Leveraging lock contention to improve OLTP application performance

Yan, Cong; Cheung, Alvin

doi:10.14778/2876473.2876479

Cited by 48 publications

(32 citation statements)

References 19 publications

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“…The approach identifies dependencies between database calls and extracts the independent statements to be run in parallel to decrease latency. A similar approach, extended to use optimization, is followed in QURO [34], to reorder statements to reduce transaction conflict under a two-phase locking protocol. However, reordering requires complex analysis for query dependency.…”

Section: Related Workmentioning

confidence: 99%

Scheduling OLTP transactions via learned abort prediction

Sheng

Tomasic

Zhang

et al. 2019

Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management

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Current main memory database system architectures are still challenged by high contention workloads and this challenge will continue to grow as the number of cores in processors continues to increase [35]. These systems schedule transactions randomly across cores to maximize concurrency and to produce a uniform load across cores. Scheduling never considers potential conflicts. Performance could be improved if scheduling balanced between concurrency to maximize throughput and scheduling transactions linearly to avoid conflicts. In this paper, we present the design of several intelligent transaction scheduling algorithms that consider both potential transaction conflicts and concurrency.To incorporate reasoning about transaction conflicts, we develop a supervised machine learning model that estimates the probability of conflict. This model is incorporated into several scheduling algorithms. In addition, we integrate an unsupervised machine learning algorithm into an intelligent scheduling algorithm. We then empirically measure the performance impact of different scheduling algorithms on OLTP and social networking workloads. Our results show that, with appropriate settings, intelligent scheduling can increase throughput by 54% and reduce abort rate by 80% on a 20-core machine, relative to random scheduling. In summary, the paper provides preliminary evidence that intelligent scheduling significantly improves DBMS performance.Transaction aborts are one of the main sources of performance loss in main memory OLTP systems [35].Current architectures for OLTP DBMS use random scheduling to assign transactions to threads. Random scheduling achieves uniform load across CPU cores and keeps all cores occupied. For workloads with a high abort rate, a large portion of work done by CPU is wasted. In contrast, the abort rate drops to zero if all transactions are scheduled sequentially into one thread. No work is wasted through aborts, but the throughput drops to the performance of a single hardware thread. Research has shown that statistical scheduling of transactions using a history can achieve low abort rate and high throughput [37] for partitionable workloads. We propose a more systematic machine learning approach to schedule transactions that achieves low abort rate and high throughput for both partitionable and non-partitionable workloads. 2 Y. Sheng, et al.The fundamental intuitions of this paper are that (i) the probability that a transactions will abort will high probability can be modeled through machine learning, and (ii) given that an abort is predicted, scheduling can avoid the conflict without loss of response time or throughput. Several research works [1,20,25,27] perform exact analyses of aborts of transaction statements that are complex and not easily generalizable over different workloads or DBMS architectures. Our approach uses machine learning algorithms to model the probability of aborts. Our approach is to (i) build a machine learning model that helps to group transactions that are likely to abort with each ot...

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Section: Related Workmentioning

confidence: 99%

Scheduling OLTP transactions via learned abort prediction

Sheng

Tomasic

Zhang

et al. 2019

Proceedings of the Second International Workshop on Exploiting Artificial Intelligence Techniques for Data Management

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“…Sloth [11] and Pyxis [10] are tools that use program analysis to reduce the network communication between the application and DBMS. QURO [42] tracks dependencies among transaction statements to reorder transaction execution for 2PL. DORA [30] also partitions transaction codes to exploit intra-transaction parallelism provided by multi-core server for 2PL.…”

Section: Related Workmentioning

confidence: 99%

“…To deal with program statements, it is straightforward for us to adopt the standard dataflow algorithm [26,42]. For simplicity, in this paper, we regard the if-statement and loop-statement as a whole statement, although sophisticated techniques such as loop fossil can make fine-grained dependency tracking [39,42].…”

Section: Reordering Block Extractionmentioning

confidence: 99%

“…In this way, we will not miss highly contended operations enclosed by reordering blocks with many less contended operations. Existing method uses waiting time and its variation on each statement to estimate the degree of contention [42]. (2) Estimating waiting time and its variance suffers from thrashing.…”

Section: Contention Estimationmentioning

confidence: 99%

“…Because delay is mainly caused by conflicting operations, much work focuses on extracting more fine-grained contention information from transaction semantics to benefit transaction pipeline [13,36,41,42]. However, finegrained contention information reduces false contention, while Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reordering Transaction Execution to Boost High-Frequency Trading Applications

Zhou

Zhang

et al. 2017

Data Sci. Eng.

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High-frequency trading (HFT) has always been welcomed because it benefits not only personal benefits but also the whole social welfare. While the recent advance of portfolio selection in HFT market enables to bring about more profit, it yields much contended OLTP workloads. Featuring exploiting the abundant parallelism, transaction pipeline, the state-of-the-art concurrency control (CC) mechanism, however, suffers from limited concurrency confronted with HFT workloads. Its variants that enable more parallel execution by leveraging fine-grained contention information also take little effect. To solve this problem, we for the first time observe and formulate the source of restricted concurrency as harmful ordering of transaction statements. To resolve harmful ordering, we propose PARE, a pipeline-aware reordered execution, to improve application performance by rearranging statements in order of their degrees of contention. In concrete, two mechanisms are devised to ensure the correctness of statement rearrangement and identify the degrees of contention of statements, respectively. We also study the off-line reordering problem. We prove that this problem is NP-hard and present an off-line reordering approach to approximate the optimal reordering strategy. Experiment results show that PARE can improve transaction throughput and reduce transaction latency on HFT applications by up to an order of magnitude than the state-of-the-art CC mechanism.

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In-memory transaction processing: efficiency and scalability considerations

Zhou

Zhu

et al. 2019

Knowl Inf Syst

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Leveraging lock contention to improve OLTP application performance

Cited by 48 publications

References 19 publications

Scheduling OLTP transactions via learned abort prediction

Scheduling OLTP transactions via learned abort prediction

Reordering Transaction Execution to Boost High-Frequency Trading Applications

In-memory transaction processing: efficiency and scalability considerations

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