dynamodb

AWS DynamoDB NoSQL database for scalable data storage. Use when designing table schemas, writing queries, configuring indexes, managing capacity, implementing single-table design, or troubleshooting performance issues.

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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.

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:

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:

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

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

# 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

# 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

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

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:

# 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:

# 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:

Verify key values exactly match (case-sensitive)
Check key types (S, N, B)
Confirm table/index name
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

# 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

Repository: github.com/itsmostafa/aws-agent-skills
Last updated: about 1 month ago
Created: about 1 month ago

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dynamodb

AWS DynamoDB

Table of Contents

Core Concepts

Keys

Secondary Indexes

Capacity Modes

Common Patterns

Create a Table

Basic CRUD Operations

Query Operations

Batch Operations

Create GSI

Conditional Writes

CLI Reference

Table Operations

Item Operations

Batch Operations

Best Practices

Data Modeling

Performance

Cost Optimization

Troubleshooting

Throttling

Hot Partitions

Query Returns No Items

Scan Performance

References

Is this your skill?