Application-Level Caching

Application-Level Caching Overview

At its core, application-level caching 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 application-level caching 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, GetCommand, PutCommand } from '@aws-sdk/lib-dynamodb';

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

// reuse docClient across the module for efficient, typed access
await docClient.send(new PutCommand({ TableName: 'Orders', Item: { pk: '1' } }));

The DynamoDBDocumentClient maps plain JavaScript objects to DynamoDB item format for you.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to application-level caching.

How Application-Level Caching Works in DynamoDB

Application-Level Caching 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.

The DynamoDBDocumentClient maps plain JavaScript objects to DynamoDB item format for you.

• 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 Application-Level Caching

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

Key Takeaways

• Application-Level Caching is a core part of working effectively with DynamoDB.
• Start small and keep application-level caching focused on a single responsibility.
• Apply consistent patterns so application-level caching scales across your project.
• Test and document application-level caching to keep it maintainable over time.