Single-table design methodology in DynamoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define the primary key attribute name for a DynamoDB table.

DynamoDB

KeySchema: [{ AttributeName: '[1]', KeyType: 'HASH' }]

Drag options to blanks, or click blank then click option'

AUserId

BTimestamp

CEmail

DStatus

Attempts:

3 left

💡 Hint

Common Mistakes

Using an attribute that is not unique as the primary key.

Confusing sort key with partition key.

The primary key attribute name is usually a unique identifier like 'UserId' in single-table design.

2fill in blank

medium

Complete the code to add a sort key attribute name in the table schema.

DynamoDB

KeySchema: [ { AttributeName: 'UserId', KeyType: 'HASH' }, { AttributeName: '[1]', KeyType: 'RANGE' } ]

Drag options to blanks, or click blank then click option'

ACategory

BEmail

CStatus

DTimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-sortable attribute as the sort key.

Omitting the sort key when multiple items share the partition key.

The sort key often represents a time or sequence attribute like 'Timestamp' to order items.

3fill in blank

hard

Fix the error in the DynamoDB query to fetch items by partition key.

DynamoDB

Table.query({ KeyConditionExpression: 'UserId = :userId', ExpressionAttributeValues: { ':userId': [1] } })

Drag options to blanks, or click blank then click option'

AuserId

B'12345'

C12345

D:userId

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the variable name instead of its value.

Omitting quotes around string values.

The value for ExpressionAttributeValues must be a string or number wrapped correctly. Here, the user ID should be a string in quotes.

4fill in blank

hard

Fill both blanks to create a DynamoDB item with partition key and sort key attributes.

DynamoDB

Item: { 'UserId': [1], 'Timestamp': [2], 'Data': 'Sample' }

Drag options to blanks, or click blank then click option'

A'user123'

B'2024-06-01T12:00:00Z'

C123456

D'dataValue'

Attempts:

3 left

💡 Hint

Common Mistakes

Using numbers instead of strings for keys.

Mixing up partition key and sort key values.

The partition key 'UserId' is a string like 'user123', and the sort key 'Timestamp' is a string timestamp in ISO format.

5fill in blank

hard

Fill all three blanks to write a DynamoDB update expression that sets a new attribute and increments a counter.

DynamoDB

UpdateExpression: 'SET [1] = :val, [2] = [3] + :inc', ExpressionAttributeValues: { ':val': 'active', ':inc': 1 }

Drag options to blanks, or click blank then click option'

AStatus

BCounter

CCount

DState

Attempts:

3 left

💡 Hint

Common Mistakes

Using different attribute names for increment operation.

Forgetting to repeat the attribute name in the increment expression.

'Status' is set to a new value, and 'Counter' is incremented by 1. The same attribute name 'Counter' is used twice in the update expression.

Practice

1. What is the main advantage of using single-table design in DynamoDB?

easy

A. It requires multiple tables for each data type to keep data separate.

B. It allows storing different types of related data in one table for faster queries.

C. It automatically creates indexes for all attributes without setup.

D. It stores data only in JSON format for easy reading.

Solution

1. Step 1: Understand single-table design purpose

   Single-table design stores multiple related data types in one table to reduce the number of queries needed.

2. Step 2: Compare options with this purpose

   It allows storing different types of related data in one table for faster queries. matches this purpose. Options B, C, and D describe incorrect or unrelated features.

3. Final Answer:

   It allows storing different types of related data in one table for faster queries. -> Option B

4. Quick Check:

   Single-table design = faster queries with one table [OK]

Hint: Single-table design means one table for related data [OK]

Common Mistakes:

• Thinking single-table means multiple tables
• Assuming automatic index creation
• Confusing data format with design method

2. Which of the following is the correct way to define a partition key in a DynamoDB table using single-table design?

easy

A. PartitionKey: orderId, SortKey: userId

B. PartitionKey: userId#orderId

C. PartitionKey: userId, SortKey: orderId

D. PartitionKey: orderId#userId

Solution

1. Step 1: Recall key structure in single-table design

   Single-table design uses a partition key and a sort key to organize data efficiently.

2. Step 2: Identify correct key assignment

   PartitionKey: userId, SortKey: orderId correctly assigns userId as partition key and orderId as sort key, which is common for grouping orders by user.

3. Final Answer:

   PartitionKey: userId, SortKey: orderId -> Option C

4. Quick Check:

   Partition and sort keys = userId and orderId [OK]

Hint: Partition key groups items; sort key orders them [OK]

Common Mistakes:

• Using combined keys as partition key only
• Swapping partition and sort keys incorrectly
• Using concatenated keys without sort key

3. Given a DynamoDB table with partition key PK and sort key SK, which query will fetch all orders for user with ID 'user123' if orders are stored with PK = 'USER#user123' and SK = 'ORDER#orderId'?

medium

A. Query with KeyConditionExpression: PK = 'ORDER#orderId' AND SK = 'USER#user123'

B. Query with KeyConditionExpression: PK = 'USER#user123' AND SK = 'ORDER#orderId'

C. Scan with FilterExpression: PK = 'USER#user123'

D. Query with KeyConditionExpression: PK = 'USER#user123' AND SK begins_with 'ORDER#'

Solution

1. Step 1: Understand key structure for orders

   Orders have PK as 'USER#user123' and SK starting with 'ORDER#', so all orders share the same PK but different SKs.

2. Step 2: Identify query to fetch all orders for user

   Query with KeyConditionExpression: PK = 'USER#user123' AND SK begins_with 'ORDER#' queries PK equal to 'USER#user123' and SK that begins with 'ORDER#', which fetches all orders for that user.

3. Final Answer:

   Query with KeyConditionExpression: PK = 'USER#user123' AND SK begins_with 'ORDER#' -> Option D

4. Quick Check:

   PK fixed, SK prefix filter = all user orders [OK]

Hint: Use begins_with on sort key to get related items [OK]

Common Mistakes:

• Swapping PK and SK in query
• Using scan instead of query
• Filtering SK with exact match instead of prefix

4. You wrote this query to get all comments for a post in a single-table design DynamoDB table:

KeyConditionExpression: PK = 'POST#123' AND SK = 'COMMENT#'

Why does this query return no results?

medium

A. Because SK uses '=' instead of 'begins_with' to match multiple comments.

B. Because PK should be 'COMMENT#' and SK should be 'POST#123'.

C. Because the table does not support queries on SK.

D. Because the query syntax is missing a FilterExpression.

Solution

1. Step 1: Analyze the KeyConditionExpression

   The query uses SK = 'COMMENT#', which looks for an exact match on SK.

2. Step 2: Understand why no results appear

   Comments likely have SK starting with 'COMMENT#' plus an ID, so exact match fails. Using 'begins_with' on SK is needed to get all comments.

3. Final Answer:

   Because SK uses '=' instead of 'begins_with' to match multiple comments. -> Option A

4. Quick Check:

   Use begins_with for multiple SK matches [OK]

Hint: Use begins_with for partial sort key matches [OK]

Common Mistakes:

• Using '=' instead of begins_with on SK
• Swapping PK and SK in query
• Assuming FilterExpression fixes key condition

5. You want to design a single DynamoDB table to store users, their orders, and order items. Which key design best supports fetching all orders and items for a user efficiently?

hard

A. PartitionKey: USER#userId, SortKey: ORDER#orderId for orders; PartitionKey: ORDER#orderId, SortKey: ITEM#itemId for items

B. PartitionKey: userId, SortKey: orderId#itemId for all data types

C. PartitionKey: USER#userId, SortKey: ORDER#orderId#ITEM#itemId for all items and orders

D. PartitionKey: ITEM#itemId, SortKey: ORDER#orderId for all data

Solution

1. Step 1: Understand data grouping needs

   We want to fetch all orders and items for a user efficiently, so grouping orders by user and items by order is key.

2. Step 2: Evaluate key designs

   PartitionKey: USER#userId, SortKey: ORDER#orderId for orders; PartitionKey: ORDER#orderId, SortKey: ITEM#itemId for items uses USER#userId as partition key for orders and ORDER#orderId as partition key for items, allowing efficient queries by user and order. Other options mix keys or combine too much, making queries inefficient.

3. Final Answer:

   PartitionKey: USER#userId, SortKey: ORDER#orderId for orders; PartitionKey: ORDER#orderId, SortKey: ITEM#itemId for items -> Option A

4. Quick Check:

   Group orders by user, items by order = PartitionKey: USER#userId, SortKey: ORDER#orderId for orders; PartitionKey: ORDER#orderId, SortKey: ITEM#itemId for items [OK]

Hint: Group data by parent keys for efficient queries [OK]

Common Mistakes:

• Using one partition key for all data types
• Combining all keys into one long sort key
• Using item ID as partition key for all data