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What are the limitations of using a plain Redis list as a message queue, and when should you consider Redis Streams instead? How do you implement a priority queue pattern using Redis, where certain tasks should be processed before others? How do you monitor the length and health of a Redis based queue to ensure workers are keeping up with incoming tasks? How would you implement a delayed task queue in Redis, where a task should only become available for processing after a specific future time?
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What patterns help ensure exactly once or at least once processing semantics when using Redis as a message queue for critical tasks?

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Answer

For at least once processing, you use a reliable pattern like moving tasks into a dedicated processing list while they are being worked on, using RPOPLPUSH or LMOVE, so a crashed worker's tasks remain visible and can be retried, while achieving true exactly once processing generally requires additional application level logic like idempotency keys to safely handle the rare case of a task being processed more than once.
RPOPLPUSH tasks processing
-- Process the task
-- Use an idempotency key to safely handle potential duplicate processing
SET processed:task123 1 NX
Real-world example A payment processing system combines a reliable queue pattern with idempotency keys, ensuring that even if a task is accidentally processed twice due to a worker crash and retry, the customer is never charged more than once.

Common follow-ups: What is an idempotency key, and how does it prevent duplicate processing side effects?;Why is true exactly once delivery considered very difficult to achieve in distributed systems?

Distributed Locks with Redis;Streams
All 7 Redis as a Message Queue questions