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.

Practice

1. What is the main reason DynamoDB enforces batch limits when using batch write operations?

easy

A. To allow unlimited retries automatically

B. To increase the size of each item stored

C. To prevent sending too many items at once and avoid throttling

D. To reduce the number of tables in the database

Solution

1. Step 1: Understand batch write limits

   DynamoDB limits batch write operations to a maximum of 25 items per request to avoid overloading the service.

2. Step 2: Reason about the purpose of limits

   These limits help prevent throttling and ensure stable performance by controlling request size.

3. Final Answer:

   To prevent sending too many items at once and avoid throttling -> Option C

4. Quick Check:

   Batch limits = prevent throttling [OK]

Hint: Batch limits protect DynamoDB from overloads [OK]

Common Mistakes:

• Thinking batch limits increase item size
• Assuming retries are automatic without checks
• Confusing batch limits with table count

2. Which of the following is the correct maximum number of items you can send in a single BatchWriteItem request in DynamoDB?

easy

A. 25 items

B. 50 items

C. 100 items

D. 10 items

Solution

1. Step 1: Recall DynamoDB batch write limits

   DynamoDB allows a maximum of 25 items per BatchWriteItem request.

2. Step 2: Compare options with the limit

   Only 25 items matches the correct limit of 25 items.

3. Final Answer:

   25 items -> Option A

4. Quick Check:

   BatchWriteItem max = 25 items [OK]

Hint: Remember 25 items max per batch write [OK]

Common Mistakes:

• Choosing 50 or 100 items which exceed the limit
• Confusing batch write with batch get limits
• Assuming 10 items is the max without checking docs

3. Consider this Python snippet using boto3 DynamoDB client:

response = client.batch_write_item(RequestItems={
  'MyTable': [
    {'PutRequest': {'Item': {'ID': {'S': '1'}}}},
    {'PutRequest': {'Item': {'ID': {'S': '2'}}}}
  ]
})
print(response.get('UnprocessedItems'))

If the response prints {}, what does it mean?

medium

A. The batch write failed completely

B. The request exceeded batch limits

C. Only the first item was processed

D. All items were processed successfully with no retries needed

Solution

1. Step 1: Understand UnprocessedItems meaning

   The UnprocessedItems key contains items that DynamoDB did not process and need retrying.

2. Step 2: Interpret empty dictionary response

   An empty dictionary {} means no unprocessed items, so all were processed successfully.

3. Final Answer:

   All items were processed successfully with no retries needed -> Option D

4. Quick Check:

   Empty UnprocessedItems = all processed [OK]

Hint: Empty UnprocessedItems means success [OK]

Common Mistakes:

• Assuming empty means failure
• Thinking only partial items processed
• Confusing batch limits with processing status

4. You wrote code to batch write 30 items to DynamoDB but got an error. Which fix will solve the problem?

medium

A. Split the 30 items into two batches of 25 and 5 items

B. Send all 30 items in one batch and increase timeout

C. Use batch get instead of batch write

D. Reduce each item size to half

Solution

1. Step 1: Identify batch write limit violation

   DynamoDB batch write limits max 25 items per request, so 30 items exceed this limit.

2. Step 2: Apply correct fix by splitting batches

   Splitting into two batches (25 and 5) respects the limit and avoids errors.

3. Final Answer:

   Split the 30 items into two batches of 25 and 5 items -> Option A

4. Quick Check:

   Split batches to respect 25 item limit [OK]

Hint: Split batches over 25 items to fix errors [OK]

Common Mistakes:

• Trying to send all items at once ignoring limits
• Using batch get which is for reads, not writes
• Reducing item size does not fix batch count limit

5. You have a batch write operation that returns some unprocessed items due to throttling. What is the best approach to ensure all items are eventually written?

hard

A. Retry the entire batch including processed items

B. Immediately retry only the unprocessed items with exponential backoff

C. Ignore unprocessed items and assume they are saved

D. Increase the batch size to include more items

Solution

1. Step 1: Understand unprocessed items cause

   Unprocessed items occur when DynamoDB throttles requests; these items need retrying.

2. Step 2: Apply best retry strategy

   Retry only unprocessed items with exponential backoff to avoid repeated throttling and ensure eventual success.

3. Final Answer:

   Immediately retry only the unprocessed items with exponential backoff -> Option B

4. Quick Check:

   Retry unprocessed items with backoff [OK]

Hint: Retry unprocessed items with backoff, not whole batch [OK]

Common Mistakes:

• Ignoring unprocessed items causing data loss
• Retrying entire batch wastes resources
• Increasing batch size worsens throttling