In celery I can call task with <func_name>.delay().

How can I do it in Zappa?

I have task:

@task()
def task_name():
    pass

TL;DR;

They are meant to different purposes. Celery is distributed task queue system. That's why you can delay tasks. They are sent to a queue to be processed later.

On the other, hand Zappa enables you to deploy a serverless application on AWS Lambda.

If you want to send a request to Zappa, you can do this as a normal endpoint request, but this is not a queue system.

Introduction

From Celery Documentation:

Celery - Distributed Task Queue Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system.

From Zappa Documentation:

Zappa makes it super easy to build and deploy server-less, event-driven Python applications (including, but not limited to, WSGI web apps) on AWS Lambda + API Gateway. Think of it as "serverless" web hosting for your Python apps. That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments!

Python Celery and Zappa serve different purposes in the context of web development and task management. Here’s a breakdown of their key differences:

1. Purpose and Use Case

• Celery:

  • Celery is a distributed task queue system. It’s used to run asynchronous and background tasks (e.g., sending emails, processing images, etc.).
  • It enables the execution of long-running tasks in the background without blocking the main application.
  • Celery is often used with frameworks like Django or Flask to offload tasks to workers running separately from the main application.

• Zappa:

  • Zappa is a framework that allows deploying Python WSGI applications (like Django and Flask) to AWS Lambda.
  • It transforms your Python web application into a serverless architecture by handling the deployment to AWS Lambda and API Gateway.
  • Zappa’s primary focus is enabling Python applications to scale without managing servers.

2. Architecture

• Celery:

  • Follows a distributed architecture where multiple worker processes execute tasks from a queue.
  • Celery requires a message broker (like RabbitMQ, Redis) to hold tasks and communicate between the main app and worker nodes.
  • The architecture is focused on ensuring asynchronous task processing with retries and scheduling capabilities.

• Zappa:

  • Zappa packages your Python web app and deploys it to AWS Lambda, a serverless compute service.
  • No persistent server is required; the app only runs when triggered by events like HTTP requests (via API Gateway).
  • Zappa abstracts away server management, auto-scaling, and load balancing by relying on AWS’s serverless infrastructure.

3. Task Handling

• Celery:

  • Handles long-running and asynchronous tasks.
  • It allows tasks to be distributed across multiple workers and can schedule them for later execution.
  • Celery also provides mechanisms for task retries, monitoring, and result storage.

• Zappa:

  • Primarily handles web requests and event-driven logic in a serverless manner.
  • Although AWS Lambda can handle long-running tasks, it’s limited by a maximum execution time (up to 15 minutes).
  • Zappa is not designed specifically for background task processing like Celery but can integrate with AWS services like Step Functions or SQS for task handling.

4. Scalability

• Celery:

  • Scales by adding more worker nodes to process tasks in parallel.
  • Task queues can be spread across multiple servers, making it highly scalable for distributed systems.

• Zappa:

  • Zappa relies on AWS Lambda’s automatic scaling capabilities, scaling with incoming requests or events.
  • No need to manage workers or servers; AWS manages scaling automatically based on demand.

5. Deployment and Hosting

• Celery:

  • Celery workers need to be deployed and managed separately from the main web app.
  • Requires setting up and maintaining a message broker and backend for result storage.

• Zappa:

  • Zappa handles deployment to AWS Lambda, making it easier to go serverless without worrying about infrastructure.
  • You don’t need to manage servers, workers, or brokers; AWS takes care of scaling, load balancing, and other infrastructure concerns.

6. Integration with AWS

• Celery:

  • Celery is not tied to any specific cloud provider. It can run on various platforms as long as the required components (workers, message broker) are available.
  • You can configure Celery to use AWS SQS as a message broker, but it’s not natively integrated with AWS services.

• Zappa:

  • Zappa is deeply integrated with AWS services, specifically AWS Lambda and API Gateway.
  • It simplifies deploying Python applications on AWS’s serverless infrastructure, leveraging native services like S3, DynamoDB, and CloudWatch.

7. Common Use Cases

• Celery:

  • Offloading long-running tasks, such as sending emails or processing files.
  • Handling scheduled or periodic tasks (e.g., daily reports, cron jobs).
  • Distributed task processing across multiple servers.

• Zappa:

  • Deploying Python web applications to AWS Lambda to run them without managing servers.
  • Building serverless APIs, microservices, and event-driven applications.
  • Scaling Python applications without needing to manage infrastructure.

Summary:

• Celery is focused on managing and executing background tasks asynchronously, requiring a broker and workers to handle distributed tasks.
• Zappa focuses on deploying Python web applications to AWS Lambda, enabling serverless architecture with automatic scaling and infrastructure management by AWS.