PUBLISHED APRIL 13, 2024
Indepth Exploration of the Mongoose Populate Feature
Explore Mongoose Populate, from basic syntax to advanced features, learning efficient querying and best practices for optimizing document population.
Prerequisite
Before diving into Mongoose Populate, ensure familiarity with MongoDB basics, including collections, documents, and querying.
Additionally, a solid understanding of JavaScript and Node.js is necessary, as Mongoose is a MongoDB ODM for Node.js. Prior knowledge of relational database concepts like foreign keys is beneficial.
Mongoose, an elegant MongoDB object modeling tool designed to work in an asynchronous environment like Node.js, offers powerful features for managing data relationships.
Among these features, Mongoose Populate stands out as a crucial tool for enhancing query capabilities and simplifying data retrieval operations.
What is population?
In MongoDB, documents can reference other documents within the same collection or in different collections.
However, querying and retrieving referenced documents can be cumbersome without proper tools. This is where Mongoose Populate comes in.
It provides a convenient way to retrieve referenced documents by automatically populating them in query results.
Instead of performing separate queries to fetch referenced documents, developers can rely on Mongoose Populate to seamlessly populate these documents in a single query result, simplifying code and improving efficiency.
Understanding Mongoose Models
In Mongoose, models are an essential part of defining the structure and behavior of data within MongoDB collections.
They act as constructors for documents, providing a schema definition that defines the shape of the documents and methods for interacting with them.
They define the schema, which specifies the properties and their types, default values, validation rules, and more.
By creating and using models, developers can ensure consistency and structure in their MongoDB data.
Let's demonstarte by creating and using a simple mongoose model.
NB: Setup a node environment which you will use throughout this guide. Also for a smooth follow up with examples, please visit mongodb Atlas, create an account if you don't already have one, then create a new project and finally, save the mongodb connection string.If you encounter any challenges, please check this guide from mongodb.
Ready? Then let's proceed with our example.
// config/dbconfig.js
const {connection, connect} = require('mongoose');
export const connectDb = async () => {
connection.on('error', (error) => {
console.log(`A db error occured : ${error?.message??"Unknown"}`)
})
connection.on('reconnected', () => {
console.log('MONGODB Connection Reestablished')
});
connection.on('disconnected', () => {
console.log('MONGODB Disconnected!');
});
connection.on('close', () => {
console.log('MONGODB Connection Closed');
});
await connect('your-mongo-db-connection-string');
}// models/userModel.js
const mongoose = require('mongoose');
// Define a schema
const userSchema = new mongoose.Schema({
name: String,
age: Number,
email: {
type: String,
required: true,
unique: true
}
});
// Compile the schema into a model
const User = mongoose.model('User', userSchema);
module.exports = User;
// index.js
const User = require('./models/userModel');
const {connectDb} = require('./config/dbconfig.js');
connectDb().then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using callback pattern)
newUser.save()
.then(user => {
console.log('User saved:', user);
})
.catch(error => {
console.error('Error saving user:', error);
});
});
Breakdown: In the code examples above,
- We setup a dbconfig module for connecting to atlas db cluster.
- We define a schema for a user document with properties for name, age, and email. We then compile this schema into a model named `User`
- Finally we created a User document and saved it in our database. If you get the user document log in the console, then you successfully created a new user document 😁.
Reference in Mongoose. What are they really?
In Mongoose, references are a key concept for establishing relationships between documents in MongoDB collections.
They enable developers to create associations between documents, facilitating data organization and retrieval in a structured manner.
These references establish relationships between documents, enabling developers to model complex data structures and query related data efficiently.
Creating References ?
To create a reference between documents, you define a property in one schema that holds the ObjectId of a document in another collection. This establishes a logical connection between the two documents, allowing for easy retrieval of related data
Here is simple illustrative scenario: In our scenario, we have two collections: `users` and `posts`. Each post is authored by a user, creating a one-to-many relationship between users and posts.
We can represent this relationship using references in Mongoose like so:
// models/postModel.js
// Post schema with reference to User (Previously defined!)
const postSchema = new mongoose.Schema({
title: String,
content: String,
author: {
type: mongoose.Schema.Types.ObjectId,
ref: 'User'
}
});
const Post = mongoose.model('Post', postSchema);
module.exports = Post ;
How it works under the scene
When a query involves populated fields, Mongoose performs an additional query to fetch the referenced documents and replaces the ObjectId references with the actual documents in the query result.
This process seamlessly integrates referenced documents into the query result, allowing for easy access and manipulation of related data.
Let's go through some of the more common use cases.
1. Adding a Refs to document(s):
References without immediate population allows for storing ObjectId references in fields and populating them later as needed.
// models/postModel.js
const mongoose = require('mongoose');
const postSchema = new mongoose.Schema({
title: String,
author: {
type: mongoose.Schema.Types.ObjectId,
ref: 'User'
}
});
const Post = mongoose.model('Post', postSchema);
module.exports = Post;
Now let's create a user and post document, then query and populate post collection.
// index.js
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
const newPost = new Post({ title: 'New Post', author: user._id });
newPost.save()
.then(() => {
//query and populate the post
Post.find().populate('author').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Populated Posts:', posts);
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
In the above code the populate() method populates the `author` field of each `Post` document with the corresponding `User` document.
2. Checking Whether a Field is Populated or Not
The `isPopulated` method provides a boolean which helps to determine whether a field has been populated in a document, enabling conditional logic based on the presence or absence of populated fields.
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
const newPost = new Post({ title: 'New Post', author: user._id });
newPost.save()
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find().populate('author').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
posts.forEach(post => {
if (post.author && post.author.isPopulated('name')) {
console.log('Author name:', post.author.name);
}
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});NB: When there's no document, post.author will be null.
3. What Happens When There's No Foreign Document?
Handling cases where referenced documents may not exist involves gracefully handling `null` values or other specified defaults to ensure robustness and consistency in data retrieval operations.
For example:
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
connectDb()
.then(() => {
console.log('Db is connected!');
// create and save a new post referencing this user
const newPost = new Post({ title: 'Another Post' });
newPost.save()
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find().populate('author').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('posts:', posts);
});
});
});In this example, if a referenced `author` document does not exist for a `Post`, the `author` field in the populated `Post` document will be `null`.
This ensures that the query results maintain integrity and consistency, even in the absence of referenced documents.
4. Field Selection
Sometime we want only a subset of fields on the referenced document.
Specifying which fields to populate in query results allows for selective population of only relevant fields, optimizing query performance and minimizing unnecessary data transfer.
Let's see an example:
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
const newPost = new Post({ title: 'New Post', author: user._id });
newPost.save()
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find().populate({ path: 'author', select: 'name' }).exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('posts:', posts)
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});In the above code, only the `name` field of the referenced `author` documents will be populated in the query results, reducing data transfer and improving query performance by excluding unnecessary fields.
For multiple field selection, we could simply he desired field(s) to the select param like so: select: 'name email' and we could exclude a field by prefixing a `-` like so select: 'name email -_id'.
5. Populating Multiple Paths
Populating multiple paths in a single query enables efficient retrieval of related data across multiple levels of document nesting, reducing the need for additional queries.
For Example, let's add a tag model to the lots and create a reference for it in the post model.
- Create a tag model
// models/tagModel.js
const mongoose = require('mongoose');
const tagSchema = new mongoose.Schema({
name: String,
status: String
});
const Tag = mongoose.model('Tag', tagSchema);
- update the post model bu adding another field tags
// models/postModel.js
const mongoose = require('mongoose');
const postSchema = new mongoose.Schema({
title: String,
author: {
type: mongoose.Schema.Types.ObjectId,
ref: 'User'
},
tags: [{
type: mongoose.Schema.Types.ObjectId,
ref: 'Tag'
}]
});
const Post = mongoose.model('Post', postSchema);
Now let's query and populate our post collection.
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
Tag.create({name: 'Populate Playbook'})
.then(tag => {
const newPost = new Post({ title: 'New Post', author: user._id, tags: [tag._id] });
newPost.save()
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find().populate('author').populate('tags').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('posts:', posts);
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
6. Populate with Query Options
Advanced options such as query conditions, limiting population depth, and other configurations provide fine-grained control over population behavior, allowing developers to tailor query results to their specific needs.
Let say we are interested only in author aged above 20 and tags with status active. Here is how we could handle that.
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
Tag.create({name: 'Populate Playbook', status: 'active'})
.then(tag => {
const newPost = new Post({ title: 'New Post', author: user._id, tags: [tag._id] });
newPost.save()
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find()
.populate({
path: 'author',
match: { age: { $gte: 20 } },
select: 'name'
})
.populate({
path: 'tags',
match: { status: 'active'},
options: { limit: 5 }
}).exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('posts:', posts);
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
In our example, additional options are provided to the populate() method to specify query conditions (match), Select fields of interest (select) and limit the number of populated tag documents (options.limit).
NB: The `match` option doesn't filter out `author` documents. If there are no documents that satisfy `match`, we'll get a `post` document with a `null` author field.
However, if there are no documents that satisfy the match option for tags, then the `posts` will have `empty tags array`.
7. Limit vs. perDocumentLimit
Mongoose offers options to control the number of documents populated for each parent document (`perDocumentLimit`) and the total number of documents populated across all parent documents (`limit`), providing flexibility in managing population performance and resource usage.
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
Tag.creates([
{name: 'Populate Playbook One', status: 'active'},
{name: 'Populate Playbook Two', status: 'active'},
{name: 'Populate Playbook Three', status: 'inactive'},
{name: 'Populate Playbook Four', status: 'active'},
{name: 'Populate Playbook five', status: 'inactive'}
])
.then(tags => {
const newPosts = [];
for (let i = 0; i < tags.length; i++) {
newPosts.push({ title: `Post Number ${i+1}`, author: user._id, tags: [tag[i]._id]});
}
Post.create(newPosts)
.then(() => {
//query and conditionally log based on whether field is populated is not
Post.find()
.populate({ path: 'tags', perDocumentLimit: 3, limit: 10 })
.exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Populated Posts:', posts);
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
Breakdown: In this example, the `perDocumentLimit` option limits the number of tags populated for each post document, while the `limit` option restricts the total number of populated tags across all post documents.
These options help control population performance and prevent excessive resource consumption.
8. Populating across Multiple Levels
Populating fields across multiple levels of document nesting enables retrieval of deeply nested data structures in a single query, reducing the need for additional queries.
So suppose we re-model our schema such that the tags field actually exists on the User model.
In this case here is how we can retrieve that deep nested tags in the author field of each post document.
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
Tag.creates([
{name: 'Populate Playbook One', status: 'active'},
{name: 'Populate Playbook Two', status: 'active'},
{name: 'Populate Playbook Three', status: 'inactive'},
{name: 'Populate Playbook Four', status: 'active'},
{name: 'Populate Playbook five', status: 'inactive'}
])
then(tags => {
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com',
tags: tags
});
newUser.save()
.then(user => {
const newPost = new Post({ title: 'New Post', author: user._id });
newPost.save()
.then(() => {
Post.find()
.populate({ path: 'author', populate: { path: 'tags' } })
.exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Populated Posts:', posts);
});
});
})
})
});
In our example, the author field of each `Post` document is populated with the corresponding array of `User` document, and the tags field of each `User` document is populated in a single query.
This allows for efficient retrieval of deeply nested data.
9. Dynamic References: ref vs refPath
Dynamic referencing in Mongoose allows for dynamically determining the target model of a reference field.
This can be achieved using either the `ref` option, where the target model is explicitly specified, or the `refPath` option, where the target model is determined dynamically based on the value of another field in the document.
For example let's surpose, we have a comment model that reference either User collection or admin collection depending of yet another one its fields. Let's examine this scenerio with both cases (with ref and refPath).
- With Ref:
// models/commentmodel.js
const commentSchema = new mongoose.Schema({
content: String,
authorType: String,
authorId: { type: mongoose.Schema.Types.ObjectId, ref: 'User' }
});
const Comment = mongoose.model('Comment', commentSchema);
In this case, the authorId field in the Comment schema contains references to User collection.
The target model of the reference is explicitly specified using the ref option, indicating that the authorId field will reference documents in the User collection.
- With refPath
// models/commentModel.js
const commentSchema = new mongoose.Schema({
content: String,
authorType: { type: String, enum: ['User', 'Admin'] },
authorId: { type: mongoose.Schema.Types.ObjectId, refPath: 'authorType' }
});
const Comment = mongoose.model('Comment', commentSchema);
In this case, the authorId field in the Comment schema contains references to documents whose target model is determined dynamically based on the value of the `authorType` field. The refPath option is used to specify that the target model of the reference will be determined based on the value of the authorType field, which can be either 'User' or 'Admin'.
10. Populate in Middleware
Populating fields in middleware allows for automatically populating referenced documents before certain operations, such as document retrieval or population of virtual fields, enhancing data consistency and convenience.
Let's illustrate using the user model and post schema middleware.
// models/postModel.js
const mongoose = require('mongoose');
const postSchema = new mongoose.Schema({
title: String,
author: {
type: mongoose.Schema.Types.ObjectId,
ref: 'User'
},
tags: [{
type: mongoose.Schema.Types.ObjectId,
ref: 'Tag'
}]
});
postSchema.pre('find', function() {
this.populate('author');
});
const Post = mongoose.model('Post', postSchema);In our example, a `pre-find` middleware is defined for the Post schema, which automatically populates the author field before any find operation is executed.
This ensures that the author field is populated whenever documents are retrieved using the find method.
We can also populate multiple path in middleware. Let say we also wanted to populate tags field too. We'd proceed as shown below.
postSchema.pre('find', function() {
this.populate('author').populate('comments');
});
There are other interesting options which aren't covered her like populate with transform. You can read that from the official documentation.
In Mongoose, the `lean()` and `exec()` methods provide control over how query results are processed and returned.
When used in conjunction with populate, they offer additional flexibility in managing memory usage and query performance.
When to Use lean() and exec():
- ➢Use lean(): When you want raw JavaScript objects instead of Mongoose documents. This reduces memory overhead and can improve query performance, especially for large result sets.
- ➢Use exec(): When you need to execute a query and handle its results asynchronously. This method is typically used at the end of query chains to trigger query execution.
Effects of lean():
- ➢Before populate(): If `lean()` is called before `populate()`, the entire query result, including populated fields, will be returned as plain JavaScript objects.
This can be more memory efficient but means that virtuals, getters, and other Mongoose features will not be applied to the result.
// index.js
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
Tag.creates([
{name: 'Populate Playbook One', status: 'active'},
{name: 'Populate Playbook Two', status: 'active'},
{name: 'Populate Playbook Three', status: 'inactive'},
{name: 'Populate Playbook Four', status: 'active'},
{name: 'Populate Playbook five', status: 'inactive'}
])
.then(tags => {
const newPosts = [];
for (let i = 0; i < tags.length; i++) {
newPosts.push({ title: `Post Number ${i+1}`, author: user._id, tags: [tag[i]._id]});
}
Post.create(newPosts)
.then(() => {
Post.find().lean().populate('author').populate('author').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Lean Populated Posts:', posts); //
console.log(posts[0] instanceof mongoose.Document) // false
console.log(posts[0].author instanceof mongoose.Document) // false
console.log(posts[0].tags[0] instanceof mongoose.Document) // false
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
Here, lean() is called before populate(), resulting in the entire query result being returned as plain JavaScript objects.
- ➢After populate(): If
lean()is called afterpopulate(), only the populated fields will be converted to plain JavaScript objects, while other fields remain as Mongoose documents.
This can be useful when you want to retain Mongoose features for some fields while reducing memory usage for populated fields.
// index.js
const {connectDb} = require('./config/dbconfig');
const Post = require('./models/postModel');
const User = require('./models/userModel');
const Tag = require('./models/tagModel');
connectDb()
.then(() => {
console.log('Db is connected!');
const newUser = new User({
name: 'me myself',
age: 30,
email: 'me@myself.com'
});
// Save the document to the database (using cal)
newUser.save()
.then(user => {
// create and save a new post referencing this user
Tag.creates([
{name: 'Populate Playbook One', status: 'active'},
{name: 'Populate Playbook Two', status: 'active'},
{name: 'Populate Playbook Three', status: 'inactive'},
{name: 'Populate Playbook Four', status: 'active'},
{name: 'Populate Playbook five', status: 'inactive'}
])
.then(tags => {
const newPosts = [];
for (let i = 0; i < tags.length; i++) {
newPosts.push({ title: `Post Number ${i+1}`, author: user._id, tags: [tag[i]._id]});
}
Post.create(newPosts)
.then(() => {
Post.find().populate('author').populate('author').lean().exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Lean Populated Posts:', posts); //
console.log(posts[0] instanceof mongoose.Document) // true
console.log(posts[0].author instanceof mongoose.Document) // false
console.log(posts[0].tags[0] instanceof mongoose.Document) // false
});
});
});
})
.catch(error => {
console.error('Error saving user:', error);
});
});
Here, lean() is called after populate(), causing only the populated `author` and `tags` fields to be converted to plain JavaScript objects.
Other fields in the query result remain as Mongoose documents, retaining Mongoose features.
Before concludion this guide, let's explore the some practices, common pitfalls, and conclude with key takeaways for effectively using populate in Mongoose queries.
By understanding these aspects, you can optimize query performance, avoid common mistakes, and ensure efficient data retrieval and manipulation.
Best Practices:
When using populate in Mongoose, certain best practices can enhance query performance and improve overall application efficiency. Let's explore some of these practices:
1. Selective Population: Before populating fields, it's essential to consider which fields are necessary for the current operation.
By selectively populating only the required fields, unnecessary database queries can be avoided, leading to improved performance.
// play.js
Post.find().populate('author', 'name').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Populated Posts:', posts);
});
For the above case, only the `name` field of the author reference is populated, reducing the number of database queries and improving performance.
2. Use of lean(): Leveraging the lean() method before populating fields can result in reduced memory overhead by retrieving plain JavaScript objects instead of Mongoose documents.
This optimization is particularly beneficial for applications dealing with large datasets.
//play.js
Post.find().lean().populate('author').exec((err, posts) => {
if (err) {
console.error('Error:', err);
return;
}
console.log('Lean Populated Posts:', posts);
});
By using `lean()` in this example, memory overhead is minimized as the populated posts are retrieved as plain JavaScript objects instead of Mongoose documents.
3. Denormalization: Denormalizing data by storing redundant information, such as the author's name alongside their ID, can reduce the need for population across multiple documents and improve query performance.
// models/postModels.js
const mongoose = require('mongoose');
const postSchema = new mongoose.Schema({
title: String,
content: String,
authorName: String // Denormalized field
});
const Post = mongoose.model('Post', postSchema);
module.exports = Post;In this schema example, the author's name is denormalized, allowing for efficient querying without the need for population.
4. Indexing and Caching: Indexing fields involved in population can significantly improve query performance by reducing the time taken to retrieve related documents.
Additionally, implementing caching mechanisms can further optimize query execution by storing frequently accessed data.
// models/postModels.js
const mongoose = require('mongoose');
const postSchema = new mongoose.Schema({
title: String,
content: String,
author: {
type: mongoose.Schema.Types.ObjectId,
ref: 'User'
}
});
postSchema.index({ author: 1 });
const Post = mongoose.model('Post', postSchema);
module.exports = Post;
Indexing the `author` field can accelerate queries that involve population, leading to faster response times.
Conclusion:
Effectively using populate in Mongoose queries requires adherence to best practices while avoiding common pitfalls.
By selectively populating fields, utilizing lean() for reduced memory overhead, considering denormalization, and implementing indexing and caching, you can optimize query performance and enhance overall application efficiency.
It's crucial to be mindful of overpopulation, memory overhead, and the importance of indexing when working with populate.
By following these guidelines, you can harness the full potential of populate to enhance data retrieval and manipulation in your applications.
References:
- ➢Mongoose Population Documentation
- ➢MongoDB Indexing Strategies
- ➢Denormalization in MongoDB
- ➢MongoDB Caching Strategies
