Asynchronous Task Queue with Django, Celery AWS And SQS

When dealing with heavy workload functionalities that could have a big effect on web utility performance, and you may face the need of running it asynchronously scheduled or not. These asynchronous background tasks cannot be best appreciably to enhance the scalability of the utility with the aid of using shifting the ones fantastically ingesting operations to the background, but additionally, improve the functionalities. Once unfold on one-of-a-kind components, every with its personal responsibility, your code may even appear cleaner, more isolated, and maybe easier to maintain.

Component descriptions

It allows the opportunity of moving a specific code execution outside the HTTP request-response cycle. This way, your server can reply as speedy as feasible for a selected request, because it spawns an asynchronous activity as a worker to execute the specific piece of code, which improves your server reaction. Also with Celery AWS, you have the option of jogging jobs in the background.

Why is a task to queue necessary?

Tasks are handled asynchronously either because they are not initiated by a hypertext transfer protocol request or because they are long-jogging jobs that would dramatically reduce the performance of an HTTP response. For example, a web application could ballot the GitHub API every ten minutes to collect the names of the top hundred starred repositories. Another example is when a database query would take too long during the HTTP request-reaction cycle. The query could be performed in the background on a fixed interval with the consequences saved in the database. Once a hypertext transfer protocol request comes in that needs and those results a query would simply fetch the precalculated result instead of executing the longer query.

Asynchronous tasks with Django

If a long-running process is a major part of your application’s workflows rather than blocking the response, you should handle the background, outside the normal request and response to flow. Perhaps your web application requires users to submit a thumbnail and confirm their email when they register. If your application processed and the image sent to be confirmation email, which directly requests the handler, then the end-user would have to wait unnecessarily for them both to finish processing before the page loads. Instead, you will be want to pass these processes to a task queue and let a separated worker process deal with it. So you can immediately send a response back to the end-user, which can do other things on the client-side while your application is also unengaged to respond to the request from other users and clients.

Asynchronous processing with Django on Lambda

The main reason for this conversion is that don’t want to run any long-running instances and I need to minimize the consumed resources and the required maintenance. AWS Lambda all these of the code is ready to execute in a few dozen milliseconds. After this request hits AWS Lambda services and resources are eaten up only for the duration of the task.

Why should we use Celery AWS?

We can work around this issue using the cache, optimizing database queries, and so on. But there are some cases, that there is no other option than heavy work has to be done. A report page, export a big amount of data, video/image processing are a few examples where you may want to use Celery AWS. We don’t use celery AWS throughout the whole project, but only for specific tasks that are time-consuming.

Conclusion

The idea here is to respond to the user as quickly as possible, and pass the time-consuming tasks to the queue to be executed in the background, and always keep the server ready to respond to new requests.