Distributed job queue

[Ash Case]

How practical is PostgreSQL as the foundation for a distributed job queue in a production application, especially when multiple workers need to process background tasks concurrently? I’m interested in how developers handle job locking, worker coordination, retries, failed tasks, scheduling, and duplicate execution while keeping the system reliable and avoiding unnecessary database load. PostgreSQL has useful features such as transactions, row-level locking, and SKIP LOCKED, but I wonder how well they perform when thousands of jobs are waiting to be processed. What architecture and database design would you recommend for this kind of system, how do you monitor queue performance, and at what point does it make more sense to switch to a dedicated message broker like Redis or RabbitMQ?

[ClayGlobal]

<span data-sheets-root=”1″>I’ve always found background job processing interesting because it’s one of those things that seems straightforward until something goes wrong. A task fails halfway through, a worker stops responding, or several jobs need to be processed at the same time. That’s why I think tracking job statuses and having a reliable retry mechanism are really important. PostgreSQL can handle this kind of workload when the queue is designed properly. There are also dedicated approaches worth exploring, such as Spooled https://spooled.cloud/, a PostgreSQL-backed job queue with retries, scheduling, and workflow support. I’m curious how other developers decide when a database-backed queue is sufficient and when a separate messaging system becomes necessary.</span>

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