Process Stream Events

Process Stream Events Overview

Process Stream Events lets you structure DynamoDB work so it stays readable, testable, and easy to scale. Instead of ad-hoc code, you follow a clear pattern that other developers can recognise immediately.

The key is to keep process stream events focused and predictable. Start from the minimal example here, then layer in only the complexity your feature actually needs.

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.

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

Amazon DynamoDB Cheatsheet

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

How Process Stream Events Works in DynamoDB

Process Stream Events 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 Process Stream Events

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

Key Takeaways

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