AWS DVA-C02 Drill: Idempotent API Design - Handling Duplicates in Lambda-backed REST APIs

Jeff’s Note

#

Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.

For DVA-C02 candidates, the confusion often lies in handling duplicate requests in highly variable traffic patterns without data loss or inconsistency. In production, this is about knowing exactly how to implement idempotency checks using AWS services with Lambda functions and when to choose DynamoDB over RDS or caching solutions. Let’s drill down.

The Certification Drill (Simulated Question)

#

Scenario

#

A startup named BrightSensors is developing a serverless application deployed on a fleet of IoT edge devices that send sensor telemetry data. This data is ingested via a RESTful API, which is implemented as an AWS Lambda function behind Amazon API Gateway. Each API call includes a unique request ID generated by the device.

The volume of requests can spike unpredictably any time of day. During periods of throttling or temporary connectivity issues, the devices might retry the same API call multiple times. BrightSensors needs the API backend to handle these duplicate requests gracefully — making sure no duplicate data is ingested and no records are lost or overwritten incorrectly.

The Requirement:

#

Design a solution that ensures idempotent processing of API requests by verifying whether a unique request ID has already been processed before handling it.

The Options

#

• A) Use an Amazon RDS for MySQL instance to store unique request IDs. Modify the Lambda function to query the database to check if the request ID exists before processing.
• B) Use an Amazon DynamoDB table to store unique request IDs. Modify the Lambda function to check the table before processing the request.
• C) Use an Amazon DynamoDB table to store unique request IDs. Modify the Lambda function to return a client error when duplicate requests are detected.
• D) Use an Amazon ElastiCache for Memcached cluster to store unique request IDs temporarily. Modify the Lambda function to check the cache before processing the request.

Google adsense

#

leave a comment:

Correct Answer

#

B

Quick Insight: The Developer Imperative

#

• For DVA-C02, the key is understanding Lambda idempotency and state management via DynamoDB. DynamoDB offers single-digit millisecond latency and built-in support for conditional writes, which you can use to atomically check and insert the unique request ID — effectively preventing duplicates. RDS adds latency and operational overhead, Memcached is ephemeral and unsafe for durable idempotency, and returning a client error on duplicates may not fit all use cases.

Content Locked: The Expert Analysis

#

You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?

The Expert’s Analysis

#

Correct Answer

#

Option B

The Winning Logic

#

Using Amazon DynamoDB to store the unique request identifiers is the ideal choice because:

• DynamoDB provides fast, highly scalable, and fully managed NoSQL storage with extremely low read/write latency, critical for serverless APIs handling spiky IoT workloads.
• You can use DynamoDB’s conditional writes (PutItem with ConditionExpression) to atomically insert a new unique ID only if it does not already exist, guaranteeing exactly-once processing from the Lambda function.
• This approach ensures idempotency transparently and efficiently without introducing excess latency or complexity.
• Persistent storage is mandatory since API retries must survive function restarts and scale independently; DynamoDB fits perfectly.

The Trap (Distractor Analysis)

#

• Option A (RDS for MySQL): While functionally possible, RDS introduces higher latency, connection management overhead, and complexity. It also does not handle spiky workloads as effortlessly as DynamoDB without scaling complexities.

• Option C (DynamoDB + client error on duplicates): Returning a client error (like 409 Conflict) is not appropriate in all cases. Retries are common for transient failures, so it is better to silently detect duplicates and prevent processing rather than fail with an error.

• Option D (ElastiCache Memcached): Memcached is an in-memory cache with eventual consistency and no durability guarantees. Stored request IDs may be evicted on memory pressure, causing duplication and data loss.

The Technical Blueprint

#

Code Snippet: DynamoDB Conditional Write in Lambda (Node.js Example)

#

const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB.DocumentClient();

exports.handler = async (event) => {
  const requestId = event.requestId;
  
  const params = {
    TableName: process.env.DDB_TABLE,
    Item: { id: requestId, processedAt: new Date().toISOString() },
    ConditionExpression: 'attribute_not_exists(id)'
  };
  
  try {
    await dynamodb.put(params).promise();
    // Process the request here after successful insert (id not seen before)
    return { statusCode: 200, body: "Processed" };
  } catch (err) {
    if (err.code === 'ConditionalCheckFailedException') {
      // Duplicate request detected; idempotent handling - ignore or return 200
      return { statusCode: 200, body: "Duplicate request ignored" };
    }
    // Other errors
    throw err;
  }
};

The Comparative Analysis

#

Real-World Application (Practitioner Insight)

#

Exam Rule

#

“For the exam, always pick DynamoDB when asked about idempotent APIs with high variability in serverless applications.”

Real World

#

“In production, sometimes hybrid approaches use DynamoDB for idempotency with caching layers for read-heavy parts — but durability and atomicity always rely on DynamoDB’s conditional writes.”

(CTA) Stop Guessing, Start Mastering

#

Disclaimer

This is a study note based on simulated scenarios for the DVA-C02 exam.