DynamoDB Streams

Streams Overview

Streams is a building block you will reach for often in DynamoDB. It keeps related logic together and makes your intent obvious to reviewers and future maintainers.

When you learn streams properly, you avoid the guesswork that leads to bugs and rework. The example below shows the shape you will use in most real DynamoDB projects.

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.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to streams.

How Streams Works in DynamoDB

Streams 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 Streams

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

Key Takeaways

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