This allows us to gracefully retry messages even when a worker crashes. If we don’t acknowledge a message and worker loses connection, RabbitMQ redelivers that message to other workers. So we have to manually acknowledge a message that we have processed it. While making the call to Consume, we have set autoAck to false. Meanwhile in the goroutine, we’re ranging over the messages, processing the message body, handling the errors and finally acknowledging messages. We can use a channel and listen on it to keep waiting indefinitely. So we need a way to ensure our main cli (worker) running on the foreground doesn’t reach it’s end and quit. We’re launching a goroutine with the go func() call. This should help us through the tedious error handling scenario of Go. The consumer app needs to connect to RabbitMQ, declare a queue it wants to listen to and then start consuming messages.īefore we begin, we’re going to build an error handling function. We created a consumer directory inside which, we will build our consumer app. go get /streadway/amqp Building a consumer You may go get it, or use a dependency management system. Once we have RabbitMQ installed, we need to have the Go AMQP package installed on our system. For Windows, there should be installable packages. On a linux distro, it’s probably available from the package manager. On a MacBook, I installed using Homebrew. The RabbitMQ installation might vary from platform to platform.
#Work queue install#
For us developers, what’s a better place than localhost? Let’s install RabbitMQ on our local machine and have it running. Initial setupīefore we can start building out million dollar calculator, we need to have RabbitMQ installed somewhere we can connect to. And we need to make it web scale, so we need to use Go and RabbitMQ. We are going to build a very sophisticated (?!?) calculator that can take two numbers and output their sum on the standard output. We will then run multiple instances of the consumers in parallel to show how we can scale this system by adding more workers. And a consumer which will consume messages.
We will build a command line publisher tool which will publish/produce messages. So we will do things on the command line.
#Work queue full#
In this post, we really don’t want to go into building a full fledged web app with file uploads. We can, in many ways, customize the way messages are delivered to the different queues. RabbitMQ comes with very powerful message routing features. Consumers consume messages from the queues. Exchanges receive messages from producers and deliver them to queues. The http handler / web app is the producer, the background workers are the consumers.Įxchange and Queues: From the name, you can guess they are somehow related to message handling. In our example, when a file upload happens, the http handler produces a message for our workers to consume. Producers & Consumers: A producer is someone who creates new messages / work. There are several concepts we should keep in mind while working with RabbitMQ. In our example, we’re going to use RabbitMQ with Go to create a very simple work queue system. But RabbitMQ is a better choice in some aspects. Redis is very popular and I personally use it a lot with Celery / Bull. There are several tools that can help us with it - Redis, Kafka, RabbitMQ, ZeroMQ, IronMQ, AWS SQS - there are plenty of choices. We need a system where we can pass messages and this system will deliver these messages to our workers. To build such a distributed system, we need a centralized message queue / message broker. There can be many source of work and many workers processing those. We want to have a distributed work queue system, where the workers and the work producers would live on different servers. To accomplish what we want to do, we need a system that can work as a queue of messages. Instead, when a photo is uploaded, store the photo somewhere convenient and pass the details to background workers which will then process the photo and upload wherever needed. Do you want to do this inside your http handler? You should not. A common example I use often to describe work queues - if your app handles user uploaded photos, creates several versions (thumbnails, different sizes) and shares them on different social media, how would you handle these? Resizing the photos and uploading them on other sites will definitely take time. A work queue system is ideal for background jobs which can take time, take longer than your average http request. Workers will actively monitor the queue and do these work as they come. We want to maintain a queue of tasks / work where we would push new work.
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