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http://dx.doi.org/10.7236/JIIBC.2022.22.4.17

A Tombstone Filtered LSM-Tree for Stable Performance of KVS  

Lee, Eunji (School of AI Convergence, Soongsil University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.22, no.4, 2022 , pp. 17-22 More about this Journal
Abstract
With the spread of web services, data types are becoming more diversified. In addition to the form of storing data such as images, videos, and texts, the number and form of properties and metadata expressing the data are different for each data. In order to efficiently process such unstructured data, a key-value store is widely used for state-of-the-art applications. LSM-Tree (Log Structured Merge Tree) is the core data structure of various commercial key-value stores. LSM-Tree is optimized to provide high performance for small writes by recording all write and delete operations in a log manner. However, there is a problem in that the delay time and processing speed of user requests are lowered as batches of deletion operations for expired data are inserted into the LSM-Tree as special key-value data. This paper presents a Filtered LSM-Tree (FLSM-Tree) that solves the above problem by separating the deleted key from the main tree structure while maintaining all the advantages of the existing LSM-Tree. The proposed method is implemented in LevelDB, a commercial key-value store and it shows that the read performance is improved by up to 47% in performance evaluation.
Keywords
Key-value Store; LSM-Tree; NoSQL System;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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