Sparse index pattern in DynamoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a sparse index pattern in DynamoDB?

A sparse index pattern is a way to create a secondary index that only includes items with a specific attribute, making the index smaller and more efficient.

beginner

Why use a sparse index pattern in DynamoDB?

To improve query performance and reduce costs by indexing only a subset of items that have a particular attribute, instead of all items in the table.

intermediate

How do you create a sparse index in DynamoDB?

You create a secondary index on an attribute that only some items have. Items without that attribute are not included in the index, making it sparse.

beginner

What happens to items without the indexed attribute in a sparse index?

Items without the indexed attribute are not included in the sparse index, so they do not appear in queries on that index.

intermediate

Give a real-life example of using a sparse index pattern.

If you have a table of users and only some have a "premium" status attribute, you can create a sparse index on "premium" to quickly find premium users without indexing all users.

What does a sparse index in DynamoDB include?

A Items with null values for the indexed attribute

B All items in the table

C Only items with the indexed attribute

D Only items without the indexed attribute

A sparse index only includes items that have the attribute used for the index.

Why might you use a sparse index pattern?

A To include items without the attribute

B To index all items regardless of attributes

C To increase storage costs

D To reduce index size and improve query speed

Sparse indexes reduce size and speed up queries by indexing only relevant items.

If an item does not have the attribute used in a sparse index, what happens?

A It is excluded from the index

B It causes an error

C It is included in the index with a null value

D It is indexed with a default value

Items without the indexed attribute are excluded from the sparse index.

Which DynamoDB feature is typically used to implement a sparse index?

A Local Secondary Index (LSI)

B Global Secondary Index (GSI)

C Primary Key only

D Streams

Sparse indexes are typically implemented using Global Secondary Indexes on attributes that only some items have.

What is a benefit of querying a sparse index?

A You get only items with the indexed attribute quickly

B You get all items in the table

C You get items without the attribute

D You get slower query results

Sparse indexes allow fast queries on only the items that have the indexed attribute.

Explain the sparse index pattern in DynamoDB and why it is useful.

Think about indexing only a subset of your data.

Describe a scenario where using a sparse index pattern would improve your DynamoDB application's performance.

Consider a user table with premium users.

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