For beginners, MongoDB's straightforward start can be misleading, and as their understanding deepens and project requirements evolve, they may encounter increasing difficulties. These challenges often arise because MongoDB, while powerful, is not always the best fit for tasks traditionally handled by relational databases, such as managing complex relationships and ensuring data integrity.
5 Common Myths and Misconceptions about MongoDB
Myth Number 1 - NodeJs goes well only with MongoDB
While MongoDB is a popular NoSQL database in the Node.js ecosystem owing to its flexibility and scalability, Node.js also supports a wide range of relational databases via ORM tools and direct drivers.
ORM libraries like Sequelize, as well as query builders, allow developers to connect with relational databases such as MySQL, PostgreSQL, SQLite, and others. These technologies include benefits like as ACID compliance, SQL query capabilities, and data integrity enforcement, making them ideal for applications that require complex data connections and transactional integrity.
The database should be chosen based on the application's requirements, rather than being chosen merely by the use of Node.js as the runtime environment. This flexibility enables developers to pick the database technology that best meets their application's scalability, data format, and transactional requirements. As a result, the notion that Node.js requires the usage of MongoDB over relational databases is incorrect, as Node.js offers strong support for both types of databases.
Node.js with MongoDB and PostgreSQL
This page demonstrates how Node.js can be used with both MongoDB (a NoSQL database) and PostgreSQL (a relational database). Below are the example codes for each:
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydatabase', {
useNewUrlParser: true,
useUnifiedTopology: true,
});
const UserSchema = new mongoose.Schema({
name: String,
email: String,
});
const User = mongoose.model('User', UserSchema);
const createUser = async () => {
const user = new User({ name: 'John Doe', email: 'john@example.com' });
await user.save();
console.log('User created:', user);
};
createUser().catch(err => console.error(err));const { Sequelize, DataTypes } = require('sequelize'); const sequelize = new Sequelize('postgres://user:pass@localhost:5432/mydatabase'); const User = sequelize.define('User', { name: { type: DataTypes.STRING, allowNull: false, }, email: { type: DataTypes.STRING, allowNull: false, }, }); const createUser = async () => { await sequelize.sync(); const user = await User.create({ name: 'Jane Doe', email: 'jane@example.com' }); console.log('User created:', user.toJSON()); }; createUser().catch(err => console.error(err));
Myth Number 2- MongoDB is Very FAST
The myth that MongoDB is intrinsically quicker than relational databases like MySQL is a common misconception. MongoDB, being a NoSQL database, excels in specific applications, notably unstructured data, horizontal scalability, and quick prototyping, thanks to its flexible schema.
However, performance is greatly dependent on the use case, data type, and query patterns. If you store all data in a single MongoDB collection, similar to a single MySQL table, the performance gains may be reduced.
MySQL and other relational databases can perform extraordinarily well with proper indexing, normalization, and query optimization. Also, relational databases benefit from ACID compliance, which provides strong transaction management that is critical for many applications. Thus, MongoDB's perceived speed advantage is not universal and should be assessed depending on unique requirements and workloads.
MongoDB vs MySQL Performance
The myth that MongoDB is intrinsically quicker than relational databases like MySQL is a common misconception. Below are examples demonstrating performance depends on specific use cases, data type, and query patterns:
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const UserSchema = new mongoose.Schema({ name: String, email: String });
const User = mongoose.model('User', UserSchema);
const createUser = async () => {
const user = new User({ name: 'John Doe', email: 'john@example.com' });
await user.save();
console.log('User created:', user);
};
createUser().catch(err => console.error(err));
const { Sequelize, DataTypes } = require('sequelize');
const sequelize = new Sequelize('mysql://user:pass@localhost:3306/mydb');
const User = sequelize.define('User', {
name: { type: DataTypes.STRING, allowNull: false },
email: { type: DataTypes.STRING, allowNull: false },
});
const createUser = async () => {
await sequelize.sync();
const user = await User.create({ name: 'Jane Doe', email: 'jane@example.com' });
console.log('User created:', user.toJSON());
};
createUser().catch(err => console.error(err));
Myth Number 3- MongoDB is very Straightforward
Since MongoDB looks more flexible and schema-less than relational databases, designing relationships in it might be more difficult. In a relational database, relationships between tables are clearly specified with foreign keys, making it simple to maintain referential integrity and manage data relationships. In comparison, MongoDB demands a more complex approach to relationship modeling.
Developers must choose between embedding documents or using references, both of which have pros and cons. Embedding can result in data duplication and larger documents, which affects speed and storage, whereas referencing might complicate searches and demand manual joins inside application logic.
MongoDB does not inherently allow joins in the same manner that relational databases do, forcing the usage of application-side joins or the aggregation framework, which can add complexity.
Designing Relationships in MongoDB vs MySQL
MongoDB, while flexible and schema-less, presents more complexity in relationship design compared to relational databases like MySQL. Below are examples illustrating this:
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const PostSchema = new mongoose.Schema({
title: String,
content: String,
comments: [{ body: String, date: Date }]
});
const Post = mongoose.model('Post', PostSchema);
const createPost = async () => {
const post = new Post({
title: 'First Post',
content: 'This is my first post!',
comments: [{ body: 'Great post!', date: new Date() }]
});
await post.save();
console.log('Post created:', post);
};
createPost().catch(err => console.error(err));
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const CommentSchema = new mongoose.Schema({ body: String, date: Date });
const Comment = mongoose.model('Comment', CommentSchema);
const PostSchema = new mongoose.Schema({
title: String,
content: String,
comments: [{ type: mongoose.Schema.Types.ObjectId, ref: 'Comment' }]
});
const Post = mongoose.model('Post', PostSchema);
const createPost = async () => {
const comment = new Comment({ body: 'Great post!', date: new Date() });
await comment.save();
const post = new Post({ title: 'First Post', content: 'This is my first post!', comments: [comment._id] });
await post.save();
console.log('Post created:', post);
};
createPost().catch(err => console.error(err));
const { Sequelize, DataTypes } = require('sequelize');
const sequelize = new Sequelize('mysql://user:pass@localhost:3306/mydb');
const Post = sequelize.define('Post', {
title: { type: DataTypes.STRING, allowNull: false },
content: { type: DataTypes.STRING, allowNull: false }
});
const Comment = sequelize.define('Comment', {
body: { type: DataTypes.STRING, allowNull: false },
date: { type: DataTypes.DATE, allowNull: false }
});
Post.hasMany(Comment, { as: 'comments' });
Comment.belongsTo(Post);
const createPost = async () => {
await sequelize.sync();
const post = await Post.create({ title: 'First Post', content: 'This is my first post!' });
const comment = await Comment.create({ body: 'Great post!', date: new Date(), PostId: post.id });
console.log('Post created:', post.toJSON(), 'with comment:', comment.toJSON());
};
createPost().catch(err => console.error(err));
Myth Number 4 - MongoDB Pagination
Pagination in MongoDB has unique issues compared to standard relational databases.
While MongoDB has skip and limit methods for paginating across results, utilizing the skip method can prove inefficient with large data sets. This is because the MongoDB skip method must traverse all previous pages, resulting in decreased performance as the offset increases.
Pagination in MongoDB vs MySQL
While MongoDB has skip and limit methods for pagination, the skip method can become inefficient with large datasets. In contrast, MySQL's LIMIT and OFFSET remain efficient. Below are examples illustrating this:
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const UserSchema = new mongoose.Schema({ name: String });
const User = mongoose.model('User', UserSchema);
const paginateUsers = async (page, limit) => {
const users = await User.find().skip((page - 1) * limit).limit(limit);
console.log('Users:', users);
};
paginateUsers(5, 10).catch(err => console.error(err));
const mysql = require('mysql2/promise');
const paginateUsers = async (page, limit) => {
const connection = await mysql.createConnection({ host: 'localhost', user: 'user', password: 'pass', database: 'mydb' });
const [rows] = await connection.execute('SELECT * FROM users LIMIT ? OFFSET ?', [limit, (page - 1) * limit]);
console.log('Users:', rows);
};
paginateUsers(5, 10).catch(err => console.error(err));
Myth Number 5 - No Need for Indexing
It is a misconception that MongoDB's NoSQL architecture allows it to run effectively without indexing, assuming that its flexible, schema-free nature maximizes efficiency by default.
However, much like with relational databases, proper indexing in MongoDB is critical for efficient query execution. Without indexes, MongoDB must execute a full collection scan to get documents, which causes considerable performance reduction as data volume grows.
Indexes allow MongoDB to quickly find and retrieve the data required for queries.
It is a misconception that MongoDB's NoSQL architecture can run efficiently without indexing. Proper indexing is crucial for efficient query execution. Here are examples to illustrate this:
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const UserSchema = new mongoose.Schema({ name: String });
const User = mongoose.model('User', UserSchema);
const findUserWithoutIndex = async () => {
const users = await User.find({ name: 'John Doe' }); // Full collection scan
console.log('Users:', users);
};
// Simulate querying without indexes
findUserWithoutIndex().catch(err => console.error(err));
const mongoose = require('mongoose');
mongoose.connect('mongodb://localhost:27017/mydb', { useNewUrlParser: true, useUnifiedTopology: true });
const UserSchema = new mongoose.Schema({ name: { type: String, index: true } }); // Index added
const User = mongoose.model('User', UserSchema);
const findUserWithIndex = async () => {
const users = await User.find({ name: 'John Doe' }); // Indexed query
console.log('Users:', users);
};
// Simulate querying with indexes
findUserWithIndex().catch(err => console.error(err));
MongoDB is a highly capable database, but it’s essential to use it only when your data is genuinely non-relational and you have a clear understanding of why a NoSQL solution is necessary. If you cannot articulate the need for NoSQL, opting for a relational database is often a better choice.
While MongoDB is praised for its performance, flexibility, and rapid development capabilities, using it inappropriately can lead to significant drawbacks. In such cases, the system may become slow, difficult to manage, and resolving performance issues could require substantial changes to both the data and code structure.