Batch limits and retries in DynamoDB - Mini Project: Build & Apply

Handling Batch Limits and Retries in DynamoDB

📖 Scenario: You are managing a DynamoDB table that stores customer orders for an online store. You need to insert multiple orders at once using batch operations. However, DynamoDB limits batch writes to 25 items per request. Also, sometimes requests may not process all items, so you need to retry unprocessed items until all are saved.

🎯 Goal: Build a DynamoDB batch write operation that respects the 25-item limit per batch and retries unprocessed items until all orders are successfully written.

📋 What You'll Learn

Create a list called orders with exactly 30 order items, each with OrderId and CustomerName attributes.

Define a batch size variable called BATCH_SIZE set to 25.

Write code to split orders into batches of size BATCH_SIZE.

Implement a loop to send each batch to DynamoDB using batch_write_item and retry unprocessed items until none remain.

💡 Why This Matters

🌍 Real World

Batch writing is common when inserting or updating many items in DynamoDB efficiently while respecting service limits.

💼 Career

Understanding batch limits and retries is essential for backend developers and cloud engineers working with AWS DynamoDB to build scalable and reliable applications.

1

Create the orders list with 30 items

Create a list called orders containing 30 dictionaries. Each dictionary must have keys 'OrderId' with values from 1 to 30 as integers, and 'CustomerName' with values 'Customer1' to 'Customer30' as strings.

DynamoDB

# Create the list 'orders' with 30 order dictionaries
# Your code here

Hint

Use a list comprehension with range(1, 31) to create 30 dictionaries.

2

Define the batch size variable

Define an integer variable called BATCH_SIZE and set it to 25.

DynamoDB

orders = [{'OrderId': i, 'CustomerName': f'Customer{i}'} for i in range(1, 31)]
# Define BATCH_SIZE as 25
# Your code here

Hint

Just assign the number 25 to the variable BATCH_SIZE.

3

Split orders into batches

Create a list called batches that splits orders into sublists each of size BATCH_SIZE. Use a list comprehension with slicing and range.

DynamoDB

orders = [{'OrderId': i, 'CustomerName': f'Customer{i}'} for i in range(1, 31)]
BATCH_SIZE = 25
# Create batches by slicing orders into chunks of BATCH_SIZE
# Your code here

Hint

Use a list comprehension with range(0, len(orders), BATCH_SIZE) and slice orders[i:i + BATCH_SIZE].

4

Write batch write with retries for unprocessed items

Write code that loops over each batch in batches. For each batch, create a RequestItems dictionary with key 'Orders' and value as a list of {'PutRequest': {'Item': item}} for each item in the batch. Then, use a while loop to call dynamodb.batch_write_item(RequestItems=RequestItems) and retry unprocessed items until none remain. Assume dynamodb is a valid DynamoDB client.

DynamoDB

orders = [{'OrderId': i, 'CustomerName': f'Customer{i}'} for i in range(1, 31)]
BATCH_SIZE = 25
batches = [orders[i:i + BATCH_SIZE] for i in range(0, len(orders), BATCH_SIZE)]
# Loop over batches and write to DynamoDB with retries
# Your code here

Hint

Use a for loop over batches, build RequestItems, then a while loop to call batch_write_item and update RequestItems['Orders'] with unprocessed items.

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