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Insertion speed of indexed spatial data

This Github repository holds the code used by group 2 in the course IT3010 in 2022. This was used to generate data for our paper, the full title of which is:

Insertion speed of indexed spatial data: comparing MySQL, PostgreSQL and MongoDB

Contributors

  • Lars-Olav Vågene
  • Ingvild Løver Thon
  • Eirik Schøien
  • Christian Axell
  • Lukas Tveiten

Setup

Prerequisites:

  • Python
  • Docker

Steps:

  1. Make a copy of the .env-template file and rename it .env
  2. Download the dataset from https://www.microsoft.com/en-us/research/publication/geolife-gps-trajectory-dataset-user-guide/ and place the user folders (000-181) in the folder ./data

Experiment

To run the experiments with the CLI:

# Start Docker containers for all DBMSs
docker-compose --compatibility up

# Drop and create SQLite tables for storing experimental results
py cli.py prepare

# Run experiment with desired iterations and total size
py cli.py run -i 3 -n 5000

The results are stored in a SQLite database, which can be easily accessed with Python or a GUI tool like DB Browser.