MongoDB

Overview

MongoDB is a document database that stores JSON-like (BSON) documents with flexible schemas, nested objects, and arrays. It provides a rich query language, aggregation pipelines for data transformation, multiple index types (text, TTL, wildcard, geospatial), ACID transactions, and horizontal scaling via sharding. MongoDB Atlas adds managed cloud hosting with full-text search and vector search.

Instructions

• When designing schemas, embed data that is read together and reference data that is shared across documents, optimizing for read patterns rather than normalization since MongoDB penalizes joins ($lookup).
• When querying, use dot notation for nested fields, array operators ($in, $elemMatch, $all) for array queries, and projection to return only needed fields.
• When transforming data, use aggregation pipelines with stages like $match, $group, $project, $lookup, and $unwind rather than pulling data to application code for processing.
• When creating indexes, add indexes for every production query pattern, use explain("executionStats") to verify usage, and choose TTL indexes for auto-expiring data like sessions and logs.
• When enforcing data integrity, use JSON Schema validation on collections, set writeConcern: "majority" for critical writes, and use multi-document transactions only when atomicity across documents is required.
• When scaling, choose shard keys with high cardinality and even distribution, set up replica sets for high availability, and configure read preferences based on consistency requirements.

Examples

Example 1: Build an e-commerce product catalog