A Brief Guide to Python Message Queues | Iron.io
Did you know that Iron.io supports a huge range of programming languages, including Ruby, PHP, and Python? That’s why we get a lot of queries about Python message queues and their functionality. Python is a popular language for web and software development, workflow creation, and the development of web-based applications. We’ve taken a look at how Python can also work with or even create a useful task and message queue.
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What is a Message Queue?
A message queue (MQ) allows data to flow between various services and applications within a system. Producers create messages, or data, while consumers receive data. Workable MQs need to be highly scalable so that they use the right resources for the number of messages sent and received.
The Benefits of a Message Queue
A message queue handles a variety of tasks asynchronously. The end-user doesn’t have to wait for one task to complete to go onto the next one. Think about placing an order online. You click the “buy now” button, and data gets sent off to shipping and distribution systems, but you don’t need to wait for that to happen. Instantly, you’re told that your order has gone through, and usually, you have an email confirmation and an order reference. A message queue will be shuttling these different bits of information where they need to go. It distributes them between systems and services, providing a better user experience and a smoother running system.
Persistent Message Queues
It’s highly desirable that MQs are persistent. This means that the MQ will continue trying to deliver to the consumer even if it’s not available right now. That could be due to system downtime. Persistent MQs, like IronMQ, will continue trying until they get a notification from the consumer that it’s received the data. A persistent MQ is a reliable way to distribute tasks across a system or network of systems.
Python Message Queues
Many developers interested in Python message queue will have heard of Python Celery. The Celery project is Python but purely for message queues. Developers can implement the Celery protocol in various languages, including Python, of course, but also Node.js and PHP.
Python Celery is free and open-source, making it a popular choice for those who have to keep budget as a top priority. However, it can have a steep learning curve as the focus is on being feature-rich rather than user-friendly.
Skilled Python programmers can code their own queues, using the functions enqueue and dequeue to determine when messages join or leave the queue. Programmers also need to ensure that their code allows for the queue to alert users when it is unable to take more messages or instructions and that the messages leave the queue in the correct order. Python Lists is often used to create the correct functionality, as data can be added and removed dynamically allowing developers to manipulate queues more easily.
Using Python with IronMQ
Of course, developers can use Python with any message queue system they want. To take advantage of the speed and persistence of IronMQ, developers could create their own Python virtual environment to create producer and consumer workers that use IronMQ to distribute the tasks asynchronously.
If creating your own workers seems like too much of a chore, speak to us about IronWorker. Built from the ground up to work with containers, IronWorker can scale to the exact needs of your business thanks to our shared infrastructure. Using IronWorker and IronMQ together means you can run any number of tasks as and when you need them, neatly tying your distributed systems together.
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IronWorker can scale to the exact needs of your business thanks to our shared infrastructure. Using IronWorker and IronMQ together means you can run any number of tasks.
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