Index capacity and cost in DynamoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

How does a Global Secondary Index (GSI) affect write capacity?

In DynamoDB, when you add a Global Secondary Index (GSI) to a table, how does it impact the write capacity units (WCUs) consumed?

A Writes consume WCUs for the base table, but GSIs consume read capacity units (RCUs) instead.

B Writes only consume WCUs for the base table; GSIs do not affect write capacity.

C Writes to the table consume WCUs for both the base table and the GSI, effectively doubling the write capacity usage.

D Writes consume WCUs only for the GSI, not the base table.

💡 Hint

Think about how data is duplicated in GSIs.

When you write to a table with a GSI, DynamoDB writes the data to both the base table and the GSI, so write capacity is consumed for both.

❓ query_result

intermediate

Calculate read capacity units for a query on a Local Secondary Index (LSI)

You have a DynamoDB table with 4 KB items. You query a Local Secondary Index (LSI) and retrieve 3 items. How many read capacity units (RCUs) are consumed if you use eventually consistent reads?

A 2 RCUs

B 6 RCUs

C 1 RCUs

D 12 RCUs

💡 Hint

Each 4 KB item read with eventually consistent reads consumes 0.5 RCU.

Each 4 KB item read with eventually consistent reads consumes 0.5 RCU. For 3 items, total is 3 * 0.5 = 1.5, rounded up to 2 RCUs.

📝 Syntax

advanced

Identify the correct syntax to create a GSI with provisioned capacity

Which of the following AWS CLI commands correctly creates a Global Secondary Index (GSI) with provisioned read and write capacity?

DynamoDB

aws dynamodb update-table --table-name Music --attribute-definitions AttributeName=Artist,AttributeType=S AttributeName=AlbumTitle,AttributeType=S --global-secondary-index-updates '[{"Create":{"IndexName":"ArtistAlbumIndex","KeySchema":[{"AttributeName":"Artist","KeyType":"HASH"},{"AttributeName":"AlbumTitle","KeyType":"RANGE"}],"Projection":{"ProjectionType":"ALL"},"ProvisionedThroughput":{"ReadCapacityUnits":5,"WriteCapacityUnits":5}}}]'

A The command is missing the --provisioned-throughput flag outside the GSI definition.

B The command as shown is correct and will create the GSI with provisioned capacity.

C The KeySchema must have only one attribute for a GSI, so this command is invalid.

D The ProjectionType must be KEYS_ONLY for provisioned GSIs, so this command will fail.

💡 Hint

Check the JSON structure inside the --global-secondary-index-updates parameter.

The command correctly specifies the GSI creation with KeySchema, Projection, and ProvisionedThroughput inside the Create object.

❓ optimization

advanced

Optimize read capacity usage for a frequently queried attribute

You have a DynamoDB table with a large number of items. You frequently query by a non-key attribute. Which approach optimizes read capacity usage?

A Add the attribute as a sort key in the base table and query with a filter expression.

B Scan the entire table and filter results on the client side.

C Use a Local Secondary Index (LSI) on that attribute and query it.

D Create a Global Secondary Index (GSI) on that attribute and query the GSI instead of the base table.

💡 Hint

Think about how indexes reduce the amount of data scanned.

Creating a GSI on the attribute allows efficient queries using indexed keys, reducing read capacity usage compared to scanning.

🔧 Debug

expert

Diagnose unexpected high write capacity consumption with GSIs

A DynamoDB table has two GSIs. Suddenly, write capacity usage spikes unexpectedly. Which is the most likely cause?

A The GSIs have large projected attributes causing more data to be written and higher WCUs consumed.

B The table's read capacity units are too low, causing throttling on writes.

C The table's partition key was changed, invalidating the GSIs and causing retries.

D The GSIs are using eventually consistent reads, which double the write capacity usage.

💡 Hint

Consider how projected attributes affect write costs.

Large projected attributes in GSIs increase the amount of data written to the index, raising write capacity consumption.

Practice

1. What is the main reason to use indexes in DynamoDB?

easy

A. To speed up query performance by allowing efficient data access

B. To reduce the storage size of the table

C. To automatically backup data

D. To encrypt data at rest

Solution

1. Step 1: Understand the purpose of indexes

   Indexes in DynamoDB are designed to speed up queries by providing alternate ways to access data efficiently.

2. Step 2: Eliminate unrelated options

   Options about storage size, backup, and encryption are unrelated to index functionality.

3. Final Answer:

   To speed up query performance by allowing efficient data access -> Option A

4. Quick Check:

   Indexes improve query speed = B [OK]

Hint: Indexes speed queries, not reduce storage or backup [OK]

Common Mistakes:

• Thinking indexes reduce storage cost
• Confusing indexes with backup features
• Assuming indexes encrypt data

2. Which of the following is the correct way to specify a Global Secondary Index (GSI) in a DynamoDB table creation JSON?

easy

A. {
"LocalSecondaryIndexes": [
{
"IndexName": "MyGSI",
"KeySchema": [
{"AttributeName": "UserId", "KeyType": "HASH"}
],
"Projection": {"ProjectionType": "ALL"}
}
]
}

B. {
"GlobalSecondaryIndexes": [
{
"IndexName": "MyGSI",
"KeySchema": [
{"AttributeName": "UserId", "KeyType": "HASH"}
],
"Projection": {"ProjectionType": "ALL"},
"ProvisionedThroughput": {"ReadCapacityUnits": 5, "WriteCapacityUnits": 5}
}
]
}

C. {
"GlobalSecondaryIndexes": {
"IndexName": "MyGSI"
}
}

D. {
"Indexes": [
{
"Name": "MyGSI"
}
]
}

Solution

1. Step 1: Identify correct GSI syntax

   Global Secondary Indexes require an array under "GlobalSecondaryIndexes" with full details: IndexName, KeySchema, Projection, and ProvisionedThroughput.

2. Step 2: Compare options

   { "GlobalSecondaryIndexes": [ { "IndexName": "MyGSI", "KeySchema": [ {"AttributeName": "UserId", "KeyType": "HASH"} ], "Projection": {"ProjectionType": "ALL"}, "ProvisionedThroughput": {"ReadCapacityUnits": 5, "WriteCapacityUnits": 5} } ] } correctly uses "GlobalSecondaryIndexes" as an array with all required fields. Others use wrong keys or incomplete structures.

3. Final Answer:

   { "GlobalSecondaryIndexes": [ { "IndexName": "MyGSI", "KeySchema": [ {"AttributeName": "UserId", "KeyType": "HASH"} ], "Projection": {"ProjectionType": "ALL"}, "ProvisionedThroughput": {"ReadCapacityUnits": 5, "WriteCapacityUnits": 5} } ] } -> Option B

4. Quick Check:

   GSI syntax requires array with full details = A [OK]

Hint: GSIs need full details in GlobalSecondaryIndexes array [OK]

Common Mistakes:

• Using LocalSecondaryIndexes instead of GlobalSecondaryIndexes
• Providing object instead of array for GSIs
• Omitting ProvisionedThroughput for GSIs

3. Given a DynamoDB table with a provisioned write capacity of 10 units and a Global Secondary Index (GSI) with 5 write capacity units, what is the total write capacity units consumed when writing one item to the table and the GSI?

medium

A. 5 units

B. 10 units

C. 15 units

D. 20 units

Solution

1. Step 1: Understand write capacity consumption

   Writing one item consumes write capacity units on the main table plus on each index that is updated.

2. Step 2: Add main table and GSI write units

   Main table uses 10 units, GSI uses 5 units, total = 10 + 5 = 15 units.

3. Final Answer:

   15 units -> Option C

4. Quick Check:

   Total write capacity = main + GSI = 15 [OK]

Hint: Add main table and index write units for total cost [OK]

Common Mistakes:

• Counting only main table write units
• Counting only GSI write units
• Multiplying instead of adding capacities

4. You notice your DynamoDB table's write capacity is being exceeded even though your application writes only a few items per second. Which of the following is the most likely cause related to indexes?

medium

A. You have a Global Secondary Index with high write capacity units causing extra cost

B. You forgot to create any indexes

C. Your table has no read capacity units set

D. You are using Local Secondary Indexes which do not affect write capacity

Solution

1. Step 1: Analyze write capacity usage

   Writes to the table also consume write capacity on GSIs, increasing total usage.

2. Step 2: Identify cause of exceeded capacity

   If GSIs have high write capacity or many writes, they can cause capacity to be exceeded even if main table writes are low.

3. Final Answer:

   You have a Global Secondary Index with high write capacity units causing extra cost -> Option A

4. Quick Check:

   GSIs increase write capacity usage = C [OK]

Hint: Check GSIs for hidden write capacity usage [OK]

Common Mistakes:

• Assuming no indexes means no extra capacity
• Confusing read and write capacity units
• Thinking Local Secondary Indexes increase write capacity

5. You want to optimize costs for a DynamoDB table with a Global Secondary Index (GSI). The table has 20 write capacity units and the GSI has 10 write capacity units. If your application writes 5 items per second, how can you reduce costs without affecting query performance on the GSI?

hard

A. Disable the table's write capacity units

B. Remove the GSI to save all index costs

C. Increase the table write capacity units to 30

D. Reduce the GSI write capacity units to 5 and enable auto scaling

Solution

1. Step 1: Understand cost drivers

   Write capacity units on both table and GSI affect cost. Reducing GSI units lowers cost but must still handle write load.

2. Step 2: Apply auto scaling and adjust capacity

   Reducing GSI write units to match actual writes (5) and enabling auto scaling balances cost and performance.

3. Final Answer:

   Reduce the GSI write capacity units to 5 and enable auto scaling -> Option D

4. Quick Check:

   Lower GSI units + auto scaling = cost optimization [OK]

Hint: Match GSI capacity to writes and use auto scaling [OK]

Common Mistakes:

• Removing GSI and losing query performance
• Increasing table capacity unnecessarily
• Disabling write capacity causing failures