Mastering Serverless (Part II): Mastering AWS DynamoDB Batch Write Failures for a Smoother Experience.

7 min readJan 10, 2024

A modern, clean workspace with a computer displaying code and AWS management console on the screen, surrounded by notes about DynamoDB, AWS SQS, and error handling strategies. The environment is well-lit, suggesting a productive and tech-savvy atmosphere. There’s a coffee cup, a notepad with AWS and DynamoDB diagrams, and some tech gadgets on the desk, embodying a developer’s workspace.

Hey there, DynamoDB enthusiasts! Remember our chat about DynamoDB helper methods? Well, it turns out there’s more to explore! Today, we’re taking a deeper dive, particularly into the world of Batch Writes. Ever faced those tricky situations where things don’t go as planned? Yes, we’re talking about those pesky batch write failures.

Read Part I of the series:
Mastering Serverless (Part I): Enhancing DynamoDB Interactions with Document Client

Why should you care? Well, handling failures gracefully isn’t just about fixing temporary glitches; it’s about crafting a robust and resilient system. A system that stands strong in the face of adversities and keeps your data safe and sound. It’s about ensuring that as a developer, you’re equipped with the right tools and strategies to turn potential setbacks into opportunities for improvement. And let’s not forget the end-users! A smooth, uninterrupted experience for them is the ultimate goal, right?

So, in this article, let’s explore some smart strategies to handle batch write failures in DynamoDB. From retrying failed batches to intelligent error logging and beyond, we’ll look at how each approach can make your life easier and your application more reliable. Whether you’re a seasoned AWS navigator or just starting, there’s something here for everyone. So, grab your favorite beverage, and let’s dive in! And remember, your thoughts and experiences are what make this journey richer, so feel free to chime in with your insights and questions in the comments section below.

Alternatives for Elegantly Managing DynamoDB Batch Failures

Retrying Failed Batches:

Use Case: Imagine you’re running a time-sensitive application, like a stock trading platform, where data needs to be updated rapidly. In such cases, if a batch write fails due to transient issues like temporary network glitches or short bursts of high load, retrying makes perfect sense.

Example: Let’s say you’re pushing real-time stock price updates. A retry with exponential backoff ensures that temporary hiccups don’t cause data loss, keeping your platform accurate and reliable.

Logging and Alerting:

Use Case: Consider an e-commerce platform during a big sale event. You expect high traffic, and any failure in updating inventory can lead to overselling or customer dissatisfaction.

Example: If batch writes fail during such critical periods, logging the failures and setting up alerts can be a lifesaver. This way, your team can quickly intervene, perhaps manually adjusting inventory levels, ensuring the platform remains trustworthy and customer-friendly.

Returning Failed Items:

Use Case: In a content management system where multiple users are updating content, not all updates are equally critical.

Example: If a batch update containing user-generated content fails, your system can return these failed items. The calling function can then decide, perhaps based on the content’s priority, whether to retry immediately, save the updates for later, or notify the content creator of the issue.

Storing Failed Items for Later Processing:

Use Case: In data analytics workflows, where large volumes of data are processed and stored for future analysis, not every piece of data needs to be processed in real-time.

Example: If batch writes of log data fail, placing these items into a queue for later processing ensures that you don’t lose data. It’s not critical to have every log processed instantly, so this approach balances efficiency with data integrity.

Combination of Strategies:

Use Case: A multi-tiered application, like a social media platform, where both real-time and non-critical data are being processed.

Example: For user posts (real-time), you might use retries and alerts. For non-critical data, like user behavior analytics, using queues for later processing might be more appropriate. This combined approach ensures that the platform remains engaging and responsive while also managing less critical data smartly.

Handling Partial Failures:

Use Case: In IoT applications, where multiple devices send data simultaneously, partial failures are common due to the volume and variety of data.

Example: If a batch write partially fails while processing data from various sensors, your system should identify which data points failed and apply the most suitable strategy for each. This ensures that your IoT application remains robust and can handle the diverse nature of data streams effectively.

Evolving Our DynamoDB Batch Write Helper: Mastering Failure Detection and Management

In our journey to optimize DynamoDB interactions, it’s vital to focus on how our dynamodbBatchWriteHelper method can be more resilient and intelligent in handling failures. Let's dive into refining this method, spotlighting two key aspects: retrying failures and strategically managing unprocessed items.

export const dynamodbBatchWriteHelper = async (TableName, Items) => {
    // ... existing code ...

    let failedItems = [];

    for (const batch of writeBatches) {
        const params = { /* ... */ };

        try {
            const response = await docClient.send(new BatchWriteCommand(params));
            // Handle partial failures here
            if (response.UnprocessedItems && response.UnprocessedItems.length > 0) {
                failedItems.push(...response.UnprocessedItems);
                // Optional: Implement retry logic for failedItems here
            }
        } catch (error) {
            logger.error(`Error in dynamodbBatchWriteHelper: ${error}`);
            // Optional: Push entire batch to failedItems or handle differently
            throw error;
        }
    }

    if (failedItems.length > 0) {
        // Handle failed items (e.g., log, alert, enqueue, etc.)
    }

    logger.info(`Successfully Written: ${Items.length - failedItems.length} items to ${TableName}`);
    return failedItems; // Optional: return failed items for further handling
}

Understanding the Updated Method Structure

Our trusty dynamodbBatchWriteHelper method, up to this point, has adeptly managed batch write operations. But now, we're injecting an extra dose of sophistication. We aim to not just perform operations but also to be aware of how well they perform. This means detecting failures and making smart decisions on how to handle them.

Implementing Failure Detection

The crux of our method is where we send the batch write command: await docClient.send(new BatchWriteCommand(params)). In our enhanced version, we're not just sending requests; we're keenly observing the responses, particularly the UnprocessedItems. This attribute is our window into understanding partial failures—instances where DynamoDB couldn't process some items in the batch.

for (const batch of writeBatches) {
    const params = { /* ... */ };

    try {
        const response = await docClient.send(new BatchWriteCommand(params));
        if (response.UnprocessedItems && response.UnprocessedItems.length > 0) {
            failedItems.push(...response.UnprocessedItems);
            // Consider implementing retry logic here
        }
    } catch (error) {
        logger.error(`Error in dynamodbBatchWriteHelper: ${error}`);
        // Decide how to handle the complete batch failure
        throw error;
    }
}

Here, we meticulously collect these unprocessed items into our failedItems array, setting the stage for further action.

Strategies for Retrying Failures

Upon identifying unprocessed items, one immediate strategy is to retry. But, caution is key. Implementing a retry mechanism requires a thoughtful approach:

Backoff Strategy: Implement an exponential backoff strategy to prevent overwhelming DynamoDB with rapid, successive retries.
Limiting Retries: Set a cap on the number of retries to avoid infinite loops, ensuring our method doesn’t get stuck in a retry quagmire.

Handling Unprocessed Items

So, what about the items in failedItems? This is where strategy comes into play. Do we retry them immediately, log them for manual intervention, or queue them for later processing? This decision depends on the nature of your application and the criticality of the data.

Logging and Alerting: For critical data, where immediate attention is required, logging the failure and triggering alerts can be a smart move.
Queuing for Later Processing: For less critical data, queuing these items for later processing can ensure that no data is lost while keeping the system efficient.

if (failedItems.length > 0) {
    // Implement your chosen strategy here
}

logger.info(`Successfully Written: ${Items.length - failedItems.length} items to ${TableName}`);
return failedItems; // Optional: return for further handling

Sending Failures to a Queue. An Example of a Strategy.

Implementing a feature to send failedItems to a queue requires selecting a suitable queuing service and writing code to interact with that service. For AWS, a common choice is Amazon Simple Queue Service (SQS). Below is an example of how you could modify the dynamodbBatchWriteHelper function to send failedItems to an SQS queue if there are any.

const AWS = require('aws-sdk');
const sqs = new AWS.SQS({ apiVersion: '2012-11-05' });
const queueUrl = "YOUR_SQS_QUEUE_URL"; // Replace with your SQS queue URL

export const dynamodbBatchWriteHelper = async (TableName, Items) => {
    // ... existing code ...

    let failedItems = [];

    for (const batch of writeBatches) {
        const params = { /* ... */ };

        try {
            const response = await docClient.send(new BatchWriteCommand(params));
            if (response.UnprocessedItems && response.UnprocessedItems.length > 0) {
                failedItems.push(...response.UnprocessedItems);
            }
        } catch (error) {
            logger.error(`Error in dynamodbBatchWriteHelper: ${error}`);
            throw error;
        }
    }

    if (failedItems.length > 0) {
        // Send failed items to an SQS queue
        const sqsParams = {
            MessageBody: JSON.stringify(failedItems),
            QueueUrl: queueUrl
        };

        try {
            await sqs.sendMessage(sqsParams).promise();
            logger.info(`Failed items sent to SQS queue: ${queueUrl}`);
        } catch (error) {
            logger.error(`Error sending failed items to SQS queue: ${error}`);
        }
    }

    logger.info(`Successfully Written: ${Items.length - failedItems.length} items to ${TableName}`);
    return failedItems;
}

In this example:

SQS Initialization: We initialize the AWS SQS client and specify your queue’s URL.
Sending Failed Items to SQS: If there are any failedItems, we serialize them into a string (since SQS messages are strings) and send them to the specified SQS queue.
Error Handling: There’s basic error handling for the SQS message sending. If it fails, it logs the error.

Make sure to replace "YOUR_SQS_QUEUE_URL" with the actual URL of your SQS queue. Also, ensure that your AWS credentials are properly configured to allow access to both DynamoDB and SQS.

This example provides a basic implementation. Depending on your use case, you might need to enhance the error handling, possibly batch the messages if failedItems is large, or apply other logic specific to your application's needs.