@imranbarbhuiya/mongoose-fuzzy-searching
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    Mongoose Fuzzy Searching

    Note

    *Notice: This is a fork of the original Mongoose Fuzzy Searching

    As that project is no longer maintained, So I'm going to work on it for my personal use. I'll recommend use the original one. Also if VassilisPallas starts maintaining the original project, I'll use that instead.

    mongoose-fuzzy-searching is simple and lightweight plugin that enables fuzzy searching in documents in MongoDB. This code is based on this article.

    npm: npm docs: docs

    Features

    Install

    Install using npm

    npm i @imranbarbhuiya/mongoose-fuzzy-searching

    or using yarn

    yarn add @imranbarbhuiya/mongoose-fuzzy-searching

    Getting started

    Initialize plugin

    const mongoose = require("mongoose");
    
    return mongoose.connect(URL);

    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 mongoose_fuzzy_searching = require("@imranbarbhuiya/mongoose-fuzzy-searching");
    
    const UserSchema = new Schema({
      firstName: String,
      lastName: String,
      email: String,
      age: Number,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      fields: ["firstName", "lastName"],
    });
    const User = mongoose.model("User", UserSchema);
    module.exports = { User };
    const user = new User({
      firstName: "Joe",
      lastName: "Doe",
      email: "joe.doe@mail.com",
      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": "joe.doe@mail.com",
      //   "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);
    }

    Example with typescript

    import mongoose_fuzzy_searching, {MongoosePluginModel} from "@imranbarbhuiya/mongoose-fuzzy-searching";
    
    export interface IUser extends mongoose.Document{
      firstName: string;
      lastName: string;
    }
    
    const UserSchema<IUser> = new Schema({
      firstName: String,
      lastName: String,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      fields: ["firstName"],
    });
    
    const UserModel = mongoose.model<IUser>("User", UserSchema) as MongoosePluginModel<IUser>

    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 mongoose_fuzzy_searching = require("@imranbarbhuiya/mongoose-fuzzy-searching");
    
    const UserSchema = new Schema({
      firstName: String,
      lastName: String,
      email: String,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      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 mongoose_fuzzy_searching = require('mongoose-fuzzy-searching');
    
    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(mongoose_fuzzy_searching, {
      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'],
        },
      ],
    });

    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", ...)
    • 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 mongoose_fuzzy_searching = require("@imranbarbhuiya/mongoose-fuzzy-searching");
    
    const UserSchema = new Schema({
      firstName: String,
      lastName: String,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      fields: ["firstName"],
      middlewares: {
        preSave: function () {
          // do something before the object is saved
        },
      },
    });

    Middlewares can also be asynchronous functions:

    const mongoose_fuzzy_searching = require('mongoose-fuzzy-searching');
    
    const UserSchema = new Schema({
      firstName: String,
      lastName: String,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      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);
      }
    });

    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 mongoose_fuzzy_searching = require("@imranbarbhuiya/mongoose-fuzzy-searching");
    
    const UserSchema = new Schema({
      firstName: String,
      lastName: String,
      email: String,
      age: Number,
    });
    
    UserSchema.plugin(mongoose_fuzzy_searching, {
      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 cursor = Model.find().cursor();
    cursor.next(function (error, doc) {
      const obj = attrs.reduce((acc, attr) => ({ ...acc, [attr]: doc[attr] }), {});
      return Model.findByIdAndUpdate(doc._id, obj);
    });

    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 cursor = Model.find().cursor();
    cursor.next(function (error, doc) {
      const $unset = attrs.reduce(
        (acc, attr) => ({ ...acc, [`${attr}_fuzzy`]: 1 }),
        {}
      );
      return Model.findByIdAndUpdate(
        data._id,
        { $unset },
        { new: true, strict: false }
      );
    });

    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

    License

    MIT License

    Credit

    Credit goes to the original package owner VassilisPallas

    Install

    npm i @imranbarbhuiya/mongoose-fuzzy-searching

    DownloadsWeekly Downloads

    53

    Version

    3.0.5

    License

    MIT

    Unpacked Size

    82 kB

    Total Files

    40

    Last publish

    Collaborators

    • imranbarbhuiya