Skill: mongoid

What I do

• Document Modelling: Design document structures using fields, embedding, and referencing.
• Querying: Build complex queries and aggregations using Mongoid's criteria API.
• Associations: Manage relationships (embeds_one/many, has_many, belongs_to).
• Atomic Operations: Perform efficient updates (inc, set, push, pull) without full document rewrites.
• Optimisation: Design indices and implement eager loading (includes) to prevent N+1 queries.

When to use me

• Building Ruby/Rails applications with MongoDB.
• Storing hierarchical or flexible-schema data.
• Implementing complex aggregations or geospatial queries.
• Optimising MongoDB performance in a Ruby environment.

Core principles

• Embedding vs Referencing: Prefer embedding for 1-to-few/static data; reference for 1-to-many/unbounded data.
• ActiveModel Integration: Leverage Rails-style validations and callbacks for data integrity.
• Atomic Persistence: Use inc, set, and push to avoid race conditions.
• Index Strategy: Ensure all frequent query patterns are covered by background indices.

Patterns & examples

Document Definition (Ruby)

class Order
  include Mongoid::Document
  include Mongoid::Timestamps
  
  field :status, type: String, default: 'pending'
  field :total, type: BigDecimal
  
  belongs_to :user
  embeds_many :line_items
  
  index({ user_id: 1, created_at: -1 }, { background: true })
end

Anti-patterns to avoid

❌ Over-Embedding: Unbounded document growth causing performance degradation. ❌ N+1 Queries: Not using .includes(:association) for referenced documents. ❌ Missing Indices: Performing full collection scans on frequent queries.

KB Reference

~/vaults/baphled/3. Resources/Knowledge Base/AI Development System/Skills/Database-Persistence/Mongoid.md