Abstract
The exponential growth of geospatial data resulting from the development of earth observation technology has created significant challenges for traditional relational databases. While NoSQL databases based on distributed file systems can handle massive data storage, they often struggle to cope with real-time query. Column-storage databases, on other hand, are highly effective at both
storage and query processing for large-scale datasets. In this paper, we propose a spatial version of ClickHouse that leverages R-Tree indexing to enable efficient storage and real-time analysis of massive remote sensing data. ClickHouse is a column-oriented, open source database management system designed for handling large-scale datasets. By integrating R-Tree indexing, we have created a
highly efficient system for storing and querying geospatial data. To evaluate the performance of our system, we compare it with HBase, a popular distributed, NoSQL database system. Our experimental results show that ClickHouse outperforms HBase in handling spatial data queries, with a response time approximately three times faster than HBase. We attribute this performance gain
to the highly efficient R-Tree indexing used in ClickHouse, which allows for fast spatial data query.
storage and query processing for large-scale datasets. In this paper, we propose a spatial version of ClickHouse that leverages R-Tree indexing to enable efficient storage and real-time analysis of massive remote sensing data. ClickHouse is a column-oriented, open source database management system designed for handling large-scale datasets. By integrating R-Tree indexing, we have created a
highly efficient system for storing and querying geospatial data. To evaluate the performance of our system, we compare it with HBase, a popular distributed, NoSQL database system. Our experimental results show that ClickHouse outperforms HBase in handling spatial data queries, with a response time approximately three times faster than HBase. We attribute this performance gain
to the highly efficient R-Tree indexing used in ClickHouse, which allows for fast spatial data query.
Original language | English |
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Title of host publication | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Subtitle of host publication | 39th International Symposium on Remote Sensing of Environment (ISRSE-39) “From Human Needs to SDGs” |
Place of Publication | Antalya, Türkiye |
Pages | 65-72 |
Number of pages | 8 |
Volume | 48 |
Edition | M-1-2023 |
DOIs | |
Publication status | Published (in print/issue) - 25 Apr 2023 |
Event | 39th International Symposium on Remote Sensing of Environment - Antalya, Turkey Duration: 24 Apr 2023 → 28 Apr 2023 Conference number: 39 https://isprs-archives.copernicus.org/articles/XLVIII-M-1-2023/65/2023/ |
Publication series
Name | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
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Publisher | International Society for Photogrammetry and Remote Sensing |
ISSN (Print) | 1682-1750 |
Conference
Conference | 39th International Symposium on Remote Sensing of Environment |
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Abbreviated title | ISRSE |
Country/Territory | Turkey |
City | Antalya |
Period | 24/04/23 → 28/04/23 |
Internet address |
Bibliographical note
Publisher Copyright:© 2023 International Society for Photogrammetry and Remote Sensing. All rights reserved.
Keywords
- ClickHouse
- vector spatial data
- query processing
- HBase
- remote sensing