MongoDB

MongoDB documents, schema design, indexes, aggregation, Mongoose, and database exercises.

Study order: Documents → CRUD → Queries → Schema design → Embedding vs references → Indexes → Aggregation → Mongoose → Exercises

• What is MongoDB? — What MongoDB is, why document databases exist, and where it fits.
• Documents and Collections — How MongoDB stores JSON-like records.
• MongoDB vs SQL — How document databases differ from relational databases.
• CRUD Operations in MongoDB — Create, read, update, and delete documents.
• Query and Update Operators — Use operators like $set, $inc, $in, $gt, and $or.
• Schema Design in MongoDB — Design documents around access patterns.
• Embedding vs Referencing — Choose nested documents or separate linked collections.
• Indexes in MongoDB — Speed up queries with indexes and understand write tradeoffs.
• Aggregation Pipeline — Transform and summarize data with pipeline stages.
• Mongoose Schema and Model — Define application-level models for MongoDB documents.
• Mongoose Validation — Validate document shape and values before saving.
• Population in Mongoose — Replace referenced ids with related documents.
• Transactions in MongoDB — Make multiple writes succeed or fail together.
• Compound and Text Indexes — Use multi-field and text search indexes.
• MongoDB Performance Mistakes — Avoid unindexed queries, huge documents, and bad pagination.
• MongoDB Data Modeling Patterns — Use common document modeling patterns for real apps.
• User Post Comment Schema — Design social-style collections and relationships.
• CRUD with Mongoose — Build model-based create, read, update, and delete operations.
• Search and Pagination in MongoDB — Implement query search and paginated lists.
• Aggregation Reports — Calculate grouped summaries using aggregation pipeline.
• Order Placement with Transaction — Use transactions for multi-document order flow.