DynamoDB Scan

Scan Overview

At its core, scan 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 scan 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, ScanCommand } from '@aws-sdk/lib-dynamodb';

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

const { Items } = await docClient.send(new ScanCommand({
  TableName: 'Orders',
  FilterExpression: '#s = :status',
  ExpressionAttributeNames: { '#s': 'status' },
  ExpressionAttributeValues: { ':status': 'NEW' },
}));

ScanCommand reads the whole table and filters afterwards — use it sparingly.

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

Amazon DynamoDB Cheatsheet

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

How Scan Works in DynamoDB

Scan 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.

ScanCommand reads the whole table and filters afterwards — use it sparingly.

• 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 Scan

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

Key Takeaways

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