DynamoDB Stream Records

Stream Records Overview

At its core, stream records 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 stream records pays off across the whole codebase: fewer surprises, easier testing, and smoother onboarding. The snippet below is a solid starting point.

export const handler = async (event) => {
  for (const record of event.Records) {
    if (record.eventName === 'INSERT') {
      const newItem = record.dynamodb.NewImage;
      console.log('new item', newItem);
    }
  }
};

A Lambda triggered by DynamoDB Streams reacts to item-level INSERT, MODIFY, and REMOVE events.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to stream records.

How Stream Records Works in DynamoDB

Stream Records 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.

A Lambda triggered by DynamoDB Streams reacts to item-level INSERT, MODIFY, and REMOVE events.

• 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 Stream Records

In production, stream records 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 stream records.
• Leaving stream records untested, so regressions slip into production.
• Over-engineering stream records before you actually need the extra flexibility.
• Ignoring documentation, which makes stream records hard for the next developer to change.

Key Takeaways

• Stream Records is a core part of working effectively with DynamoDB.
• Start small and keep stream records focused on a single responsibility.
• Apply consistent patterns so stream records scales across your project.
• Test and document stream records to keep it maintainable over time.