Computer Science > Distributed, Parallel, and Cluster Computing
[Submitted on 23 May 2023 (v1), last revised 25 Sep 2023 (this version, v2)]
Title:NCC: Natural Concurrency Control for Strictly Serializable Datastores by Avoiding the Timestamp-Inversion Pitfall
View PDFAbstract:Strictly serializable datastores greatly simplify the development of correct applications by providing strong consistency guarantees. However, existing techniques pay unnecessary costs for naturally consistent transactions, which arrive at servers in an order that is already strictly serializable. We find these transactions are prevalent in datacenter workloads. We exploit this natural arrival order by executing transaction requests with minimal costs while optimistically assuming they are naturally consistent, and then leverage a timestamp-based technique to efficiently verify if the execution is indeed consistent. In the process of designing such a timestamp-based technique, we identify a fundamental pitfall in relying on timestamps to provide strict serializability, and name it the timestamp-inversion pitfall. We find timestamp-inversion has affected several existing works.
We present Natural Concurrency Control (NCC), a new concurrency control technique that guarantees strict serializability and ensures minimal costs -- i.e., one-round latency, lock-free, and non-blocking execution -- in the best (and common) case by leveraging natural consistency. NCC is enabled by three key components: non-blocking execution, decoupled response control, and timestamp-based consistency check. NCC avoids timestamp-inversion with a new technique: response timing control, and proposes two optimization techniques, asynchrony-aware timestamps and smart retry, to reduce false aborts. Moreover, NCC designs a specialized protocol for read-only transactions, which is the first to achieve the optimal best-case performance while ensuring strict serializability, without relying on synchronized clocks. Our evaluation shows that NCC outperforms state-of-the-art solutions by an order of magnitude on many workloads.
Submission history
From: Haonan Lu [view email][v1] Tue, 23 May 2023 17:21:30 UTC (581 KB)
[v2] Mon, 25 Sep 2023 15:30:42 UTC (781 KB)
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