nosql-database-design

name	nosql-database-design
description	Design NoSQL database schemas for MongoDB and DynamoDB. Use when modeling document structures, designing collections, or planning NoSQL data architectures.

NoSQL Database Design

Overview

Design scalable NoSQL schemas for MongoDB (document) and DynamoDB (key-value). Covers data modeling patterns, denormalization strategies, and query optimization for NoSQL systems.

When to Use

• MongoDB collection design
• DynamoDB table and index design
• Document structure modeling
• Embedding vs. referencing decisions
• Query pattern optimization
• NoSQL indexing strategies
• Data denormalization planning

MongoDB Schema Design

Document Structure Design

MongoDB - Embedded Documents:

// Single document with embedded arrays
db.createCollection("users")

db.users.insertOne({
  _id: ObjectId("..."),
  email: "[e-mail]",
  name: "John Doe",
  createdAt: new Date(),

  // Embedded address
  address: {
    street: "123 Main St",
    city: "New York",
    state: "NY",
    zipCode: "10001"
  },

  // Embedded array of items
  orders: [
    {
      orderId: ObjectId("..."),
      date: new Date(),
      total: 149.99
    },
    {
      orderId: ObjectId("..."),
      date: new Date(),
      total: 89.99
    }
  ]
})

MongoDB - Referenced Documents:

// Separate collections with references
db.createCollection("users")
db.createCollection("orders")

db.users.insertOne({
  _id: ObjectId("..."),
  email: "[e-mail]",
  name: "John Doe"
})

db.orders.insertMany([
  {
    _id: ObjectId("..."),
    userId: ObjectId("..."),  // Reference to user
    orderDate: new Date(),
    total: 149.99
  },
  {
    _id: ObjectId("..."),
    userId: ObjectId("..."),
    orderDate: new Date(),
    total: 89.99
  }
])

// Query with $lookup for JOINs
db.orders.aggregate([
  {
    $match: { userId: ObjectId("...") }
  },
  {
    $lookup: {
      from: "users",
      localField: "userId",
      foreignField: "_id",
      as: "user"
    }
  }
])

Indexing in MongoDB

// Single field index
db.users.createIndex({ email: 1 })
db.orders.createIndex({ createdAt: -1 })

// Compound index
db.orders.createIndex({ userId: 1, createdAt: -1 })

// Text index for search
db.products.createIndex({ name: "text", description: "text" })

// Geospatial index
db.stores.createIndex({ location: "2dsphere" })

// TTL index for auto-expiration
db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 })

// Sparse index (only documents with field)
db.users.createIndex({ phone: 1 }, { sparse: true })

// Check index usage
db.users.aggregate([{ $indexStats: {} }])

Schema Validation

// Define collection validation schema
db.createCollection("products", {
  validator: {
    $jsonSchema: {
      bsonType: "object",
      required: ["name", "price", "category"],
      properties: {
        _id: { bsonType: "objectId" },
        name: {
          bsonType: "string",
          description: "Product name (required)"
        },
        price: {
          bsonType: "decimal",
          minimum: 0,
          description: "Price must be positive"
        },
        category: {
          enum: ["electronics", "clothing", "food"],
          description: "Category must be one of listed values"
        },
        tags: {
          bsonType: "array",
          items: { bsonType: "string" }
        },
        createdAt: {
          bsonType: "date"
        }
      }
    }
  }
})

DynamoDB Schema Design

Table Structure

// DynamoDB table with single primary key
const TableName = "users"
const params = {
  TableName,
  KeySchema: [
    { AttributeName: "userId", KeyType: "HASH" }  // Partition key
  ],
  AttributeDefinitions: [
    { AttributeName: "userId", AttributeType: "S" }  // String
  ],
  BillingMode: "PAY_PER_REQUEST"  // On-demand
}

// DynamoDB table with composite primary key
const ordersParams = {
  TableName: "orders",
  KeySchema: [
    { AttributeName: "userId", KeyType: "HASH" },      // Partition key
    { AttributeName: "orderId", KeyType: "RANGE" }    // Sort key
  ],
  AttributeDefinitions: [
    { AttributeName: "userId", AttributeType: "S" },
    { AttributeName: "orderId", AttributeType: "S" }
  ],
  BillingMode: "PAY_PER_REQUEST"
}

Global Secondary Indexes (GSI)

// Add GSI for querying by email
const gsiParams = {
  TableName: "users",
  AttributeDefinitions: [
    { AttributeName: "email", AttributeType: "S" }
  ],
  GlobalSecondaryIndexes: [
    {
      IndexName: "emailIndex",
      KeySchema: [
        { AttributeName: "email", KeyType: "HASH" }
      ],
      Projection: {
        ProjectionType: "ALL"  // Return all attributes
      },
      BillingMode: "PAY_PER_REQUEST"
    }
  ]
}

// GSI with composite key for time-based queries
const timeIndexParams = {
  GlobalSecondaryIndexes: [
    {
      IndexName: "userCreatedIndex",
      KeySchema: [
        { AttributeName: "userId", KeyType: "HASH" },
        { AttributeName: "createdAt", KeyType: "RANGE" }
      ],
      Projection: { ProjectionType: "ALL" },
      BillingMode: "PAY_PER_REQUEST"
    }
  ]
}

DynamoDB Item Operations

// Put item (insert/update)
const putParams = {
  TableName: "users",
  Item: {
    userId: { S: "user-123" },
    email: { S: "[e-mail]" },
    name: { S: "John Doe" },
    createdAt: { N: Date.now().toString() },
    metadata: {
      M: {
        joinDate: { N: Date.now().toString() },
        source: { S: "web" }
      }
    }
  }
}

// Query using GSI
const queryParams = {
  TableName: "users",
  IndexName: "emailIndex",
  KeyConditionExpression: "email = :email",
  ExpressionAttributeValues: {
    ":email": { S: "[e-mail]" }
  }
}

// Batch get items
const batchGetParams = {
  RequestItems: {
    "users": {
      Keys: [
        { userId: { S: "user-123" } },
        { userId: { S: "user-456" } }
      ]
    }
  }
}

Denormalization Patterns

MongoDB - Embedding for Performance:

// Embed frequently accessed data to avoid lookups
db.orders.insertOne({
  _id: ObjectId("..."),
  userId: ObjectId("..."),
  userEmail: "[e-mail]",      // Denormalized
  userName: "John Doe",                // Denormalized
  createdAt: new Date(),
  items: [
    {
      productId: ObjectId("..."),
      productName: "Laptop",            // Denormalized
      productPrice: 999.99,             // Denormalized
      quantity: 1
    }
  ]
})

DynamoDB - Denormalization with Consistency:

// Store related data in same item to ensure consistency
const params = {
  TableName: "orders",
  Item: {
    userId: { S: "user-123" },
    orderId: { S: "order-456" },
    orderDate: { N: Date.now().toString() },

    // User data snapshot at order time
    userSnapshot: {
      M: {
        email: { S: "[e-mail]" },
        address: { S: "123 Main St" }
      }
    },

    // Items with product information
    items: {
      L: [
        {
          M: {
            productId: { S: "prod-789" },
            name: { S: "Laptop" },
            price: { N: "999.99" },
            quantity: { N: "1" }
          }
        }
      ]
    }
  }
}

Design Patterns

MongoDB - Time-Series Pattern:

// Efficient time-series data storage
db.sensor_data.insertOne({
  _id: ObjectId("..."),
  sensorId: "sensor-123",
  date: ISODate("2024-01-15"),
  measurements: [
    { time: "12:00", temperature: 72.5, humidity: 45 },
    { time: "12:01", temperature: 72.6, humidity: 45.2 },
    { time: "12:02", temperature: 72.4, humidity: 44.8 }
  ]
})

// Index for efficient queries
db.sensor_data.createIndex({ sensorId: 1, date: -1 })

DynamoDB - One-to-Many Relationship:

// Store one-to-many relationships efficiently
// User comments using userId as partition key, commentId as sort key
const commentParams = {
  TableName: "comments",
  Item: {
    userId: { S: "user-123" },           // Partition key
    commentId: { S: "comment-789" },     // Sort key
    postId: { S: "post-456" },
    content: { S: "Great article!" },
    createdAt: { N: Date.now().toString() }
  }
}

Capacity Planning

MongoDB - Horizontal Scaling:

// Sharding for large collections
sh.shardCollection("ecommerce.orders", { userId: "hashed" })

// Monitor shard distribution
db.orders.aggregate([
  { $group: { _id: "$userId", count: { $sum: 1 } } },
  { $sort: { count: -1 } }
])

DynamoDB - Partition Key Design:

// Good: Distribute across many keys
// Partition key: "USER#123" (spreads across partitions)
// Sort key: "ORDER#2024-01"

// Bad: Hot partition
// Partition key: "ADMIN" (all admin operations hit same partition)

// Solution: Add timestamp or random suffix
// Partition key: "ADMIN#20240115#random"

Migration Considerations

• Plan data migration strategy
• Consider consistency requirements
• Test query patterns before finalizing schema
• Monitor performance after deployment
• Document relationships and access patterns
• Plan for schema evolution

Resources

• MongoDB Schema Design Best Practices
• DynamoDB Best Practices
• MongoDB vs Relational Data Modeling