Performance Analysis of PostgreSQL and MongoDB Databases for Unstructured Data

DOI

10.2991/mbdasm-19.2019.14How to use a DOI?

Keywords

unstructured data; MongoDB; PostgreSQL;storage; GeoTIFF

Abstract

The storage of unstructured data plays an important role in the implementation of big data environment, thus, choosing an efficient database can provide an excellent solution for data mining. In this paper, two database technologies, MongoDB and PostgreSQL, are used as performance tests for storing unstructured data. Remote sensing data in GeoTIFF format is the most representative unstructured data. Large amounts of data are stored in MongoDB and PostgreSQL databases by designing metadata table for GeoTIFF data to test the performance of both. The results show that MongoDB storage is six times faster than PostgreSQL, however PostgreSQL compresses data up to 95%. Therefore, MongoDB is suitable for rapid storage of remote sensing data, while PostgreSQL is more suitable for operations with small data volumes. In a word, this research work has completed the database performance test of unstructured remote sensing data.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yinyi Cheng
AU  - Kefa Zhou
AU  - Jinlin Wang
PY  - 2019/10
DA  - 2019/10
TI  - Performance Analysis of PostgreSQL and MongoDB Databases for Unstructured Data
BT  - Proceedings of the 2019 International Conference on Mathematics, Big Data Analysis and Simulation and Modelling (MBDASM 2019)
PB  - Atlantis Press
SP  - 60
EP  - 62
SN  - 2352-538X
UR  - https://doi.org/10.2991/mbdasm-19.2019.14
DO  - 10.2991/mbdasm-19.2019.14
ID  - Cheng2019/10
ER  -