Unprocessed items handling in DynamoDB - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are unprocessed items in DynamoDB batch operations?

Unprocessed items are those that DynamoDB could not process in a batch write or batch get request due to throttling or internal errors. They need to be retried to ensure all data is handled.

beginner

How does DynamoDB indicate unprocessed items in a batch write response?

DynamoDB returns unprocessed items in the response under the key 'UnprocessedItems'. This contains the items that were not written and should be retried.

intermediate

Why is it important to handle unprocessed items in DynamoDB batch operations?

Because DynamoDB may throttle requests or face temporary issues, unprocessed items ensure you don't lose data. Retrying them guarantees data consistency and completeness.

intermediate

What is a common strategy to handle unprocessed items in DynamoDB batch writes?

A common strategy is to check the 'UnprocessedItems' in the response and retry sending them after a short delay, often using exponential backoff to avoid repeated throttling.

beginner

Can unprocessed items occur in both BatchWriteItem and BatchGetItem operations?

Yes, unprocessed items can occur in both BatchWriteItem and BatchGetItem operations when DynamoDB cannot process all requested items in a single batch.

What does DynamoDB return when some items in a batch write are not processed?

A An error and no data

B A partial success without details

C A success message with all items processed

D A list of unprocessed items under 'UnprocessedItems'

DynamoDB returns the unprocessed items under the 'UnprocessedItems' key so you can retry them.

Why might DynamoDB return unprocessed items in a batch operation?

A Because the table is deleted

B Because the items are invalid

C Due to throttling or internal errors

D Because the request was too small

Unprocessed items usually happen because DynamoDB throttles requests or encounters temporary internal issues.

What is the best practice after receiving unprocessed items from DynamoDB?

A Ignore them

B Retry after a delay, possibly with exponential backoff

C Retry immediately without delay

D Delete the unprocessed items

Retrying after a delay with exponential backoff helps avoid repeated throttling and increases success chances.

In which DynamoDB operations can unprocessed items appear?

A Both BatchWriteItem and BatchGetItem

B Only BatchGetItem

C Only BatchWriteItem

D Only PutItem

Unprocessed items can appear in both batch write and batch get operations.

What happens if you do not handle unprocessed items in DynamoDB batch writes?

A Some data may be lost or not written

B The request fails completely

C DynamoDB automatically retries them forever

D All data is saved anyway

If unprocessed items are ignored, some data may not be saved, causing inconsistency.

Explain what unprocessed items are in DynamoDB batch operations and why you need to handle them.

Think about what happens when DynamoDB can't finish all requests at once.

Describe a simple approach to handle unprocessed items returned by DynamoDB batch write requests.

Consider how to avoid hammering DynamoDB repeatedly.

Practice

1. What does DynamoDB return when some items in a batch write are not processed immediately?

easy

A. An error stopping the entire batch operation

B. A partial success with no retry information

C. A success message without details

D. A list of unprocessed items to retry later

Solution

1. Step 1: Understand batch write behavior

   DynamoDB may not process all items in a batch due to throttling or limits.

2. Step 2: Identify returned data for unprocessed items

   It returns unprocessed items so the client can retry sending them.

3. Final Answer:

   A list of unprocessed items to retry later -> Option D

4. Quick Check:

   Unprocessed items = retry list [OK]

Hint: Unprocessed items mean retry those items later [OK]

Common Mistakes:

• Thinking DynamoDB throws an error instead
• Assuming all items are processed without feedback
• Ignoring unprocessed items in code

2. Which of the following is the correct way to check for unprocessed items in a DynamoDB BatchWriteItem response in Python?

easy

A. if 'UnprocessedItems' in response and response['UnprocessedItems']:

B. if response['UnprocessedItems'] == None:

C. if response['UnprocessedItems'] is False:

D. if 'UnprocessedItems' not in response:

Solution

1. Step 1: Understand response structure

   The response includes 'UnprocessedItems' key if any items were not processed.

2. Step 2: Correctly check for unprocessed items

   Check if 'UnprocessedItems' exists and is not empty to retry those items.

3. Final Answer:

   if 'UnprocessedItems' in response and response['UnprocessedItems']: -> Option A

4. Quick Check:

   Check key exists and is not empty [OK]

Hint: Check key exists and is not empty to find unprocessed items [OK]

Common Mistakes:

• Checking for None instead of empty dict
• Using 'not in' which misses unprocessed items
• Checking for False which is incorrect

3. Given this Python snippet handling DynamoDB batch write:

response = client.batch_write_item(RequestItems=batch)
if response.get('UnprocessedItems'):
    retry_items = response['UnprocessedItems']
else:
    retry_items = None
print(retry_items)

What will be printed if all items were processed successfully?

medium

A. List of all items

B. {}

C. None

D. An error

Solution

1. Step 1: Understand response when all items processed

   If all items are processed, 'UnprocessedItems' key is empty or missing.

2. Step 2: Analyze code behavior

   response.get('UnprocessedItems') returns None or empty dict; if empty dict is falsy, else branch sets retry_items to None.

3. Final Answer:

   None -> Option C

4. Quick Check:

   No unprocessed items means retry_items = None [OK]

Hint: If no unprocessed items, retry_items is None [OK]

Common Mistakes:

• Expecting empty dict instead of None
• Assuming an error is raised
• Thinking retry_items contains all items

4. You wrote this code to resend unprocessed items but it never stops retrying:

while True:
    response = client.batch_write_item(RequestItems=items)
    items = response.get('UnprocessedItems', {})
    if not items:
        break

What is the likely bug causing infinite retries?

medium

A. Missing a delay between retries causing throttling

B. Using 'break' inside the loop incorrectly

C. Not updating 'items' with the new unprocessed items correctly

D. Not checking for empty dictionary properly

Solution

1. Step 1: Analyze the retry loop

   The code updates items correctly but retries immediately without delay.

2. Step 2: Identify the cause of infinite retries

   Missing delay or exponential backoff leads to rapid retries, causing persistent throttling. DynamoDB continually returns the same unprocessed items.

3. Final Answer:

   Missing a delay between retries causing throttling -> Option A

4. Quick Check:

   Add sleep/backoff between retries [OK]

Hint: Add delay or backoff between batch retries to avoid throttling [OK]

Common Mistakes:

• Blaming item update logic instead of throttling
• Assuming empty dict check prevents infinite loops
• Overlooking need for backoff in tight retry loops

5. You want to ensure all items in a large batch are saved in DynamoDB without loss. Which approach best handles unprocessed items efficiently?

hard

A. Split items into single writes to avoid unprocessed items

B. Check for unprocessed items after each batch write and retry them until none remain

C. Send all items once and ignore unprocessed items since they are rare

D. Use batch write once and log unprocessed items without retrying

Solution

1. Step 1: Understand batch write limitations

   DynamoDB batch writes can return unprocessed items due to throttling or limits.

2. Step 2: Best practice for data safety

   Retry unprocessed items repeatedly until all are saved to avoid data loss.

3. Final Answer:

   Check for unprocessed items after each batch write and retry them until none remain -> Option B

4. Quick Check:

   Retry unprocessed items until empty for safe writes [OK]

Hint: Always retry unprocessed items until all are saved [OK]

Common Mistakes:

• Ignoring unprocessed items causing data loss
• Writing items one by one causing inefficiency
• Logging without retrying unprocessed items