Managing Persistent Redis Connections in Django

For your use case of maintaining a persistent Redis connection to respond immediately to incoming messages using BLPOP, there are several production-ready approaches you can consider:

1. Dedicated Worker Process

The most common approach is to create a dedicated worker process outside of your Django application that maintains the persistent Redis connection:

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# redis_listener.py
import redis
import django
import os
import time

# Set up Django environment
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'your_project.settings')
django.setup()

# Import your Django models/functions after setup
from your_app.models import YourModel
from your_app.tasks import process_message

def listen_for_messages():
    r = redis.Redis(host='your_redis_host', port=6379, db=0)
    
    while True:
        try:
            # BLPOP blocks until a message is available
            key, message = r.blpop(['your_queue'], timeout=0)
            
            # Process the message
            process_message(message.decode('utf-8'))
            
        except Exception as e:
            print(f"Error processing message: {e}")
            time.sleep(1)  # Prevent tight loop in case of errors

if __name__ == "__main__":
    listen_for_messages()

You can run this script as a systemd service or using a process manager like Supervisor.

2. Django Management Command

Create a custom Django management command that runs the persistent connection:

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# your_app/management/commands/redis_listener.py
from django.core.management.base import BaseCommand
import redis
import time

class Command(BaseCommand):
    help = 'Listens for messages on Redis queue'

    def handle(self, *args, **options):
        r = redis.Redis(host='your_redis_host', port=6379, db=0)
        
        self.stdout.write(self.style.SUCCESS('Starting Redis listener...'))
        
        while True:
            try:
                key, message = r.blpop(['your_queue'], timeout=0)
                self.stdout.write(f"Received message: {message.decode('utf-8')}")
                
                # Process the message or trigger a Celery task
                from your_app.tasks import process_message
                process_message.delay(message.decode('utf-8'))
                
            except Exception as e:
                self.stdout.write(self.style.ERROR(f"Error: {e}"))
                time.sleep(1)

Run it with: python manage.py redis_listener

3. Improved Celery Approach

Instead of running a task for 9 seconds every 10 seconds, you can use Celery's ability to chain tasks:

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# tasks.py
from celery import shared_task
from celery.signals import worker_ready
import redis

@shared_task(bind=True)
def listen_for_redis_messages(self, timeout=5):
    r = redis.Redis(host='your_redis_host', port=6379, db=0)
    
    try:
        # Use a reasonable timeout to prevent task from running too long
        result = r.blpop(['your_queue'], timeout=timeout)
        
        if result:
            key, message = result
            # Process the message
            process_message.delay(message.decode('utf-8'))
    finally:
        # Always chain to the next execution
        listen_for_redis_messages.delay(timeout=timeout)
    
    return None

@worker_ready.connect
def at_start(sender, **k):
    # Start the listener when Celery worker starts
    listen_for_redis_messages.delay()

@shared_task
def process_message(message):
    # Process the actual message
    pass

4. Use Redis Pub/Sub with Channels

For a more Django-integrated approach, consider using Django Channels with Redis as the backing store:

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# consumers.py
from channels.consumer import SyncConsumer
from channels.layers import get_channel_layer
from asgiref.sync import async_to_sync

class RedisMessageConsumer(SyncConsumer):
    def redis_message(self, message):
        # Process the message
        print(f"Received: {message['content']}")
        
# In your application code
channel_layer = get_channel_layer()
async_to_sync(channel_layer.send)('redis_listener', {
    'type': 'redis.message',
    'content': 'Your message here'
})

Recommendation

For your production environment, I recommend either:

1. The dedicated worker process with a process manager like Supervisor
2. Django Channels if you're already using or planning to use WebSockets

These approaches provide better separation of concerns and reliability than trying to maintain persistent connections within the Django request/response cycle.