What is rq?
RQ (Redis Queue) is a simple, efficient, and robust Python library for queueing jobs and processing them in the background with workers. It uses Redis as its message broker.
-
Key Features:
- Simple API: RQ provides an easy-to-use API for enqueuing jobs and starting workers.
- Redis-backed: Leverages the speed and reliability of Redis for message passing.
- Concurrency: Supports concurrent job processing through multiple workers.
- Job Management: Provides features for tracking job status, retrying failed jobs, and scheduling jobs.
- Worker Management: Tools for monitoring and managing worker processes.
- Integration: Easily integrates with existing Python applications and frameworks like Flask and Django.
- Prioritization: Supports different queues with varying priorities.
- Timeouts: Configurable timeouts for jobs to prevent them from running indefinitely.
-
Core Components:
- Queue: Represents a list of jobs waiting to be processed. Each job is added to a specific Queue.
- Worker: A process that listens on one or more queues and executes jobs as they become available. Workers pick jobs from the queue.
- Job: Represents a unit of work to be executed. A Job encapsulates the function and its arguments.
- Redis Connection: RQ relies on a connection to a Redis%20Connection instance for storing and retrieving job data.
-
Workflow:
- A job is enqueued (added) to a queue.
- One or more workers are started and listen for jobs on the queue.
- When a worker finds a job, it retrieves the job's information from Redis.
- The worker executes the job.
- The job's status (e.g., completed, failed) is updated in Redis.
-
Use Cases:
- Asynchronous task processing (e.g., sending emails, generating reports)
- CPU-intensive tasks (e.g., image processing, data analysis)
- Background tasks in web applications
- Deferring tasks to avoid blocking the main thread
-
Error Handling:
- RQ provides mechanisms for handling job failures. Failed jobs can be retried, moved to a failed queue, or logged for investigation.