Celery is an asynchronous task queue/job queue system based on distributed message passing. It's used to execute tasks asynchronously (in the background) and schedule periodic tasks. Key use cases include:

• Sending emails without blocking the main application
• Processing large datasets in the background
• Performing time-consuming computations
• Integrating with third-party APIs that might be slow
• Scheduling periodic tasks like generating reports

Celery consists of several key components:

• Client/Producer: The application that creates and sends tasks
• Message Broker: Middleware that routes tasks from producers to workers (e.g., RabbitMQ, Redis)
• Worker: Process that executes the actual tasks
• Result Backend: Optional storage for task results (e.g., Redis, Database)
• Beat: Scheduler for periodic tasks

Celery supports multiple message brokers:

• RabbitMQ: Most feature-complete, supports all Celery features, provides message persistence and delivery guarantees
• Redis: Simpler setup, good performance, but less robust than RabbitMQ for message durability
• Amazon SQS: Good for AWS deployments, but limited feature support
• Apache Kafka: Good for high-throughput scenarios

RabbitMQ is recommended for production due to its reliability and full feature support.

Both methods are used to execute tasks asynchronously:

• delay(): Shortcut method, simpler syntax for basic task execution
• apply_async(): More powerful, allows passing execution options

# Using delay
task.delay(arg1, arg2)

# Using apply_async with options
task.apply_async(args=[arg1, arg2], 
                 countdown=10,
                 retry=True)

Celery serializes task messages and results to transmit them over the network. Supported serializers include:

• JSON: Default, cross-platform compatible, human-readable
• Pickle: Python-specific, supports complex objects but has security risks
• YAML: Human-readable, less common
• msgpack: Binary format, more efficient than JSON

Configuration example:

app.conf.task_serializer = 'json'
app.conf.result_serializer = 'json'
app.conf.accept_content = ['json']

Tasks are defined using the @app.task decorator:

from celery import Celery

app = Celery('tasks', broker='redis://localhost')

@app.task
def add(x, y):
    return x + y

• Unbound tasks: Regular tasks that don't have access to the task instance
• Bound tasks: Tasks that receive self as the first argument, providing access to task metadata

# Bound task
@app.task(bind=True)
def debug_task(self):
    print(f'Request: {self.request!r}')