Data Modeling in MongoDB
Data modeling in MongoDB involves designing schemas and relationships between documents.
// Relationship: Embedding data in documents
db.users.insertOne({
username: "john_doe",
email: "john@example.com",
posts: [
{ title: "Post 1", content: "Content 1" },
{ title: "Post 2", content: "Content 2" }
]
})
// Relationship: Referencing documents
db.comments.insertOne({
user_id: ObjectId("user_id_here"),
post_id: ObjectId("post_id_here"),
content: "Comment content"
})
// Specify JSON schema validation
db.createCollection("collection_name", {
validator: {
$jsonSchema: {
bsonType: "object",
required: ["field1", "field2"],
properties: {
field1: {
bsonType: "string"
},
field2: {
bsonType: "number"
}
}
}
}
})
// Scaling in MongoDB involves sharding, replication, and proper index usage to distribute data across multiple servers.
We demonstrate embedding data in documents and referencing documents to model relationships between collections. JSON schema validation ensures data integrity by enforcing structure and data types. Scaling in MongoDB involves strategies like sharding and replication to handle large volumes of data.
MongoDB Cheat Sheet
MongoDB is a document-oriented NoSQL database that revolutionizes data storage with its flexibility and scalability. By storing data in JSON-like documents, MongoDB offers developers a powerful and intuitive way to handle complex data structures. From basic CRUD operations to advanced aggregation techniques, MongoDB empowers users to build robust and dynamic applications with ease.
In this MongoDB cheat sheet, we’ll delve into MongoDB’s key concepts, including data types, CRUD operations, query techniques, aggregation framework, indexing strategies, transaction support, and data modeling approaches. Each section is packed with examples and explanations to help you grasp MongoDB’s functionalities quickly and efficiently.
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