K6 Load & Performance Testing: Implementation Guide

Hey guys! In this article, we're diving deep into implementing load and performance testing using Grafana k6. Our goal is to ensure our platform rocks solid, meets its Service Level Objectives (SLOs), and aligns with our Site Reliability Engineering (SRE) reliability goals. We want to proactively spot those pesky performance bottlenecks and scalability limits before they hit production. Let's get started!

Why Grafana k6?

We've chosen Grafana k6 because it aligns perfectly with our "test-as-code" philosophy. This means our developers can craft sophisticated load tests using JavaScript/TypeScript, keep them version-controlled in Git, and seamlessly integrate them into our CI/CD pipeline. This approach offers flexibility, maintainability, and collaboration across teams. Plus, it jives well with our existing observability stack.

Standardizing on k6

Standardizing on Grafana k6 brings a unified approach to performance testing across our organization. This standardization simplifies the process of writing, running, and analyzing load tests, ensuring consistency and accuracy in our results. With k6, developers can easily define test scenarios, simulate user behavior, and monitor system performance under various load conditions. This proactive approach helps us identify and address potential bottlenecks before they impact our users, ultimately improving the reliability and scalability of our applications. Choosing k6 helps break down silos, promote knowledge sharing, and empower teams to take ownership of performance testing, leading to a more resilient and efficient software development lifecycle. Moreover, k6's integration with our existing observability stack, including Prometheus and Grafana, enables us to correlate performance metrics with system-level data, providing a holistic view of our application's health and performance.

Advantages of k6

The advantages of k6 are multifaceted. First and foremost, its scriptability via JavaScript/TypeScript allows developers to write expressive and maintainable tests. The "test-as-code" paradigm means tests can be version-controlled, reviewed, and integrated seamlessly into CI/CD pipelines. This allows for automated and repeatable testing processes, ensuring that performance is continuously validated with each code change. Secondly, k6 is designed for performance from the ground up. It can generate substantial load from a single machine, which is critical for simulating real-world traffic scenarios. Its efficient use of system resources means tests can be run without requiring extensive infrastructure. Thirdly, k6's native support for modern protocols such as HTTP/2 and WebSockets allows us to accurately test the performance of our APIs and applications. Lastly, the extensive ecosystem and integration capabilities of k6 enable it to fit seamlessly into our existing toolchain. From exporting metrics to Prometheus and visualizing them in Grafana, to integrating with CI/CD tools like Jenkins and GitLab, k6 provides the flexibility and extensibility we need to build a comprehensive performance testing strategy.

Test-as-Code Philosophy

Adopting a test-as-code philosophy is pivotal for modern software development. This approach treats tests as first-class citizens, integrating them directly into the development lifecycle. By writing tests as code, we gain several advantages. First, tests become version-controlled, allowing us to track changes, collaborate effectively, and maintain a history of test iterations. Second, tests can be automated and integrated into CI/CD pipelines, ensuring continuous validation of our application's functionality and performance. Third, tests become more maintainable and reusable. Code-based tests are easier to read, understand, and modify, reducing the risk of test obsolescence and improving overall test quality. Fourth, the test-as-code approach fosters a culture of collaboration between developers, testers, and operations teams. By sharing tests as code, teams can collectively contribute to the testing effort, leading to more comprehensive and effective testing practices. By embracing the test-as-code philosophy, we transform testing from a separate activity to an integral part of the software development process, resulting in higher quality software, faster release cycles, and improved overall team productivity.

Definition of Done (DoD)

Before we declare victory, let's make sure we've ticked all the boxes:

• [ ] k6 is installed and documented as a standard developer tool.
• [ ] A new, dedicated package (apps/load-tests) is created in the Monorepo to store all k6 test scripts.
• [ ] An ADR (adr-00X-why-k6-for-load-testing.mdx) is written, formalizing the choice over JMeter.
• [ ] A sample k6 script (scenarios/login-spike.js) is created, simulating a "spike" (e.g., 1000 virtual users in 30 seconds) against the auth-service login endpoint.
• [ ] The k6 script is configured with Thresholds (e.g., http_req_failed < 0.01, http_req_duration{p(95)} < 250ms).

Installing and Documenting k6

Installing and documenting k6 as a standard developer tool is the cornerstone of our load testing initiative. To begin, we must ensure that every developer can easily install k6 on their local machine, irrespective of their operating system. Detailed installation guides should be provided for each supported platform, including macOS, Windows, and Linux. These guides should cover all necessary dependencies, configuration steps, and troubleshooting tips. In addition to installation instructions, comprehensive documentation is essential. This documentation should outline the basic concepts of k6, its command-line interface, scripting capabilities, and available options. It should also include tutorials and examples to help developers get started quickly. Moreover, the documentation should be regularly updated to reflect the latest features, changes, and best practices. By providing clear and comprehensive documentation, we empower developers to effectively use k6 for load testing, ensuring that our applications meet performance requirements and deliver a seamless user experience. Furthermore, we should establish a dedicated channel for developers to ask questions, share knowledge, and collaborate on load testing efforts, fostering a culture of continuous learning and improvement. By prioritizing ease of installation and accessibility to information, we ensure that k6 becomes an integral part of our development workflow, enabling us to build robust and scalable applications.

Creating a Dedicated Package

Creating a dedicated package, specifically apps/load-tests, within our Monorepo serves as the central repository for all k6 test scripts. This package should adhere to a well-defined structure, promoting consistency and maintainability across all load tests. Within the apps/load-tests package, we should establish clear conventions for organizing test scripts, configuration files, and supporting resources. For example, we might organize tests by service, feature, or scenario, ensuring that each test script is easily discoverable and understandable. Additionally, we should provide a template or boilerplate for creating new k6 test scripts, outlining the required structure, dependencies, and best practices. This template should include sections for defining test scenarios, configuring thresholds, and exporting metrics. Furthermore, we should establish guidelines for naming test scripts, defining variables, and handling environment-specific configurations. By centralizing all load tests in a dedicated package and adhering to consistent conventions, we ensure that our tests are easily accessible, maintainable, and reusable, contributing to a more robust and efficient load testing process. Moreover, this approach facilitates collaboration among developers, allowing them to share, review, and improve load tests collectively, leading to higher quality tests and better overall application performance.

Writing an Architecture Decision Record (ADR)

Writing an Architecture Decision Record (ADR), specifically adr-00X-why-k6-for-load-testing.mdx, formalizes our decision to choose k6 over alternatives like JMeter. This ADR should provide a detailed rationale for our choice, outlining the key factors that influenced our decision and the benefits of k6 over other options. The ADR should begin by summarizing the context of the decision, including the problem we are trying to solve and the goals we aim to achieve with load testing. It should then describe the various alternatives considered, such as JMeter, Gatling, and Locust, along with their respective strengths and weaknesses. Next, the ADR should present a detailed analysis of k6, highlighting its key features, advantages, and how it aligns with our requirements. This analysis should cover aspects such as scripting language, performance, scalability, integration capabilities, and community support. The ADR should also address any potential drawbacks or limitations of k6 and how we plan to mitigate them. Furthermore, the ADR should include a decision matrix or comparison table that summarizes the key criteria and how each alternative fares against them. Finally, the ADR should conclude with a clear statement of our decision to adopt k6 for load testing, along with a summary of the expected benefits and outcomes. By documenting our decision-making process in an ADR, we ensure that our choice of k6 is well-reasoned, transparent, and aligned with our overall architectural principles. This documentation also serves as a valuable reference for future decisions and helps onboard new team members to our load testing strategy.

Integration with Observability

Let's get those k6 metrics flowing into our observability stack. This means:

• [ ] Configuring the k6 test runner to output metrics to our Prometheus/Mimir instance.
• [ ] Creating a dedicated Grafana Dashboard ("Load Test Results").

Configuring k6 Metrics

Configuring k6 metrics to output to our Prometheus/Mimir instance is crucial for gaining actionable insights into our application's performance under load. We can achieve this integration by leveraging k6's built-in support for Prometheus and Mimir. First, we need to configure the k6 test runner to expose its metrics in Prometheus-compatible format. This can be done by specifying the --out flag with the prometheus or prometheus-rw option when running k6. For example, k6 run script.js --out prometheus=addr=http://prometheus:9090 will configure k6 to expose its metrics on the specified Prometheus endpoint. Alternatively, we can use the prometheus-rw option to enable remote write functionality, allowing k6 to directly push metrics to a Prometheus-compatible remote storage like Mimir. Once k6 is configured to expose its metrics, we need to configure Prometheus or Mimir to scrape those metrics. This involves adding a new scrape configuration to the Prometheus or Mimir configuration file, specifying the target endpoint where k6 is exposing its metrics. For example, we can add a scrape configuration like this:

scrape_configs:
  - job_name: 'k6'
    static_configs:
      - targets: ['k6:9100']

This configuration tells Prometheus to scrape metrics from the k6:9100 endpoint, where k6 is exposing its metrics. After configuring Prometheus or Mimir to scrape k6 metrics, we can visualize those metrics in Grafana using our dedicated "Load Test Results" dashboard. By integrating k6 metrics with our observability stack, we gain a holistic view of our application's performance, allowing us to identify bottlenecks, optimize resource utilization, and ensure a seamless user experience.

Creating a Grafana Dashboard

Creating a dedicated Grafana Dashboard, named "Load Test Results," is essential for visualizing and analyzing k6 metrics in a meaningful way. This dashboard should provide a comprehensive overview of our application's performance under load, enabling us to identify bottlenecks, track trends, and make informed decisions. The dashboard should include a variety of panels, each displaying a specific set of metrics relevant to load testing. For example, we can include panels for displaying request rates, response times, error rates, CPU utilization, memory usage, and database performance. Each panel should be carefully configured to display the metrics in a clear and concise manner, using appropriate visualizations such as graphs, charts, and tables. We should also configure thresholds and alerts to highlight potential issues and trigger notifications when performance degrades. In addition to displaying raw metrics, the dashboard should also provide aggregated views and calculated metrics to help us understand the overall performance of our application. For example, we can calculate the average response time, the 95th percentile response time, and the total number of requests processed. Furthermore, the dashboard should allow us to filter and drill down into specific time ranges, test scenarios, and endpoints to investigate performance issues in more detail. By creating a well-designed and informative Grafana Dashboard, we empower our teams to effectively monitor and analyze load test results, enabling us to optimize our application's performance and ensure a seamless user experience.

End-to-End Validation

Time to put it all together and see if it works! Let's:

• [ ] Run the login-spike.js test against the staging environment.
• [ ] Use the Grafana dashboard to visualize both the k6-reported metrics and the impact on the auth-service.

Running the Login Spike Test

Running the login-spike.js test against the staging environment is a critical step in validating our load testing setup and assessing the performance of our auth-service under a sudden surge of user logins. Before running the test, we need to ensure that our staging environment is properly configured and representative of our production environment. This includes ensuring that the auth-service is running with the appropriate resources and configurations. Once the staging environment is ready, we can execute the login-spike.js test using the k6 command-line interface. The test should simulate a sudden spike in user logins, gradually increasing the number of virtual users over a short period, such as 30 seconds. As the test runs, k6 will generate metrics related to request rates, response times, error rates, and other performance indicators. These metrics will be streamed to our Prometheus/Mimir instance, where they can be visualized in our Grafana dashboard. By running the login-spike.js test, we can observe how the auth-service behaves under a realistic load scenario and identify any potential bottlenecks or performance issues. This information can then be used to optimize the auth-service and ensure that it can handle sudden spikes in user traffic without impacting the user experience.

Visualizing Metrics in Grafana

Visualizing metrics in Grafana is essential for gaining a comprehensive understanding of our application's performance during the load test. Our dedicated "Load Test Results" dashboard should provide a single pane of glass view, displaying both the k6-reported metrics (client-side) and the impact on the auth-service (server-side). On the client-side, we should visualize metrics such as request rates, response times, error rates, and the number of virtual users. These metrics provide insights into the performance experienced by the simulated users during the test. On the server-side, we should visualize metrics such as CPU utilization, memory usage, database performance, and network latency for the auth-service. These metrics provide insights into the resource consumption and overall health of the auth-service under load. By correlating the client-side and server-side metrics, we can identify potential bottlenecks and understand how the auth-service is responding to the simulated user traffic. For example, if we observe high response times on the client-side accompanied by high CPU utilization on the server-side, it may indicate that the auth-service is struggling to handle the load. In addition to visualizing raw metrics, we should also configure alerts and thresholds to notify us when performance degrades or when certain metrics exceed predefined limits. By visualizing metrics in Grafana and setting up alerts, we can proactively monitor our application's performance during load tests and quickly identify and address any potential issues.

CI/CD Integration

Let's automate this thing! We need to:

• [ ] Create a new GitHub Actions workflow (load-test.yml).
• [ ] Configure the workflow to be manually triggered against the staging environment.
• [ ] Ensure the CI job runs the k6 test and fails if thresholds are breached.

Creating a GitHub Actions Workflow

Creating a new GitHub Actions workflow, named load-test.yml, is essential for automating our load testing process and integrating it into our CI/CD pipeline. This workflow should define the steps required to run our k6 tests against the staging environment whenever we make changes to our codebase. The workflow should begin by checking out the latest version of our code from the repository. Next, it should install k6 and any dependencies required to run our tests. Then, it should execute our k6 tests, specifying the appropriate test scripts and configurations. Finally, the workflow should analyze the test results and determine whether the defined thresholds have been breached. If any thresholds have been breached, the workflow should fail, indicating that the changes have introduced performance issues. The workflow should also be configured to output the test results and any relevant logs to a centralized location, such as a cloud storage bucket or a reporting tool. This allows us to easily track and analyze the performance of our application over time. By creating a GitHub Actions workflow, we automate our load testing process, ensuring that our application is continuously tested for performance issues whenever we make changes to our codebase. This helps us to identify and address performance issues early in the development cycle, reducing the risk of introducing performance regressions into production.

Configuring Manual Triggering

Configuring the GitHub Actions workflow for manual triggering against the staging environment allows us to initiate load tests on demand, providing us with greater control over the testing process. Manual triggering is particularly useful for testing specific features, scenarios, or configurations that may not be automatically triggered by our CI/CD pipeline. To configure manual triggering, we can use the workflow_dispatch event in our load-test.yml file. This event allows us to define inputs that can be specified when manually triggering the workflow. For example, we can define an input for specifying the test script to run, the environment to test against, or the number of virtual users to simulate. When we manually trigger the workflow, we will be prompted to enter values for these inputs, allowing us to customize the test execution. In addition to manual triggering, we can also configure the workflow to be triggered by other events, such as pull requests or pushes to specific branches. This allows us to automate load testing for different scenarios and ensure that our application is continuously tested for performance issues. By configuring manual triggering and other event triggers, we create a flexible and automated load testing process that fits seamlessly into our CI/CD pipeline.

Ensuring CI Job Failure on Threshold Breaches

Ensuring that the CI job fails when k6 thresholds are breached is crucial for preventing performance regressions from being introduced into our codebase. This mechanism serves as a safety net, alerting us to potential performance issues before they impact our users. To achieve this, we need to configure our GitHub Actions workflow to monitor the test results and check whether any of the defined thresholds have been breached. If any thresholds have been breached, the workflow should exit with a non-zero exit code, causing the CI job to fail. We can achieve this by using the k6 command-line interface to specify the thresholds and the --no-thresholds flag to prevent k6 from automatically exiting with a non-zero exit code when thresholds are breached. Instead, we can use a script or a command within our workflow to check the test results and exit with a non-zero exit code if any thresholds have been breached. This ensures that the CI job fails and alerts us to the performance issue. In addition to failing the CI job, we should also configure the workflow to output detailed information about the breached thresholds, including the metric name, the threshold value, and the actual value. This information helps us to quickly identify the cause of the performance issue and take corrective action. By ensuring that the CI job fails when k6 thresholds are breached, we prevent performance regressions from being introduced into our codebase and maintain the performance and stability of our application.

Wrapping Up

Alright, guys! That's a wrap on implementing load and performance testing with Grafana k6. By following these steps, you'll be well on your way to building a robust and scalable platform. Happy testing!