Batch limits and retries in DynamoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

BatchWriteItem request size limit

You want to write 150 items to a DynamoDB table using BatchWriteItem. What will happen if you send all 150 items in one batch request?

A The request will succeed and write all 150 items at once.

B The request will automatically split into multiple batches and write all items.

C The request will partially succeed and write only the first 100 items.

D The request will fail because the maximum batch size is 25 items.

💡 Hint

Remember the maximum number of items allowed in a single BatchWriteItem request.

DynamoDB limits BatchWriteItem requests to a maximum of 25 items per batch. Sending more than 25 items in one request causes the entire request to fail.

🧠 Conceptual

intermediate

Handling unprocessed items in BatchWriteItem

When you use BatchWriteItem, some items may be returned as unprocessed. What is the best practice to handle these unprocessed items?

A Manually retry sending the unprocessed items until they succeed.

B Ignore unprocessed items because DynamoDB will retry them automatically.

C Delete the unprocessed items from your source data to avoid errors.

D Increase the batch size to include unprocessed items in the next request.

💡 Hint

Think about how DynamoDB handles unprocessed items and what your application should do.

DynamoDB returns unprocessed items when it cannot process all items in a batch due to throttling or limits. Your application should retry these unprocessed items until they succeed.

📝 Syntax

advanced

Correct syntax for BatchWriteItem with retries

Which of the following code snippets correctly implements retry logic for unprocessed items in a DynamoDB BatchWriteItem operation using AWS SDK?

A do { const response = await dynamoDB.batchWrite({ RequestItems: itemsToWrite }).promise(); itemsToWrite = response.UnprocessedItems; } while (itemsToWrite && Object.keys(itemsToWrite).length > 0);

B const response = await dynamoDB.batchWrite({ RequestItems: itemsToWrite }).promise(); if (response.UnprocessedItems) { itemsToWrite = response.UnprocessedItems; }

C for (let i = 0; i < 3; i++) { await dynamoDB.batchWrite({ RequestItems: itemsToWrite }).promise(); }

D while (itemsToWrite.length > 0) { const response = await dynamoDB.batchWrite({ RequestItems: itemsToWrite }).promise(); itemsToWrite = response.UnprocessedItems; }

💡 Hint

Look for a loop that continues while unprocessed items exist.

Option A uses a do-while loop that retries the batch write as long as there are unprocessed items returned by DynamoDB. This ensures all items are eventually written.

❓ optimization

advanced

Optimizing batch write throughput with retries

You want to optimize writing 1000 items to DynamoDB using BatchWriteItem. Which approach best balances throughput and retry handling?

A Split items into batches of 100, send batches in parallel, and ignore unprocessed items to save time.

B Split items into batches of 25, send batches sequentially, and retry unprocessed items with exponential backoff.

C Send all 1000 items in one batch request and retry unprocessed items immediately without delay.

D Send items one by one using PutItem to avoid unprocessed items and retries.

💡 Hint

Consider DynamoDB batch size limits and best retry practices.

BatchWriteItem supports up to 25 items per batch. Sending batches sequentially with exponential backoff retries for unprocessed items balances throughput and avoids throttling.

🔧 Debug

expert

Diagnosing persistent unprocessed items in BatchWriteItem

You notice that your BatchWriteItem requests keep returning unprocessed items even after multiple retries with exponential backoff. What is the most likely cause?

A You are using the wrong AWS region for your DynamoDB table.

B Your batch size exceeds the 25 item limit, causing automatic throttling.

C Your provisioned write capacity is too low to handle the write volume.

D Your retry logic is missing the required <code>RequestItems</code> parameter.

💡 Hint

Think about what causes throttling and unprocessed items despite retries.

Persistent unprocessed items usually indicate that the table's write capacity is insufficient to handle the volume, causing throttling. Increasing capacity or using on-demand mode can help.