![]() That MongoDB’s full-text search is not proposed as a complete replacement of searchĮngine databases like Elastic, SOLR, etc. Using any of these solutions increases the architectural complexity of theĪpplication, since MongoDB now has to talk to an additional external database. ![]() Search-centric applications, there are alternative solutions like Elastic (like searching for ‘movies released in 2015’) or weighted searches.Īpart from these approaches, for more advanced and complex These expressions do not effectively utilize indexes.Īddition to that, none of these techniques can be used to perform any phrase searches Regular expressions is not efficient from the performance point of view, since Keyword searches would require the creation of multi-key indexes, which are not None of these approaches supports functionalities like stemming, stop words, However, using any of these approaches had its own limitations: Would either model our data to support keyword searches or use regular expressions for implementing such searchįunctionalities. have a common base stand.Ī relative ranking to measure which of the search results isīefore MongoDB came up with the concept of text indexes, we ![]() Stemming is the process of reducing the words to their stem.įor example: words like standing, stands, stood, etc. For example: a, an, the, is, at, which, etc. Stop words are the irrelevant words that should be filtered These terms are applicable to anyįull-text search implementation (and not MongoDB-specific). To full-text search which you should know. Search to find all the posts which contain the keyword cats in them or to be more complex, all the posts which haveīefore we move on, there are certain general terms related These search results are sorted by relevance based on theirĪnother example, consider a social networking site where the user can make a The search engine brings up results of all the articles related to the keywords/phrase you searched for (even if those keywords were used deep inside Getting back the relevant results sorted by their ranking.Īre some more scenarios where we would see a full-text search happening: Other search application) by entering certain string keywords/phrases and It is something similar to how we search any content on Google (or in fact any Mapping Relational Databases and SQL to MongoDBīefore we get into any details, let us look at some background.įull-text search refers to the technique of searching a full-text database against the search criteria specified by the user.If you are new to MongoDB, I recommend that you read theįollowing articles on Envato Tuts+ that will help you understand the basic concepts ![]() In this article we are going to explore the full-text searchįunctionalities of MongoDB right from fundamentals. This feature has nowīecome an integral part of the product (and is no longer an experimentalįeature). Version 2.4, MongoDB began with an experimental feature supporting Full-Text Search using Text Indexes. Scans and hence limiting the number of documents MongoDB searches. Indexes, which support efficient execution of queries by avoiding full-collection At the core of this fast performance lies MongoDB Is well known for its fast performance, flexible schema, scalability and MongoDB, one of the leading NoSQL databases, ![]()
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