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Chapter 7

Boosting Node.js and MongoDB with Mongoose

I first learned about Mongoose when I worked at Storify. Mongoose is a fully developed object document mapping (ODM) library for Node.js and MongoDB. We used it to simplify business logic in our Node API apps. We had a lot of connections between different database documents and Mongoose models allows us to save all the related logic. Mongoose worked fine except for one extra complex query which I wrote using native driver, not Mongoose.

The disadvantage of Mongoose is that it could make certain queries slower due to a lot of code that Mongoose has to go through. Contrary, the advantages of using ODM are many and go far beyond code organization or the ease of development. Typical ODM is a crucial piece of modern software engineering, especially enterprise engineering.

The main benefit of Mongoose is that it abstracts everything from the database, and the application code interacts only with objects and their methods. ODM also allows specifying relationships between different types of objects and putting business logic (related to those objects) in the classes.

In addition, Mongoose has built-in validation and type casting that can be extended and customized according to needs. When used together with Express.js, Mongoose makes the stack truly adherent to the MVC concept.

Also, Mongoose uses a similar interface to those of Mongo shell, native MongoDB driver, and Mongoskin. Mongoose provides its own methods while making available methods from the native driver. The main Mongoose functions such as find, update, insert, save, remove, and so on, do what you they say they do. It'll help us to get started with Mongoose faster.

Buckle up because in this chapter we learn at the following:

  • Mongoose installation
  • Connection establishment in a standalone Mongoose script
  • Mongoose schemas
  • Hooks for keeping code organized
  • Custom static and instance methods
  • Mongoose models
  • Relationships and joins with population
  • Nested documents
  • Virtual fields
  • Schema type behavior amendment
  • Express.js + Mongoose = true MVC

The source code for this chapter is in the code/ch7/blog-express directory of the practical node GitHub repository (https://github.com/azat-co/practicalnode).

Mongoose Installation

First, we should install Mongoose with npm. Among many variations, this is one of the ways we can install Mongoose 4.13.0 into an empty folder:

$ npm init -y
$ npm i mongoose@4.13.0 -SE

DB Connection in a Standalone Mongoose Script

Mongoose can be used as a standalone MongoDB library. To illustrate this, here’s a banal script that establishes a connection, creates a Mongoose model definition, instantiates the practicalNodeBook object, and then saves it to the database.

Let's create a rather simple mongoose-example (that's in the folder in code/ch7). To have access to the library, we need to include the mongoose module in our program:

const mongoose = require('mongoose')

Unlike the Node.js native MongoDB driver, which requires us to write a few lines of code, Mongoose can connect to the database server in one line. Mongoose requests are buffered, so we don’t have to wait for the established connection like we do with the native driver, which requires developers to put all the code in the callback of open().

To connect to DB, just call mongoose.connect() with at least the uri argument (first) or with optional options and callback (second and third). The uniform resource identifier (URI), a.k.a. a connection string, is the only required parameter. It follows a standard format of:

mongodb://username:password@host:port/database_name

In our example we use the default values. The host is localhost, and the port is 27017. The database name is test while there's no password or username:

mongoose.connect('mongodb://localhost:27017/test', {useMongoClient: true})
mongoose.Promise = global.Promise

The line with Promise makes Mongoose use native ES6 promise implementation. Developers can supply another promise implementation if they want (for example, flow bluebird).

For situations that are more advanced, options and callbacks can be passed to connect. The options object supports all properties of the native MongoDB driver (http://mongodb.github.io/node-mongodb-native/driver-articles/mongoclient.html#mongoclient-connect-options).

Note It’s a common practice in Node.js apps (and Mongoose) to open a database connection once, when the program starts, and then to keep it open until termination. This applies to web apps and servers as well. Ergo, there's no need to open and close connections.

This is easy so far, right? The next step is an important distinction that Mongoose introduces compared with Mongoskin and other lightweight MongoDB libraries. The step creates a model with the model() function by passing a string and a schema (more on schemas later). The model is usually stored in a capitalized literal:

const Book = mongoose.model('Book', { name: String })

Now the configuration phase is over, and we can create a document that represents a particular instance of the model Book:

const practicalNodeBook = new Book({ name: 'Practical Node.js' })

Mongoose documents come with very convenient built-in methods (http://mongoosejs.com/docs/api.html#document-js) such as validate, isNew, update, and so on. Just keep in mind that these methods apply to this particular document, not the entire collection or model.

The difference between documents and models is that a document is an instance of a model; a model is something abstract. It’s like your real MongoDB collection, but it is supported by a schema and is presented as a Node.js class with extra methods and attributes. Collections in Mongoose closely resemble collections in Mongoskin or the native driver. Strictly speaking, models, collections, and documents are different Mongoose classes.

Usually we don’t use Mongoose collections directly, and we manipulate data via models only. Some of the main model methods look strikingly familiar to the ones from Mongoskin or native MongoDB driver, such as find, insert(), save(), and so forth.

To finish our small script and make it write a document to the database, let’s use one of the document methods—document.save(). This method is a document methods that will save the document into the database. The method is asynchronous, which by now you know will require a callback (or a promise or an async/await function). The method's callback has an error-first signature:

practicalNodeBook.save((err, results) => {
  if (err) {
    console.error(err)
    process.exit(1)
  } else {
    console.log('Saved: ', results)
    process.exit(0)
  }
})

Here is the full source code for the mongoose.js file from the code/ch7/mongoose-example, which creates a new document with the property name:

const mongoose = require('mongoose')
mongoose.connect('mongodb://localhost:27017/test', {useMongoClient: true})
mongoose.Promise = global.Promise
const Book = mongoose.model('Book', { name: String })

const practicalNodeBook = new Book({ name: 'Practical Node.js' })
practicalNodeBook.save((err, results) => {
  if (err) {
    console.error(err)
    process.exit(1)
  } else {
    console.log('Saved: ', results)
    process.exit(0)
  }
})

To run this snippet, execute the $ node mongoose.js command (MongoDB server must be running in parallel). The results of the script should output the newly created object with its ObjectId, as seen in Figure 7-1.

alt

Figure 7-1. Running a standalone Mongoose script that creates objects

So far, our schema was very basic. It had only one field name with the type String. Next we'll study what other type of fields are supported.

Mongoose Schemas

Schema is a JSON-ish class that has information about properties/field types of a document. It also can store information about validation and default values, and whether a particular property is required. Schemas can contain business logic and other important information. In other words, schemas serve as blueprints for documents.

Schemas include validation and enables more robust adherence to the data structure. This is a major benefit. For example, upon saving a document, Mongoose will ignore fields that are not in the schema. Or as another example, Mongoose will not save a document when fields required in its schema are missing from the document.

To work with Mongoose, developers use documents (it's ODM after all), but Mongoose documents and models require schemas. That's why first developers create schemas to define models, which they in turn use to create documents.

Thus, before we can use models properly, we need to define their schemas, e.g., the book schema with the name property of string type can be defined right in the model as you saw before or by itself with the Schema method from mongoose. Simply invoke Schema with an object and save it in a variable:

const bookSchema = mongoose.Schema({
  name: String
})

Warning Mongoose ignores those properties that aren’t defined in the model's schema but allows the documents to be created, updated, or saved. On the other hand, any violation of a type or omission of a required field will lead to an error and the document NOT being saved, updated, or created.

Mongoose Schema supports various types of data. Some of these types are similar to JavaScript and thus Node types, but some are new. These are the Mongoose data types:

  • String: A standard JavaScript/Node.js string (a sequence of characters) type
  • Number: A standard JavaScript/Node number type up to 253 (64-bit); larger numbers with mongoose-long https://www.npmjs.org/package/mongoose-long and https://github.com/aheckmann/mongoose-long
  • Boolean: A standard JavaScript/Node Boolean type—true or false
  • Buffer: A Node.js binary type (images, PDFs, archives, and so on)
  • Date: An ISODate-formatted date type, such as 2014-12-31T12:56:26.009Z
  • Array: A standard JavaScript/Node array type
  • Schema.Types.ObjectId A typical, MongoDB 24-character hex string of a 12-byte binary number (e.g., 52dafa354bd71b30fa12c441)
  • Schema.Types.Mixed: Any type of data (i.e., flexible free type)

Warning Mongoose does not listen to mixed-type object changes, so call markModified() before saving the object to make sure changes in the mixed-type field are persistent.

ObjectId is added automatically as a primary _id key if omitted in the insert() or save() methods; _id key can be used to sort documents chronologically (http://docs.mongodb.org/manual/reference/object-id). They are available through Schema.Types or mongoose.Schema.Types, e.g., Schema.Types.Mixed.

We have a great deal of flexibility in defining our document schemas—for example, here's a schema with strings, dates, buffers, objects (mixed type), and ObjectIds. Moreover, you can set the default values right there in the schema. Default values simplify development because they allow to omit values. How? Default values will be used when no values are provided.

But that's not all. We can define a function as a default value too. This is a dynamic way to set the value. Finally, using [] means the fields, value will be an array with each individual item of that array having the type specified in the square braces []. For example, contributors is an array of ObjectIds (referring to the collection of contributors).

const ObjectId = mongoose.Schema.Types.ObjectId
const Mixed = mongoose.Schema.Types.Mixed

const bookSchema = mongoose.Schema({
  name: String,
  created_at: Date,
  updated_at: {
    type: Date, 
    default: Date.now // Current timestamp
  },
  published: Boolean,
  authorId : { 
    type: ObjectId, 
    required: true // Require field
  },
  description: { 
    type: String, 
    default: null 
  },
  active: {
    type: Boolean, 
    default: false
  },
  keywords: { // Array of strings
    type: [String], 
    default: [] 
  },
  description: {
    body: String,
    image: Buffer // Binary or string data
  },
  version: {
    type: Number, 
    default: () => { // Dynamic value
      return 1
    }
  },
  notes: Mixed,
  contributors: [ObjectId]
})

It’s possible to create and use custom types that already have the rules for the ubiquitous email and URL types, e.g., there’s a module mongoose-types (https://github.com/bnoguchi/mongoose-types).

Mongoose schemas are pluggable, which means, by creating a plugin, certain functionality can be extended across all schemas of the application.

For better code organization and code reuse, in the schema, we can set up static and instance methods, apply plugins, and define hooks.

Tip For validation in Node.js in addition to Mongoose and before it, consider using the validator.js and express-validator modules.

Hooks for Keeping Code Organized

In a complex application with a lot of interrelated objects, we might want to execute certain logic before saving an object. Hooks are a good place to store such logic. For example, we might want to upload a PDF to the web site before saving a book document:

bookSchema.pre('save', (next) => {
  // Prepare for saving
  // Upload PDF 
  return next()
})

On the other hand, before removing, we need to make sure there are no pending purchase orders for this book:

bookSchema.pre('remove', (next) => {
  // Prepare for removing
  return next(e) // e is an instance of Error or null
})

Developers can set up pre and post hooks on save, remove, and validate as well as on custom methods.

Custom Static and Instance Methods

In addition to dozens of built-in Mongoose model methods, we can add custom ones. For example, to initiate a purchase, we can call the buy method on the document practicalNodeBook after we implement the custom instance method buy():

bookSchema.method({ // Instance methods
  buy: function (quantity, customer, callback) {
    const bookToPurchase = this // Exact book with id, title, etc.
    // Create a purchase order and invoice customer
    // Any document/instance method like save, valid, etc. will work on "this"
    return callback(results)
  },
  refund: function (customer, callback) {
    // Process the refund
    return callback(results)
  }
})

The custom instance methods are better to use instead of re-implementing the same logic over and over again.

On the other hand, there are static methods. Static methods are useful when we either don’t have a particular document object or we don’t need it. For example, we don't need a particular book ID to run a report to get how much books have 0 inventory in the warehouse or to get how many books of a particular kind we have in the store:

bookSchema.static({ // Static methods for generic, not instance/document specific logic
  getZeroInventoryReport: function(callback) {
    // Run a query on all books and get the ones with zero inventory
    // Document/instance methods would not work on "this"
    return callback(books)
  },
  getCountOfBooksById: function(bookId, callback){
    // Run a query and get the number of books left for a given book
    // Document/instance methods would not work on "this"
    return callback(count)
  }
})

Note Hooks and methods must be added to the schemas before compiling them to models—in other words, before calling the mongoose.model() method.

Mongoose Models

As in many other ORMs/ODMs, in Mongoose, the cornerstone object is a model. To compile a schema into a model, use mongoose.model(name, schema). For example, to create a book model from bookSchema, use mongoose.model:

const Book = mongoose.model('Book', bookSchema)

The first parameter is just a string, which we can use later to pull an instance of this model. Usually, this string is the same as the object literal for the model. It's usually capitalized, e.g., Book. By default, Mongoose will use the model name to tie it to a collection name by pluralizing it. For example, the Book model will use books collection.

Models are used to create documents (actual data). To do so, call new ModelName(data)—for example, this is how to create two documents for two different books using one Book model:

const practicalNodeBook = new Book({ name: 'Practical Node.js' })
const javascriptTheGoodPartsBook = new Book({ name: "JavaScript The Good Parts"})

It’s better to assign the initial value through the constructor new Book() versus using the document.set() method later, because Mongoose has to process fewer function calls and our code remains more compact and better organized. Of course, this is possible only if we know the values when we create the instances. ;-)

Don’t confuse static with instance model methods. If we call a method on practicalNodeBook, it’s an instance method; if we call it on the Book object, it’s a static class method.

Models have static built-in methods that are very similar to Mongoskin and native MongoDB methods, such as find(), create(), and update().

A list of the static Mongoose model methods (invoked on a capitalized object, e.g., Book) along with their meaning, follows:

  • Model.create(data, [callback (error, doc)]): Creates a new Mongoose document and saves it to the database
  • Model.remove(query, [callback(error)]): Removes documents from the collection that match the query; when finished, calls callback with error
  • Model.find(query, [fields], [options], [callback(error, docs)]): Finds documents that match the query (as a JSON object); possible to select fields (http://mongoosejs.com/docs/api.html#query_Query-select) and use options (http://mongodb.github.io/node-mongodb-native/api-generated/collection.html#find)
  • Model.update(query, update, [options], [callback(error, affectedCount, raw)]): Updates documents, similar to native update
  • Model.populate(docs, options, [callback(error, doc)]): Populates documents using references to other collections; an alternative to another approach described in the next section
  • Model.findOne(query, [fields], [options], [callback(error, doc)]): Finds the first document that matches the query
  • Model.findById(id, [fields], [options], [callback(error, doc)]): Finds the first element for which _id equals the id argument (cast based on the schema)
  • Model.findOneAndUpdate([query], [update], [options], [callback(error, doc)]): Finds the first document that matches the query (if present) and updates it, returning the document; uses findAndModify (http://mongodb.github.io/node-mongodb-native/api-generated/collection.html#findandmodify)
  • Model.findOneAndRemove(query, [options], [callback(error, doc)]): Finds the first document that matches the query and removes it when returning the document
  • Model.findByIdAndUpdate(id, [update], [options], [callback(error, doc)]): Similar to findOneAndUpdate using only the ID
  • Model.findByIdAndRemove(id, [options], [callback(error, doc)]): Similar to findOneAndRemove using only the ID

Warning Not all the Mongoose model methods trigger hooks. Some of them are executed directly. For example, calling Model.remove() does not trigger the remove hook, because no Mongoose documents are involved (instances of Model that use lowercase literals, e.g., practicalNodeBook).

The complete list of the methods is extensive; therefore, refer to the official Mongoose API documentation (http://mongoosejs.com/docs/api.html#model-js). The most used instance (document) methods are as follows:

  • doc.model(name): Returns another Mongoose model
  • doc.remove([callback(error, doc)]): Removes this document
  • doc.save([callback(error, doc, affectedCount)]): Saves this document
  • doc.update(doc, [options], [callback(error, affectedCount, raw)]): Updates the document with doc properties, and options parameters, and then upon completion fires a callback with error, number of affectedCount, and the database output
  • doc.toJSON([option]): Converts a Mongoose document to JSON (options are listed later)
  • doc.toObject([option]): Converts a Mongoose document to a plain JavaScript object (options are listed later)
  • isModified([path]): True/false, respectively, if some parts (or the specific path) of the document are or are not modified
  • markModified(path): Marks a path manually as modified, which is useful for mixed (Schema.Types.Mixed) data types because they don't trigger the modified flag automatically
  • doc.isNew: True/false, respectively, whether the document is new or not new
  • doc.id: Returns the document ID
  • doc.set(path, value, [type], [options]): Sets value at a path
  • doc.validate(callback(error)): Checks validation manually (triggered automatically before save())

Most often, you'll need to get data from your document, e.g., to send back to a client using res.send(). But the document object will have some additional Mongoose properties and methods. The two methods listed above will help you to get just the data. They are toObject() and toJSON(). They take options, listed for toObject() and toJSON() are as follows:

  • getters: True/false, calls all getters including path and virtual types
  • virtuals: True/false, includes virtual getters and can override the getters option
  • minimize: True/false, removes empty properties/objects (defaults to true)
  • transform: Transforms the function called right before returning the object

That's it for Mongoose methods for the most part. Of course, Mongoose has other methods for more edge case scenarios and advanced uses. You can learn about them by opening this Mongoose document API link: http://mongoosejs.com/docs/api.html#document-js.

Relationships and Joins with Population

Although, Node developers cannot query MongoDB on complex relationships (like they can in MySQL), they can do so in the application layer with the help of Mongoose. This becomes really convenient in larger applications because they tend to have complex relationships between documents.

To give you an example, in an e-commerce store, an order refers to its products by IDs. To get more than just product ID, developers need to write two queries: one to fetch order and another to fetch its products. Instead of making two queries developers can one Mongoose query. They can use Mongoose to fetch order with products fields.

Mongoose makes connecting two entities by their relationship easier. Mongoose provides a feature called population. No. This population is not about people living in a certain area but it somehow related. Mongoose population is about adding more data to your query using relationships. It allows us to fill certain parts of the document from a different collection.

Let’s say we have posts and users documents. Users can write posts. There are two approaches to implement this. We can use one collection. The users collection can have the posts array field. This will require a single query, but this structure is limited in many ways because posts cannot be indexed or accessed separately from users.

Or we can use two collections (and models). In this case, the structure is more flexible but requires at least two queries if we want to fetch a user and his posts.

Don't fret. Mongoose is here to help. We can reference posts in the user schema and then populate the posts. In order to use populate(), we must define ref and the name of the model such as in the posts field of userSchema:

const mongoose = require('mongoose')
const Schema = mongoose.Schema
  
const userSchema = Schema({
  _id: Number,
  name: String,
  posts: [{ 
    type: Schema.Types.ObjectId, 
    ref: 'Post' 
  }]
})

The actual postSchema does not have any mentions about the user model. It just has some string fields:

const postSchema = Schema({
  _creator: { type: Number, ref: 'User' },
  title: String,
  text: String
})

The next few lines are where we create models, and then bang! We can pull posts data with a single query, not two as we would have done without referencing and without populate(). Here's how to construct the query and then call exec() to run it:

const Post  = mongoose.model('Post', postSchema)
const User = mongoose.model('User', userSchema)

User.findOne({ name: /azat/i })
  .populate('posts')
  .exec((err, user) => {
    if (err) return handleError(err) // Defined elsewhere
    console.log('The user has % post(s)', user.posts.length)
  })

Note ObjectId, Number, String, and Buffer are valid data types to use as references, meaning they will work as foreign keys in the relational DB terminology.

In the previous query, we used a regular expression (RegExp) /azat/i, which means "Find me all the names matching the string azat case-insensitively". This feature is not exclusive to Mongoose. In fact, the native driver and its other wrappers, along with the mongo console, all support RegExps. The syntax is the same as in normal JavaScript/Node.js RegExp patterns. Therefore, in a way, we perform a join query on our Post and User models.

Okay. It’s possible to return only a portion of populated results. For example, we can limit the number of posts to the first ten (10) only:

  .populate({     
    path: 'posts',
    options: { limit: 10, sort: 'title' }
  })

Sometimes it’s more practical to return only certain fields instead of the full document. This can be done with select:

  .populate({
      path: 'posts',
      select: 'title',
      options: { 
        limit: 10, 
        sort: 'title' 
      }
    })

In addition, Mongoose can filter the populated results by a query! For example, we can apply RegExp for “node.js” to the text (a match query property):

  .populate({
    path: 'posts',
    select: '_id title text',
    match: {text: /node\.js/i},
    options: { 
      limit: 10, 
      sort: '_id' 
    }
  })

The query selects properties using select by the field names of _id, title, text. You see, queries can be as customized as you want them to be! The best practice is to query and populate only the required fields because this avoids potential leakage of sensitive information and reduces overhead on the system.

The populate method also works on multiple document queries. For example, we can use find instead of findOne:

User.find({}, {
    limit: 10, 
    sort: { _id: -1}})
  .populate('posts')
  .exec((err, user) => {
    if (err) return handleError(err)
    console.log('The user has % post(s)', user.posts.length)
  })

Tip For custom sorting, we can add properties using name: -1 or name: 1 patterns and can pass the resulting object to the sort option. Again, this is a standard MongoDB interface and is not exclusive to Mongoose.

Nested Documents

In the previous section, we saw how to populate a query on one collection with the data from another collection. That's a more traditional approach to designing your database in the sense that it mimics relational database design with its normal forms and strict atomization of data.

The document storage model in NoSQL databases is well suited to use nested documents. This is better when you know what queries are run most frequently. You can optimize your database to make it be biased to a certain query. For example, if we know that the most typical use case is to read user profiles, then instead of having two collections—posts and users—we can have a single collection (users), with each item of that collection having posts.

The decision whether to use separate collections or nested documents is more of an architectural question, and its answer depends on usage. For example, if posts are used only in the context of users (their authors)—say, on the users’ profile pages—then it’s best to use nested documents. However, if the blog features multiple users’ posts that need to be queried independently of their (posts) user context, then separate collections fit better.

To implement nested documents, we can use the type Schema.Types.Mixed in Mongoose schemas (Schema, e.g., bookSchema or postSchema) or we can create a new schema for the nested document. An example of the former approach is as follows:

const userSchema = new mongoose.Schema({
  name: String,
  posts: [mongoose.Schema.Types.Mixed]
})
// Attach methods, hooks, etc.
const User = mongoose.model('User', userSchema)

However, the latter approach of using a distinct new subschema is more flexible and powerful. Take a look at the next example in which we define two schemas and then one is used in an array field of another schema. This approach is better for code reuse because it lets you to use the nested schema elsewhere, maybe in several more schemas.

Here I nested postSchema in an array field of userSchema, because users can have posts, and querying by users is the most typical use case for this app:

const postSchema = new mongoose.Schema({
  title: String,
  text: String
})
// Attach methods, hooks, etc., to post schema
const userSchema = new mongoose.Schema({
  name: String,
  posts: [postSchema]
})
// Attach methods, hooks, etc., to user schema
const User = mongoose.model('User', userSchema)

To create a new user document or to save a post to an existing user when working with a nested posts document, treat the posts property as an array and just use the push method from the JavaScript/Node.js API, or use the MongoDB $push operand (http://docs.mongodb.org/manual/reference/operator/update/push/).

For example, we can use MongoDB's $push in the update() query to add a post (newPost) to a user object, which is found by a matching ID (_id is userId):

User.update(
  {_id: userId}, 
  {$push: {posts: newPost}}, 
  function (error, results) {
    // Handle error and check results
})

Fields can be like ghosts. Sometime you see 'em, other time you don't. Let's study yet another Mongoose feature—virtual fields.

Virtual Fields

Virtual fields (or virtuals) are fields that don’t exist in the database, but act just like regular fields in a Mongoose document when accessed in a document. To oversimplify, virtual fields are mock or fake fields that pretend to act like and be normal ones.

Virtual fields are awesome for dynamic data or creating aggregate fields. For example, if our system requires to have first name, last name, and the full name (which is just a concatenation of the first two names)—there’s no need to store the full name values in addition to the first and last name values! All we need to do is concatenate the first and last name in a full-name virtual field.

Another use case is to make the database backward compatible. That's how I avoided writing and running database migrations at Storify. Every time there was a new DB schema, I just added a virtual to support old documents.

For example, we might have thousands of user items in a MongoDB collection, and we want to start collecting their locations. We have two options: run a migration script to add the default location (“none”) to the thousands of old user documents or use a virtual field and apply defaults at runtime!

To define a virtual we need to do two things:

  1. Call the virtual(name) method to create a virtual type (Mongoose API) (http://mongoosejs.com/docs/api.html#document-js).
  2. Apply a getter function with get(fn) (Mongoose API) (http://mongoosejs.com/docs/api.html#virtualtype_VirtualType-get).

As an example, let's build a Gravatar link generator to pull images from Gravatar. (http://en.gravatar.com is a service that hosts profile images, a.k.a., avatars, to be used universally by various websites.)

A Gravatar URL is always an md5 hash of the user’s email: (http://en.gravatar.com/site/implement/hash). This allows us to construct a Gravatar link for any user by his/her email. Therefore, we can get the virtual value (gravatarUrl) on the fly by hashing instead of storing the value (less overhead!).

In this example, I intentionally made the input email mixed cased and with a trailing space, and then applied core Node module crypto for the md5 hashing:

const crypto = require('crypto')

Identity.virtual('gravatarUrl')
  .get(function() { // Not fatty catty ()=>{}
    if (!this.email) return null // "this" is an instance/document
    let email = this.email // For example: email = "HI@azat.co "
    email = email.trim().toLowerCase()
    const hash = crypto
      .createHash('md5')
      .update(email)
      .digest('hex')
    const gravatarBaseUrl = 'https://secure.gravatar.com/avatar/'
    return gravatarBaseUrl + hash
  })

Or the case mentioned earlier, getting a full name out of first and last, is implemented by concatenating the names into one string, as follows:

userSchema.virtual('fullName')
  .get(function() {
    // "this" is an instance/document
    return `${this.firstName}  ${this.lastName}` 
  })

Another example is when only a subset of the full document must be exposed and not the full details, as in the user model, which has tokens and passwords. Thus we omit fields that we want to hide by whitelisting only the fields we want to expose, such as username and avatar, but not token, password, or salt:

userSchema.virtual('info')
  .get(function() {
    return {
      service: this.service,
      username: this.username,
      name: this.name,
      date: this.date,
      url: this.url,
      avatar: this.avatar
    }
  })

We used get for the virtual. Let's dig deeper into the getter, as well as it's close kin setter.

Schema Type Behavior Amendment

Schemas are not just static boring type definitions. Developers can add functions to bring the dynamism to the fields in the schema. Mongoose allows us to define/write getters (get), setters (set), and defaults (default) right in the Schema! Same goes for validate and some other useful methods.

get is invoked when a field is read, while set when the field is assigned a value. Developers can modify the actual value being read or assigned from/to the actual database document. For example, the URL field can have a set() method that enforces all strings into lowercase. Validate is triggered for the field validation and is typically used for some custom types such as emails.

Mongoose offers four methods: set(), get(), default() and validate(). They do what you think they do. Here are examples of defining methods and their purpose

  • set(): To transform a string to a lower case when the value is assigned
  • get(): To add a "thousands" comma to a number when the number is extracted/accessed
  • default(): To generate a new ObjectId,
  • validate(): To check for email patterns; is triggered upon save()

We can define the aforementioned four methods all right there, in the fields of the JSON-like Mongoose Schema on the same level as type:

postSchema = new mongoose.Schema({
  slug: { 
    type: String, 
    set: function(slug) { 
      return slug.toLowerCase()
    }
  },
  numberOfLikes: {
    type: Number,
    get: function(value) {
      return value.toString().replace(/\B(?=(\d{3})+(?!\d))/g, ",")
    }
  },  
  posted_at: { 
    type: String, 
    get: function(value) { 
      if (!value) return null;
      return value.toUTCString()
    }
  },  
  authorId: { 
    type: ObjectId, 
    default: function() { 
      return new mongoose.Types.ObjectId() 
    } 
  },
  email: { 
    type: String, 
    unique: true, 
    validate: [ 
      function(email) {
        return (email.match(/[a-z0-9!#$%&'*+\/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+\/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?/i) != null)}, 
      'Invalid email'
    ] 
  }
})

If defining custom methods in the Schema definition is not an option for some reason (maybe our system requires us to do it dynamically), Mongoose offers another approach to amending Schema behavior—chained methods, which require two steps:

  1. Use Schema.path(name) to get SchemaType (official docs) (http://mongoosejs.com/docs/api.html#schema_Schema-path).
  2. Use SchemaType.get(fn) to set the getter method (official docs) (http://mongoosejs.com/docs/api.html#schematype_SchemaType-get).

For example, we can create a getter method for the numberOfPosts field not in the Schema definition, but after userSchema is created:

userSchema
  .path('numberOfPosts')
  .get(function() {
    return this.posts.length
  })

In Mongoose, path is just a fancy name for the nested field name and its parent objects. For example, if we have ZIP code (zip) as a child of contact.address, such as user.contact.address.zip, then the contact.address.zip is a path.

Express.js + Mongoose = True MVC

To avoid rebuilding all other components unrelated to ODM, such as templates, routes, and so forth, we can factor the existing Blog from the previous chapter by making it use Mongoose instead of Mongoskin. This requires minimal effort but produces an abstraction layer between MongoDB and the request handlers. As always, the fully functional code is available on GitHub, in the ch7 folder. (https://github.com/azat-co/practicalnode/tree/master/ch7).

The process of refactoring starts with the creation of a new branch: mongoose. You can use the final solution in the GitHub repository. (https://github.com/azat-co/blog-express/tree/mongoose). First, we need to remove Mongoskin and install Mongoose:

$ npm uninstall mongoskin –save
$ npm install mongoose@4.13.0 --save

package.json is amended to include mongoose and looks similar to this:

{
  "name": "blog-mongoose",
  "version": "1.0.1",
  "private": true,
  "scripts": {
    "start": "make start",
    "seed": "sh ./seed.sh",
    "test": "make test",
    "st": "standard app.js && standard tests/index.js && standard routes/*"
  },
  "author": "Azat Mardan (http://azat.co/)",
  "license": "MIT",  
  "dependencies": {
    "body-parser": "1.18.2",
    "cookie-parser": "1.4.3",
    "errorhandler": "1.5.0",
    "everyauth": "0.4.9",
    "express": "4.16.2",
    "express-session": "1.15.6",
    "method-override": "2.3.10",
    "mongoose": "4.13.0",
    "morgan": "1.9.0",
    "pug": "2.0.0-rc.4",
    "serve-favicon": "2.4.5",
    "stylus": "0.54.5"
  },
  "devDependencies": {
    "expect.js": "0.3.1",
    "mocha": "4.0.1",
    "standard": "10.0.3",
    "superagent": "3.8.0"
  }
}

Now, in the app.js file, we can remove the Mongoskin inclusion (mongoskin = require('mongoskin'),) and add a new import statement for Mongoose:

const mongoose = require('mongoose')

Mongoose uses models, but Mongoskin does not. So let’s create a folder models in our project folder (use bash: $ mkdir models) and include the folder with (it really includes index.js, which we have yet to create):

const models = require('./models')

Substitute the Mongoskin db, and articles and users db.collection() statements shown next:

const db = mongoskin.db(dbUrl, {safe: true})
const collections = {
  articles: db.collection('articles'),
  users: db.collection('users')
}

with just the Mongoose connection statement, leaving out the collections object entirely because in Mongoose we'll be working with models not collections directly:

const db = mongoose.connect(dbUrl, {useMongoClient: true})

In the collection middleware, we remove if/else and req.collections lines inside the app.use():

app.use((req, res, next) => {
  if (!collections.articles || ! collections.users) // <--- REMOVE
    return next(new Error('No collections.'))  // <--- UPDATE
  req.collections = collections // <--- REMOVE
})

Then, add the if/else validation for Article and User models (coming from models/article.js and models/user.js), and the models in the request with the req.models = models statement:

app.use((req, res, next) => {
  if (!models.Article || !models.User) { // <--- ADD
    return next(new Error('No models.')) // <--- UPDATE
  }
  req.models = models // <--- ADD
  return next()
})

That’s it! The upgrade from Mongoskin to Mongoose is complete. ;-) For your reference, the full code of the resulting app.js is in the code/ch7/blog-mongoose/app.js.

Next, let's implement the schemas. In the Article schema, title is required and it's limited to 120 characters with validate. The published defaults to false if not specified upon object creation. The slug should never have spaces due to the set method.

To illustrate code reuse, we abstract the find method from the routes (routes/article.js) into the model (models/article.js). This can be done with all database methods:

articleSchema.static({
  list: function (callback) {
    this.find({}, null, {sort: {_id: -1}}, callback)
  }
})

Then, we compile the schema and methods into a model:

module.exports = mongoose.model('Article', articleSchema)

The full source code of article.js with schema and a static method is as follows:

const mongoose = require('mongoose')

const articleSchema = new mongoose.Schema({
  title: {
    type: String,
    required: true,
    validate: [function (value) {
      return value.length <= 120
    }, 'Title is too long (120 max)'],
    default: 'New Post'
  },
  text: String,
  published: {
    type: Boolean,
    default: false
  },
  slug: {
    type: String,
    set: function (value) {
      return value.toLowerCase().replace(' ', '-')
    }
  }
})

articleSchema.static({
  list: function (callback) {
    this.find({}, null, {sort: {_id: -1}}, callback)
  }
})

module.exports = mongoose.model('Article', articleSchema)

The models/user.js file also begins with an inclusion and a schema:

const mongoose = require('mongoose')

const userSchema = new mongoose.Schema({
  email: {
    type: String,
    required: true,
    set: function (value) { 
      return value.trim().toLowerCase() 
    },
    validate: [
      function (email) {
        return (email.match(/[a-z0-9!#$%&'*+\/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+\/=?^_`{|}~-]+)*@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?/i) != null)
      },
      'Invalid email'
    ]
  },
  password: String,
  admin: {
    type: Boolean,
    default: false
  }
})

module.exports = mongoose.model('User', userSchema)

The email field is validated with RegExp, and is then is trimmed and forced to lowercase when it’s set.

To connect app.js and models, there must be a models/index.js file that simply acts as a layer of abstraction by importing and exporting all the models:

exports.Article = require('./article')
exports.User = require('./user')

We have models/index.js so that we don't need to import all schemas individually in our app.js and other files (potentially).

Now we modify the routes files. The routes/article.js file now needs to switch to Mongoose models instead of Mongoskin collections. So, in the show method, this Mongoskin line goes away:

req.collections.articles.findOne({slug: req.params.slug}, 
  (error, article) => {

Then this Mongoose line comes in to use the Article model from req.models:

req.models.Article.findOne({slug: req.params.slug}, 
  (error, article) => {

The resulting show uses the Mongoose method findOne() from Article model and has slug presence validation before that:

exports.show = (req, res, next) => {
  if (!req.params.slug) return next(new Error('No article slug.'))
  req.models.Article.findOne({slug: req.params.slug}, (error, article) => {
    if (error) return next(error)
    if (!article.published && !req.session.admin) return res.status(401).send()
    res.render('article', article)
  })
}

In the list method, remove the Mongoskin code show next, since we are not working with collections directly anymore:

req.collections.articles.find({}).toArray((error, articles) => {

and replace it with Mongoose model code of Article.list():

req.models.Article.list((error, articles) => {

to get the request handler that resembles this:

exports.list = (req, res, next) => {
  req.models.Article.list((error, articles) => {
    if (error) return next(error)
    res.send({articles: articles})
  })
}

Next, in the exports.add method, find this line of Mongoskin code:

req.collections.articles.insert(
  article, 
  (error, articleResponse) => {

is replaced with this Mongoose code that uses the Article model instead of a collection:

req.models.Article.create(article, (error, articleResponse) => {

The exports.edit method is trickier, and there are a few possible solutions:

  1. Find a Mongoose document (e.g., findById()) and use document methods (e.g., update()).
  2. Use the static model method findByIdAndUpdate().

In both cases, this Mongoskin piece of code goes away:

req.collections.articles.updateById(
  req.params.id, 
  {$set: req.body.article}, 
  (error, count) => {

Although there's update() in Mongoose as well, we'll use another, better approach with save(), because save() executes all the schema logic such as pre and post hooks, and proper schema validation. It's smarter than the direct update(). save() is the special sauce that Mongoose brings to the table, and it's a pity not to harness its power. So the preceding Mongoskin snippet with updateById() is replaced by this code with Mongoose's set() and save():

exports.edit = (req, res, next) => {
  if (!req.params.id) return next(new Error('No article ID.'))
  if (!req.body.article) return next(new Error('No article payload.'))
  req.models.Article.findById(req.params.id, (error, article) => {
    if (error) return next(error)
    article.set(req.body.article)
    article.save((error, savedDoc) => {
      if (error) return next(error)
      res.send(savedDoc)
    })
  })
}

Just to show you a more elegant one-step approach that uses one method findByIdAndUpdate()(the latter from the new exports.edit implementation shown earlier):

req.models.Article.findByIdAndUpdate(
  req.params.id, 
  {$set: req.body.article}, 
  (error, doc) => {
    if (error) return next(error)
    res.send(doc)
  }
)

Lastly, in the exports.del request handler, we will find the document by its ID and then invoke remove():

exports.del = (req, res, next) => {
  if (!req.params.id) return next(new Error('No article ID.'))
  req.models.Article.findById(req.params.id, (error, article) => {
    if (error) return next(error)
    if (!article) return next(new Error('Article not found'))
    article.remove((error, doc) => {
      if (error) return next(error)
      res.send(doc)
    })
  })

The exports.postArticle and exports.admin functions look like these (the functions' bodies are the same as when we used Mongoskin):

req.models.Article.create(article, (error, articleResponse) => {
  // ...
})
req.models.Article.list((error, articles) => {
  // ...
})

Again, that’s all we have to do to switch to Mongoose for this route. However, to make sure there’s nothing missing, here’s the full code of the routes/article.js file:

exports.show = (req, res, next) => {
  if (!req.params.slug) return next(new Error('No article slug.'))
  req.models.Article.findOne({slug: req.params.slug}, (error, article) => {
    if (error) return next(error)
    if (!article.published && !req.session.admin) return res.status(401).send()
    res.render('article', article)
  })
}

exports.list = (req, res, next) => {
  req.models.Article.list((error, articles) => {
    if (error) return next(error)
    res.send({articles: articles})
  })
}

exports.add = (req, res, next) => {
  if (!req.body.article) return next(new Error('No article payload.'))
  var article = req.body.article
  article.published = false
  req.models.Article.create(article, (error, articleResponse) => {
    if (error) return next(error)
    res.send(articleResponse)
  })
}

exports.edit = (req, res, next) => {
  if (!req.params.id) return next(new Error('No article ID.'))
  if (!req.body.article) return next(new Error('No article payload.'))
  req.models.Article.findById(req.params.id, (error, article) => {
    if (error) return next(error)
    article.set(req.body.article)
    article.save((error, savedDoc) => {
      if (error) return next(error)
      res.send(savedDoc)
    })
  })
}

exports.del = (req, res, next) => {
  if (!req.params.id) return next(new Error('No article ID.'))
  req.models.Article.findById(req.params.id, (error, article) => {
    if (error) return next(error)
    if (!article) return next(new Error('Article not found.'))
    article.remove((error, doc) => {
      if (error) return next(error)
      res.send(doc)
    })
  })
}

exports.post = (req, res, next) => {
  if (!req.body.title) { res.render('post') }
}

exports.postArticle = (req, res, next) => {
  if (!req.body.title || !req.body.slug || !req.body.text) {
    return res.render('post', {error: 'Fill title, slug and text.'})
  }
  var article = {
    title: req.body.title,
    slug: req.body.slug,
    text: req.body.text,
    published: false
  }
  req.models.Article.create(article, (error, articleResponse) => {
    if (error) return next(error)
    res.render('post', {error: 'Article was added. Publish it on Admin page.'})
  })
}

exports.admin = (req, res, next) => {
  req.models.Article.list((error, articles) => {
    if (error) return next(error)
    res.render('admin', {articles: articles})
  })
}

The routes/index.js file, which serves the home page, is as follows:

exports.article = require('./article')
exports.user = require('./user')

exports.index = (req, res, next) => {
  req.models.Article.find(
    {published: true},
    null,
    {sort: {_id: -1}},
    (error, articles) => {
      if (error) return next(error)
      res.render('index', {articles: articles})
    }
  )
}

Finally, routes/user.js has a single line (JUST ONE LINE) to change in authenticate. Do this! Invoke findOne() from the req.models.User model to fetch the user with username and password (plain). This will check the user validity:

exports.authenticate = (req, res, next) => {
  if (!req.body.email || !req.body.password) { 
    return res.render('login', {error: 'Please enter your email and password.'}) 
  }
  req.models.User.findOne({
    email: req.body.email,
    password: req.body.password
  }, function (error, user) {
    if (error) return next(error)
    if (!user) return res.render('login', {error: 'Incorrect email&password combination.'})
    req.session.user = user
    req.session.admin = user.admin
    res.redirect('/admin')
  })
}

Of course, in real life you would not store plain passwords but use encrypted hash and salt. In other words, store salt and hash but never the plain password to prevent attackers stealing the plain passwords, which they can and will use on other websites. Most people can't remember more than 2-3 passwords, so they keep using the same ones everywhere. Gosh, they should download a password manager like Keepass, Padlock, enpass or something similar, to store unique 50-character passwords and randomly generated answers to silly questions like "What was the name of your first pet?".

To check if everything went well, simply run Blog as usual with $ node app and navigate the pages on http://localhost:3000/. In addition, we can run Mocha tests with $ npm test (which triggers a make command, which in turn triggers the mocha command).

Summary

In this chapter, we learned what Mongoose is, how to install it, how to establish a connection to the database, and how to create Mongoose schemas while keeping the code organized with hooks and methods. We also compiled schemas into models and populated references automatically, and used virtual fields and custom schema type properties. And we refactored Blog to use Mongoose and made our app gain a true MVC architecture.

Next, we'll cover how to build REST APIs with the two Node.js frameworks: Express.js and Hapi. This is an important topic, because more and more web development is shifting toward heavy front-end logic and thin backend. Some systems even go as far as building/using free-JSON APIs or back-as-a-service services. This tendency allows teams to focus on what is the most important for end users—user interface and features—as well as what is vital for businesses: reduced iteration cycles, and lower costs of maintenance and development.

Another essential piece in this puzzle is test-driven practice. To explore it, we'll cover Mocha, a widely used Node.js testing framework. Onward to REST APIs and TDD.