Scan vs query performance in AWS - Hands-On Comparison

Scan vs Query Performance in AWS DynamoDB

📖 Scenario: You are working with AWS DynamoDB, a fast and flexible NoSQL database service. You want to understand the difference between Scan and Query operations and how they affect performance when retrieving data.Imagine you manage a table of customer orders and want to efficiently find orders for a specific customer.

🎯 Goal: Build a simple AWS DynamoDB setup and write code snippets to perform a Scan and a Query operation. You will see how to configure each operation and understand their performance implications.

📋 What You'll Learn

Create a DynamoDB table data structure with sample orders

Add a configuration variable for the customer ID to search

Write a Scan operation filtering orders by customer ID

Write a Query operation using the customer ID as the partition key

💡 Why This Matters

🌍 Real World

Understanding Scan vs Query helps optimize DynamoDB usage, saving costs and improving app speed.

💼 Career

Cloud engineers and developers must know when to use Scan or Query to design efficient, scalable applications.

1

Create DynamoDB table data structure with sample orders

Create a Python dictionary called orders representing DynamoDB items with these exact entries: {'order_id': '001', 'customer_id': 'C123', 'amount': 250}, {'order_id': '002', 'customer_id': 'C456', 'amount': 450}, and {'order_id': '003', 'customer_id': 'C123', 'amount': 150}.

AWS

# Create the orders dictionary with sample data
# Your code here

Need a hint?

Use a list of dictionaries to represent multiple orders.

2

Add a configuration variable for the customer ID to search

Create a variable called search_customer_id and set it to the string 'C123' to specify which customer's orders to find.

AWS

orders = [
    {'order_id': '001', 'customer_id': 'C123', 'amount': 250},
    {'order_id': '002', 'customer_id': 'C456', 'amount': 450},
    {'order_id': '003', 'customer_id': 'C123', 'amount': 150}
]
# Create the search_customer_id variable
# Your code here

Need a hint?

This variable will be used to filter orders.

3

Write a Scan operation filtering orders by customer ID

Write a list comprehension called scan_results that scans the orders list and includes only orders where order['customer_id'] == search_customer_id.

AWS

orders = [
    {'order_id': '001', 'customer_id': 'C123', 'amount': 250},
    {'order_id': '002', 'customer_id': 'C456', 'amount': 450},
    {'order_id': '003', 'customer_id': 'C123', 'amount': 150}
]
search_customer_id = 'C123'
# Write the scan_results list comprehension filtering by customer_id
# Your code here

Need a hint?

Scan checks every item and filters in code, which is slower for large tables.

4

Write a Query operation using the customer ID as the partition key

Write a list comprehension called query_results that simulates a DynamoDB Query by selecting orders from orders where order['customer_id'] == search_customer_id. This simulates efficient querying by partition key.

AWS

orders = [
    {'order_id': '001', 'customer_id': 'C123', 'amount': 250},
    {'order_id': '002', 'customer_id': 'C456', 'amount': 450},
    {'order_id': '003', 'customer_id': 'C123', 'amount': 150}
]
search_customer_id = 'C123'
scan_results = [order for order in orders if order['customer_id'] == search_customer_id]
# Write the query_results list comprehension simulating a Query operation
# Your code here

Need a hint?

Query uses the partition key to quickly find matching items without scanning the whole table.