Batch limits and retries in DynamoDB - Step-by-Step Execution

Concept Flow - Batch limits and retries

Start Batch Write

↓

Check Batch Size <= 25?

No→Split Batch

|Yes↓

Send Batch Request

↓

Receive Response

↓

Check Unprocessed Items?

No→Batch Complete

|Yes↓

Retry Unprocessed Items

↓

Send Batch Request

This flow shows how DynamoDB batch writes are limited to 25 items per request, and how unprocessed items are retried until all are written.

Execution Sample

DynamoDB

batch = items[:25]
while batch:
  response = dynamodb.batch_write_item(RequestItems={'YourTableName': batch})
  batch = response.get('UnprocessedItems', {}).get('YourTableName', [])
  if batch:
    wait_and_retry()

This code sends items in batches of max 25 to DynamoDB, retries unprocessed items until all are written.

Execution Table

Step	Batch Size	Action	Unprocessed Items	Retry?
1	25	Send batch write request	5	Yes
2	5	Retry unprocessed items	0	No
3	0	All items processed	-	No

💡 All items processed, no unprocessed items remain

Variable Tracker

Variable	Start	After Step 1	After Step 2	Final
batch	25 items	5 items (unprocessed)	0 items	0 items

Key Moments - 3 Insights

Why does DynamoDB limit batch writes to 25 items?

DynamoDB limits batch writes to 25 items per request to control load and ensure performance. See execution_table Step 1 where batch size is 25.

What happens to items not processed in the batch?

Unprocessed items are returned in the response and must be retried. In execution_table Step 1, 5 items were unprocessed and retried in Step 2.

Why do we retry only unprocessed items, not the whole batch?

Retrying only unprocessed items avoids duplicate writes and reduces load. The variable_tracker shows batch size shrinking after retries.

Visual Quiz - 3 Questions

Look at the execution_table, what is the batch size sent in Step 2?

A 30

B 5

C 25

D 0

💡 Hint

Check the 'Batch Size' column in execution_table row for Step 2

At which step does the batch become empty, indicating all items processed?

A Step 3

B Step 1

C Step 2

D None

💡 Hint

Look at the 'Batch Size' and 'Unprocessed Items' columns in execution_table

If the initial batch size was 50 items, how many batch requests would be needed at minimum?

A 3

B 1

C 2

D 25

💡 Hint

Recall the batch size limit of 25 items per request from concept_flow

Concept Snapshot

DynamoDB batch writes accept up to 25 items per request.
Unprocessed items are returned and must be retried.
Retries only include unprocessed items, not the whole batch.
This ensures efficient, reliable batch writing.
Always check response for unprocessed items and retry accordingly.

Full Transcript

This visual execution shows how DynamoDB batch write operations handle limits and retries. Each batch can contain up to 25 items. If the batch is larger, it is split. After sending a batch write request, DynamoDB may return some items as unprocessed due to capacity or throttling. These unprocessed items must be retried until all are written. The execution table traces batch sizes and retries step-by-step, showing how the batch shrinks as unprocessed items are retried. Key moments clarify why the limit exists, how unprocessed items are handled, and why retries focus only on those items. The quiz tests understanding of batch sizes, retry steps, and request counts. The snapshot summarizes the core rules for batch limits and retries in DynamoDB.

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