research-article

Optimizing in-memory database engine for AI-powered on-line decision augmentation using persistent memory

Authors:

Andy RudoffAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 14, Issue 5

Pages 799 - 812

https://doi.org/10.14778/3446095.3446102

Published: 01 January 2021 Publication History

Abstract

On-line decision augmentation (OLDA) has been considered as a promising paradigm for real-time decision making powered by Artificial Intelligence (AI). OLDA has been widely used in many applications such as real-time fraud detection, personalized recommendation, etc. On-line inference puts real-time features extracted from multiple time windows through a pre-trained model to evaluate new data to support decision making. Feature extraction is usually the most time-consuming operation in many OLDA data pipelines. In this work, we started by studying how existing in-memory databases can be leveraged to efficiently support such real-time feature extractions. However, we found that existing in-memory databases cost hundreds or even thousands of milliseconds. This is unacceptable for OLDA applications with strict real-time constraints. We therefore propose FEDB (<u>F</u>eature <u>E</u>ngineering <u>D</u>ata<u>b</u>ase), a distributed in-memory database system designed to efficiently support on-line feature extraction. Our experimental results show that FEDB can be one to two orders of magnitude faster than the state-of-the-art in-memory databases on real-time feature extraction. Furthermore, we explore the use of the Intel Optane DC Persistent Memory Module (PMEM) to make FEDB more cost-effective. When comparing the proposed PMEM-optimized persistent skiplist to the FEDB using DRAM+SSD, PMEM-based FEDB can shorten the tail latency up to 19.7%, reduce the recovery time up to 99.7%, and save up to 58.4% total cost of a real OLDA pipeline.

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cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 14, Issue 5

January 2021

142 pages

ISSN:2150-8097

Editors:
Xin Luna Dong
Amazon
,
Felix Naumann
HPI, University of Potsdam

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Published: 01 January 2021

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Chang CLo EYe C(2024)Biathlon: Harnessing Model Resilience for Accelerating ML Inference PipelinesProceedings of the VLDB Endowment10.14778/3675034.367505217:10(2631-2640)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675052
Liu RPark KPsallidas FZhu XMo JSen RInterlandi MKaranasos KTian YCamacho-Rodríguez J(2023)Optimizing Data Pipelines for Machine Learning in Feature StoresProceedings of the VLDB Endowment10.14778/3625054.362506016:13(4230-4239)Online publication date: 1-Sep-2023
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Song XZhu YWu JLiu BWei H(2023)ADOps: An Anomaly Detection Pipeline in Structured LogsProceedings of the VLDB Endowment10.14778/3611540.361161816:12(4050-4053)Online publication date: 1-Aug-2023
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Zhou XChen CLi KHe BLu MLiu QHuang WLi GZheng ZChen Y(2023)FEBench: A Benchmark for Real-Time Relational Data Feature ExtractionProceedings of the VLDB Endowment10.14778/3611540.361155016:12(3597-3609)Online publication date: 1-Aug-2023
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Zhang HYang JChen CWang SLi JLu M(2023)Principles and Practices of Real-Time Feature Computing Platforms for MLCommunications of the ACM10.1145/358922466:7(77-78)Online publication date: 22-Jun-2023
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Ye CXu YShen XSha YLiao XJin HSolihin YAamodt TJerger NSwift M(2023)SpecPMT: Speculative Logging for Resolving Crash Consistency Overhead of Persistent MemoryProceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 210.1145/3575693.3575696(762-777)Online publication date: 27-Jan-2023
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He YLu DHuang KWang T(2022)Evaluating persistent memory range indexesProceedings of the VLDB Endowment10.14778/3551793.355180815:11(2477-2490)Online publication date: 1-Jul-2022
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