When evaluating web server performance under stress, these industry-standard tools should be in every developer's toolkit:

// Example JMeter test plan structure
<TestPlan>
  <ThreadGroup>
    <HTTPSampler domain="example.com" path="/api" method="GET"/>
    <ConstantThroughputTimer throughput="500"/>
  </ThreadGroup>
  <SummaryReport/>
</TestPlan>

Many developers make these critical mistakes during testing:

• Testing against development environments with mock data
• Ignoring network latency between test clients and servers
• Not monitoring database performance during tests

For production-grade load testing, consider these approaches:

# Locust Python script example
from locust import HttpUser, task, between

class WebsiteUser(HttpUser):
    wait_time = between(1, 5)
    
    @task
    def load_test(self):
        self.client.get("/")
        self.client.post("/login", json={
            "username": "test",
            "password": "test123"
        })

Platforms like AWS LoadRunner and Azure Load Testing provide:

• Geographically distributed test nodes
• Auto-scaling test infrastructure
• Integration with CI/CD pipelines

After a decade of performance testing web applications, I've compiled this practical guide covering both open-source and commercial solutions with real-world implementation examples.

1. JMeter (Java) - The Swiss Army knife for load testing:

<TestPlan>
  <ThreadGroup loops="100">
    <HTTPSampler domain="example.com" path="/api" method="GET"/>
  </ThreadGroup>
</TestPlan>

2. k6 (Go) - Developer-friendly scripting:

import http from 'k6/http';
export default function() {
  http.get('https://test-api.example.com');
}

• Testing on dissimilar hardware to production
• Ignoring network latency factors
• Not monitoring system resources during tests

For microservices, consider distributed testing with Locust:

from locust import HttpUser, task
class WebsiteUser(HttpUser):
    @task
    def load_test(self):
        self.client.get("/")

Calculate required servers based on throughput:

Required_Instances = (Peak_RPS × Avg_Response_Time) / Target_Utilization
Where:
  Peak_RPS = 1000 requests/second
  Avg_Response_Time = 200ms 
  Target_Utilization = 70% → 0.7