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aws-dynamodb skill

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This skill helps you model and query DynamoDB with single-table design, providing practical access-pattern guidance and TypeScript SDK v3 examples.

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---
name: aws-dynamodb
description: AWS DynamoDB single-table design, GSI patterns, SDK v3 TypeScript/Python
---

# AWS DynamoDB Skill

*Load with: base.md + [typescript.md | python.md]*

DynamoDB is a fully managed NoSQL database designed for single-digit millisecond performance at any scale. Master single-table design and access pattern modeling.

**Sources:** [DynamoDB Docs](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/) | [SDK v3](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/dynamodb/) | [Best Practices](https://aws.amazon.com/blogs/database/single-table-vs-multi-table-design-in-amazon-dynamodb/)

---

## Core Principle

**Design for access patterns, not entities. Think access-pattern-first.**

DynamoDB requires you to know your queries before designing your schema. Model around how you'll access data, not how data relates. Single-table design stores multiple entity types in one table using generic key attributes.

---

## Key Concepts

| Concept | Description |
|---------|-------------|
| **Partition Key (PK)** | Primary key attribute - determines data distribution |
| **Sort Key (SK)** | Optional secondary key for range queries within partition |
| **GSI** | Global Secondary Index - alternate partition/sort keys |
| **LSI** | Local Secondary Index - same partition, different sort |
| **Item** | Single record (max 400 KB) |
| **Attribute** | Field within an item |

---

## Single-Table Design

### Why Single Table?
- Fetch related data in single query
- Reduce round trips and costs
- Enable transactions across entity types
- Simplify operations (backup, restore, IAM)

### Generic Key Pattern
```typescript
// Instead of entity-specific keys:
// userId, orderId, productId

// Use generic keys that work for all entities:
interface BaseItem {
  PK: string;   // Partition Key
  SK: string;   // Sort Key
  GSI1PK?: string;  // First GSI partition key
  GSI1SK?: string;  // First GSI sort key
  EntityType: string;
  // ... entity-specific attributes
}
```

### Example: E-commerce Schema
```typescript
// Users
{ PK: 'USER#123', SK: 'PROFILE', EntityType: 'User', name: 'John', email: '[email protected]' }
{ PK: 'USER#123', SK: 'ADDRESS#1', EntityType: 'Address', street: '123 Main', city: 'NYC' }

// Orders for user (1:N relationship)
{ PK: 'USER#123', SK: 'ORDER#2024-001', EntityType: 'Order', total: 99.99, status: 'shipped' }
{ PK: 'USER#123', SK: 'ORDER#2024-002', EntityType: 'Order', total: 49.99, status: 'pending' }

// Order details (query by order ID using GSI)
{ PK: 'USER#123', SK: 'ORDER#2024-001', GSI1PK: 'ORDER#2024-001', GSI1SK: 'ORDER', ... }
{ PK: 'ORDER#2024-001', SK: 'ITEM#1', GSI1PK: 'ORDER#2024-001', GSI1SK: 'ITEM#1', productId: 'PROD#456', qty: 2 }

// Products
{ PK: 'PROD#456', SK: 'PRODUCT', EntityType: 'Product', name: 'Widget', price: 29.99 }
```

### Access Patterns Covered
```
1. Get user profile          → Query PK='USER#123', SK='PROFILE'
2. Get user with addresses   → Query PK='USER#123', SK begins_with 'ADDRESS'
3. Get all user orders       → Query PK='USER#123', SK begins_with 'ORDER'
4. Get order by ID           → Query GSI1, PK='ORDER#2024-001'
5. Get order with items      → Query GSI1, PK='ORDER#2024-001'
6. Get product details       → Query PK='PROD#456', SK='PRODUCT'
```

---

## SDK v3 Setup (TypeScript)

### Install Dependencies
```bash
npm install @aws-sdk/client-dynamodb @aws-sdk/lib-dynamodb
```

### Client Configuration
```typescript
// lib/dynamodb.ts
import { DynamoDBClient } from '@aws-sdk/client-dynamodb';
import { DynamoDBDocumentClient } from '@aws-sdk/lib-dynamodb';

const client = new DynamoDBClient({
  region: process.env.AWS_REGION || 'us-east-1',
  // For local development with DynamoDB Local
  ...(process.env.DYNAMODB_LOCAL && {
    endpoint: 'http://localhost:8000',
    credentials: { accessKeyId: 'local', secretAccessKey: 'local' }
  })
});

// Document client for simplified operations
export const docClient = DynamoDBDocumentClient.from(client, {
  marshallOptions: {
    removeUndefinedValues: true,  // Important: match v2 behavior
    convertClassInstanceToMap: true
  },
  unmarshallOptions: {
    wrapNumbers: false
  }
});

export const TABLE_NAME = process.env.DYNAMODB_TABLE || 'MyTable';
```

### Type Definitions
```typescript
// types/dynamodb.ts
export interface BaseItem {
  PK: string;
  SK: string;
  GSI1PK?: string;
  GSI1SK?: string;
  EntityType: string;
  createdAt: string;
  updatedAt: string;
}

export interface User extends BaseItem {
  EntityType: 'User';
  userId: string;
  email: string;
  name: string;
}

export interface Order extends BaseItem {
  EntityType: 'Order';
  orderId: string;
  userId: string;
  total: number;
  status: 'pending' | 'paid' | 'shipped' | 'delivered';
}

// Key builders
export const keys = {
  user: (userId: string) => ({
    PK: `USER#${userId}`,
    SK: 'PROFILE'
  }),
  userOrders: (userId: string) => ({
    PK: `USER#${userId}`,
    SKPrefix: 'ORDER#'
  }),
  order: (userId: string, orderId: string) => ({
    PK: `USER#${userId}`,
    SK: `ORDER#${orderId}`,
    GSI1PK: `ORDER#${orderId}`,
    GSI1SK: 'ORDER'
  })
};
```

---

## CRUD Operations

### Put Item (Create/Update)
```typescript
import { PutCommand } from '@aws-sdk/lib-dynamodb';
import { docClient, TABLE_NAME } from './dynamodb';
import { User, keys } from './types';

async function createUser(userId: string, data: { email: string; name: string }): Promise<User> {
  const now = new Date().toISOString();
  const item: User = {
    ...keys.user(userId),
    EntityType: 'User',
    userId,
    email: data.email,
    name: data.name,
    createdAt: now,
    updatedAt: now
  };

  await docClient.send(new PutCommand({
    TableName: TABLE_NAME,
    Item: item,
    ConditionExpression: 'attribute_not_exists(PK)'  // Prevent overwrite
  }));

  return item;
}
```

### Get Item (Read)
```typescript
import { GetCommand } from '@aws-sdk/lib-dynamodb';

async function getUser(userId: string): Promise<User | null> {
  const result = await docClient.send(new GetCommand({
    TableName: TABLE_NAME,
    Key: keys.user(userId)
  }));

  return (result.Item as User) || null;
}
```

### Query (List/Search)
```typescript
import { QueryCommand } from '@aws-sdk/lib-dynamodb';

// Get all orders for a user
async function getUserOrders(userId: string): Promise<Order[]> {
  const result = await docClient.send(new QueryCommand({
    TableName: TABLE_NAME,
    KeyConditionExpression: 'PK = :pk AND begins_with(SK, :sk)',
    ExpressionAttributeValues: {
      ':pk': `USER#${userId}`,
      ':sk': 'ORDER#'
    },
    ScanIndexForward: false  // Newest first
  }));

  return (result.Items as Order[]) || [];
}

// Query GSI by order ID
async function getOrderById(orderId: string): Promise<Order | null> {
  const result = await docClient.send(new QueryCommand({
    TableName: TABLE_NAME,
    IndexName: 'GSI1',
    KeyConditionExpression: 'GSI1PK = :pk',
    ExpressionAttributeValues: {
      ':pk': `ORDER#${orderId}`
    }
  }));

  return (result.Items?.[0] as Order) || null;
}

// Paginated query
async function getUserOrdersPaginated(
  userId: string,
  pageSize: number = 20,
  lastKey?: Record<string, any>
): Promise<{ items: Order[]; lastKey?: Record<string, any> }> {
  const result = await docClient.send(new QueryCommand({
    TableName: TABLE_NAME,
    KeyConditionExpression: 'PK = :pk AND begins_with(SK, :sk)',
    ExpressionAttributeValues: {
      ':pk': `USER#${userId}`,
      ':sk': 'ORDER#'
    },
    Limit: pageSize,
    ExclusiveStartKey: lastKey
  }));

  return {
    items: (result.Items as Order[]) || [],
    lastKey: result.LastEvaluatedKey
  };
}
```

### Update Item
```typescript
import { UpdateCommand } from '@aws-sdk/lib-dynamodb';

async function updateUser(userId: string, updates: Partial<Pick<User, 'name' | 'email'>>): Promise<User> {
  // Build update expression dynamically
  const updateParts: string[] = ['#updatedAt = :updatedAt'];
  const names: Record<string, string> = { '#updatedAt': 'updatedAt' };
  const values: Record<string, any> = { ':updatedAt': new Date().toISOString() };

  if (updates.name !== undefined) {
    updateParts.push('#name = :name');
    names['#name'] = 'name';
    values[':name'] = updates.name;
  }

  if (updates.email !== undefined) {
    updateParts.push('#email = :email');
    names['#email'] = 'email';
    values[':email'] = updates.email;
  }

  const result = await docClient.send(new UpdateCommand({
    TableName: TABLE_NAME,
    Key: keys.user(userId),
    UpdateExpression: `SET ${updateParts.join(', ')}`,
    ExpressionAttributeNames: names,
    ExpressionAttributeValues: values,
    ReturnValues: 'ALL_NEW',
    ConditionExpression: 'attribute_exists(PK)'  // Must exist
  }));

  return result.Attributes as User;
}

// Atomic counter increment
async function incrementOrderCount(userId: string): Promise<void> {
  await docClient.send(new UpdateCommand({
    TableName: TABLE_NAME,
    Key: keys.user(userId),
    UpdateExpression: 'SET orderCount = if_not_exists(orderCount, :zero) + :inc',
    ExpressionAttributeValues: {
      ':zero': 0,
      ':inc': 1
    }
  }));
}
```

### Delete Item
```typescript
import { DeleteCommand } from '@aws-sdk/lib-dynamodb';

async function deleteUser(userId: string): Promise<void> {
  await docClient.send(new DeleteCommand({
    TableName: TABLE_NAME,
    Key: keys.user(userId),
    ConditionExpression: 'attribute_exists(PK)'
  }));
}
```

---

## Batch Operations

### Batch Write (Up to 25 items)
```typescript
import { BatchWriteCommand } from '@aws-sdk/lib-dynamodb';

async function batchCreateItems(items: BaseItem[]): Promise<void> {
  // DynamoDB allows max 25 items per batch
  const chunks = [];
  for (let i = 0; i < items.length; i += 25) {
    chunks.push(items.slice(i, i + 25));
  }

  for (const chunk of chunks) {
    await docClient.send(new BatchWriteCommand({
      RequestItems: {
        [TABLE_NAME]: chunk.map(item => ({
          PutRequest: { Item: item }
        }))
      }
    }));
  }
}
```

### Batch Get (Up to 100 items)
```typescript
import { BatchGetCommand } from '@aws-sdk/lib-dynamodb';

async function batchGetUsers(userIds: string[]): Promise<User[]> {
  const result = await docClient.send(new BatchGetCommand({
    RequestItems: {
      [TABLE_NAME]: {
        Keys: userIds.map(id => keys.user(id))
      }
    }
  }));

  return (result.Responses?.[TABLE_NAME] as User[]) || [];
}
```

---

## Transactions

### TransactWrite (Atomic Multi-Item)
```typescript
import { TransactWriteCommand } from '@aws-sdk/lib-dynamodb';

async function createOrderWithItems(
  userId: string,
  orderId: string,
  orderData: { total: number },
  items: { productId: string; quantity: number }[]
): Promise<void> {
  const now = new Date().toISOString();

  const transactItems = [
    // Create order
    {
      Put: {
        TableName: TABLE_NAME,
        Item: {
          ...keys.order(userId, orderId),
          EntityType: 'Order',
          orderId,
          userId,
          total: orderData.total,
          status: 'pending',
          createdAt: now,
          updatedAt: now
        },
        ConditionExpression: 'attribute_not_exists(PK)'
      }
    },
    // Update user's order count
    {
      Update: {
        TableName: TABLE_NAME,
        Key: keys.user(userId),
        UpdateExpression: 'SET orderCount = if_not_exists(orderCount, :zero) + :inc',
        ExpressionAttributeValues: { ':zero': 0, ':inc': 1 }
      }
    },
    // Add order items
    ...items.map((item, index) => ({
      Put: {
        TableName: TABLE_NAME,
        Item: {
          PK: `ORDER#${orderId}`,
          SK: `ITEM#${index}`,
          GSI1PK: `ORDER#${orderId}`,
          GSI1SK: `ITEM#${index}`,
          EntityType: 'OrderItem',
          productId: item.productId,
          quantity: item.quantity,
          createdAt: now
        }
      }
    }))
  ];

  await docClient.send(new TransactWriteCommand({
    TransactItems: transactItems
  }));
}
```

---

## GSI Patterns

### Sparse Index
```typescript
// Only items with GSI1PK attribute appear in the index
// Useful for "featured" or "flagged" items

// Featured products (only some products have GSI1PK)
{ PK: 'PROD#1', SK: 'PRODUCT', GSI1PK: 'FEATURED', GSI1SK: 'PROD#1', ... }  // In index
{ PK: 'PROD#2', SK: 'PRODUCT', ... }  // Not in index (no GSI1PK)

// Query featured products
const featured = await docClient.send(new QueryCommand({
  TableName: TABLE_NAME,
  IndexName: 'GSI1',
  KeyConditionExpression: 'GSI1PK = :pk',
  ExpressionAttributeValues: { ':pk': 'FEATURED' }
}));
```

### Inverted Index (GSI)
```typescript
// Main table: User -> Orders (PK=USER#, SK=ORDER#)
// GSI: Orders by status (GSI1PK=STATUS#, GSI1SK=ORDER#)

{ PK: 'USER#123', SK: 'ORDER#001', GSI1PK: 'STATUS#pending', GSI1SK: 'ORDER#001', ... }
{ PK: 'USER#456', SK: 'ORDER#002', GSI1PK: 'STATUS#shipped', GSI1SK: 'ORDER#002', ... }

// Get all pending orders across all users
const pending = await docClient.send(new QueryCommand({
  TableName: TABLE_NAME,
  IndexName: 'GSI1',
  KeyConditionExpression: 'GSI1PK = :pk',
  ExpressionAttributeValues: { ':pk': 'STATUS#pending' }
}));
```

### Multi-Attribute Composite Keys (Nov 2025+)
```typescript
// New feature: Up to 4 attributes per partition/sort key
// No more synthetic keys like "TOURNAMENT#WINTER2024#REGION#NA-EAST"

// Table definition (IaC)
const table = {
  AttributeDefinitions: [
    { AttributeName: 'tournament', AttributeType: 'S' },
    { AttributeName: 'region', AttributeType: 'S' },
    { AttributeName: 'score', AttributeType: 'N' }
  ],
  GlobalSecondaryIndexes: [{
    IndexName: 'TournamentRegionIndex',
    KeySchema: [
      { AttributeName: 'tournament', KeyType: 'HASH' },  // Composite PK part 1
      { AttributeName: 'region', KeyType: 'HASH' },      // Composite PK part 2
      { AttributeName: 'score', KeyType: 'RANGE' }
    ]
  }]
};
```

---

## Python (boto3)

### Setup
```python
# requirements.txt
boto3>=1.34.0

# db.py
import boto3
from boto3.dynamodb.conditions import Key, Attr
import os

dynamodb = boto3.resource(
    'dynamodb',
    region_name=os.getenv('AWS_REGION', 'us-east-1'),
    endpoint_url=os.getenv('DYNAMODB_LOCAL_ENDPOINT')  # For local dev
)

table = dynamodb.Table(os.getenv('DYNAMODB_TABLE', 'MyTable'))
```

### Operations
```python
from datetime import datetime
from typing import Optional, List
from decimal import Decimal

def create_user(user_id: str, email: str, name: str) -> dict:
    now = datetime.utcnow().isoformat()
    item = {
        'PK': f'USER#{user_id}',
        'SK': 'PROFILE',
        'EntityType': 'User',
        'userId': user_id,
        'email': email,
        'name': name,
        'createdAt': now,
        'updatedAt': now
    }

    table.put_item(
        Item=item,
        ConditionExpression='attribute_not_exists(PK)'
    )
    return item


def get_user(user_id: str) -> Optional[dict]:
    response = table.get_item(
        Key={'PK': f'USER#{user_id}', 'SK': 'PROFILE'}
    )
    return response.get('Item')


def get_user_orders(user_id: str) -> List[dict]:
    response = table.query(
        KeyConditionExpression=Key('PK').eq(f'USER#{user_id}') & Key('SK').begins_with('ORDER#'),
        ScanIndexForward=False
    )
    return response.get('Items', [])


def update_user(user_id: str, **updates) -> dict:
    update_parts = ['#updatedAt = :updatedAt']
    names = {'#updatedAt': 'updatedAt'}
    values = {':updatedAt': datetime.utcnow().isoformat()}

    for key, value in updates.items():
        update_parts.append(f'#{key} = :{key}')
        names[f'#{key}'] = key
        values[f':{key}'] = value

    response = table.update_item(
        Key={'PK': f'USER#{user_id}', 'SK': 'PROFILE'},
        UpdateExpression=f'SET {", ".join(update_parts)}',
        ExpressionAttributeNames=names,
        ExpressionAttributeValues=values,
        ReturnValues='ALL_NEW'
    )
    return response['Attributes']


def delete_user(user_id: str) -> None:
    table.delete_item(
        Key={'PK': f'USER#{user_id}', 'SK': 'PROFILE'}
    )
```

---

## Local Development

### DynamoDB Local
```bash
# Docker
docker run -d -p 8000:8000 amazon/dynamodb-local

# Create table locally
aws dynamodb create-table \
  --endpoint-url http://localhost:8000 \
  --table-name MyTable \
  --attribute-definitions \
    AttributeName=PK,AttributeType=S \
    AttributeName=SK,AttributeType=S \
    AttributeName=GSI1PK,AttributeType=S \
    AttributeName=GSI1SK,AttributeType=S \
  --key-schema \
    AttributeName=PK,KeyType=HASH \
    AttributeName=SK,KeyType=RANGE \
  --global-secondary-indexes \
    'IndexName=GSI1,KeySchema=[{AttributeName=GSI1PK,KeyType=HASH},{AttributeName=GSI1SK,KeyType=RANGE}],Projection={ProjectionType=ALL}' \
  --billing-mode PAY_PER_REQUEST
```

### NoSQL Workbench
AWS provides [NoSQL Workbench](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/workbench.html) for visual data modeling and querying.

---

## CLI Quick Reference

```bash
# Table operations
aws dynamodb create-table --cli-input-json file://table.json
aws dynamodb describe-table --table-name MyTable
aws dynamodb delete-table --table-name MyTable

# Item operations
aws dynamodb put-item --table-name MyTable --item '{"PK":{"S":"USER#1"},"SK":{"S":"PROFILE"}}'
aws dynamodb get-item --table-name MyTable --key '{"PK":{"S":"USER#1"},"SK":{"S":"PROFILE"}}'
aws dynamodb delete-item --table-name MyTable --key '{"PK":{"S":"USER#1"},"SK":{"S":"PROFILE"}}'

# Query
aws dynamodb query --table-name MyTable \
  --key-condition-expression "PK = :pk" \
  --expression-attribute-values '{":pk":{"S":"USER#1"}}'

# Scan (avoid in production)
aws dynamodb scan --table-name MyTable --limit 10
```

---

## Anti-Patterns

- **Scan operations** - Always use Query with proper key conditions
- **Hot partitions** - Distribute writes with high-cardinality partition keys
- **Large items** - Keep items under 400KB; use S3 for large data
- **Too many GSIs** - Each GSI duplicates data; design carefully
- **Ignoring capacity** - Monitor consumed capacity, use on-demand for variable loads
- **No condition expressions** - Always validate with ConditionExpression
- **Fetching all attributes** - Use ProjectionExpression to limit data
- **Multi-table design without reason** - Single-table is preferred unless access patterns don't overlap

Overview

This skill teaches opinionated single-table DynamoDB design and practical patterns for GSIs using AWS SDK v3 in TypeScript and boto3 in Python. It focuses on access-pattern-first schema modeling, secure SDK configuration, and common CRUD, batch, and transactional operations. Followable key-building conventions and examples accelerate building scalable, cost-efficient NoSQL apps.

How this skill works

The skill inspects access patterns and maps them to a single-table schema using generic keys (PK, SK) and optional GSIs for alternate query shapes. It provides client setup examples, typed TypeScript interfaces and key builders, plus idiomatic CRUD, query, batch, and transaction routines. GSI patterns (sparse, inverted, composite) and pagination are demonstrated for real-world retrievals.

When to use it

  • Building APIs that require predictable single-digit millisecond reads/writes
  • Designing an application where many entity types share query patterns (single-table design)
  • Needing global secondary indexes to support alternate query access across entities
  • Implementing atomic multi-item workflows with TransactWrite
  • Migrating from multi-table relational patterns to a NoSQL access-pattern-first model

Best practices

  • Define access patterns before choosing keys or GSIs; model queries first
  • Use generic PK/SK naming (e.g., USER#id, ORDER#id) and store EntityType for clarity
  • Keep items under 400 KB and push large blobs to object storage with pointers in DynamoDB
  • Use conditional expressions to prevent accidental overwrites and to enforce invariants
  • Use DocumentClient/typed clients and marshallOptions to remove undefined values and simplify SDK usage

Example use cases

  • E-commerce: store users, orders, order items and products in one table for single-query fetches
  • Multi-tenant SaaS: tenant-scoped PKs to isolate data and enable efficient tenant queries
  • Event sourcing: append-only items with SK timestamps for ordered streams per entity
  • Analytics flags: sparse GSI for featured or flagged records across all entities
  • Checkout flow: transactionally create an order and its items while incrementing user counters

FAQ

When should I use a GSI versus duplicating data?

Use a GSI when you need a different primary access pattern across many items. Duplicate minimal attributes into items with GSI keys when cross-partition queries would otherwise be expensive.

How do I avoid hot partitions?

Distribute traffic by including sharding tokens or time buckets in the PK for very high-throughput keys, and avoid extremely high write concentration on a single partition key.