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  1. シンポジウム
  2. シンポジウムシリーズ
  3. Asia Pacific Conference on Robot IoT System Development and Platform (APRIS)
  4. 2024

Redis Persistence Performance with Asynchronous I/O Integration

https://ipsj.ixsq.nii.ac.jp/records/241866
https://ipsj.ixsq.nii.ac.jp/records/241866
63e793dc-157e-4ddc-9422-5c335527cef5
名前 / ファイル ライセンス アクション
IPSJ-APRIS2024006.pdf IPSJ-APRIS2024006.pdf (1.3 MB)
 2026年12月27日からダウンロード可能です。
Copyright (c) 2024 by the Information Processing Society of Japan
非会員:¥0, IPSJ:学会員:¥0, EMB:会員:¥0, DLIB:会員:¥0
Item type Symposium(1)
公開日 2024-12-27
タイトル
タイトル Redis Persistence Performance with Asynchronous I/O Integration
タイトル
言語 en
タイトル Redis Persistence Performance with Asynchronous I/O Integration
言語
言語 eng
資源タイプ
資源タイプ識別子 http://purl.org/coar/resource_type/c_5794
資源タイプ conference paper
著者所属
Shibaura Institute of Technology
著者所属
Shibaura Institute of Technology
著者所属
Shibaura Institute of Technology
著者所属
Shibaura Institute of Technology
著者所属(英)
en
Shibaura Institute of Technology
著者所属(英)
en
Shibaura Institute of Technology
著者所属(英)
en
Shibaura Institute of Technology
著者所属(英)
en
Shibaura Institute of Technology
著者名 Le-gao, Chen

× Le-gao, Chen

Le-gao, Chen

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Yanzhi, Li

× Yanzhi, Li

Yanzhi, Li

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Tipporn, Laohakangvalvit

× Tipporn, Laohakangvalvit

Tipporn, Laohakangvalvit

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Midori, Sugaya

× Midori, Sugaya

Midori, Sugaya

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著者名(英) Le-gao, Chen

× Le-gao, Chen

en Le-gao, Chen

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Yanzhi, Li

× Yanzhi, Li

en Yanzhi, Li

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Tipporn, Laohakangvalvit

× Tipporn, Laohakangvalvit

en Tipporn, Laohakangvalvit

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Midori, Sugaya

× Midori, Sugaya

en Midori, Sugaya

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論文抄録
内容記述タイプ Other
内容記述 In recent years, in-memory database servers have gained popularity due to advancements in memory and CPU architecture, which enables applications such as real-time analytics. Among those servers, Redis is the most widely adopted solution across various companies. However, data persistence in Redis causes severe performance degradation and resource consumption in the server. This is especially apparent in applications where payload sizes are big. This encouraged us to investigate way s to mitigate the effects of persistence on the whole system. We discovered that traditional Redis uses synchronous I/O on a background thread during persistence thus causing the thread and I/O device to block each other. While persistence operations are ongoing, the severe memory usage also causes the server throughput to slow down. This paper proposes a Redis with asynchronous I/O persistence to soften these effects on the system. Tests with different payload sizes were done to analyze the performance of asynchronous I/O and synchronous I/O under different data loads and conditions. Our proposed method resulted in a reduction of 2 to 11 seconds of time taken for persistence operations, causing a 500MB to 1.5GB reduction in memory usage, 0.3% to 13% reduction in CPU usage, and a 0.5% to 8.4% increase in request per second in 30-second segments when payload size is above 128KB.
論文抄録(英)
内容記述タイプ Other
内容記述 In recent years, in-memory database servers have gained popularity due to advancements in memory and CPU architecture, which enables applications such as real-time analytics. Among those servers, Redis is the most widely adopted solution across various companies. However, data persistence in Redis causes severe performance degradation and resource consumption in the server. This is especially apparent in applications where payload sizes are big. This encouraged us to investigate way s to mitigate the effects of persistence on the whole system. We discovered that traditional Redis uses synchronous I/O on a background thread during persistence thus causing the thread and I/O device to block each other. While persistence operations are ongoing, the severe memory usage also causes the server throughput to slow down. This paper proposes a Redis with asynchronous I/O persistence to soften these effects on the system. Tests with different payload sizes were done to analyze the performance of asynchronous I/O and synchronous I/O under different data loads and conditions. Our proposed method resulted in a reduction of 2 to 11 seconds of time taken for persistence operations, causing a 500MB to 1.5GB reduction in memory usage, 0.3% to 13% reduction in CPU usage, and a 0.5% to 8.4% increase in request per second in 30-second segments when payload size is above 128KB.
書誌情報 Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform

巻 2024, p. 37-44, 発行日 2024-12-27
出版者
言語 ja
出版者 情報処理学会
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