api-database-mongodb

MongoDB / Mongoose Patterns

Quick Guide: Use Mongoose as the ODM for MongoDB. Define schemas with automatic TypeScript inference, use lean() for read-only queries, prefer embedding over referencing for co-accessed data, place $match early in aggregation pipelines, and always define indexes to match your query patterns.

<critical_requirements>

CRITICAL: Before Using This Skill

All code must follow project conventions in CLAUDE.md (kebab-case, named exports, import ordering, import type, named constants)

(You MUST define Mongoose middleware (pre/post hooks) BEFORE calling model() -- hooks registered after model compilation are silently ignored)

(You MUST pass { session } to EVERY operation inside a transaction -- missing session causes operations to run outside the transaction)

(You MUST use .lean() for read-only queries that send results directly to API responses -- skipping lean wastes 3x memory on hydration overhead)

(You MUST use 127.0.0.1 instead of localhost in connection strings -- Node.js 18+ prefers IPv6 and localhost can cause connection timeouts)

(You MUST NOT use findOneAndUpdate / updateOne and expect save middleware to fire -- only save() and create() trigger document middleware)

</critical_requirements>

Auto-detection: MongoDB, Mongoose, mongoose.connect, Schema, model, ObjectId, populate, aggregate, $match, $group, $lookup, lean, HydratedDocument, InferSchemaType, MongoClient, Atlas

When to use:

• Defining MongoDB schemas and models with Mongoose
• Building CRUD operations and complex queries
• Designing aggregation pipelines for analytics and reporting
• Managing indexes for query performance
• Connecting to MongoDB Atlas or local instances
• Modeling document relationships (embedding vs referencing)

Key patterns covered:

• Connection setup (Atlas URI, pooling, error handling)
• Schema definition (types, validation, defaults, enums)
• Models with TypeScript (automatic inference, methods, statics, virtuals)
• CRUD operations (create, find, update, delete, lean)
• Query building (filters, projection, sort, limit, populate)

When NOT to use:

• Highly relational data with complex joins and foreign key constraints (use a relational database)
• Strong ACID guarantees across many collections as a primary pattern (use a relational database)
• Simple key-value storage (use a dedicated key-value store)
• Fixed schemas where relational constraints are critical
• Time-series data at scale (use a dedicated time-series database)

Detailed Resources:

• For decision frameworks and anti-patterns, see [reference.md](reference.md)

Core Patterns:

• [examples/core.md](examples/core.md) - Connection, schema definition, model creation, TypeScript typing

Query Patterns:

• [examples/queries.md](examples/queries.md) - Complex queries, populate, lean, cursor, pagination

Aggregation:

• [examples/aggregation.md](examples/aggregation.md) - Aggregation pipeline, $match, $group, $lookup, $project

Advanced Patterns:

• [examples/patterns.md](examples/patterns.md) - Schema design (embedding vs referencing), transactions, middleware hooks, virtuals

Indexing:

• [examples/indexes.md](examples/indexes.md) - Index types, compound indexes, text search, geospatial, TTL, performance

<philosophy>

Philosophy

MongoDB is a document database. Mongoose provides schema-based modeling on top of it. The core principle: data that is accessed together should be stored together.

Core principles:

1. Schema-first design -- Define schemas before models. Schemas enforce structure, validation, and defaults at the application layer.
2. Embed by default -- Co-accessed data belongs in the same document. Only reference when data is shared across many documents, grows unbounded, or is frequently updated independently.
3. Lean for reads -- Use .lean() for read-only queries. It returns plain objects (3x less memory) instead of full Mongoose documents.
4. Index your queries -- Every query pattern needs a supporting index. Compound indexes follow the Equality-Sort-Range (ESR) rule.
5. Aggregation over application logic -- Push data transformation to the database with aggregation pipelines instead of processing in application code.
6. TypeScript inference -- Let Mongoose infer types from schema definitions. Avoid manually duplicating interfaces unless you need methods/statics/virtuals.

When to use MongoDB / Mongoose:

• Document-oriented data (user profiles, product catalogs, content)
• Flexible schemas that evolve over time
• Hierarchical or nested data structures
• High read throughput with embedding
• Geospatial queries and full-text search

</philosophy>

<patterns>

Core Patterns

Pattern 1: Connection Setup

Establish a single connection at startup with named constants for pool/timeout config and environment variables for credentials. See [examples/core.md](examples/core.md) for full examples including connection events and graceful shutdown.

const connection = await mongoose.connect(process.env.MONGODB_URI!, {
  maxPoolSize: POOL_SIZE_MAX,
  minPoolSize: POOL_SIZE_MIN,
  serverSelectionTimeoutMS: SERVER_SELECTION_TIMEOUT_MS,
  socketTimeoutMS: SOCKET_TIMEOUT_MS,
  retryWrites: true,
  retryReads: true,
});

Pattern 2: Schema Definition with TypeScript

Let Mongoose infer types from the schema definition. Use explicit interfaces only when adding methods, statics, or virtuals. See [examples/core.md](examples/core.md) for full typing examples with HydratedDocument, InferSchemaType, and generic parameters.

// Preferred: automatic type inference
const userSchema = new Schema(
  {
    name: { type: String, required: true, trim: true },
    email: { type: String, required: true, unique: true, lowercase: true },
    role: {
      type: String,
      enum: ["admin", "user", "moderator"] as const,
      default: "user",
    },
  },
  { timestamps: true },
);
const User = model("User", userSchema);

// For methods/statics/virtuals: explicit interfaces with Schema generics
const userSchema = new Schema<IUser, UserModel, IUserMethods, {}, IUserVirtuals>({ ... });

Pattern 3: CRUD Operations

Use .lean() for read-only queries, save() when middleware must fire, findByIdAndUpdate with { runValidators: true } for direct updates. See [examples/core.md](examples/core.md) for full CRUD examples.

const user = await User.findById(id).lean(); // read-only, 3x less memory
await User.insertMany(users, { ordered: false }); // bulk insert
await User.findByIdAndUpdate(id, update, { new: true, runValidators: true }); // direct update

Pattern 4: Query Building

Use comparison/logical operators for filters, .populate() with field selection and limits. See [examples/queries.md](examples/queries.md) for dynamic query builders, cursor-based pagination, and populate patterns.

const post = await Post.findById(id)
  .populate("author", "name email")
  .populate({
    path: "comments",
    options: { sort: { createdAt: -1 }, limit: 10 },
  })
  .lean();

Pattern 5: Schema Validation

Add custom error messages, regex validation, and array-level validators. See [examples/core.md](examples/core.md) for complete validation examples.

price: {
  type: Number,
  required: true,
  min: [0, "Price cannot be negative"],
  validate: { validator: (v: number) => Number.isFinite(v), message: "Price must be finite" },
},
sku: {
  type: String,
  required: true,
  unique: true,
  match: [/^[A-Z]{2}-\d{6}$/, "SKU must match format XX-000000"],
},

</patterns>

<red_flags>

RED FLAGS

High Priority Issues:

• Mutating a document fetched with .lean() and expecting .save() to work -- lean returns plain objects without Mongoose methods
• Registering middleware after `model()