How does Wikipedia sort though 5+ million articles to find the most relevant one for your research?
How does Facebook find the friend who you're looking for (and whose name you've misspelled), across a userbase of 2+ billion people?
How does Google search the entire internet for webpages relevant to your vague, typo-filled search query?
In this tutorial, we'll walk through setting up our own full-text search application (of an admittedly lesser complexity than the systems in the questions above). Our example app will provide a UI and API to search the complete texts of 100 literary classics such as Peter Pan, Frankenstein, and Treasure Island.
You can preview a completed version of the tutorial app here - https://search.patricktriest.com
To read more, visit - https://blog.patricktriest.com/text-search-docker-elasticsearch/
This application is 100% open-source, feel free to utilize the code however you would like.
The MIT License (MIT)
Copyright (c) 2018 Patrick Triest
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.