I’ve been looking into how to handle background jobs in applications, and I’m curious whether PostgreSQL can work well as the foundation for a distributed job queue. For smaller projects, it seems convenient to keep job data in the same database as the rest of the application, but I imagine things get more complicated when multiple workers need to process tasks at the same time. How do you usually handle job locking, retries, scheduled tasks, and failed jobs without creating too much contention in the database? I’m also interested in how to make sure jobs aren’t processed twice if a worker crashes or a task takes longer than expected. At what point does a PostgreSQL-based queue stop being a practical choice, and when would it make more sense to move to a dedicated messaging system? If you’ve built something similar, I’d love to hear what design patterns, libraries, or technical resources helped you understand the trade-offs between simplicity, reliability, and scalability.
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I’ve been looking into how to handle background jobs in applications, and I’m curious whether PostgreSQL can work well as the foundation for a distributed job queue. For smaller projects, it seems convenient to keep job data in the same database as the rest of the application, but I imagine things get more complicated when multiple workers need to process tasks at the same time. How do you usually handle job locking, retries, scheduled tasks, and failed jobs without creating too much contention in the database? I’m also interested in how to make sure jobs aren’t processed twice if a worker crashes or a task takes longer than expected. At what point does a PostgreSQL-based queue stop being a practical choice, and when would it make more sense to move to a dedicated messaging system? If you’ve built something similar, I’d love to hear what design patterns, libraries, or technical resources helped you understand the trade-offs between simplicity, reliability, and scalability.