Lambda Batch Processing

Lambda Batch Processing Overview

At its core, lambda batch processing is about doing one thing well inside your DynamoDB project. Once you understand the pattern, you can apply it consistently across features and teams.

Good lambda batch processing pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.

import { DynamoDBClient } from '@aws-sdk/client-dynamodb';
import { DynamoDBDocumentClient, BatchWriteCommand } from '@aws-sdk/lib-dynamodb';

const client = new DynamoDBClient({});
const docClient = DynamoDBDocumentClient.from(client);

await docClient.send(new BatchWriteCommand({
  RequestItems: {
    Orders: [
      { PutRequest: { Item: { pk: 'ORDER#1', sk: 'META' } } },
      { PutRequest: { Item: { pk: 'ORDER#2', sk: 'META' } } },
    ],
  },
}));

BatchWriteCommand writes or deletes up to 25 items in a single request.

Lambda Batch Processing Example

import { DynamoDBClient } from '@aws-sdk/client-dynamodb';
import { DynamoDBDocumentClient } from '@aws-sdk/lib-dynamodb';

const docClient = DynamoDBDocumentClient.from(new DynamoDBClient({}));
// docClient.send(new PutCommand(...)) etc.

• Start from a minimal Lambda Batch Processing example and grow it only as needed.
• Keep configuration explicit so Lambda Batch Processing behaves the same in every environment.
• Name things clearly so teammates understand your Lambda Batch Processing at a glance.
• Add tests around Lambda Batch Processing early to lock in expected behaviour.

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to lambda batch processing.

How Lambda Batch Processing Works in DynamoDB

Lambda Batch Processing builds on DynamoDB's key-value and document model, where every item lives in a partition chosen by its partition key and is optionally ordered by a sort key.

BatchWriteCommand writes or deletes up to 25 items in a single request.

• Design access patterns first, then model keys around them.
• Prefer Query over Scan for predictable performance.
• Use expressions to read and write only what you need.
• Keep items small and avoid hot partitions.

Practical Guidance for Lambda Batch Processing

In production, lambda batch processing should be cost-aware and resilient. Right-size capacity, handle throttling with retries, and lean on indexes to support your query patterns.

Common Mistakes

• Skipping error handling and edge cases when wiring up lambda batch processing.
• Leaving lambda batch processing untested, so regressions slip into production.
• Over-engineering lambda batch processing before you actually need the extra flexibility.
• Ignoring documentation, which makes lambda batch processing hard for the next developer to change.

Key Takeaways

• Lambda Batch Processing is a core part of working effectively with DynamoDB.
• Start small and keep lambda batch processing focused on a single responsibility.
• Apply consistent patterns so lambda batch processing scales across your project.
• Test and document lambda batch processing to keep it maintainable over time.