Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)

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Comprehensive Study of Persistence Techniques in In-memory Databases

Authors
Aryan Chaudhari1, *, Harsh Bhat1, Abhishek Belgaonkar1, Aditya Supare1, Seema Patil1
1Department of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, India
*Corresponding author. Email: aryanchaudhari44@gmail.com
Corresponding Author
Aryan Chaudhari
Available Online 26 May 2025.
DOI
10.2991/978-94-6463-716-8_83How to use a DOI?
Keywords
In-memory databases; IM-NoSQL; persistence techniques; Redis; NoSQL; Snapshotting; logging; Append-Only File (AOF); key-value stores; RAMCloud; Memcached; Apache Ignite; hybrid persistence models; crash recovery; database benchmarking; cloud-native databases; workload optimization
Abstract

In recent years, NoSQL databases have become essential for delivering Big data web services. As memory capacities increase, there is a heightened focus on In-memory NoSQL (IM-NoSQL) systems, which use dynamic random-access memory (DRAM) to enable minimal latency. However, because DRAM is volatile, IM-NoSQL systems need effective persistence and recovery methods to prevent data loss during server failures. This paper presents a detailed study of the performance of persistence and recovery techniques in IM-NoSQL databases. The evaluation examines the performance of Snapshotting and logging techniques, focusing on their effectiveness in failure recovery. Our research aims to answer critical questions: (i) This study investigates whether IM-NoSQL systems maintain efficiency under memory constraints.(ii) What are the performance trade-offs between using snapshots and logging? (iii) How quickly can an IM-NoSQL system recover after a failure? (iv)How does the choice of persistence method affect the system’s performance? This study utilizes Redis as a representative IM-NoSQL system to evaluate persistence strategies, recovery durations, and system performance metrics.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
Series
Advances in Intelligent Systems Research
Publication Date
26 May 2025
ISBN
978-94-6463-716-8
ISSN
1951-6851
DOI
10.2991/978-94-6463-716-8_83How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Aryan Chaudhari
AU  - Harsh Bhat
AU  - Abhishek Belgaonkar
AU  - Aditya Supare
AU  - Seema Patil
PY  - 2025
DA  - 2025/05/26
TI  - Comprehensive Study of Persistence Techniques in In-memory Databases
BT  - Proceedings of the International Conference on Recent Advancements and Modernisations in Sustainable Intelligent Technologies and Applications (RAMSITA 2025)
PB  - Atlantis Press
SP  - 1124
EP  - 1138
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-716-8_83
DO  - 10.2991/978-94-6463-716-8_83
ID  - Chaudhari2025
ER  -