Why table design determines performance in DynamoDB - See It in Action

Why Table Design Determines Performance in DynamoDB

📖 Scenario: You are building a simple online bookstore database using DynamoDB. You want to understand how the way you design your table affects how fast and efficient your database works.

🎯 Goal: Build a DynamoDB table with a proper primary key and a secondary index to see how table design impacts query performance.

📋 What You'll Learn

Create a DynamoDB table named Books with a partition key called ISBN (string).

Add a sort key called Title (string) to the table.

Create a Global Secondary Index (GSI) named AuthorIndex with Author as the partition key and Year as the sort key.

Insert sample items with attributes ISBN, Title, Author, and Year.

💡 Why This Matters

🌍 Real World

In real online stores or apps, designing your database tables well helps users get their data quickly without delays.

💼 Career

Database designers and backend developers must understand table design to build fast, scalable applications using DynamoDB.

1

Create the DynamoDB table with primary key

Create a DynamoDB table named Books with a partition key called ISBN of type string and a sort key called Title of type string.

DynamoDB

# Define the Books table with partition key ISBN and sort key Title
# Your code here

Need a hint?

Use KeySchema to define partition and sort keys. Partition key is HASH, sort key is RANGE.

2

Add a Global Secondary Index for author queries

Add a Global Secondary Index named AuthorIndex to the Books table with Author as the partition key and Year as the sort key. Both attributes are strings.

DynamoDB

Books = {
    'TableName': 'Books',
    'KeySchema': [
        {'AttributeName': 'ISBN', 'KeyType': 'HASH'},
        {'AttributeName': 'Title', 'KeyType': 'RANGE'}
    ],
    'AttributeDefinitions': [
        {'AttributeName': 'ISBN', 'AttributeType': 'S'},
        {'AttributeName': 'Title', 'AttributeType': 'S'},
        {'AttributeName': 'Author', 'AttributeType': 'S'},
        {'AttributeName': 'Year', 'AttributeType': 'S'}
    ],
    'GlobalSecondaryIndexes': [
        {
            'IndexName': 'AuthorIndex',
            'KeySchema': [
                {'AttributeName': 'Author', 'KeyType': 'HASH'},
                {'AttributeName': 'Year', 'KeyType': 'RANGE'}
            ],
            'Projection': {
                'ProjectionType': 'ALL'
            },
            'ProvisionedThroughput': {
                'ReadCapacityUnits': 5,
                'WriteCapacityUnits': 5
            }
        }
    ],
    'ProvisionedThroughput': {
        'ReadCapacityUnits': 5,
        'WriteCapacityUnits': 5
    }
}
# Your code here

Need a hint?

Remember to add Author and Year to AttributeDefinitions before using them in the GSI.

3

Insert sample book items

Insert three sample items into the Books table with these exact attributes and values: {'ISBN': '978-0132350884', 'Title': 'Clean Code', 'Author': 'Robert C. Martin', 'Year': '2008'}, {'ISBN': '978-0201616224', 'Title': 'The Pragmatic Programmer', 'Author': 'Andrew Hunt', 'Year': '1999'}, and {'ISBN': '978-0131103627', 'Title': 'The C Programming Language', 'Author': 'Brian W. Kernighan', 'Year': '1988'}.

DynamoDB

Books = {
    'TableName': 'Books',
    'KeySchema': [
        {'AttributeName': 'ISBN', 'KeyType': 'HASH'},
        {'AttributeName': 'Title', 'KeyType': 'RANGE'}
    ],
    'AttributeDefinitions': [
        {'AttributeName': 'ISBN', 'AttributeType': 'S'},
        {'AttributeName': 'Title', 'AttributeType': 'S'},
        {'AttributeName': 'Author', 'AttributeType': 'S'},
        {'AttributeName': 'Year', 'AttributeType': 'S'}
    ],
    'GlobalSecondaryIndexes': [
        {
            'IndexName': 'AuthorIndex',
            'KeySchema': [
                {'AttributeName': 'Author', 'KeyType': 'HASH'},
                {'AttributeName': 'Year', 'KeyType': 'RANGE'}
            ],
            'Projection': {
                'ProjectionType': 'ALL'
            },
            'ProvisionedThroughput': {
                'ReadCapacityUnits': 5,
                'WriteCapacityUnits': 5
            }
        }
    ],
    'ProvisionedThroughput': {
        'ReadCapacityUnits': 5,
        'WriteCapacityUnits': 5
    }
}
items = [
    # Your code here
]

Need a hint?

Use a list of dictionaries to represent the items with exact attribute names and values.

4

Explain how table design affects performance

Add a comment explaining why choosing the right partition key and using a Global Secondary Index improves query speed and overall performance in DynamoDB.

DynamoDB

Books = {
    # ... previous code ...
}
items = [
    # ... previous items ...
]
# Your explanation here

Need a hint?

Explain in a comment how partition keys and GSIs help DynamoDB find data faster and keep performance high.