Scan vs query performance in AWS - When to Use Which

The Big Idea

What if you could find exactly what you need in seconds instead of minutes?

The Scenario

Imagine you have a huge library of books and you want to find all books by a certain author.

If you look at every single book one by one, it will take forever.

The Problem

Manually checking each book is slow and tiring.

You might miss some books or make mistakes.

It wastes time and energy.

The Solution

Using a query is like asking the librarian to quickly find all books by that author.

The librarian knows exactly where to look, so it's fast and accurate.

Before vs After

✗ Before

Scan entire table and filter results in code

✓ After

Use Query operation with key condition to get exact items

What It Enables

It lets you get the data you want quickly and efficiently without wasting resources.

Real Life Example

When an online store wants to show all orders for a customer, using a query fetches only those orders instantly instead of scanning all orders.

Key Takeaways

Scanning checks every item, which is slow and costly.

Querying uses keys to find data fast and precisely.

Choosing the right method improves app speed and saves money.

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