Postgres remains a popular tool for job queue workloads, often running alongside OLTP and OLAP tasks in mixed environments. The core issue with queues, where rows are inserted and immediately deleted, involves Postgres' MVCC implementation. While convenient for transactional integrity, this process generates 'dead tuples' that must eventually be cleaned up by VACUUM operations.

When workers quickly claim and delete jobs, they hold short transactions, but the deleted rows aren't instantly removed. These dead tuples accumulate in indexes, forcing sequential or index scans to traverse entries pointing to non-existent data. This hidden I/O overhead degrades performance, especially when competing with slower analytical queries on the primary instance.

Maintaining a healthy queue hinges on managing this cleanup process effectively against workload accumulation. If autovacuum cannot keep pace with deletions, index bloat forces queries—like the one fetching the next pending job via `FOR UPDATE SKIP LOCKED`—to work harder, slowing down the entire application stack.

PlanetScale advises that queue health depends on configuration and the behavior of all other transactions sharing the instance. Ignoring dead tuple accumulation guarantees degraded performance, even if Postgres itself is capable of handling massive scale in isolation.