AWS DynamoDB

Amazon DynamoDB is a fully managed NoSQL key-value and document database. It delivers single-digit millisecond latency at any scale with automatic scaling, built-in security, and zero operational overhead.

Core Concepts

• Table — a collection of items (rows)
• Partition Key (PK) — required primary key for distributing data
• Sort Key (SK) — optional, enables range queries within a partition
• GSI — Global Secondary Index, alternate PK/SK for different access patterns
• LSI — Local Secondary Index, same PK but different SK (must be created at table creation)
• Streams — ordered log of item changes for event-driven processing
• TTL — automatic item expiration

Creating Tables

# Create a table with partition key and sort key
aws dynamodb create-table \
  --table-name Orders \
  --attribute-definitions \
    AttributeName=PK,AttributeType=S \
    AttributeName=SK,AttributeType=S \
  --key-schema \
    AttributeName=PK,KeyType=HASH \
    AttributeName=SK,KeyType=RANGE \
  --billing-mode PAY_PER_REQUEST \
  --tags Key=Env,Value=prod

# Create table with provisioned capacity and GSI
aws dynamodb create-table \
  --table-name Orders \
  --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"},
    "ProvisionedThroughput": {"ReadCapacityUnits":5,"WriteCapacityUnits":5}
  }]' \
  --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

Single-Table Design

# Single-table design — store multiple entity types in one table
import boto3
from datetime import datetime

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('AppData')

# Store a customer
table.put_item(Item={
    'PK': 'CUSTOMER#C001',
    'SK': 'PROFILE',
    'name': 'Alice Johnson',
    'email': '[email protected]',
    'GSI1PK': 'CUSTOMERS',
    'GSI1SK': 'Alice Johnson',
    'entity_type': 'Customer'
})

# Store an order for that customer
table.put_item(Item={
    'PK': 'CUSTOMER#C001',
    'SK': 'ORDER#2024-01-15#O001',
    'total': 149.99,
    'status': 'shipped',
    'GSI1PK': 'ORDER#O001',
    'GSI1SK': 'CUSTOMER#C001',
    'entity_type': 'Order'
})

# Query all orders for a customer (sorted by date)
response = table.query(
    KeyConditionExpression='PK = :pk AND begins_with(SK, :sk)',
    ExpressionAttributeValues={':pk': 'CUSTOMER#C001', ':sk': 'ORDER#'}
)

CRUD Operations

# Put an item
aws dynamodb put-item \
  --table-name Orders \
  --item '{
    "PK": {"S": "CUSTOMER#C001"},
    "SK": {"S": "ORDER#2024-01-15#O001"},
    "total": {"N": "149.99"},
    "status": {"S": "pending"}
  }'

# Get an item by key
aws dynamodb get-item \
  --table-name Orders \
  --key '{"PK":{"S":"CUSTOMER#C001"},"SK":{"S":"ORDER#2024-01-15#O001"}}'

# Update an item with conditional expression
aws dynamodb update-item \
  --table-name Orders \
  --key '{"PK":{"S":"CUSTOMER#C001"},"SK":{"S":"ORDER#2024-01-15#O001"}}' \
  --update-expression "SET #s = :new_status, updated_at = :ts" \
  --condition-expression "#s = :old_status" \
  --expression-attribute-names '{"#s":"status"}' \
  --expression-attribute-values '{":new_status":{"S":"shipped"},":old_status":{"S":"pending"},":ts":{"S":"2024-01-16T10:00:00Z"}}'

# Delete an item
aws dynamodb delete-item \
  --table-name Orders \
  --key '{"PK":{"S":"CUSTOMER#C001"},"SK":{"S":"ORDER#2024-01-15#O001"}}'

Queries and Scans

# Query with sort key condition
aws dynamodb query \
  --table-name Orders \
  --key-condition-expression "PK = :pk AND begins_with(SK, :prefix)" \
  --expression-attribute-values '{":pk":{"S":"CUSTOMER#C001"},":prefix":{"S":"ORDER#2024"}}' \
  --scan-index-forward false \
  --limit 10

# Query a GSI
aws dynamodb query \
  --table-name Orders \
  --index-name GSI1 \
  --key-condition-expression "GSI1PK = :pk" \
  --expression-attribute-values '{":pk":{"S":"ORDER#O001"}}'

Batch Operations

# Batch write (up to 25 items)
import boto3

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('AppData')

with table.batch_writer() as batch:
    for i in range(100):
        batch.put_item(Item={
            'PK': f'PRODUCT#P{i:04d}',
            'SK': 'DETAILS',
            'name': f'Product {i}',
            'price': round(9.99 + i * 0.5, 2)
        })

DynamoDB Streams

# Enable streams on a table
aws dynamodb update-table \
  --table-name Orders \
  --stream-specification StreamEnabled=true,StreamViewType=NEW_AND_OLD_IMAGES

# Lambda handler for DynamoDB stream events
import json

def handler(event, context):
    for record in event['Records']:
        event_name = record['eventName']  # INSERT, MODIFY, REMOVE
        new_image = record['dynamodb'].get('NewImage', {})
        old_image = record['dynamodb'].get('OldImage', {})

        if event_name == 'MODIFY':
            old_status = old_image.get('status', {}).get('S')
            new_status = new_image.get('status', {}).get('S')
            if old_status != new_status:
                print(f"Status changed: {old_status} -> {new_status}")
                # Trigger downstream processing

TTL (Time to Live)

# Enable TTL on an attribute
aws dynamodb update-time-to-live \
  --table-name Sessions \
  --time-to-live-specification Enabled=true,AttributeName=expires_at

# Set TTL when writing items (epoch timestamp)
import time

table.put_item(Item={
    'PK': 'SESSION#abc123',
    'SK': 'DATA',
    'user_id': 'U001',
    'expires_at': int(time.time()) + 86400  # 24 hours from now
})

Best Practices

• Design for access patterns first, not entity relationships
• Use single-table design to minimize the number of requests
• Use begins_with on sort keys for hierarchical data queries
• Enable on-demand (PAYPERREQUEST) for unpredictable workloads
• Use GSIs sparingly — each one duplicates data and costs extra
• Enable DynamoDB Streams + Lambda for event-driven reactions
• Use TTL to auto-expire temporary data (sessions, caches)
• Use condition expressions to prevent write conflicts