Constructing a Lightweight Key-Value Store Based on the Windows Native Features.
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| Title: | Constructing a Lightweight Key-Value Store Based on the Windows Native Features. |
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| Authors: | Kwon, Hyuk-Yoon |
| Source: | Applied Sciences (2076-3417); Sep2019, Vol. 9 Issue 18, p3801, 22p |
| Subject Terms: | WINDOWS (Graphical user interfaces), BIG data, RETAIL stores, CLINICAL trial registries |
| Abstract: | Featured Application: In this paper, we propose a lightweight key-value store for managing various types of data, which are generated from Big data applications, in a very simple form. The proposed technique can be used in any environments where Windows operating systems are running, which encompass from client environments (e.g., Windows 10) to server environments (e.g., Windows Server 2016), with the minimum effort for the installation. For the other environments without Windows operating systems, we can easily migrate data to the other any environments that support existing key-value stores by using the ETL (Extract-Transform-Load) method proposed by this paper. In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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