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Conventional non-preemptive scheduling strategies struggle to meet the latency requirements of mixed workloads: low-priority, long-running analytics can dominate CPU cores while short, high-priority transactions wait a long time to be scheduled. Although preemptive scheduling appears to be a natural solution, it has long been discouraged in DBMSs by conventional wisdom due to concerns about deadlocks and interrupt-handling overheads. In this demonstration, we highlight that this is no longer the case with PreemptDB, a modern memory-optimized DBMS that we built around (1) optimistic concurrency and (2) userspace interrupts that recently became available in x86 CPUs. PreemptDB proposes user-interruptassisted context switching to renew preemptive scheduling in modern DBMSs. Through a set of demonstration scenarios, we show that preemptive scheduling is practical and prioritizes high-priority transactions while preserving throughput and fairness.
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Low-Latency Transaction Scheduling via Userspace Interrupts: Why Wait or Yield When You Can Preempt?
Traditional non-preemptive scheduling can lead to long latency under workloads that mix long-running and short transactions with varying priorities. This occurs because worker threads tend to monopolize CPU cores until they finish processing long-running transactions. Thus, short transactions must wait for the CPU, leading to long latency. As an alternative, cooperative scheduling allows for transaction yielding, but it is difficult to tune for diverse workloads. Although preemption could potentially alleviate this issue, it has seen limited adoption in DBMSs due to the high delivery latency of software interrupts and concerns on wasting useful work induced by read-write lock conflicts in traditional lock-based DBMSs. In this paper, we propose PreemptDB, a new database engine that leverages recent userspace interrupts available in modern CPUs to enable efficient preemptive scheduling. We present an efficient transaction context switching mechanism purely in userspace and scheduling policies that prioritize short, high-priority transactions without significantly affecting long-running queries. Our evaluation demonstrates that PreemptDB significantly reduces end-to-end latency for high-priority transactions compared to non-preemptive FIFO and cooperative scheduling methods.
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- Award ID(s):
- 2339596
- PAR ID:
- 10672205
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 3
- Issue:
- 3
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1145/3725319
