Scaling Load Testing at Speed: How a Security Researcher Mastered Massive Traffic with TypeScript

Handling massive load testing is a perennial challenge for security researchers and developers tasked with validating system resilience under extreme conditions. When working under tight deadlines, the key is to leverage efficient, scalable tools and design patterns that can simulate high traffic volumes without sacrificing accuracy or control.

In this post, we explore how a security researcher utilized TypeScript to develop a high-performance load testing framework capable of generating millions of requests, all while maintaining code clarity, security, and rapid deployment.

The Challenge

The researcher faced a scenario where existing load testing tools either lacked the flexibility for custom behaviors or would take too long to configure for the required scale. The goal: simulate a load of up to 10 million requests to evaluate system robustness, latency, and fault tolerance — all within a 48-hour window.

Using TypeScript for Load Testing

TypeScript, with its static typing and modern JavaScript features, provides a productive environment for building scalable load testing tools. Its ecosystem supports asynchronous operations, stream handling, and reliable code maintenance — critical for high-load scenarios.

Designing a High-Throughput Request Generator

The core component is a request generator that can spawn numerous concurrent requests efficiently. Employing asynchronous functions and worker pools, the researcher implemented a modular system:

import axios from 'axios';

interface LoadTestConfig {
  targetUrl: string;
  concurrentRequests: number;
  totalRequests: number;
}

async function runLoadTest(config: LoadTestConfig) {
  let completedRequests = 0;

  const enqueueRequests = async (workerId: number) => {
    while (completedRequests < config.totalRequests) {
      try {
        await axios.get(config.targetUrl);
        completedRequests++;
        if (completedRequests % 1000 === 0) {
          console.log(`Requests completed: ${completedRequests}`);
        }
      } catch (error) {
        console.error(`Error at request ${completedRequests}:`, error.message);
      }
    }
  };

  // Creating worker pool
  const workers = Array.from({ length: config.concurrentRequests }, (_, i) =>
    enqueueRequests(i)
  );

  await Promise.all(workers);
  console.log('Load test completed successfully!');
}

// Example Usage
runLoadTest({ targetUrl: 'https://yourapi.com/endpoint', concurrentRequests: 500, totalRequests: 5_000_000 });

This code initializes a pool of worker functions running asynchronously. Each worker issues requests in a loop until the total request count is reached. By carefully tuning concurrentRequests, the researcher optimized throughput based on system capacity.

Handling Rate Limiting and Backpressure

To avoid overwhelming the target system or the network, the framework incorporates request pacing and rate limiting:

const RATE_LIMIT = 200; // requests per second
const interval = 1000 / RATE_LIMIT;

async function pacedRequest() {
  for (;;) {
    const startTime = Date.now();
    // issuing a request
    await axios.get('https://yourapi.com/endpoint');
    const elapsed = Date.now() - startTime;
    if (elapsed < interval) {
      await new Promise(res => setTimeout(res, interval - elapsed));
    }
  }
}

This approach ensures the load stays within acceptable bounds and reduces the risk of accidental DoS situations.

Performance Considerations

The researcher employed several strategies to ensure performance:

• Using axios with connection pool management (via http.Agent)
• Running requests in parallel across multiple worker threads or processes
• Minimizing request overhead by batching headers and payloads where possible

Python or Go might be preferred for raw speed, but TypeScript's asynchronous model combined with Node.js high-performance network libraries provides a compelling trade-off between speed and development agility.

Final Thoughts

Leveraging TypeScript for massive load testing allows security researchers to precisely control traffic patterns, adapt quickly to changing requirements, and maintain code readability — especially vital when under severe deadline pressures. Combining asynchronous programming, rate limiting, and modular design results in a scalable, reliable testing framework that can simulate real-world high-stress scenarios effectively.

In environments where rapid deployment and security are paramount, such an approach ensures that testing keeps pace with project timelines while delivering meaningful insights into system resilience.

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