15 AWS DynamoDB Interview Questions and Answers

AWS DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability. It is designed to handle large-scale applications and can automatically scale up and down to adjust for capacity and maintain performance. DynamoDB is widely used for its flexibility, reliability, and integration with other AWS services, making it a popular choice for developers and organizations looking to build robust, high-performance applications.

This article offers a curated selection of interview questions and answers focused on AWS DynamoDB. By reviewing these questions, you will gain a deeper understanding of key concepts, best practices, and real-world applications, helping you to confidently demonstrate your expertise in DynamoDB during technical interviews.

AWS DynamoDB Interview Questions and Answers

1. Describe the difference between a partition key and a sort key.

In AWS DynamoDB, a partition key and a sort key uniquely identify items within a table and organize data for efficient querying. A partition key is a single attribute used to distribute data across partitions, ensuring even distribution and horizontal scaling. A sort key, combined with the partition key, forms a composite primary key, allowing for complex querying, such as retrieving items within a specific range of sort key values.

2. What are Global Secondary Indexes (GSIs) and how do they differ from Local Secondary Indexes (LSIs)?

Global Secondary Indexes (GSIs) allow querying on non-primary key attributes and can be created at any time, with their own provisioned throughput settings. They span all items in a table, offering flexibility for querying different attributes. Local Secondary Indexes (LSIs) are created with the table, sharing the same partition key as the primary key, and allow indexing on a different sort key. LSIs share the table’s provisioned throughput and are limited to 10 GB per partition key.

Key differences:

• Creation Time: GSIs can be created anytime, while LSIs must be created with the table.
• Partition Key: GSIs can have a different partition key, whereas LSIs share the table’s partition key.
• Provisioned Throughput: GSIs have their own settings, while LSIs share the table’s throughput.
• Data Size Limit: LSIs are limited to 10 GB per partition key; GSIs are not.

3. How would you implement pagination?

Pagination in DynamoDB manages large datasets by dividing them into smaller chunks. Use the LastEvaluatedKey from a Query or Scan response to fetch the next set of results, acting as a cursor to continue from where the previous operation left off.

Example:

import boto3

def paginate_dynamodb(table_name, limit):
    dynamodb = boto3.resource('dynamodb')
    table = dynamodb.Table(table_name)
    
    response = table.scan(Limit=limit)
    items = response.get('Items', [])
    
    while 'LastEvaluatedKey' in response:
        response = table.scan(
            Limit=limit,
            ExclusiveStartKey=response['LastEvaluatedKey']
        )
        items.extend(response.get('Items', []))
    
    return items

# Usage
items = paginate_dynamodb('your_table_name', 10)
print(items)

4. Write a query to update an item conditionally based on an attribute value.

You can perform conditional updates using the UpdateItem operation, updating an item only if a specified condition is met. This ensures data integrity and prevents race conditions.

Example:

import boto3
from botocore.exceptions import ClientError

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

key = {'PrimaryKey': 'YourPrimaryKeyValue'}
update_expression = "set attributeName = :newVal"
condition_expression = "attributeName = :expectedVal"
expression_attribute_values = {
    ':newVal': 'NewValue',
    ':expectedVal': 'ExpectedValue'
}

try:
    response = table.update_item(
        Key=key,
        UpdateExpression=update_expression,
        ConditionExpression=condition_expression,
        ExpressionAttributeValues=expression_attribute_values
    )
    print("Update succeeded:", response)
except ClientError as e:
    if e.response['Error']['Code'] == 'ConditionalCheckFailedException':
        print("Update failed: Condition not met")
    else:
        print("Update failed:", e)

5. How would you handle large items that exceed the 400KB limit?

DynamoDB has a 400KB limit per item. To handle large items, consider:

• Data Segmentation: Split large items into smaller ones using a common partition key and a sort key.
• Use S3 for Large Objects: Store large objects in Amazon S3 and save the S3 object URL in DynamoDB.
• Compression: Compress data before storing it in DynamoDB.
• Normalization: Break down data into smaller, related tables and use references to link them.
• Pagination: Retrieve large datasets in chunks.

6. Explain the role of DynamoDB Streams and provide a use case.

DynamoDB Streams enable real-time responses to data changes. When an item is modified, a stream record is created, capturing details about the modification. A common use case is triggering AWS Lambda functions, such as processing new items or updating another data store.

7. How would you optimize read and write capacity units for a high-traffic application?

To optimize read and write capacity units for high-traffic applications, consider:

• Choose the Right Capacity Mode: Use on-demand mode for unpredictable traffic and provisioned mode for predictable traffic.
• Use Auto-Scaling: Enable auto-scaling to adjust capacity units based on traffic patterns.
• Optimize Partition Keys: Design tables with well-distributed partition keys to avoid hot partitions.
• Leverage Global and Local Secondary Indexes: Use GSIs and LSIs to optimize read operations.
• Implement Caching: Use Amazon DynamoDB Accelerator (DAX) to reduce read load.
• Batch Operations: Use batch operations for read and write requests to improve throughput.

8. Write a query to perform a transactional write operation involving multiple tables.

A transactional write operation allows multiple write operations across tables as a single transaction, ensuring all operations succeed or none do. Use the transact_write_items method for this.

Example:

import boto3

dynamodb = boto3.client('dynamodb')

transact_items = [
    {
        'Put': {
            'TableName': 'Table1',
            'Item': {
                'PrimaryKey': {'S': 'Key1'},
                'Attribute1': {'S': 'Value1'}
            }
        }
    },
    {
        'Update': {
            'TableName': 'Table2',
            'Key': {
                'PrimaryKey': {'S': 'Key2'}
            },
            'UpdateExpression': 'SET Attribute2 = :val',
            'ExpressionAttributeValues': {
                ':val': {'S': 'UpdatedValue'}
            }
        }
    }
]

response = dynamodb.transact_write_items(
    TransactItems=transact_items
)

print("Transaction successful:", response)

9. How would you implement a time-to-live (TTL) feature for expiring items?

DynamoDB’s Time-to-Live (TTL) feature automatically deletes items after a specified timestamp, managing data lifecycle and storage space. To implement TTL:

• Add an attribute to store the expiration timestamp in Unix epoch format.
• Enable TTL on your table and specify the attribute name.
• DynamoDB will automatically delete expired items.

10. Write a query to scan a table with a filter expression.

A scan operation reads every item in a table, and a filter expression refines results by specifying conditions items must meet. The filter expression is applied after the scan, meaning all items are read, but only matching items are returned.

Example:

import boto3

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

response = table.scan(
    FilterExpression=Attr('YourAttributeName').eq('YourAttributeValue')
)

for item in response['Items']:
    print(item)

11. How would you secure access to your tables?

To secure access to DynamoDB tables, consider:

• IAM Policies: Use IAM to create policies granting the least privilege necessary.
• Encryption: Enable encryption at rest with AWS KMS and use SSL/TLS for data in transit.
• VPC Endpoints: Use VPC endpoints to connect your VPC to DynamoDB securely.
• Fine-Grained Access Control: Restrict access to specific items or attributes using IAM policies.
• Monitoring and Logging: Enable AWS CloudTrail and Amazon CloudWatch for auditing and detecting unauthorized access.

12. Discuss best practices for data modeling.

When modeling data in DynamoDB, follow these best practices:

• Understand Access Patterns: Know your application’s read and write operations to design an efficient schema.
• Use Partition Keys and Sort Keys Wisely: Choose keys that ensure even data distribution and support query patterns.
• Leverage Secondary Indexes: Use GSIs and LSIs for additional query flexibility.
• Avoid Hot Partitions: Ensure your partition key has high cardinality and distributes traffic evenly.
• Use Projections to Optimize Read Performance: Specify which attributes should be copied to secondary indexes.
• Design for Scalability: Use sharding techniques and consider the impact on read and write capacity units.
• Implement Efficient Query Patterns: Use query operations instead of scan operations whenever possible.

13. Explain how to plan capacity.

Planning capacity involves understanding provisioned and on-demand modes. Provisioned capacity allows specifying read and write units, suitable for predictable traffic. On-demand mode adjusts automatically, ideal for variable traffic. Consider read and write patterns, cost, performance, and auto-scaling when planning capacity.

14. Discuss cost management techniques.

Cost management in DynamoDB involves strategies to optimize expenses while maintaining performance:

• On-Demand vs. Provisioned Capacity: Choose the appropriate mode based on workload.
• Auto Scaling: Enable auto-scaling to adjust capacity based on traffic patterns.
• DynamoDB Accelerator (DAX): Use DAX to cache frequently accessed data.
• Reserved Capacity: Purchase reserved capacity for long-term, predictable workloads.
• Monitoring and Alerts: Use AWS CloudWatch to monitor usage and set up alerts.
• Optimize Data Models: Design data models to minimize read and write operations.
• TTL (Time to Live): Use TTL to automatically delete expired items.

15. Explain how DynamoDB integrates with other AWS services like Lambda and S3.

DynamoDB integrates with various AWS services, enhancing functionality and enabling robust applications.

Lambda Integration: DynamoDB can trigger AWS Lambda functions using Streams, useful for real-time processing like updating derived data or sending notifications.

S3 Integration: Use DynamoDB with Amazon S3 for data storage and retrieval. Store large objects in S3, while metadata and indexing information are in DynamoDB. AWS Data Pipeline can transfer data between DynamoDB and S3 for archiving, backup, and analysis.