Efficient Database Search using MongoDB’s Full Text Search (Mongoose)

por | 28 febrero, 2018

We all know how important it is to have a great search feature for any app. As more and more apps are seen everyday, the standard for a reasonable search feature keeps rising. A lot of us end up using elastic search or any of its alternatives. This post is for those who want an easier way for a quick application without using external search engines.

Some of the things our search feature should cover are:

  1. search the database — duh
  2. indexing database for speed — if it fits your application
  3. ignore stop words like ‘this’, ‘and’ etc.
  4. reduce words to its stem. e.g. ‘baking’ to ‘bake’
  5. scoring — that is ranking results based on certain rules
  6. rank the results according to the feilds they have matched up with
  7. rank results according to the percentage match with the search phrase
  8. search similar words, similar sounding words, similarly spelled words too.
  9. display partial word matches in the end
  10. Add auto-complete

That is a lot to do, I will give some pointers on how to go about it in this post.

Mongo has this awesome feature called ‘full-text search’ which covers most of these features for us. We are using Mongoose to access Mongo.


If you already know the fields which will hold the values which you may want to search, indexing the database leads us to faster search results with the drawback of a tiny overhead whenever we insert new records.

recipeSchema.index({‘name’: ‘text’, ‘tags’: ‘text’, ‘ingredients.ingredient’: ‘text’});

Stop words, Stemming and Scoring

Mongo’s full text takes care of these for us. By default, whenever we search a phrase using full-text-search, the results are ranked in decending order of matching scores.

.find( { $text: { $search : searchPhrase } },
  { score : { $meta: “textScore” } } )
.sort( {
  score: { $meta : ‘textScore’ }
} )

Rank results according to the fields with which they have matched up

Also called as adding weight to the fields, this can be done at the time of indexing.

recipeSchema.index({‘name’: ‘text’, ‘tags’: ‘text’, ‘ingredients.ingredient’: ‘text’}, {weights: {name: 3, tags: 2, ‘ingredients.ingredient’: 1}});

Similar words, Similar sounding words, Similarly spelled words

There are a lot of APIs which can help us with this functionality. I prefer datamuse API.

API paths:

words with a meaning similar to ringing in the ears: https://api.datamuse.com/words?ml=ringing+in+the+ears

words that are spelled similarly to coneticut: https://api.datamuse.com/words?sp=coneticut

words that sound like elefint: https://api.datamuse.com/words?sl=elefint

words that are triggered by (strongly associated with) the word “cow”: https://api.datamuse.com/words?rel_trg=cow

suggestions for the user if they have typed in rawand so far: https://api.datamuse.com/sug?s=rawand


Mongoose-In-Memory Auto-Complete is a great package which can help you here.