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Introduction To MongoDB
Lesson 13: MongoDB Introduction
Objective:
Introduction to MongoDB as a NoSQL database for MERN stack.
MongoDB, a prominent NoSQL database, offers flexibility and scalability for modern web applications in the MERN (MongoDB, Express.js, React, Node.js) stack. Let's dive into its fundamentals and advantages:
Basics of NoSQL databases
- NoSQL: Stands for "Not Only SQL," diverging from traditional relational databases (SQL) by offering a more flexible data model.
- Types: NoSQL databases include document-based (like MongoDB), key-value stores, column-oriented, and graph databases.
Advantages of MongoDB
- Schema flexibility: No predefined schema allows for easy modification and addition of fields without downtime.
- Scalability: Horizontal scaling and sharding enable handling large volumes of data and high traffic.
- Querying: Powerful querying capabilities with support for complex queries.
Comparison with SQL databases
- Structure: SQL databases use tables and rows, while MongoDB uses collections and documents.
- Schema: SQL databases have a fixed schema, whereas MongoDB has a dynamic schema.
- Scaling: MongoDB provides better scalability for large-scale applications.
Lesson 14: Environment Setup of MongoDB
Objective:
Learn how to set up and configure the MongoDB environment.
Installation and configuration
- Installation: Download and install MongoDB locally on your machine or set it up through package managers.
- Configuration: Adjust settings such as data directory, port, and authentication.
Using MongoDB Atlas for cloud-based setup
- MongoDB Atlas: A cloud-based platform that manages MongoDB databases, offering scalability, security, and automated backups.
- Setup: Create an account, set up clusters, and manage databases in the cloud.
Connecting MongoDB with Node.js
- MongoDB drivers: Use MongoDB Node.js drivers like `mongodb` or `mongoose` to connect Node.js applications with MongoDB databases.
- Connection: Establish connections, perform CRUD operations, and handle data in Node.js.
Lesson 15: Database Structure
Objective:
Understand the structure and organization of databases in MongoDB.
Collections and documents
- Collections: Equivalent to tables in SQL databases, hold documents.
- Documents: JSON-like data structures containing key-value pairs, forming the basic unit of data in MongoDB.
CRUD operations (Create, Read, Update, Delete)
- Create: `insertOne`, `insertMany` to add data.
- Read: `find`, `findOne` for retrieving data.
- Update: `updateOne`, `updateMany` for modifying data.
- Delete: `deleteOne`, `deleteMany` for removing data.
Indexing and querying in MongoDB
- Indexes: Enhance query performance by indexing fields for quicker retrieval.
- Querying: Utilize operators like `$eq`, `$ne`, `$in`, etc., to perform queries on data.
Lesson 16: MongoDB Data Types
Objective:
Explore data types supported by MongoDB.
BSON data types
- BSON: Binary JSON, the format in which MongoDB stores data, includes various data types like string, integer, double, object, array, binary data, etc.
- Examples:
- String: `"Hello"`
- Integer: `42`
- Object: `{ key: "value" }`
Handling different types of data in MongoDB
- Data modeling: Considerations for embedding documents, referencing documents, and designing schemas based on application needs.
- Geospatial data: MongoDB supports geospatial queries, allowing the storage and querying of geospatial data.
Data modeling considerations
- Normalization vs. denormalization: Deciding between normalized and denormalized data structures based on read vs. write performance requirements.
- Document structure: Designing documents to optimize query performance and data retrieval.
Understanding MongoDB's structure, its querying capabilities, and data modeling techniques empowers developers to create efficient, scalable, and well-structured databases for their MERN stack applications.