NoSQL Database Design: How to Model Data in MongoDB and Redis
NoSQL databases have been gaining popularity in recent years due to their flexibility, scalability and performance. While traditional relational databases like MySQL and PostgreSQL are great for handling structured data, NoSQL databases are better suited for handling unstructured or semi-structured data. In this article, we will focus on two popular NoSQL databases, MongoDB and Redis, and how to model data in them.
MongoDB is a document-oriented NoSQL database that stores data in JSON-like documents. Each document represents a single record and can have a different structure, which means that MongoDB is schema-less. MongoDB is a great choice for applications that require high write and read throughput and need to scale horizontally.
When modeling data in MongoDB, we need to consider the following:
Data relationships: Unlike relational databases, MongoDB does not support table joins. Instead, we can embed related data within a document or use references. Embedding data is a great option when the related data is small and frequently accessed together. However, if the related data is large or accessed independently, it’s better to use references.
Query patterns: In MongoDB, we need to design our data model based on the queries we will be running. This means that we should optimize our data model for the most common queries to improve performance. We can use indexes to speed up queries.
Data growth: MongoDB is great for handling data that grows rapidly. We can add new fields or sub-documents to a document without modifying the whole schema.
Let’s say we are building a blogging platform in MongoDB. Our data model would consist of two collections, users
and posts
. Each post can have multiple comments and likes, and each user can have multiple posts.
Here’s how we can model our data:
// users collection
{
_id: ObjectId("615e9bca3c0a2d0ff29a8d1a"),
username: "john_doe",
email: "john.doe@example.com",
password: "$2a$10$qE8jC/1NzGTf6DhUJ0f8sO4/E4j4WOzvYmHfYJzjSgS1gKj56FSWS",
created_at: ISODate("2021-10-07T09:00:00.000Z")
}
// posts collection
{
_id: ObjectId("615e9c003c0a2d0ff29a8d1b"),
title: "My First Blog Post",
body: "Lorem ipsum dolor sit amet, consectetur adipiscing elit.",
author_id: ObjectId("615e9bca3c0a2d0ff29a8d1a"),
comments: [
{
_id: ObjectId("615e9c5d3c0a2d0ff29a8d1f"),
text: "Great post!",
author_id: ObjectId("615e9d8e3c0a2d0ff29a8d1c"),
created_at: ISODate("2021-10-07T09:10:00.000Z")
},
{
_id: ObjectId("615e9c6c3c0a2d0ff29a8d20"),
text: "Thanks!",
author_id: ObjectId("615e9bca3c0a2d0ff29a8d1a"),
created_at: ISODate("2021-10-07T09:11:00.000Z")
}
],
likes: [
ObjectId("615e9d8e3c0a2d0ff29a8d1c"), // user_id
ObjectId("615e9bca3c0a2d0ff29a8d1a") // user_id
],
created_at: ISODate("2021-10-07T09:05:00.000Z")
}
In this example, we have two collections, users
and posts
. We have used references to relate posts
to users
. We have also embedded comments within a post document since comments are small and accessed together with a post. Likes are stored as an array of user IDs.
Redis is a key-value NoSQL database that stores data in memory. Redis is a great choice for applications that require high-speed data access and low latency. Redis can also persist data to disk, which means that we can use it as a cache or a database.
When modeling data in Redis, we need to consider the following:
Data relationships: Unlike relational databases, Redis does not support table joins. We can use multiple keys to represent relationships between data. We can also use data structures like sets, lists, and hashes to store related data.
Query patterns: In Redis, we need to design our data model based on the queries we will be running. This means that we should optimize our data model for the most common queries to improve performance. We can use indexes to speed up queries.
Memory usage: Since Redis stores data in memory, we need to consider memory usage when designing our data model. We can use techniques like data sharding and expiration to manage memory usage.
Let’s say we are building a real-time chat application in Redis. Our data model would consist of two keys, users
and rooms
. Each room can have multiple users and messages, and each user can be in multiple rooms.
Here’s how we can model our data:
// users key
{
"user:1": {
"name": "John Doe",
"email": "john.doe@example.com"
},
"user:2": {
"name": "Jane Doe",
"email": "jane.doe@example.com"
}
}
// rooms key
{
"room:1": {
"name": "Room 1",
"users": ["user:1", "user:2"],
"messages": [
{
"text": "Hello, World!",
"user_id": "user:1",
"created_at": "2021-10-07T09:20:00.000Z"
},
{
"text": "Hi, John!",
"user_id": "user:2",
"created_at": "2021-10-07T09:21:00.000Z"
}
]
},
"room:2": {
"name": "Room 2",
"users": ["user:1"],
"messages": [
{
"text": "Hey, everyone!",
"user_id": "user:1",
"created_at": "2021-10-07T09:22:00.000Z"
}
]
}
}
In this example, we have used multiple keys to represent relationships between data. We have used a set to store the list of users in a room and a list to store the messages in a room. We have also used a hash to store user data.
In this article, we have explored how to model data in MongoDB and Redis. We have learned that data modeling in NoSQL databases is different from traditional relational databases and requires us to consider data relationships, query patterns, and data growth or memory usage. By following these best practices, we can design efficient and scalable data models that can handle real-world applications.