Building a full-text search engine in 150 traces of Python code Bart de Goede

Full-text search is in all puts. From finding a information on Scribd, a movie on Netflix, rest room paper on Amazon, or anything else on the net by means of manner of Google (like [how to do your job as a software engineer](https://localghost.dev/2019/09/everything-i-googled-in-a-week-as-a-professional-software-engineer/)), you have got searched massive amounts of unstructured wisdom a quantity of cases as we discuss. What's a lot more glorious, is that you've got although you searched tens of millions (or [billions](https://www.worldwidewebsize.com/)) of knowledge, you purchased a reaction in milliseconds. In this publish, we are going to assemble a number one full-text search engine that can search all the way through tens of millions of forms and rank them in conserving with their relevance to the query in milliseconds, in not up to 150 traces of code!
Full-text search is everywhere. From finding a book on Scribd, a movie on Netflix, toilet paper on Amazon, or anything else on the web through Google (like [how to do your job as a software engineer](https://localghost.dev/2019/09/everything-i-googled-in-a-week-as-a-professional-software-engineer/)), you've searched vast amounts of unstructured data multiple times today. What's even more amazing, is that you've even though you searched millions (or [billions](https://www.worldwidewebsize.com/)) of records, you got a response in milliseconds. In this post, we are going to build a basic full-text search engine that can search across millions of documents and rank them according to their relevance to the query in milliseconds, in less than 150 lines of code!Original article