Identifying access patterns first in DynamoDB - Mini Project: Build & Apply

Identifying Access Patterns First

📖 Scenario: You are designing a DynamoDB table for a simple online bookstore. You want to store book information and be able to quickly find books by their ISBN or by the author's name.

🎯 Goal: Create a DynamoDB table structure that supports fast lookups by ISBN and by author name. You will define the main table and a secondary index to support these access patterns.

📋 What You'll Learn

Create a DynamoDB table named Books with ISBN as the partition key.

Add a Global Secondary Index (GSI) named AuthorIndex with Author as the partition key.

Include attributes Title and Author in the table items.

💡 Why This Matters

🌍 Real World

Designing DynamoDB tables by identifying access patterns first helps build fast and scalable applications like online bookstores, social media apps, or inventory systems.

💼 Career

Understanding how to model DynamoDB tables and indexes based on access patterns is a key skill for cloud developers, backend engineers, and database architects working with AWS.

1

Create the DynamoDB table structure

Create a DynamoDB table named Books with ISBN as the partition key. Define the attributes ISBN, Title, and Author in the item structure.

DynamoDB

# Define the Books table with ISBN as the partition key
# Your code here

Need a hint?

Remember to set ISBN as the partition key in KeySchema and define it in AttributeDefinitions.

2

Add a Global Secondary Index for author lookups

Add a Global Secondary Index named AuthorIndex to the Books table. Use Author as the partition key for this index.

DynamoDB

Books = {
    'TableName': 'Books',
    'KeySchema': [
        {'AttributeName': 'ISBN', 'KeyType': 'HASH'}
    ],
    'AttributeDefinitions': [
        {'AttributeName': 'ISBN', 'AttributeType': 'S'},
        {'AttributeName': 'Author', 'AttributeType': 'S'}
    ],
    'ProvisionedThroughput': {
        'ReadCapacityUnits': 5,
        'WriteCapacityUnits': 5
    },
    # Add Global Secondary Index here
    # Your code here
}

Need a hint?

Use GlobalSecondaryIndexes with IndexName set to AuthorIndex and Author as the partition key.

3

Add sample book items

Create a list named book_items with two dictionaries representing books. Each dictionary should have keys ISBN, Title, and Author with these exact values: first book with ISBN 978-0132350884, Title Clean Code, Author Robert C. Martin; second book with ISBN 978-0201616224, Title The Pragmatic Programmer, Author Andrew Hunt.

DynamoDB

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

# Create a list named book_items with two book dictionaries
# Your code here

Need a hint?

Make sure the list is named book_items and contains exactly two dictionaries with the specified keys and values.

4

Define the final table creation parameters

Add the Item key to the book_items dictionaries to prepare them for insertion. Then, create a list named put_requests where each element is a dictionary with the key PutRequest and the value is a dictionary with the key Item set to the book item. This structure is used for batch writing items to DynamoDB.

DynamoDB

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

book_items = [
    {'ISBN': '978-0132350884', 'Title': 'Clean Code', 'Author': 'Robert C. Martin'},
    {'ISBN': '978-0201616224', 'Title': 'The Pragmatic Programmer', 'Author': 'Andrew Hunt'}
]

# Create put_requests list for batch write
# Your code here

Need a hint?

Use a list comprehension to create put_requests with each element as a dictionary containing PutRequest and Item.