DynamoDB Caching

Caching sits at the heart of caching in DynamoDB. This guide walks through the concept step by step, with examples, a cheatsheet, and common mistakes to avoid.

Caching Overview

Caching is a building block you will reach for often in DynamoDB. It keeps related logic together and makes your intent obvious to reviewers and future maintainers.

When you learn caching properly, you avoid the guesswork that leads to bugs and rework. The example below shows the shape you will use in most real DynamoDB projects.

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.

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

Amazon DynamoDB Cheatsheet

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

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 Caching Works in DynamoDB

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 Caching

In production, caching 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

Skipping error handling and edge cases when wiring up caching.
Leaving caching untested, so regressions slip into production.
Over-engineering caching before you actually need the extra flexibility.
Ignoring documentation, which makes caching hard for the next developer to change.

Key Takeaways

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

Pro Tip

Pair caching with automated tests from day one. It is far cheaper to catch DynamoDB regressions in CI than in production.