Abstract
NoSQL systems have been used to store and manage large amount of data having various data types. Many NoSQL systems are providing functions that store and manage geospatial information.
However, their performance comparisons have not been known. In this paper, we compare and evaluate the performance of NoSQL systems for processing geospatial information. For this, we choose three representative NoSQL systems that are appropriate to process geospatial information (i.e., MongoDB, Redis, and ElasticSearch) and conduct extensive experiments in various environments using 6 synthetic and real data sets. MongoDB shows overall superior performance and scalability in processing geospatial information. Redis shows the best performance when storing geospatial information. ElasticSearch shows relatively constant performance compared to the other two systems regardless of caching.
However, their performance comparisons have not been known. In this paper, we compare and evaluate the performance of NoSQL systems for processing geospatial information. For this, we choose three representative NoSQL systems that are appropriate to process geospatial information (i.e., MongoDB, Redis, and ElasticSearch) and conduct extensive experiments in various environments using 6 synthetic and real data sets. MongoDB shows overall superior performance and scalability in processing geospatial information. Redis shows the best performance when storing geospatial information. ElasticSearch shows relatively constant performance compared to the other two systems regardless of caching.
| Translated title of the contribution | A Performance Evaluation of NoSQL Systems for Processing Geospatial Information |
|---|---|
| Original language | Korean |
| Pages (from-to) | 3-19 |
| Number of pages | 17 |
| Journal | 데이타베이스연구 |
| Volume | 35 |
| Issue number | 1 |
| State | Published - 2019 |