Top 5 Task queue Management Frameworks in Python

Top 5 Task queue Management Frameworks in Python

Hello Friends ! It is very obvious that we encounter a situation where we have create a pipeline for our tasks .Specially In machine learning there you have so many task to perform like data preparation , model training etc . Even in data preparation there could be n numbers of task like cleaning , scaling , validation . In order to handle these task , you may use distributive computing . Well ! In high level you at least need some Task queue Management Frameworks . This will reduce your over head tasks . This post will assist you on Top 5 Task queue Management Frameworks in Python .

Top 5 Task queue Management Frameworks in Python

Before you start , I would like to acknowledge that the ranking in the task queue managers are not meant that one which is coming after another is lesser efficient .It is just an order to display the information . Each framework has its own advantages and disadvantages . Lets start –


1. Celery: Distributed Task Queue –

It support distribute message passing . The core for Celery is in python but its different version support multiple languages . You will awesome documentation on celery like – How to use celery , related bugs and their fixes etc . There is big community support for celery . Obviously celery is my first choice for task queue management . Once it is come the implementation , It has simple interface . Celery support multiple message broker like Rabbit MQ , Redis , BeanStalk etc .

python task queue manager

2. Redis Queue –

Awesome implementation in python . You may put jobs in queue and handle them with n numbers of workers . This is very popular framework .Actually you will so many application which are built on the top of Redis Queue .


Redis queue Task manager
Redis queue Task manager

3. huey –

The back end for huey is Redis but It does not ,mean they both are perfectly similar . But yes you may say Huey is simpler version for Redis . Apart from scheduling stuffs , it can also help you in retrying failure events .  Corporate have high adaptability for huey . In huey you may create separate pipeline for distributed tasks. You may use timer to automate and schedule the jobs.

 huey task queue manager
huey task queue manager

4. Taskmaster

It is a master piece in handling large number of tasks . See ! it works like the above mention but with the difference of handling large amount of queue tasks .  If you have such requirement , it comes at first place for you .

taskmaster- queue- manager

5. Others –

As I never put a single name at last bucket . How can I do with Task queue Management Frameworks ? I will provide you couple of name here . You may go through them –

5.1 – Kuyruk

5.2 -Dramatiq

Conclusion –

Well how did you find this article – Top 5 Task queue Management Frameworks in Python . The scope of this article is to introduce you with relevant frameworks . Let me clear you detailing was not in the scope of this article .If you willing for reading each of them in details . Please subscribe us for  latest update on Task queue Management Frameworks . 

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