Sparse index pattern in DynamoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a sparse index by specifying the attribute to index.

DynamoDB

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

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

ACategory

BPrice

CDescription

DStock

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing an attribute that exists on all items, which makes the index not sparse.

Using an attribute that is not defined in the table's attribute definitions.

The sparse index uses the 'Category' attribute as the partition key to index only items that have this attribute.

2fill in blank

medium

Complete the code to define the projection type for the sparse index.

DynamoDB

Projection: { ProjectionType: [1] }

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

AKEYS_ONLY

BALL

CINCLUDE

DNONE

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ALL' projection which copies all attributes and increases index size.

Using 'NONE' which is not a valid projection type.

Using 'KEYS_ONLY' projection includes only the index keys in the index, which is common for sparse indexes to save space.

3fill in blank

hard

Fix the error in the attribute definition for the sparse index.

DynamoDB

AttributeDefinitions: [{ AttributeName: 'Category', AttributeType: [1] }]

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

AB

BN

CBOOL

DS

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'N' for a string attribute causes type mismatch errors.

Using 'BOOL' which is not a valid AttributeType.

The 'Category' attribute is a string, so its type must be 'S' for string in DynamoDB.

4fill in blank

hard

Fill both blanks to complete the sparse index creation with correct key schema and projection.

DynamoDB

GlobalSecondaryIndexes: [{ IndexName: 'CategoryIndex', KeySchema: [{ AttributeName: [1], KeyType: 'HASH' }], Projection: { ProjectionType: [2] } }]

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

ACategory

BALL

CKEYS_ONLY

DPrice

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Price' as partition key which is not the sparse attribute.

Using 'ALL' projection which increases index size unnecessarily.

The sparse index uses 'Category' as the partition key and 'KEYS_ONLY' projection to index only items with 'Category' attribute efficiently.

5fill in blank

hard

Fill all three blanks to define a sparse index with attribute definitions, key schema, and projection type.

DynamoDB

AttributeDefinitions: [{ AttributeName: [1], AttributeType: [2] }], GlobalSecondaryIndexes: [{ IndexName: 'CategoryIndex', KeySchema: [{ AttributeName: [3], KeyType: 'HASH' }], Projection: { ProjectionType: 'KEYS_ONLY' } }]

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

ACategory

BS

CPrice

DN

Attempts:

3 left

💡 Hint

Common Mistakes

Mismatch between attribute names in definitions and key schema.

Using 'Price' or 'N' when the sparse attribute is a string.

The sparse index uses 'Category' as the attribute name with type 'S' (string) in attribute definitions and as the partition key in the key schema.

Practice

1. What is the main benefit of using a sparse index pattern in DynamoDB?

easy

A. It indexes all items regardless of attributes, increasing storage.

B. It indexes only items with a specific attribute, making queries faster and cheaper.

C. It duplicates all data to another table for backup.

D. It automatically deletes items without a key attribute.

Solution

1. Step 1: Understand sparse index purpose

   Sparse index only includes items that have the indexed attribute, skipping others.

2. Step 2: Identify benefit of selective indexing

   This selective indexing reduces query cost and speeds up queries for that subset.

3. Final Answer:

   It indexes only items with a specific attribute, making queries faster and cheaper. -> Option B

4. Quick Check:

   Sparse index = selective fast queries [OK]

Hint: Sparse index targets only items with the attribute [OK]

Common Mistakes:

• Thinking sparse index includes all items
• Confusing sparse index with full table scan
• Assuming sparse index duplicates data

2. Which of the following is the correct way to define a sparse index in DynamoDB?

easy

A. Create a Global Secondary Index (GSI) with a partition key only on items having the attribute.

B. Create a Local Secondary Index (LSI) on all items regardless of attribute presence.

C. Create a GSI with a partition key that is always null.

D. Create a GSI with a partition key that duplicates the primary key.

Solution

1. Step 1: Identify sparse index creation method

   Sparse indexes use GSIs with keys only on items having the attribute.

2. Step 2: Check options for correctness

   Create a Global Secondary Index (GSI) with a partition key only on items having the attribute. matches this method; others either index all items or use invalid keys.

3. Final Answer:

   Create a Global Secondary Index (GSI) with a partition key only on items having the attribute. -> Option A

4. Quick Check:

   Sparse index = GSI with selective keys [OK]

Hint: Sparse index uses GSI with keys only on some items [OK]

Common Mistakes:

• Confusing LSI with sparse index
• Using null keys in GSI partition key
• Duplicating primary key in GSI

3. Given a DynamoDB table with items where only some have attribute status, and a sparse GSI on status, what will this query return?

Query on GSI where status = 'active'

medium

A. Only items without the status attribute.

B. All items in the table regardless of status.

C. All items in the table with status 'active'.

D. An error because status is not a primary key.

Solution

1. Step 1: Understand sparse GSI query behavior

   The sparse GSI includes only items with the status attribute, so querying status='active' returns those items.

2. Step 2: Eliminate incorrect options

   All items in the table regardless of status. is wrong because items without status are not in GSI; C is wrong because query targets status='active'; D is wrong because GSI keys can be queried.

3. Final Answer:

   All items in the table with status 'active'. -> Option C

4. Quick Check:

   Query sparse GSI = items with attribute value [OK]

Hint: Query sparse GSI returns only items with that attribute [OK]

Common Mistakes:

• Expecting all table items in result
• Thinking query fails without primary key
• Confusing sparse index with full scan

4. You created a sparse GSI on attribute category, but your query returns no results even though items have category. What is a likely cause?

medium

A. Sparse indexes do not support queries.

B. The table has no items at all.

C. You queried the base table instead of the GSI.

D. The GSI partition key is not set to category.

Solution

1. Step 1: Check GSI key configuration

   If the GSI partition key is not category, the index won't include those items properly.

2. Step 2: Evaluate other options

   Table having no items is unlikely if items exist; querying base table returns items but not via GSI; sparse indexes do support queries.

3. Final Answer:

   The GSI partition key is not set to category. -> Option D

4. Quick Check:

   Wrong GSI key = no results [OK]

Hint: Check GSI partition key matches attribute queried [OK]

Common Mistakes:

• Querying base table instead of GSI
• Assuming sparse index can't be queried
• Ignoring GSI key setup

5. You want to quickly find all orders with a deliveryDate attribute set, but most orders don't have this attribute. How can you use a sparse index pattern to optimize queries?

hard

A. Create a GSI with deliveryDate as the partition key; only orders with this attribute appear in the index.

B. Add deliveryDate as a sort key in the main table to speed up queries.

C. Create a GSI with a constant partition key for all items and deliveryDate as sort key.

D. Scan the entire table filtering on deliveryDate attribute.

Solution

1. Step 1: Use sparse index with deliveryDate attribute

   Creating a GSI with deliveryDate as partition key includes only items having that attribute, making queries efficient.

2. Step 2: Evaluate other options

   A constant partition key approach requires adding an extra attribute to all items, incurring write costs; main table sort key doesn't filter missing attributes; full table scan is inefficient.

3. Final Answer:

   Create a GSI with deliveryDate as the partition key; only orders with this attribute appear in the index. -> Option A

4. Quick Check:

   Sparse GSI on attribute = fast filtered queries [OK]

Hint: Sparse GSI on attribute filters only items having it [OK]

Common Mistakes:

• Using scan instead of sparse index
• Indexing all items losing sparsity
• Misusing sort key for filtering