DynamoDB Error Handling

Error Handling Overview

Error Handling 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 error handling focused and predictable. Start from the minimal example here, then layer in only the complexity your feature actually needs.

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.

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

Amazon DynamoDB Cheatsheet

Handy DynamoDB (AWS SDK v3) reference related to error handling.

How Error Handling Works in DynamoDB

Error Handling 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 Error Handling

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

Common Mistakes

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

Key Takeaways

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