Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices
As the importance of data increases, data is continuously collected from diverse sources such as sensors, IoT devices, and edge computing devices. To manage these continuously monitored data, it is often organized chronologically with time which is referred as time-series data. By managing the data...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10305082/ |
| _version_ | 1797454727641825280 |
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| author | Sangmyung Lee Yongseok Son Sunggon Kim |
| author_facet | Sangmyung Lee Yongseok Son Sunggon Kim |
| author_sort | Sangmyung Lee |
| collection | DOAJ |
| description | As the importance of data increases, data is continuously collected from diverse sources such as sensors, IoT devices, and edge computing devices. To manage these continuously monitored data, it is often organized chronologically with time which is referred as time-series data. By managing the data using time, data from different streams can be analyzed in a comprehensive manner with an identical index which is time. However, due to the unique characteristics of time-series data, it is essential for the underlying database systems to understand the characteristics of the time-series data. To handle this, time-series database systems, which specially target time-series data, are emerging. These database systems have different performance characteristics due to the unique characteristics of the data which should be investigated to efficiently store and analyze the data. In this paper, we analyze the time-series database from the perspective of I/O using various storage devices from HDD, SATA and NVMe SSD. First, we analyze the I/O characteristics such as runtime, throughput and size of total requests using various storage devices. In addition, we analyze the effect of unique time-series database features such as data chunk interval, compression and number of workers. Our analysis results show that adapting high-performance devices can greatly improve the performance of the database by up to <inline-formula> <tex-math notation="LaTeX">$33.22\times $ </tex-math></inline-formula>. |
| first_indexed | 2024-03-09T15:42:15Z |
| format | Article |
| id | doaj.art-d05e7ce55e65435b9eba1ff712b2e023 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T15:42:15Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d05e7ce55e65435b9eba1ff712b2e0232023-11-25T00:01:12ZengIEEEIEEE Access2169-35362023-01-011112899812900810.1109/ACCESS.2023.332947410305082Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage DevicesSangmyung Lee0https://orcid.org/0009-0004-9003-1042Yongseok Son1Sunggon Kim2https://orcid.org/0000-0002-2295-3385Department of Computer Science and Engineering, Seoul National University of Science and Technology, Gongneung-ro, Nowon-gu, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of KoreaDepartment of Computer Science and Engineering, Seoul National University of Science and Technology, Gongneung-ro, Nowon-gu, Seoul, Republic of KoreaAs the importance of data increases, data is continuously collected from diverse sources such as sensors, IoT devices, and edge computing devices. To manage these continuously monitored data, it is often organized chronologically with time which is referred as time-series data. By managing the data using time, data from different streams can be analyzed in a comprehensive manner with an identical index which is time. However, due to the unique characteristics of time-series data, it is essential for the underlying database systems to understand the characteristics of the time-series data. To handle this, time-series database systems, which specially target time-series data, are emerging. These database systems have different performance characteristics due to the unique characteristics of the data which should be investigated to efficiently store and analyze the data. In this paper, we analyze the time-series database from the perspective of I/O using various storage devices from HDD, SATA and NVMe SSD. First, we analyze the I/O characteristics such as runtime, throughput and size of total requests using various storage devices. In addition, we analyze the effect of unique time-series database features such as data chunk interval, compression and number of workers. Our analysis results show that adapting high-performance devices can greatly improve the performance of the database by up to <inline-formula> <tex-math notation="LaTeX">$33.22\times $ </tex-math></inline-formula>.https://ieeexplore.ieee.org/document/10305082/Performance analysisNVMe SSDSATA SSDtime-series databenchmarkdatabase |
| spellingShingle | Sangmyung Lee Yongseok Son Sunggon Kim Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices IEEE Access Performance analysis NVMe SSD SATA SSD time-series data benchmark database |
| title | Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices |
| title_full | Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices |
| title_fullStr | Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices |
| title_full_unstemmed | Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices |
| title_short | Analyzing I/O Characteristics of Time-Series Data Using High Performance Storage Devices |
| title_sort | analyzing i o characteristics of time series data using high performance storage devices |
| topic | Performance analysis NVMe SSD SATA SSD time-series data benchmark database |
| url | https://ieeexplore.ieee.org/document/10305082/ |
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