Įvadas į „Mongoose“, skirtą „MongoDB“

„Mongoose“ yra objektų duomenų modeliavimo (ODM) biblioteka, skirta „MongoDB“ ir „Node.js“. Jis valdo ryšius tarp duomenų, teikia schemos patvirtinimą ir yra naudojamas vertimui tarp objektų kode ir tų objektų atvaizdavimui MongoDB.

„MongoDB“ yra be schemų „NoSQL“ dokumentų duomenų bazė. Tai reiškia, kad joje galite saugoti JSON dokumentus, o šių dokumentų struktūra gali skirtis, nes ji nėra vykdoma kaip SQL duomenų bazės. Tai yra vienas iš „NoSQL“ naudojimo pranašumų, nes jis paspartina programų kūrimą ir sumažina diegimo sudėtingumą.

Toliau pateikiamas pavyzdys, kaip duomenys saugomi „Mongo“ ir „SQL“ duomenų bazėje:

Terminijos

Kolekcijos

„Kolekcijos“ mongo kalba yra lygiavertės reliacinių duomenų bazių lentelėms. Jie gali laikyti kelis JSON dokumentus.

Dokumentai

„Dokumentai“ yra lygiaverčiai SQL įrašams ar duomenų eilutėms. Nors SQL eilutėje galima nurodyti duomenis kitose lentelėse, „Mongo“ dokumentai paprastai juos sujungia dokumente.

Laukai

„Laukai“ arba atributai yra panašūs į SQL lentelės stulpelius.

Schema

Nors „Mongo“ neturi schemų, SQL schemą apibrėžia naudodamasis lentelės apibrėžimu. „Mongoose“ schema yra dokumento duomenų struktūra (arba dokumento forma), kuri yra vykdoma per programos sluoksnį.

Modeliai

„Modeliai“ yra aukštesnės eilės konstruktoriai, kurie imasi schemos ir sukuria dokumento egzempliorių, lygiavertį reliacinės duomenų bazės įrašams.

Darbo pradžia

„Mongo“ diegimas

Prieš pradėdami, nustatykime „Mongo“. Galite pasirinkti vieną iš šių variantų (šiame straipsnyje naudojame 1 parinktį):

  1. Iš „MongoDB“ svetainės atsisiųskite savo operacinei sistemai tinkamą „MongoDB“ versiją ir vykdykite jų diegimo instrukcijas
  2. „MLab“ sukurkite nemokamą „sandbox“ duomenų bazės prenumeratą
  3. Įdiekite „Mongo“ naudodami „Docker“, jei norite naudoti „Docker“

Peržiūrėkime kai kuriuos „Mongoose“ pagrindus, įgyvendindami modelį, kuris atspindi supaprastintos adresų knygos duomenis.

Aš naudoju „Visual Studio Code“, „Node 8.9“ ir „NPM 5.6“. Suaktyvinkite savo mėgstamą IDE, sukurkite tuščią projektą ir pradėkime! „Node“ naudosime ribotą ES6 sintaksę, todėl nekonfigūruosime „Babel“.

„NPM Install“

Eikime į projekto aplanką ir inicijuokime savo projektą

npm init -y

Įdiekime „Mongoose“ ir patvirtinimo biblioteką naudodami šią komandą:

npm install mongoose validator

Pirmiau nurodyta diegimo komanda įdiegs naujausią bibliotekų versiją. Šiame straipsnyje esanti „Mongoose“ sintaksė būdinga „Mongoose v5“ ir naujesnėms versijoms.

Duomenų bazių jungtis

Sukurkite failą ./src/database.jspagal projekto šaknį.

Tada pridėsime paprastą klasę su metodu, kuris prisijungs prie duomenų bazės.

Jūsų ryšio eilutė skirsis priklausomai nuo jūsų diegimo.

let mongoose = require('mongoose'); const server = '127.0.0.1:27017'; // REPLACE WITH YOUR DB SERVER const database = 'fcc-Mail'; // REPLACE WITH YOUR DB NAME class Database { constructor() { this._connect() } _connect() { mongoose.connect(`mongodb://${server}/${database}`) .then(() => { console.log('Database connection successful') }) .catch(err => { console.error('Database connection error') }) } } module.exports = new Database()

require(‘mongoose’)aukščiau pateiktas skambutis grąžina „Singleton“ objektą. Tai reiškia, kad pirmą kartą paskambinus require(‘mongoose’)sukuriamas „Mongoose“ klasės egzempliorius ir jis grąžinamas. Vėlesnių skambučių metu jis grąžins tą patį egzempliorių, kuris buvo sukurtas ir grąžintas jums pirmą kartą dėl to, kaip modulių importavimas / eksportavimas veikia ES6.

Panašiai mes pavertėme savo duomenų bazės klasę pavieniu simboliu, module.exportssakinyje grąžindami klasės egzempliorių, nes mums reikia tik vieno ryšio su duomenų baze.

ES6 mums labai lengva sukurti pavienį (vieno egzemplioriaus) modelį dėl to, kaip modulio krautuvas veikia talpindamas anksčiau importuoto failo atsakymą.

„Mongoose Schema vs. Model“

„Mongoose“ modelis yra „Mongoose“ schemos apvalkalas. „Mongoose“ schema apibrėžia dokumento struktūrą, numatytąsias reikšmes, tikrintuvus ir kt., O „Mongoose“ modelis suteikia sąsają su duomenų baze, kad būtų galima kurti, pateikti užklausas, atnaujinti, ištrinti įrašus ir kt.

Sukurti „Mongoose“ modelį pirmiausia sudaro trys dalys:

1. Nurodo Mongoose

let mongoose = require('mongoose')

This reference will be the same as the one that was returned when we connected to the database, which means the schema and model definitions will not need to explicitly connect to the database.

2. Defining the Schema

A schema defines document properties through an object where the key name corresponds to the property name in the collection.

let emailSchema = new mongoose.Schema({ email: String })

Here we define a property called email with a schema type String which maps to an internal validator that will be triggered when the model is saved to the database. It will fail if the data type of the value is not a string type.

The following Schema Types are permitted:

  • Array
  • Boolean
  • Buffer
  • Date
  • Mixed (A generic / flexible data type)
  • Number
  • ObjectId
  • String

Mixed and ObjectId are defined under require(‘mongoose’).Schema.Types.

3. Exporting a Model

We need to call the model constructor on the Mongoose instance and pass it the name of the collection and a reference to the schema definition.

module.exports = mongoose.model('Email', emailSchema)

Let’s combine the above code into ./src/models/email.jsto define the contents of a basic email model:

let mongoose = require('mongoose') let emailSchema = new mongoose.Schema({ email: String }) module.exports = mongoose.model('Email', emailSchema)

A schema definition should be simple, but its complexity is usually based on application requirements. Schemas can be reused and they can contain several child-schemas too. In the example above, the value of the email property is a simple value type. However, it can also be an object type with additional properties on it.

We can create an instance of the model we defined above and populate it using the following syntax:

let EmailModel = require('./email') let msg = new EmailModel({ email: '[email protected]' })

Let’s enhance the Email schema to make the email property a unique, required field and convert the value to lowercase before saving it. We can also add a validation function that will ensure that the value is a valid email address. We will reference and use the validator library installed earlier.

let mongoose = require('mongoose') let validator = require('validator') let emailSchema = new mongoose.Schema({ email: { type: String, required: true, unique: true, lowercase: true, validate: (value) => { return validator.isEmail(value) } } }) module.exports = mongoose.model('Email', emailSchema)

Basic Operations

Mongoose has a flexible API and provides many ways to accomplish a task. We will not focus on the variations because that is out of scope for this article, but remember that most of the operations can be done in more than one way either syntactically or via the application architecture.

Create Record

Let’s create an instance of the email model and save it to the database:

let EmailModel = require('./email') let msg = new EmailModel({ email: '[email protected]' }) msg.save() .then(doc => { console.log(doc) }) .catch(err => { console.error(err) })

The result is a document that is returned upon a successful save:

{ _id: 5a78fe3e2f44ba8f85a2409a, email: '[email protected]', __v: 0 }

The following fields are returned (internal fields are prefixed with an underscore):

  1. The _id field is auto-generated by Mongo and is a primary key of the collection. Its value is a unique identifier for the document.
  2. The value of the email field is returned. Notice that it is lower-cased because we specified the lowercase:true attribute in the schema.
  3. __v is the versionKey property set on each document when first created by Mongoose. Its value contains the internal revision of the document.

If you try to repeat the save operation above, you will get an error because we have specified that the email field should be unique.

Fetch Record

Let’s try to retrieve the record we saved to the database earlier. The model class exposes several static and instance methods to perform operations on the database. We will now try to find the record that we created previously using the find method and pass the email as the search term.

EmailModel .find({ email: '[email protected]' // search query }) .then(doc => { console.log(doc) }) .catch(err => { console.error(err) })

The document returned will be similar to what was displayed when we created the record:

{ _id: 5a78fe3e2f44ba8f85a2409a, email: '[email protected]', __v: 0 }

Update Record

Let’s modify the record above by changing the email address and adding another field to it, all in a single operation. For performance reasons, Mongoose won’t return the updated document so we need to pass an additional parameter to ask for it:

EmailModel .findOneAndUpdate( { email: '[email protected]' // search query }, { email: '[email protected]' // field:values to update }, { new: true, // return updated doc runValidators: true // validate before update }) .then(doc => { console.log(doc) }) .catch(err => { console.error(err) })

The document returned will contain the updated email:

{ _id: 5a78fe3e2f44ba8f85a2409a, email: '[email protected]', __v: 0 }

Delete Record

We will use the findOneAndRemove call to delete a record. It returns the original document that was removed:

EmailModel .findOneAndRemove({ email: '[email protected]' }) .then(response => { console.log(response) }) .catch(err => { console.error(err) })

Helpers

We have looked at some of the basic functionality above known as CRUD (Create, Read, Update, Delete) operations, but Mongoose also provides the ability to configure several types of helper methods and properties. These can be used to further simplify working with data.

Let’s create a user schema in ./src/models/user.js with the fieldsfirstName and lastName:

let mongoose = require('mongoose') let userSchema = new mongoose.Schema({ firstName: String, lastName: String }) module.exports = mongoose.model('User', userSchema)

Virtual Property

A virtual property is not persisted to the database. We can add it to our schema as a helper to get and set values.

Let’s create a virtual property called fullName which can be used to set values on firstName and lastName and retrieve them as a combined value when read:

userSchema.virtual('fullName').get(function() { return this.firstName + ' ' + this.lastName }) userSchema.virtual('fullName').set(function(name) { let str = name.split(' ') this.firstName = str[0] this.lastName = str[1] })

Callbacks for get and set must use the function keyword as we need to access the model via the thiskeyword. Using fat arrow functions will change what this refers to.

Now, we can set firstName and lastName by assigning a value to fullName:

let model = new UserModel() model.fullName = 'Thomas Anderson' console.log(model.toJSON()) // Output model fields as JSON console.log() console.log(model.fullName) // Output the full name

The code above will output the following:

{ _id: 5a7a4248550ebb9fafd898cf, firstName: 'Thomas', lastName: 'Anderson' } Thomas Anderson

Instance Methods

We can create custom helper methods on the schema and access them via the model instance. These methods will have access to the model object and they can be used quite creatively. For instance, we could create a method to find all the people who have the same first name as the current instance.

In this example, let’s create a function to return the initials for the current user. Let’s add a custom helper method called getInitials to the schema:

userSchema.methods.getInitials = function() { return this.firstName[0] + this.lastName[0] }

This method will be accessible via a model instance:

let model = new UserModel({ firstName: 'Thomas', lastName: 'Anderson' }) let initials = model.getInitials() console.log(initials) // This will output: TA

Static Methods

Similar to instance methods, we can create static methods on the schema. Let’s create a method to retrieve all users in the database:

userSchema.statics.getUsers = function() { return new Promise((resolve, reject) => { this.find((err, docs) => { if(err) { console.error(err) return reject(err) } resolve(docs) }) }) }

Calling getUsers on the Model class will return all the users in the database:

UserModel.getUsers() .then(docs => { console.log(docs) }) .catch(err => { console.error(err) })

Adding instance and static methods is a nice approach to implement an interface to database interactions on collections and records.

Middleware

Middleware are functions that run at specific stages of a pipeline. Mongoose supports middleware for the following operations:

  • Aggregate
  • Document
  • Model
  • Query

For instance, models have pre and post functions that take two parameters:

  1. Type of event (‘init’, ‘validate’, ‘save’, ‘remove’)
  2. A callback that is executed with this referencing the model instance

Let’s try an example by adding two fields called createdAt and updatedAt to our schema:

let mongoose = require('mongoose') let userSchema = new mongoose.Schema({ firstName: String, lastName: String, createdAt: Date, updatedAt: Date }) module.exports = mongoose.model('User', userSchema)

When model.save() is called, there is a pre(‘save’, …) and post(‘save’, …) event that is triggered. For the second parameter, you can pass a function that is called when the event is triggered. These functions take a parameter to the next function in the middleware chain.

Let’s add a pre-save hook and set values for createdAt and updatedAt:

userSchema.pre('save', function (next) { let now = Date.now() this.updatedAt = now // Set a value for createdAt only if it is null if (!this.createdAt) { this.createdAt = now } // Call the next function in the pre-save chain next() })

Let’s create and save our model:

let UserModel = require('./user') let model = new UserModel({ fullName: 'Thomas Anderson' } msg.save() .then(doc => { console.log(doc) }) .catch(err => { console.error(err) })

You should see values for createdAt and updatedAt when the record that is created is printed:

{ _id: 5a7bbbeebc3b49cb919da675, firstName: 'Thomas', lastName: 'Anderson', updatedAt: 2018-02-08T02:54:38.888Z, createdAt: 2018-02-08T02:54:38.888Z, __v: 0 }

Plugins

Suppose that we want to track when a record was created and last updated on every collection in our database. Instead of repeating the above process, we can create a plugin and apply it to every schema.

Let’s create a file ./src/model/plugins/timestamp.js and replicate the above functionality as a reusable module:

module.exports = function timestamp(schema) { // Add the two fields to the schema schema.add({ createdAt: Date, updatedAt: Date }) // Create a pre-save hook schema.pre('save', function (next) { let now = Date.now() this.updatedAt = now // Set a value for createdAt only if it is null if (!this.createdAt) { this.createdAt = now } // Call the next function in the pre-save chain next() }) }

To use this plugin, we simply pass it to the schemas that should be given this functionality:

let timestampPlugin = require('./plugins/timestamp') emailSchema.plugin(timestampPlugin) userSchema.plugin(timestampPlugin)

Query Building

Mongoose has a very rich API that handles many complex operations supported by MongoDB. Consider a query where we can incrementally build query components.

In this example, we are going to:

  1. Find all users
  2. Skip the first 100 records
  3. Limit the results to 10 records
  4. Sort the results by the firstName field
  5. Select the firstName
  6. Execute that query
UserModel.find() // find all users .skip(100) // skip the first 100 items .limit(10) // limit to 10 items .sort({firstName: 1} // sort ascending by firstName .select({firstName: true} // select firstName only .exec() // execute the query .then(docs => { console.log(docs) }) .catch(err => { console.error(err) })

Closing

We have barely scratched the surface exploring some of the capabilities of Mongoose. It is a rich library full of useful and and powerful features that make it a joy to work with data models in the application layer.

While you can interact with Mongo directly using Mongo Driver, Mongoose will simplify that interaction by allowing you to model relationships between data and validate them easily.

Įdomus faktas: „ Mongoose “ sukūrė Valeris Karpovaskas yra nepaprastai talentingas inžinierius! Jis sukūrė terminą „PRASMĖS krūva“ .

Jei šis straipsnis buvo naudingas, ??? ir Sekite mane „Twitter“.