一篇技术博文介绍了将PostgreSQL支持的队列系统吞吐量从每秒约100个工作流提升至30000个工作流的优化方案1。该方案由DBOS团队发布，通过三个关键优化策略解决了队列系统在高并发场景下的性能瓶颈1。
首个优化针对多worker之间的行级竞争问题，采用FOR UPDATE SKIP LOCKED锁定语句1。第二个优化涉及事务隔离级别的条件调整：当工作流吞吐量超过每秒1000个时，REPEATABLE READ隔离级别会导致序列化失败，改用READ COMMITTED隔离级别可消除这一问题，前提是不需要全局流控制1。第三个优化通过部分索引提高索引效率，在吞吐量超过每秒8000个工作流时能显著降低CPU使用率，同时减少了索引的维护和autovacuum成本1。
优化后的系统达到每秒30000个工作流执行的吞吐量，相当于每月80亿次操作1。
A technical post demonstrates that PostgreSQL-backed queue systems can achieve significant throughput through targeted optimizations.1 The unoptimized baseline processes approximately 100 workflows per second, but three key enhancements enable the system to reach 30,000 workflows per second—equivalent to 80 billion per month.1
The first optimization addresses contention among multiple workers by using FOR UPDATE SKIP LOCKED, which allows concurrent processes to lock rows without blocking each other.1 The second involves conditional adjustment of transaction isolation levels: at throughput exceeding 1,000 workflows per second, REPEATABLE READ isolation causes serialization failures, but switching to READ COMMITTED eliminates these failures when global flow control is not required.1 The third optimization tackles inefficient indexing that emerges at throughput above 8,000 workflows per second, driving up CPU usage; partial indexes both improve efficiency and reduce maintenance and autovacuum costs.1
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