developer-roadmap/src/data/roadmaps/mongodb/content/100-mongodb-basics/index.md

MongoDB Basics

MongoDB is a popular NoSQL database system that stores data in Flexible JSON-like documents, making it suitable for working with large scale and unstructured data.

Key MongoDB Concepts

• Database: Stores all your collections within a MongoDB instance.
• Collection: A group of related documents, similar to a table in a relational database.
• Document: A single record within a collection, which is stored as BSON (Binary JSON) format.
• Field: A key-value pair within a document.
• _id: A unique identifier automatically generated for each document within a collection.

Basic Operations

• Insert: To insert a single document, use db.collection.insertOne(). For inserting multiple documents, use db.collection.insertMany().
• Find: Fetch documents from a collection using db.collection.find(), and filter the results with query criteria like {field: value}. To fetch only one document, use db.collection.findOne().
• Update: Update fields or entire documents by using update operators like $set and $unset with db.collection.updateOne() or db.collection.updateMany().
• Delete: Remove documents from a collection using db.collection.deleteOne() or db.collection.deleteMany() with query criteria.
• Drop: Permanently delete a collection or a database using db.collection.drop() and db.dropDatabase().

Indexes and Aggregations

• Indexes: Improve the performance of searches by creating indexes on fields within a collection using db.collection.createIndex() or build compound indexes for querying multiple fields.
• Aggregations: Perform complex data processing tasks like filtering, grouping, transforming, and sorting using aggregation operations like $match, $group, $project, and $sort.

Data Modeling

MongoDB's flexible schema allows for various data modeling techniques, including:

• Embedded Documents: Store related data together in a single document, which is suitable for one-to-one or one-to-few relationships.
• Normalization: Store related data in separate documents with references between them, suitable for one-to-many or many-to-many relationships.
• Hybrid Approach: Combine embedded documents and normalization to balance performance and storage needs.

In conclusion, MongoDB's flexible and feature-rich design makes it a powerful choice for modern applications dealing with large scale and unstructured data. Understanding the basics of MongoDB can help you effectively use it as your data storage solution.