Soft Delete Pattern

Soft Delete Pattern Overview

At its core, soft delete pattern 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 soft delete pattern 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, DeleteCommand } from '@aws-sdk/lib-dynamodb';

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

await docClient.send(new DeleteCommand({
  TableName: 'Orders',
  Key: { pk: 'ORDER#123', sk: 'META' },
}));

DeleteCommand removes a single item identified by its primary key.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to soft delete pattern.

How Soft Delete Pattern Works in DynamoDB

Soft Delete Pattern 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.

DeleteCommand removes a single item identified by its primary key.

• 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 Soft Delete Pattern

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

Key Takeaways

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