Throughput Analysis in Distributed Systems

Throughput measures the number of requests a system can handle per second (RPS). Analyzing throughput is critical for ensuring that a system can meet performance requirements under different loads. It helps identify the maximum workload a system can sustain before performance, such as latency, starts to degrade.

1. Simulating Workloads with Apache JMeter or k6

To measure throughput, we simulate concurrent requests to a system under test (SUT) using tools like Apache JMeter or k6. These tools help create realistic traffic patterns to stress-test the system.

Using Apache JMeter

Apache JMeter is a popular open-source tool for load testing. Below is a step-by-step guide to simulate workloads:

  1. Download and Install JMeter:

  2. Configure the Test Plan:

    • Add a Thread Group to simulate concurrent users:
      • Define the number of threads (users), ramp-up time, and loop count.
    • Add an HTTP Request Sampler:
      • Specify the target URL or endpoint.
    • Add a Listener:
      • Use “View Results Tree” or “Aggregate Report” to collect data.

  3. Run the Test:

    • Start the test to send concurrent requests to the server.
    • Monitor throughput, response times, and error rates in real-time.

  4. Analyze Results:

    • Review the Aggregate Report for:
      • Throughput: Requests per second (RPS).
      • Average Response Time: Time taken for requests to complete.
      • Error Rate: Percentage of failed requests.

Using k6 for Workload Simulation

k6 is a modern, scriptable tool for load testing. It uses JavaScript to define scenarios and is ideal for developers comfortable with coding.

  1. Install k6:

  2. Create a Load Test Script: Write a script to define the test scenario.

    import http from 'k6/http';
import { check, sleep } from 'k6';
    
export let options = {
    stages: [
        { duration: '30s', target: 50 }, // Ramp up to 50 users
        { duration: '1m', target: 50 },  // Steady state
        { duration: '30s', target: 0 },  // Ramp down
    ],
};
    
export default function () {
    let res = http.get('https://httpbin.org/get');
    check(res, { 'status was 200': (r) => r.status === 200 });
    sleep(1); // Simulate user think time
}

  3. Run the Test: Execute the test with:

    k6 run load_test.js

  4. Review Results:

    • Throughput (RPS) and latency will be displayed in the terminal.
    • k6 also integrates with visualization tools like Grafana for advanced analysis.

2. Measuring Maximum Throughput

To determine the system’s maximum throughput:

  1. Gradually Increase Load:

    • Start with a small number of concurrent users and increase gradually.
    • Observe metrics like RPS, average latency, and error rates.

  2. Identify the Tipping Point:

    • The tipping point occurs when:
      • Latency starts increasing sharply.
      • Error rates rise significantly.
      • RPS no longer increases with more users.

  3. Document Results:

    • Record the maximum RPS achieved before performance degrades.
    • Note other metrics like response time and resource utilization (CPU, memory).

3. Example Analysis

Let’s consider a test scenario where a backend API is load-tested: | Metric | Value | |———————|—————–| | Concurrent Users | 100 | | Average RPS | 500 requests/s | | Average Latency | 200 ms | | P99 Latency | 500 ms | | Error Rate | 0.5% |

Observations:

4. Visualizing Results

To visualize the results, use tools like Excel, Grafana, or Python scripts. A sample Python code to plot throughput and latency:

import matplotlib.pyplot as plt

# Sample data
concurrent_users = [10, 50, 100, 200, 300, 400]
rps = [100, 300, 500, 600, 600, 590]  # Throughput
latency = [100, 150, 200, 300, 500, 800]  # Average latency in ms

# Plot throughput
plt.figure(figsize=(10, 6))
plt.plot(concurrent_users, rps, label="Throughput (RPS)", marker="o")
plt.plot(concurrent_users, latency, label="Average Latency (ms)", marker="x")
plt.xlabel("Concurrent Users")
plt.ylabel("Metric")
plt.title("Throughput and Latency vs Concurrent Users")
plt.legend()
plt.grid()
plt.show()

5. Practical Insights

By simulating workloads and analyzing throughput, you gain insights into how a system performs under stress, allowing you to make informed decisions about scaling and optimization.