In today’s fast-paced world, people want everything fast and quickly. Similarly, on the internet, they want their process/request to be completed instantaneously and get the required results in seconds. Small tasks can be executed within a second or microseconds, but heavy tasks like sending emails and fetching large chunks of data may take some time.
So here the question arises, how to achieve fast loading while our big tasks are getting processed?
To achieve this, we need some asynchronous processes that perform their operations in the background. To perform these types of tasks, Celery comes into play for parallel processing/execution of tasks. Celery will execute its task when the system is sitting ideally.
What is celery?
Celery is an open-source Python library that enables users to perform jobs asynchronously. It is a based system that holds tasks and forwards them to workers. It supports real-time operations and scheduled tasks (to run regular interval tasks). It enhanced end-user performance, allowing users to achieve anything quickly. Celery has various types of message brokers, such as RabbitMQ and Redis.
Why is it useful?
In today’s world, we have systems that work with APIs more often for fetching data, sending emails, and many more tasks that take time. Celery can schedule these types of tasks.
In another case, A user tries to access a page but it is taking time. In this case, Celery will be a great asset by performing partial functionality while loading the page, and the rest of the work will be achieved in the background on the same server or another server.
Celery’s workers can then update the UI via callbacks, process files, send emails, make changes in the database, and many more.
The main advantage of Celery is that our application can continue to respond to client requests. So the end-users don’t have to wait unnecessarily.
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