Optimizing Postgres for Scalable Queue Workloads Using SKIP LOCKED

Scaling Postgres Queues

Conventional wisdom suggests that Postgres is not suitable for high-scale queueing systems, often recommending dedicated solutions like RabbitMQ or Redis. This perception stems from the demanding nature of queue workloads on databases, where thousands of workers concurrently poll tables, leading to contention and index churn. However, with specific optimizations, Postgres can handle these workloads effectively.

Addressing Worker Contention

A primary challenge in scaling Postgres-backed queues is contention among multiple workers trying to dequeue the same workflows. Without proper mechanisms, concurrent queries by workers to find and dequeue the oldest tasks result in most workers failing to find new work, creating a bottleneck that limits task processing speed.

Utilizing SKIP LOCKED for Concurrency

Postgres provides locking clauses, specifically `FOR UPDATE SKIP LOCKED`, to resolve this contention. This clause allows workers to select rows while simultaneously locking them, preventing other workers from selecting the same rows. Crucially, it also skips any rows that are already locked, ensuring that each worker can efficiently pull unique tasks without waiting or retrying. This mechanism enables many workers to process workflows concurrently, significantly boosting throughput beyond the typical ~100 workflows per second limit without it.