Best Practices for Distributed Performance Testing Configuration

Performance testing is a quality assurance technique aimed at evaluating and benchmarking the speed, responsiveness, and overall efficiency of a software system or application. Performance testing is like maintaining a smoothly flowing traffic system on a busy city road. However, when dealing with distributed performance testing, it’s more like orchestrating the synchronized movement of multiple traffic signals, which can be intricate and challenging. In distributed performance testing, you run tests from multiple machines or virtual instances to simulate a realistic user load. Distributed Performance Testing Setup Properly is crucial for obtaining meaningful results.

In this blog, we will explore the best practices for setting up and configuring a distributed performance testing environment, covering key concepts and tools commonly used in the industry.

1. Choose Your Instrument: The Right Testing Tool

Just as a skilled conductor selects the perfect instruments for the orchestra, you need to choose the right tool for your distributed performance testing.

• Apache JMeter: An open-source tool for load and performance testing that supports various protocols.
• Gatling: A Scala-based tool for load testing web applications with HTTP and other protocol support.
• Locust: An open-source Python tool known for its user-friendliness and flexibility.
• k6: A developer-centric open-source tool for performance testing with JavaScript scripting capabilities.
• LoadRunner: A widely used commercial performance testing tool with comprehensive distributed testing features.

k6, a powerful open-source tool, is an excellent choice for this role. It not only supports distributed testing but also offers scalability to match the complexity of your performance tests. In this blog also we will be using K6 for instance.

2. Set the Stage with Clear Objectives

Before diving into distributed performance testing, it’s essential to set clear objectives, just as a conductor defines the musical masterpiece’s tempo and mood. Ask yourself, what are you trying to achieve with your performance tests? Are you aiming to see how your application scales under load, uncover hidden performance aspects or ensure your service level agreements (SLAs) are met? These objectives will guide your testing strategy and configuration.

These objectives might include:

• Load Testing: Analyzing how well your application performs under a specific number of concurrent users or requests.
• Stress Testing: Identifying the application’s breaking point under extreme loads.
• Endurance Testing: Assessing how your application performs over time to detect potential memory leaks or performance degradation.
• Scalability Testing: Evaluating how well your application scales with increased resources.

3. Set Up a Scalable Testing Environment

For distributed performance testing, you need a scalable infrastructure capable of generating substantial load on your application. We can consider these infrastructure aspects:

• Cloud-Based Resources: Utilize cloud providers like AWS, Azure, or Google Cloud for scalable and geographically distributed environments.
• Containerization: Employ containers (e.g., Docker) for packaging and easy scaling.
• Orchestration: Use container orchestration platforms like Kubernetes to manage, scale, and automate infrastructure deployment.

In this blog, we will be using a Docker Compose file to define a scalable k6 testing environment:

version: '3'
services:
  k6-master:
    image: loadimpact/k6
    command: run -u 1000 -d 10s /scripts/perf_test.js
  k6-slave1:
    image: loadimpact/k6
  k6-slave2:
    image: loadimpact/k6

4. Geographic Distribution

One of the primary goals of distributed performance testing is to assess how your application behaves for users in different locations. To achieve this, distribute your load generators geographically. This can be accomplished through:

• Cloud Regions: Deploy load generators in different cloud regions or data centers to simulate users from various locations.
• Content Delivery Networks (CDNs): Utilize CDNs to cache and deliver content to users closer to their geographical locations.

For instance, we are using k6 cloud execution for geographical distribution:

# Run the test in the US
k6 cloud perf_test.js
# Run the test in Europe
k6 cloud --region europe-west perf_test.js

5. Compose Realistic Scenarios

Think of your performance test scenarios as musical compositions. To create harmonious, real-world simulations, design scenarios that closely mimic how your users interact with your application. Capture the user behavior, concurrency levels, and data variations that matter most. These scenarios should be well-documented, just like a musical score, and carefully organized.

// Define realistic user behavior scenarios
export default function () {
  group('User Scenarios', function () {
    // Simulate user actions like clicking, browsing, etc.
    // Use k6 HTTP requests to mimic real user interactions
    http.get('https://testwebappcom/page1');
    http.get('https://testwebapp.com/page2');
  });
}

6. Monitor and Collect Metrics

Integrate robust monitoring and metrics collection into your performance tests. This data will help you to track the progress of your tests and analyze results. Key performance metrics may include:

• Response time
• Throughput
• Error rates
• Resource utilization (CPU, memory, disk I/O)
• Network latency
• Server and database performance

Collecting performance metrics using k6 and exporting them to an InfluxDB database, Click here to see how we can integrate k6 with Influx DB and Grafana

import { check } from 'k6';
import http from 'k6/http';

export let options = {
  thresholds: {
    'http_req_duration{type:GET}': ['p(95)<500'], // 95% of requests must complete in under 500ms
  },
  ext: {
    loadimpact: {
      name: 'Sample Page',
      projectID: 12345,
    },
  },
};

export default function () {
  let res = http.get('https://testwebapp.com/page1');
  check(res, {
    'is status 200': (r) => r.status === 200,
  });
}

7. CI/CD Integration

Automation is key for distributed performance testing setup. Automate test execution, data collection, and analysis using CI/CD pipelines.

stages:
  - test
  - deploy

performance_test:
  stage: test
  script:
    - k6 run perf_test.js
  artifacts:
    paths:
      - k6-results.json

deploy_to_production:
  stage: deploy
  script:
    - deploy_to_production_script.sh
  only:
    - tags

8. Results Analysis and Reporting

Configure your testing tool to collect and store detailed test results, including response times, error rates, and other relevant metrics. Use dashboards and reporting tools to analyze the results effectively. Ensure that these reports are easily shareable with your team and stakeholders.

// Export and save test results for analysis
export let options = {
  thresholds: {
    http_req_duration: ['p(95)<500'],
  },
  noConnectionReuse: true,
  userAgent: 'k6 Distributed Test',
};

9. Harmonizing Data Management

Handling data in performance testing is like tuning each instrument in an orchestra. Synchronize data effectively, especially when your scenarios involve data creation, modification, or deletion. Use techniques like data seeding or database snapshots to ensure a consistent test environment.

// Manage data synchronization within test scenarios
export default function () {
  group('User Scenarios', function () {
    // You can seed data before running your test scenarios
    let data = generateTestData();
    http.post('https://testwebapp.com/api/data', JSON.stringify(data));
    
    // Then use the data in your test scenarios
    http.get('https://testwebapp.com/page1');
  });
}

10. Test, Refine, and Repeat

Distributed performance testing is an ongoing symphony. After each test, analyze the results and fine-tune your scenarios. Gradually increase the load and complexity to achieve your performance goals. Just as musical compositions evolve with each performance, so do your test scenarios. Gradually increase the load and complexity to reach your performance goals.

In conclusion, Distributed Performance Testing Setup requires careful planning, the right tools, and adherence to best practices. By following these guidelines, you can effectively assess the performance of your application, identify bottlenecks, and improve its overall user experience. Remember that performance testing is not a one-time activity but an ongoing process to ensure that your application can meet the demands of your users in the real world.

References:

https://k6.io/docs/testing-guides/running-distributed-tests/

https://getopentest.org/docs/distributed-testing.html