Batch limits and retries in DynamoDB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the maximum number of items allowed in a single batch write request.

DynamoDB

max_items = [1]

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

A25

B50

C100

D10

Attempts:

3 left

💡 Hint

Common Mistakes

Using a number higher than 25 causes the batch request to fail.

Confusing batch write limits with batch get limits.

The maximum number of items allowed in a single DynamoDB batch write request is 25.

2fill in blank

medium

Complete the code to retry unprocessed items after a batch write operation.

DynamoDB

if response.get('UnprocessedItems'):
    retry_items = response['[1]']

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

AProcessedItems

BUnprocessedItems

CItems

DFailedItems

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ProcessedItems' which contains items already handled.

Assuming all items are processed without checking this key.

UnprocessedItems contains the items that DynamoDB did not process in the batch write and need to be retried.

3fill in blank

hard

Fix the error in the code to correctly handle batch write retries with exponential backoff.

DynamoDB

import time

retry_count = 0
while retry_count < 5:
    response = client.batch_write_item(RequestItems=batch)
    unprocessed = response.get('UnprocessedItems')
    if not unprocessed:
        break
    batch = unprocessed
    time.sleep([1])
    retry_count += 1

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

Aretry_count

Bretry_count * 2

C5

D2 ** retry_count

Attempts:

3 left

💡 Hint

Common Mistakes

Using a constant or linear wait time causes inefficient retries.

Not increasing wait time can lead to repeated throttling.

Using exponential backoff with 2 ** retry_count seconds helps avoid throttling by increasing wait time after each retry.

4fill in blank

hard

Fill both blanks to create a batch write request with a limit and handle unprocessed items.

DynamoDB

batch_items = items[:[1]]
response = client.batch_write_item(RequestItems=[2])

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

A25

B{'MyTable': batch_items}

C50

D{'TableName': batch_items}

Attempts:

3 left

💡 Hint

Common Mistakes

Using more than 25 items in a batch write.

Incorrect dictionary key for RequestItems.

The batch write limit is 25 items, and the request must be a dictionary with the table name as the key.

5fill in blank

hard

Fill all three blanks to implement a retry loop with batch write, exponential backoff, and unprocessed items handling.

DynamoDB

max_retries = [1]
retry = 0
while retry < max_retries:
    response = client.batch_write_item(RequestItems=[2])
    unprocessed = response.get('UnprocessedItems')
    if not unprocessed:
        break
    [3](2 ** retry)
    retry += 1

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

A5

B{'OrdersTable': batch}

Ctime.sleep

Dclient.batch_write_item

Attempts:

3 left

💡 Hint

Common Mistakes

Not using exponential backoff in retries.

Passing incorrect structure to RequestItems.

Forgetting to sleep between retries.

Set max retries to 5, pass the batch dictionary with table name, and use time.sleep for exponential backoff.

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