AWS DynamoDB

Amazon DynamoDB is a fully managed NoSQL database service providing fast, predictable performance at any scale. It supports key-value and document data structures.

Table of Contents

• Core Concepts
• Common Patterns
• CLI Reference
• Best Practices
• Troubleshooting
• References

Core Concepts

Keys

| Key Type | Description | |----------|-------------| | Partition Key (PK) | Required. Determines data distribution | | Sort Key (SK) | Optional. Enables range queries within partition | | Composite Key | PK + SK combination |

Secondary Indexes

| Index Type | Description | |------------|-------------| | GSI (Global Secondary Index) | Different PK/SK, separate throughput, eventually consistent | | LSI (Local Secondary Index) | Same PK, different SK, shares table throughput, strongly consistent option |

Capacity Modes

| Mode | Use Case | |------|----------| | On-Demand | Unpredictable traffic, pay-per-request | | Provisioned | Predictable traffic, lower cost, can use auto-scaling |

Common Patterns

Create a Table

AWS CLI:

bash
aws dynamodb create-table \
  --table-name Users \
  --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

boto3:

python
import boto3

dynamodb = boto3.resource('dynamodb')

table = dynamodb.create_table(
    TableName='Users',
    KeySchema=[
        {'AttributeName': 'PK', 'KeyType': 'HASH'},
        {'AttributeName': 'SK', 'KeyType': 'RANGE'}
    ],
    AttributeDefinitions=[
        {'AttributeName': 'PK', 'AttributeType': 'S'},
        {'AttributeName': 'SK', 'AttributeType': 'S'}
    ],
    BillingMode='PAY_PER_REQUEST'
)

table.wait_until_exists()

Basic CRUD Operations

python
import boto3
from boto3.dynamodb.conditions import Key, Attr

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

# Put item
table.put_item(
    Item={
        'PK': 'USER#123',
        'SK': 'PROFILE',
        'name': 'John Doe',
        'email': 'john@example.com',
        'created_at': '2024-01-15T10:30:00Z'
    }
)

# Get item
response = table.get_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'}
)
item = response.get('Item')

# Update item
table.update_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'},
    UpdateExpression='SET #name = :name, updated_at = :updated',
    ExpressionAttributeNames={'#name': 'name'},
    ExpressionAttributeValues={
        ':name': 'John Smith',
        ':updated': '2024-01-16T10:30:00Z'
    }
)

# Delete item
table.delete_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'}
)

Query Operations

python
# Query by partition key
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123')
)

# Query with sort key condition
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123') & Key('SK').begins_with('ORDER#')
)

# Query with filter
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123'),
    FilterExpression=Attr('status').eq('active')
)

# Query with projection
response = table.query(
    KeyConditionExpression=Key('PK').eq('USER#123'),
    ProjectionExpression='PK, SK, #name, email',
    ExpressionAttributeNames={'#name': 'name'}
)

# Paginated query
paginator = dynamodb.meta.client.get_paginator('query')
for page in paginator.paginate(
    TableName='Users',
    KeyConditionExpression='PK = :pk',
    ExpressionAttributeValues={':pk': {'S': 'USER#123'}}
):
    for item in page['Items']:
        print(item)

Batch Operations

python
# Batch write (up to 25 items)
with table.batch_writer() as batch:
    for i in range(100):
        batch.put_item(Item={
            'PK': f'USER#{i}',
            'SK': 'PROFILE',
            'name': f'User {i}'
        })

# Batch get (up to 100 items)
dynamodb = boto3.resource('dynamodb')
response = dynamodb.batch_get_item(
    RequestItems={
        'Users': {
            'Keys': [
                {'PK': 'USER#1', 'SK': 'PROFILE'},
                {'PK': 'USER#2', 'SK': 'PROFILE'}
            ]
        }
    }
)

Create GSI

bash
aws dynamodb update-table \
  --table-name Users \
  --attribute-definitions AttributeName=email,AttributeType=S \
  --global-secondary-index-updates '[
    {
      "Create": {
        "IndexName": "email-index",
        "KeySchema": [{"AttributeName": "email", "KeyType": "HASH"}],
        "Projection": {"ProjectionType": "ALL"}
      }
    }
  ]'

Conditional Writes

python
from botocore.exceptions import ClientError

# Only put if item doesn't exist
try:
    table.put_item(
        Item={'PK': 'USER#123', 'SK': 'PROFILE', 'name': 'John'},
        ConditionExpression='attribute_not_exists(PK)'
    )
except ClientError as e:
    if e.response['Error']['Code'] == 'ConditionalCheckFailedException':
        print("Item already exists")

# Optimistic locking with version
table.update_item(
    Key={'PK': 'USER#123', 'SK': 'PROFILE'},
    UpdateExpression='SET #name = :name, version = version + :inc',
    ConditionExpression='version = :current_version',
    ExpressionAttributeNames={'#name': 'name'},
    ExpressionAttributeValues={
        ':name': 'New Name',
        ':inc': 1,
        ':current_version': 5
    }
)

CLI Reference

Table Operations

| Command | Description | |---------|-------------| | aws dynamodb create-table | Create table | | aws dynamodb describe-table | Get table info | | aws dynamodb update-table | Modify table/indexes | | aws dynamodb delete-table | Delete table | | aws dynamodb list-tables | List all tables |

Item Operations

| Command | Description | |---------|-------------| | aws dynamodb put-item | Create/replace item | | aws dynamodb get-item | Read single item | | aws dynamodb update-item | Update item attributes | | aws dynamodb delete-item | Delete item | | aws dynamodb query | Query by key | | aws dynamodb scan | Full table scan |

Batch Operations

| Command | Description | |---------|-------------| | aws dynamodb batch-write-item | Batch write (25 max) | | aws dynamodb batch-get-item | Batch read (100 max) | | aws dynamodb transact-write-items | Transaction write | | aws dynamodb transact-get-items | Transaction read |

Best Practices

Data Modeling

• Design for access patterns — know your queries before designing
• Use composite keys — PK for grouping, SK for sorting/filtering
• Prefer query over scan — scans are expensive
• Use sparse indexes — only items with index attributes are indexed
• Consider single-table design for related entities

Performance

• Distribute partition keys evenly — avoid hot partitions
• Use batch operations to reduce API calls
• Enable DAX for read-heavy workloads
• Use projections to reduce data transfer

Cost Optimization

• Use on-demand for variable workloads
• Use provisioned + auto-scaling for predictable workloads
• Set TTL for expiring data
• Archive to S3 for cold data

Troubleshooting

Throttling

Symptom: ProvisionedThroughputExceededException

Causes:

• Hot partition (uneven key distribution)
• Burst traffic exceeding capacity
• GSI throttling affecting base table

Solutions:

python
# Use exponential backoff
import time
from botocore.config import Config

config = Config(
    retries={
        'max_attempts': 10,
        'mode': 'adaptive'
    }
)
dynamodb = boto3.resource('dynamodb', config=config)

Hot Partitions

Debug:

bash
# Check consumed capacity by partition
aws cloudwatch get-metric-statistics \
  --namespace AWS/DynamoDB \
  --metric-name ConsumedReadCapacityUnits \
  --dimensions Name=TableName,Value=Users \
  --start-time $(date -d '1 hour ago' -u +%Y-%m-%dT%H:%M:%SZ) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%SZ) \
  --period 60 \
  --statistics Sum

Solutions:

• Add randomness to partition keys
• Use write sharding
• Distribute access across partitions

Query Returns No Items

Debug checklist:

1. Verify key values exactly match (case-sensitive)
2. Check key types (S, N, B)
3. Confirm table/index name
4. Review filter expressions (they apply AFTER read)

Scan Performance

Issue: Scans are slow and expensive

Solutions:

• Use parallel scan for large tables
• Create GSI for the access pattern
• Use filter expressions to reduce returned data

python
# Parallel scan
import concurrent.futures

def scan_segment(segment, total_segments):
    return table.scan(
        Segment=segment,
        TotalSegments=total_segments
    )

with concurrent.futures.ThreadPoolExecutor() as executor:
    results = list(executor.map(
        lambda s: scan_segment(s, 4),
        range(4)
    ))

References

• DynamoDB Developer Guide
• DynamoDB API Reference
• DynamoDB CLI Reference
• boto3 DynamoDB
• DynamoDB Best Practices