Benchmarking geospatial database on Kubernetes cluster
Abstract Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. D...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2021-07-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-021-00754-2 |
| _version_ | 1819080470201106432 |
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| author | Bharti Sharma Poonam Bansal Mohak Chugh Adisakshya Chauhan Prateek Anand Qiaozhi Hua Achin Jain |
| author_facet | Bharti Sharma Poonam Bansal Mohak Chugh Adisakshya Chauhan Prateek Anand Qiaozhi Hua Achin Jain |
| author_sort | Bharti Sharma |
| collection | DOAJ |
| description | Abstract Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments. |
| first_indexed | 2024-12-21T19:45:23Z |
| format | Article |
| id | doaj.art-b1a2d0d9ad224df58275ea22fb8f326b |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-12-21T19:45:23Z |
| publishDate | 2021-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-b1a2d0d9ad224df58275ea22fb8f326b2022-12-21T18:52:21ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802021-07-012021112910.1186/s13634-021-00754-2Benchmarking geospatial database on Kubernetes clusterBharti Sharma0Poonam Bansal1Mohak Chugh2Adisakshya Chauhan3Prateek Anand4Qiaozhi Hua5Achin Jain6MSIT, GGSIPUMSIT, GGSIPUMSIT, GGSIPUMSIT, GGSIPUNSUTComputer School, Hubei University of Arts and ScienceBharati Vidyapeeth’s College of EngineeringAbstract Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.https://doi.org/10.1186/s13634-021-00754-2Distributed data processingGeospatial databasesCluster computingGeospatial-dataGeospatial-databasesBenchmarking |
| spellingShingle | Bharti Sharma Poonam Bansal Mohak Chugh Adisakshya Chauhan Prateek Anand Qiaozhi Hua Achin Jain Benchmarking geospatial database on Kubernetes cluster EURASIP Journal on Advances in Signal Processing Distributed data processing Geospatial databases Cluster computing Geospatial-data Geospatial-databases Benchmarking |
| title | Benchmarking geospatial database on Kubernetes cluster |
| title_full | Benchmarking geospatial database on Kubernetes cluster |
| title_fullStr | Benchmarking geospatial database on Kubernetes cluster |
| title_full_unstemmed | Benchmarking geospatial database on Kubernetes cluster |
| title_short | Benchmarking geospatial database on Kubernetes cluster |
| title_sort | benchmarking geospatial database on kubernetes cluster |
| topic | Distributed data processing Geospatial databases Cluster computing Geospatial-data Geospatial-databases Benchmarking |
| url | https://doi.org/10.1186/s13634-021-00754-2 |
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