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

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

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

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

Hint

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

Practice

1. Which AWS DynamoDB operation is faster when you know the exact primary key of the item you want to retrieve?

easy

A. Update

B. Scan

C. Query

D. Delete

Solution

1. Step 1: Understand Query operation

   Query retrieves items by specifying the primary key, making it fast and efficient.

2. Step 2: Compare with Scan operation

   Scan reads the entire table, which is slower and more costly when you know the key.

3. Final Answer:

   Query -> Option C

4. Quick Check:

   Known key = Query [OK]

Hint: Use Query for known keys, Scan reads all data [OK]

Common Mistakes:

• Thinking Scan is faster for known keys
• Confusing Update or Delete with data retrieval
• Assuming Query reads the whole table

2. Which of the following is the correct syntax to perform a Query operation in AWS DynamoDB using the AWS CLI?

easy

A. aws dynamodb query --table-name MyTable --key-condition-expression 'Id = :id' --expression-attribute-values '{":id":{"S":"123"}}'

B. aws dynamodb scan --table-name MyTable --key-condition-expression 'Id = :id' --expression-attribute-values '{":id":{"S":"123"}}'

C. aws dynamodb get-item --table-name MyTable --key '{"Id":{"S":"123"}}'

D. aws dynamodb update-item --table-name MyTable --key '{"Id":{"S":"123"}}'

Solution

1. Step 1: Identify Query command syntax

   The Query operation uses 'aws dynamodb query' with key-condition-expression and expression-attribute-values.

2. Step 2: Check other options

   Scan reads the entire table (and does not support --key-condition-expression), get-item retrieves a single item by exact key, update-item modifies items, so only the Query syntax is correct.

3. Final Answer:

   aws dynamodb query --table-name MyTable --key-condition-expression 'Id = :id' --expression-attribute-values '{":id":{"S":"123"}}' -> Option A

4. Quick Check:

   Query syntax uses 'query' command [OK]

Hint: Query uses 'aws dynamodb query' command [OK]

Common Mistakes:

• Using Scan command instead of Query
• Confusing GetItem with Query
• Incorrect expression-attribute-values format

3. Given a DynamoDB table with 1000 items, what will be the main difference in performance between these two operations?

1. Query with KeyConditionExpression on partition key
2. Scan without any filter

medium

A. Both read the same number of items and have similar speed

B. Scan reads fewer items and is faster; Query reads all items and is slower

C. Both read all items but Query is slower due to filtering

D. Query reads fewer items and is faster; Scan reads all items and is slower

Solution

1. Step 1: Analyze Query operation

   Query uses the partition key to directly find matching items, reading fewer items and faster.

2. Step 2: Analyze Scan operation

   Scan reads every item in the table, making it slower and more expensive.

3. Final Answer:

   Query reads fewer items and is faster; Scan reads all items and is slower -> Option D

4. Quick Check:

   Query = fewer reads, Scan = full table [OK]

Hint: Query targets keys, Scan reads whole table [OK]

Common Mistakes:

• Assuming Scan is faster than Query
• Thinking Query reads all items
• Ignoring cost differences

4. You wrote this DynamoDB Scan command but it is very slow:

aws dynamodb scan --table-name Users

What is the best way to improve performance if you only want users with UserId = '123'?

medium

A. Replace Scan with Query using KeyConditionExpression on UserId

B. Add a FilterExpression to the Scan command

C. Increase the Read Capacity Units of the table

D. Use UpdateItem to modify the UserId

Solution

1. Step 1: Identify problem with Scan

   Scan reads all items, causing slowness when filtering after reading.

2. Step 2: Use Query for known key

   Query with KeyConditionExpression on UserId reads only matching items, improving speed.

3. Final Answer:

   Replace Scan with Query using KeyConditionExpression on UserId -> Option A

4. Quick Check:

   Known key = Query, not Scan [OK]

Hint: Use Query with key condition, not Scan with filter [OK]

Common Mistakes:

• Adding filter to Scan instead of using Query
• Increasing capacity without optimizing query
• Using UpdateItem unrelated to read performance

5. You have a DynamoDB table with 1 million items. You need to retrieve all items where the attribute 'Status' equals 'Active'. The table's primary key is 'UserId'. Which approach gives the best performance and cost efficiency?

hard

A. Use Scan with FilterExpression 'Status = Active' to get all matching items

B. Create a Global Secondary Index (GSI) on 'Status' and use Query on the GSI

C. Use Query on the primary key 'UserId' with FilterExpression 'Status = Active'

D. Use Scan without any filter and filter results in your application code

Solution

1. Step 1: Understand Scan with filter

   Scan reads all 1 million items, filtering after reading, which is slow and costly.

2. Step 2: Use GSI for efficient querying

   Creating a GSI on 'Status' allows Query to directly find 'Active' items efficiently.

3. Step 3: Evaluate other options

   Query on primary key can't filter by 'Status' efficiently; scanning without filter wastes resources.

4. Final Answer:

   Create a Global Secondary Index (GSI) on 'Status' and use Query on the GSI -> Option B

5. Quick Check:

   GSI + Query = best performance for non-key attribute [OK]

Hint: Use GSI and Query for non-key attribute filtering [OK]

Common Mistakes:

• Using Scan with filter on large tables
• Querying primary key with filter instead of GSI
• Filtering in application after full Scan