Event Source Mapping

Event Source Mapping is an important part of building production-ready DynamoDB systems. This lesson explains what event source mapping means, how it works, and how to apply it with practical examples you can reuse.

Event Source Mapping Overview

Event Source Mapping 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 event source mapping 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.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to event source mapping.

Operation	Command	Purpose
Create/replace	`PutCommand`	Write an item
Read one	`GetCommand`	Fetch by primary key
Update	`UpdateCommand`	Modify attributes
Delete	`DeleteCommand`	Remove an item
Query	`QueryCommand`	Efficient key-based read
Scan	`ScanCommand`	Full-table read (avoid)
Transaction	`TransactWriteCommand`	Atomic multi-item writes

How Event Source Mapping Works in DynamoDB

Event Source Mapping 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 Event Source Mapping

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

Concern	Recommendation
Performance	Query by key; avoid table scans
Cost	Use on-demand or right-sized provisioned capacity
Modeling	Design for known access patterns
Reliability	Retry throttled requests with backoff

Common Mistakes

Copying event source mapping snippets without understanding what each line does.
Skipping error handling and edge cases when wiring up event source mapping.
Leaving event source mapping untested, so regressions slip into production.
Over-engineering event source mapping before you actually need the extra flexibility.

Key Takeaways

Event Source Mapping is a core part of working effectively with DynamoDB.
Start small and keep event source mapping focused on a single responsibility.
Apply consistent patterns so event source mapping scales across your project.
Test and document event source mapping to keep it maintainable over time.

Pro Tip

When you get stuck on event source mapping, reduce it to the smallest reproducible example first — most DynamoDB issues become obvious once the noise is gone.