If you find this utility library useful, you can buy me a coffee to keep me energized for creating libraries like this.

Mongoose Fuzzy Searching

Harness the power of fuzzy logic with fuzzily-mongoose, an open source, simple and lightweight plugin that enables fuzzy searching in documents in MongoDB. This repo is a fork from VassilisPallas/mongoose-fuzzy-searching.

The reason for a fork and a new npm library is simply from the limitation that text query based on fuzzy logic scans all the documents in a given collection and only then you can filter out documents based on values of other fields. This makes the query inefficient. With the introduction of equalityPredicate, you can first filter out the documents and then perform a text query on the filtered documents. See Performance section for improvement in search with fuzzily-mongoose plugin. With the help of this plugin, you can enable partial text search efficiently in self hosted Mongodb installation without going for paid services of Mongodb Atlas or using solutions like Elasticsearch etc. This is very helpful for startups during initial days when cost is a concern and also for developers who are working on their own ideas.

• Features
• Install
• Getting started
  • Initialize plugin
  • Plugin options
    • Fields
      • String field
      • Object field
    • Equality Predicate
    • Middlewares
• Query parameters
  • Instance method
  • Query helper
• Working with pre-existing data
  • Update all pre-existing documents with ngrams
  • Delete old ngrams from all documents
• Testing and code coverage
  • All tests
  • Available test suites
• Performance
• License

Features

• Creates Ngrams for the selected keys in the collection
• Add fuzzySearch method on model
• Work with pre-existing data

Install

Install using npm

$ npm i fuzzily-mongoose

or using yarn

$ yarn add fuzzily-mongoose

Getting started

Initialize plugin

Before starting, for best practices and avoid any issues, handle correctly all the Deprecation Warnings.

In order to let the plugin create the indexes, you need to set useCreateIndex to true. The below example demonstrates how to connect with the database.

const options = {
  useNewUrlParser: true,
  useUnifiedTopology: true,
  useFindAndModify: false,
  useCreateIndex: true,
};

mongoose.Promise = global.Promise;
return mongoose.connect(URL, options);

In the below example, we have a User collection and we want to make fuzzy searching in firstName and lastName.

const { Schema } = require('mongoose');
const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
  email: String,
  age: Number,
});

UserSchema.plugin(fuzzily_mongoose, { fields: ['firstName', 'lastName'] });
const User = mongoose.model('User', UserSchema);
module.exports = { User };

const user = new User({ firstName: 'Joe', lastName: 'Doe', email: '[email protected]', age: 30 });

try {
  await user.save(); // mongodb: { ..., firstName_fuzzy: [String], lastName_fuzzy: [String] }
  const users = await User.fuzzySearch('jo');

  console.log(users);
  // each user object will not contain the fuzzy keys:
  // Eg.
  // {
  //   "firstName": "Joe",
  //   "lastName": "Doe",
  //   "email": "[email protected]",
  //   "age": 30,
  //   "confidenceScore": 34.3 ($text meta score)
  // }
} catch (e) {
  console.error(e);
}

The results are sorted by the confidenceScore key. You can override this option.

try {
  const users = await User.fuzzySearch('jo').sort({ age: -1 }).exec();
  console.log(users);
} catch (e) {
  console.error(e);
}

Plugin options

Options can contain fields and middlewares.

Fields

Fields attribute is mandatory and should be either an array of Strings or an array of Objects.

String field

If you want to use the default options for all your fields, you can just pass them as a string.

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
  email: String,
});

UserSchema.plugin(fuzzily_mongoose, { fields: ['firstName', 'lastName'] });

Object field

In case you want to override any of the default options for your arguments, you can add them as an object and override any of the values you wish. The below table contains the expected keys for this object.

| key | type | default | description | | ----------------------- | ----------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | name | String | null | Collection key name | | minSize | Integer | 2 | N-grams min size. Learn more about N-grams | | weight | Integer | 1 | Denotes the significance of the field relative to the other indexed fields in terms of the text search score. Learn more about index weights | | prefixOnly | Boolean | false | Only return ngrams from start of word. (It gives more precise results) | | escapeSpecialCharacters | Boolean | true | Remove special characters from N-grams. | | keys | Array[String] | null | If the type of the collection attribute is Object or [Object] (see example), you can define which attributes will be used for fuzzy searching |

Example:

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
  email: String,
  content: {
      en: String,
      de: String,
      it: String
  }
  text: [
    {
      title: String,
      description: String,
      language: String,
    },
  ],
});

UserSchema.plugin(fuzzily_mongoose, {
  fields: [
    {
      name: 'firstName',
      minSize: 2,
      weight: 5,
    },
    {
      name: 'lastName',
      minSize: 3,
      prefixOnly: true,
    },
    {
      name: 'email',
      escapeSpecialCharacters: false,
    },
    {
      name: 'content',
      keys: ['en', 'de', 'it'],
    },
    {
      name: 'text',
      keys: ['title', 'language'],
    },
  ],
});

Equality Predicate

equalityPredicate is an optional Object.

e.g.

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
  email: String,
  orgId: String
});

UserSchema.plugin(fuzzily_mongoose, { fields: ['firstName'], equalityPredicate: {orgId: 1} });

This will create a fuzzy text index:

key: { orgId: 1, _fts: 'text', _ftsx: 1 },
    name: 'fuzzy_text',

User.fuzzySearch({ query: 'jo', prefixOnly: true, minSize: 4 }, {orgId: 'ORG100'})
  .then(console.log)
  .catch(console.error);

The above code will first filter out documents based on org id and then run text query on filtered documents. This improves the number of documents scanned and hence improves the performance of query in a big way.

Middlewares

Middlewares is an optional Object that can contain custom pre middlewares. This plugin is using these middlewares in order to create or update the fuzzy elements. That means that if you add pre middlewares, they will never get called since the plugin overrides them. To avoid that problem you can pass your custom midlewares into the plugin. Your middlewares will be called first. The middlewares you can pass are:

• preSave
  • stands for schema.pre("save", ...)
• preInsertMany
  • stands for schema.pre("insertMany", ...)
• preUpdate
  • stands for schema.pre("update", ...)
• preUpdateOne
  • stands for schema.pre("updateOne", ...)
• preFindOneAndUpdate
  • stands for schema.pre("findOneAndUpdate", ...)
• preUpdateMany
  • stands for schema.pre("updateMany", ...)

If you want to add any of the middlewares above, you can add it directly on the plugin.

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
});

UserSchema.plugin(fuzzily_mongoose, {
  fields: ['firstName'],
  middlewares: {
    preSave: function () {
      // do something before the object is saved
    },
  },
});

Middlewares can also be asynchronous functions:

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
});

UserSchema.plugin(fuzzily_mongoose, {
  fields: ['firstName'],
  middlewares: {
    preUpdateOne: async function {
      // do something before the object is updated (asynchronous)
    }
  }
});

Query parameters

The fuzzy search query can be used either as static function, or as a helper, which let's you to chain multiple queries together. The function name in either case is surprise, surprise, fuzzySearch.

Instance method

Instance method can accept up to three parameters. The first one is the query, which can either be either a String or an Object. This parameter is required. The second parameter can either be eiter an Object that contains any additional queries (e.g. age: { $gt: 18 }), or a callback function. If the second parameter is the queries, then the third parameter is the callback function. If you don't set a callback function, the results will be returned inside a Promise.

The below table contains the expected keys for the first parameter (if is an object)

| key | type | deafult | description | | ---------- | ----------- | ----------- | --------------------------------------------------------------------------------- | | query | String | null | String to search | | minSize | Integer | 2 | N-grams min size. | | prefixOnly | Boolean | false | Only return ngrams from start of word. (It gives more precise results) the prefix | | exact | Boolean | false | Matches on a phrase, as opposed to individual terms |

Example:

/* With string that returns a Promise */
User.fuzzySearch('jo').then(console.log).catch(console.error);

/* With additional options that returns a Promise */
User.fuzzySearch({ query: 'jo', prefixOnly: true, minSize: 4 })
  .then(console.log)
  .catch(console.error);

/* With additional queries that returns a Promise */
User.fuzzySearch('jo', { age: { $gt: 18 } })
  .then(console.log)
  .catch(console.error);

/* With string and a callback */
User.fuzzySearch('jo', (err, doc) => {
  if (err) {
    console.error(err);
  } else {
    console.log(doc);
  }
});

/* With additional queries and callback */
User.fuzzySearch('jo', { age: { $gt: 18 } }, (err, doc) => {
  if (err) {
    console.error(err);
  } else {
    console.log(doc);
  }
});

Query helper

You can also use the query is a helper function, which is like instance methods but for mongoose queries. Query helper methods let you extend mongoose's chainable query builder API.

Query helper can accept up to two parameters. The first one is the query, which can either be either a String or an Object. This parameter is required. The second parameter can be an Object that contains any additional queries (e.g. age: { $gt: 18 }), which is optional. This helpers doesn't accept a callback function. If you pass a function it will throw an error. More about query helpers.

Example:

const user = await User.find({ age: { $gte: 30 } })
  .fuzzySearch('jo')
  .exec();

Working with pre-existing data

The plugin creates indexes for the selected fields. In the below example the new indexes will be firstName_fuzzy and lastName_fuzzy. Also, each document will have the fields firstName_fuzzy[String] and lastName_fuzzy[String]. These arrays will contain the anagrams for the selected fields.

const fuzzily_mongoose = require('fuzzily-mongoose');

const UserSchema = new Schema({
  firstName: String,
  lastName: String,
  email: String,
  age: Number,
});

UserSchema.plugin(fuzzily_mongoose, { fields: ['firstName', 'lastName'] });

In other words, this plugin creates anagrams when you create or update a document. All the pre-existing documents won't contain these fuzzy arrays, so fuzzySearch function, will not be able to find them.

Update all pre-existing documents with ngrams

In order to create anagrams for pre-existing documents, you should update each document. The below example, updates the firstName attribute to every document on the collection User.

const query = {};
let promiseArr = [];
let count = 0;

for await (const item of Model.find(query).cursor()) {
  const obj = attrs.reduce((acc, attr) => ({ ...acc, [attr]: item[attr] }), {});
  promiseArr.push(databaseService.findByIdAndUpdate(modelName, item._id, obj));

  if (count % 50 === 0) {
    await Promise.all(promiseArr);
    promiseArr = [];
  }

  count++;
}

if (promiseArr.length !== 0) {
  await Promise.all(promiseArr);
}

Delete old ngrams from all documents

In the previous example, we set firstName and lastName as the fuzzy attributes. If you remove the firstName from the fuzzy fields, the firstName_fuzzy array will not be removed by the collection. If you want to remove the array on each document you have to unset that value.

// const attrs = ['field_name'];
const query = {};
let promiseArr = [];
let count = 0;

for await (const item of Model.find(query).cursor()) {
  const $unset = attrs.reduce(
    (acc, attr) => ({ ...acc, [`${attr}_fuzzy`]: 1 }),
    {}
  );

  promiseArr.push(databaseService.update(modelName, item._id, { $unset }));

  if (count % 50 === 0) {
    await Promise.all(promiseArr);
    promiseArr = [];
  }

  count++;
}

if (promiseArr.length !== 0) {
  await Promise.all(promiseArr);
}

Testing and code coverage

All tests

We use jest for all of our unit and integration tests.

$ npm test

Note: this will run all suites serially to avoid mutliple concurrent connection on the db.

This will run the tests using a memory database. If you wish for any reason to run the tests using an actual connection on a mongo instance, add the environment variable MONGO_DB:

$ docker run --name mongo_fuzzy_test -p 27017:27017 -d mongo
$ MONGO_DB=true npm test

Available test suites

unit tests

$ npm run test:unit

Integration tests

$ npm run test:integration

Performance

Let us define a collection as below

const mongoose = require('mongoose');
const fuzzySearchPlugin = require('fuzzily-mongoose');

const productSchema = new mongoose.Schema({
  name: String,
  description: String,
  category: String,
});

productSchema.plugin(fuzzySearchPlugin, {
  fields: ['name'],  // Specify the fields for fuzzy searching
  equalityPredicate: { category: 1 }, // Predicate for filtering
});

const Product = mongoose.model('Product', productSchema);

Now insert data into this collection and perform query:

const results = await Product.fuzzySearch(
   { query: "Smartphone" },
   { category: "Electronics" }
);

console.log(results);

Without Equality Predicate (Old Approach)

In the old approach, MongoDB runs a fuzzy search across the entire collection, resulting in a high number of documents and keys being examined: Total Keys Examined: 36,854 Total Docs Examined: 24,298 Execution Time: 88 ms

These numbers represent the large search space MongoDB has to sift through, which slows down query performance, especially as the collection size grows.

With Equality Predicate (New Approach)

By applying the equality predicate and utilizing a compound text index, I was able to significantly narrow down the search space. Here's how the new approach performed: Total Keys Examined: 1739 Total Docs Examined: 566 Execution Time: 6 ms

The improvement here is substantial. By reducing the number of documents and keys MongoDB needs to examine, the query becomes far more efficient, leading to faster response times and reduced load on the database.

Read more in detail at Building Fuzzy Search in MongoDB: An Open-Source Solution

Comparison

Fuzzy search comparison - Compare both approaches for yourself by running the test cases we've set up. See the performance improvement in real-time!

Tech Blog

Read my blog at Tech Insights: Personal Tech Blog

License

MIT License

Copyright (c) 2024 Manish Prasad

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.

If you find this utility library useful, you can buy me a coffee to keep me energized for creating libraries like this.