Handling Massive Load Testing in TypeScript: A Security Researcher’s Approach Without Documentation

In large-scale software systems and security research, load testing plays a critical role in evaluating system resilience against traffic spikes and malicious attacks. However, when attempting to implement robust load testing tools using TypeScript without proper documentation or existing frameworks, developers face unique challenges. This post explores a practical, code-centric approach to designing an effective load testing system optimized for massive loads, driven entirely through code and architecture decisions.

The Problem Space

Handling massive load testing involves generating million-plus requests efficiently without overwhelming the target or wasting resources. Security researchers often need to simulate malicious or abnormal traffic patterns to identify vulnerabilities like DoS (Denial of Service) vulnerabilities or to stress-test infrastructure.

Architectural Considerations

Given the lack of documentation, the first step is to establish a minimal, scalable architecture:

• Concurrency Control: Use Node.js's event-driven nature combined with worker threads for parallel processing.
• Request Batching: Send requests in batches to optimize throughput.
• Resource Management: Monitor CPU, memory, and network utilization dynamically.
• Data Collection: Log system metrics and request/response details for analysis.

Implementation Approach

Setting Up the Environment

First, initialize a TypeScript project with necessary dependencies:

npm init -y
npm install typescript @types/node axios
npx tsc --init

Create loadTester.ts for core logic.

Core Load Testing Logic

Leveraging async functions, worker threads, and efficient HTTP libraries such as Axios, here’s an example structure:

import axios from 'axios';
import { Worker, isMainThread, parentPort, workerData } from 'worker_threads';

const TARGET_URL = 'https://targetsystem.example/api/stress';
const TOTAL_REQUESTS = 1_000_000;
const BATCH_SIZE = 5000;

async function sendBatch(batchNumber: number, requestsCount: number) {
  const promises = [];
  for (let i = 0; i < requestsCount; i++) {
    promises.push(
      axios.get(TARGET_URL).then(response => {
        // Log success or handle response
      }).catch(error => {
        // Log errors for failures
      })
    );
  }
  await Promise.all(promises);
  console.log(`Batch ${batchNumber} completed.`);
}

function runWorker(start: number, end: number, workerId: number) {
  const totalBatches = Math.ceil((end - start) / BATCH_SIZE);
  for (let batch = 0; batch < totalBatches; batch++) {
    const batchStart = start + batch * BATCH_SIZE;
    const batchEnd = Math.min(batchStart + BATCH_SIZE, end);
    sendBatch(batch, batchEnd - batchStart);
  }
  parentPort?.postMessage({ workerId });
}

if (isMainThread) {
  const workerCount = 4;
  const requestsPerWorker = Math.ceil(TOTAL_REQUESTS / workerCount);

  for (let i = 0; i < workerCount; i++) {
    const start = i * requestsPerWorker;
    const end = Math.min(start + requestsPerWorker, TOTAL_REQUESTS);
    const worker = new Worker(__filename, { workerData: { start, end, workerId: i } });
    worker.on('message', msg => {
      console.log(`Worker ${msg.workerId} finished.`);
    });
  }
}

This architecture distributes the load generation across multiple worker threads, allowing high concurrency while managing resource utilization.

Monitoring and Data Collection

Integrate real-time statistics collection to monitor throughput, latency, and errors. Use a combination of process metrics and application-layer logs to analyze system behavior under load.

// Example of simple statistics update
let requestsMade = 0;
let errors = 0;
// inside sendBatch
requestsMade += 1;
// upon error
errors += 1;

Optimizations and Best Practices

• Connection Reuse: Utilize HTTP Keep-Alive headers for persistent connections.
• Rate Limiting: Implement controlled request rates to avoid unintentional denial of service.
• Dynamic Scaling: Adjust worker count based on system feedback.

Final Thoughts

Building a load testing tool purely through code without substantial documentation presents challenges but also offers deep insight into system design. Emphasizing modularity, concurrency, resource management, and monitoring enables security researchers to push systems to their limits in a controlled, insightful manner.

This approach demonstrates that with a focused, code-driven methodology, effective massive load testing can be achieved in TypeScript even in documentation-deficient environments, ultimately improving both security posture and system robustness.

References

• Node.js Worker Threads
• Axios HTTP Client
• High Performance Load Testing Techniques

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