Celery

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.

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.

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.

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

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.