{ "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2-final" }, "orig_nbformat": 2, "kernelspec": { "name": "Python 3.8.2 64-bit ('stackoverflow': conda)", "display_name": "Python 3.8.2 64-bit ('stackoverflow': conda)", "metadata": { "interpreter": { "hash": "f3131ab0c53afd587e929ab5f3e0081bb3b1675889ab2c259292a9bd8773e05a" } } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "# Time series databases" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "Time series databases ([TSDB][1]) are specifically designed to store time series data, most importantly by storing time/value pairs. The use of time as a key index is perhaps the most important factor distinguishing them from, for example, relational ones.\n", "\n", "Because of the increased use of machine-generated data (e.g. IoT devices) they gained popularity relative to other database categories (by 180% over the last 24 months based on [DB-Engines][2])\n", "\n", "## Main types\n", "\n", "There are many different types of time series databases. As of October 2020 [DB-Engines][2] ranks the following ones as the top five:\n", "\n", "\n", "1. InfluxDB\n", "2. Kdb+\n", "3. Prometheus\t\n", "4. Graphite\t\n", "5. RRDtool\t\n", "\n", "\n", "[1]: https://db-engines.com/en/ranking/time+series+dbms\n", "[2]: https://en.wikipedia.org/wiki/Time_series_database" ], "cell_type": "markdown", "metadata": {} } ] }