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Tutorial-Codebase-Knowledge/docs/Celery/index.md
2025-04-04 13:48:54 -04:00

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---
layout: default
title: "Celery"
nav_order: 5
has_children: true
---
# Tutorial: Celery
Celery is a system for running **distributed tasks** *asynchronously*. You define *units of work* (Tasks) in your Python code. When you want a task to run, you send a message using a **message broker** (like RabbitMQ or Redis). One or more **Worker** processes are running in the background, listening for these messages. When a worker receives a message, it executes the corresponding task. Optionally, the task's result (or any error) can be stored in a **Result Backend** (like Redis or a database) so you can check its status or retrieve the output later. Celery helps manage this whole process, making it easier to handle background jobs, scheduled tasks, and complex workflows.
**Source Repository:** [https://github.com/celery/celery/tree/d1c35bbdf014f13f4ab698d75e3ea381a017b090/celery](https://github.com/celery/celery/tree/d1c35bbdf014f13f4ab698d75e3ea381a017b090/celery)
```mermaid
flowchart TD
A0["Celery App"]
A1["Task"]
A2["Worker"]
A3["Broker Connection (AMQP)"]
A4["Result Backend"]
A5["Canvas (Signatures & Primitives)"]
A6["Beat (Scheduler)"]
A7["Configuration"]
A8["Events"]
A9["Bootsteps"]
A0 -- "Defines and sends" --> A1
A0 -- "Uses for messaging" --> A3
A0 -- "Uses for results" --> A4
A0 -- "Loads and uses" --> A7
A1 -- "Updates state in" --> A4
A2 -- "Executes" --> A1
A2 -- "Fetches tasks from" --> A3
A2 -- "Uses for lifecycle" --> A9
A5 -- "Represents task invocation" --> A1
A6 -- "Sends scheduled tasks via" --> A3
A8 -- "Sends events via" --> A3
A9 -- "Manages connection via" --> A3
```