Sharing opportunities for OLTP workloads in different isolation levels

Author:

Rehrmann Robin1,Binnig Carsten2,Böhm Alexander3,Kim Kihong4,Lehner Wolfgang1

Affiliation:

1. TU Dresden, Dresden, Germany

2. TU Darmstadt, Darmstadt, Germany

3. SAP SE, Walldorf, Germany

4. SAP Labs, Seoul, Korea

Abstract

OLTP applications are usually executed by a high number of clients in parallel and are typically faced with high throughput demand as well as a constraint latency requirement for individual statements. Interestingly, OLTP workloads are often read-heavy and comprise similar query patterns, which provides a potential to share work of statements belonging to different transactions. Consequently, OLAP techniques for sharing work have started to be applied also to OLTP workloads, lately. In this paper, we present an approach for merging read statements within interactively submitted multi-statement transactions consisting of reads and writes. We first define a formal framework for merging transactions running under a given isolation level and provide insights into a prototypical implementation of merging within a commercial database system. In our experimental evaluation, we show that, depending on the isolation level, the load in the system and the read-share of the workload, an improvement of the transaction throughput by up to a factor of 2.5X is possible without compromising the transactional semantics.

Publisher

VLDB Endowment

Subject

General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient Partitioning;IEEE Transactions on Parallel and Distributed Systems;2022-08-01

2. SwitchTx;Proceedings of the VLDB Endowment;2022-07

3. GaccO - A GPU-accelerated OLTP DBMS;Proceedings of the 2022 International Conference on Management of Data;2022-06-10

4. GalOP;Proceedings of the 17th International Workshop on Data Management on New Hardware (DaMoN 2021);2021-06-20

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