capacity to provision. And when load is dynamic, it is tedious and inefficient to do it manually.

Jon and Henri decided to build GuvScale, a feature that autoscaled workers in Heroku for RabbitMQ.

GuvScale implements a simple proportional scaling model. It does not measure CPU or memory usage, it’s based on the current number of jobs in the queue, and an estimate of job processing time – from this it calculates the number of workers required for all work to be completed within a configured deadline.

GuvScale on Heroku
At The Grid we’ve had GuvScale make hundreds of thousands of scaling operations per month, running background dynos at more than 90% efficiency.

GuvScale is now available in a public beta. If you're using RabbitMQ for distributing work to background dynos hosted by Heroku (i.e if you are using CloudAMQP via Heroku), GuvScale can monitor the queues for you and scale the number of workers up and down automatically.

The add-on lets people that use CloudAMQP on Heroku quickly set up autoscaling for their workers. The autoscaling saves them money by automatically scaling servers down when not needed, and lets them worry less about performance by automatically scaling servers up under load.

Thanks for your time, Jon and Henri!

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