Celery

Automations are what makes a CRM powerful. With Celery and RabbitMQ we've been able to make powerful automations that truly works for our clients. Such as for example, automatic daily reports, reminders for their activities, important notifications regarding their client activities and actions on the website and more.

We use Celery basically for everything that needs to be scheduled for the future, and using RabbitMQ as our Queue-broker is amazing since it fully integrates with Django and Celery storing on our database results of the tasks done so we can see if anything fails immediately.

Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

We use AppOptics. I am curious what are the current leaders for APM for small companies (50 employees) that use Python, MariaDB, RabbitMQ, and Google Cloud Storage. We run both Celery and Gunicorn services. We are considering Datadog or some other deep code profiling tool that can spot I/O, DB, or other response time/request rate issues

I am just a beginner at these two technologies.

Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.

Here these users can be treated as messages. Each REST API request is a task.

I am confused about whether I should go for RabbitMQ alone or Celery.

If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.

If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.

I'm analyzing companies with stacks similar to my company, as we are in a process of breaking the monolith for microservices. I noticed that your stack is very similar to ours, Python, Django, Celery, and so on. Analyzing the technology you use I could see the use of Go and Kafka which made me think that you also went through a similar process. So here's my question I would like to know what were some of the reasons why you adopted GO in your ecosystem?? scalability, performance?? We are looking into the possibility of starting to use GO but for that, I wanted to know why use GO instead of Python?? or why to use both together?? It may be something trivial but all experience and opinion are important to us?? Thanks.

A big part of our needs fits perfectly into what Django has to offer: an ORM with support for PostgreSQL , the amazing auto-generated admin interface, consolidated tooling around the application lifecycle and a well-established community with solutions to the majority of common problems.

We use Django whenever we need the auto-generated admin and the friendly templating language to build capable web applications which are relatively easy to maintain for a comparably long time. The excellent integrations for Celery and Django REST framework make it easy to build the necessary integrations with other services.