mongodb

name	mongodb
description	MongoDB schema design, aggregation pipelines, indexing strategies, change streams, transactions, and production optimization
layer	domain
category	database
triggers	["mongodb","mongo","mongoose","nosql","document database","aggregation pipeline"]
inputs	["Data modeling requirements","Query patterns","Scaling requirements"]
outputs	["Schema designs with embedding/referencing strategies","Aggregation pipeline implementations","Indexing and performance recommendations"]
linksTo	["nodejs","python","fastapi","redis","microservices"]
linkedFrom	["error-handling","monitoring"]
preferredNextSkills	["redis","nodejs","microservices"]
fallbackSkills	["postgresql"]
riskLevel	low
memoryReadPolicy	selective
memoryWritePolicy	none
sideEffects	[]

MongoDB Domain Skill

Purpose

Provide expert-level guidance on MongoDB schema design, the embedding vs. referencing trade-off, aggregation pipelines, indexing strategies, change streams, transactions, sharding, and production optimization.

Key Design Principle

Design for your query patterns, not for data normalization. MongoDB schema design is driven by how you read data, not how you write it.

Key Patterns

1. Schema Design: Embed vs. Reference

// EMBED when:
// - Data is read together (1:1 or 1:few)
// - Child data doesn't change independently
// - Document size stays under 16MB

// Embedded: Blog post with comments
{
  _id: ObjectId("..."),
  title: "Understanding MongoDB",
  author: {                    // Embedded 1:1
    name: "Jane Doe",
    email: "[e-mail]"
  },
  tags: ["mongodb", "nosql"], // Embedded array
  comments: [                  // Embedded 1:few
    {
      _id: ObjectId("..."),
      user: "Bob",
      text: "Great article!",
      createdAt: ISODate("2024-01-15")
    }
  ],
  createdAt: ISODate("2024-01-10"),
  updatedAt: ISODate("2024-01-15")
}

// REFERENCE when:
// - Data is accessed independently






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