Continuous Load Testing

Building load test infrastructure is tricky and poses many questions. How can we identify performance regressions in newly deployed builds, given the overhead of spinning up test clients? To gather the most representative results, should we load test at our peak hours or when there’s a lull? How do we incentivize engineers to invest time…

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Slack
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Shreya Ramesh
•16 min read•advanced•
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AWSChefDynamoDBJenkinsJSONKubernetesPrometheus

Overview

The article discusses the implementation of continuous load testing at Slack using a tool called Koi Pond. It highlights the challenges faced, the technical background of the solution, and the benefits of integrating load testing into the development process.

What You'll Learn

1

How to implement continuous load testing using Koi Pond

2

Why building a culture of performance is crucial in software development

3

How to ensure safety and resilience in load testing environments

Prerequisites & Requirements

  • Understanding of load testing concepts and practices
  • Familiarity with Kubernetes and AWS services(optional)

Key Questions Answered

What is Koi Pond and how does it facilitate load testing?
Koi Pond is a load testing tool at Slack that simulates user behavior by making API requests and sending messages over WebSocket. It operates within Kubernetes pods and allows for continuous load testing, enabling engineers to identify performance regressions in real-time.
What safety measures are implemented in continuous load testing?
Safety measures include the Automatic Shutdown service, which halts load testing if performance metrics fall below defined thresholds. This ensures that load tests do not negatively impact production services and helps maintain system integrity.
How does Koi Pond ensure resilience during load testing?
Koi Pond has been backed by AWS DynamoDB to persist load test data, allowing it to maintain state even during pod restarts. This resilience is crucial for continuous testing and helps in analyzing historical performance data.
What are the benefits of integrating load testing into release cycles?
Integrating load testing into release cycles allows teams to verify the performance of features before deployment. It helps catch performance regressions early, ensuring a smoother user experience and reducing the risk of incidents post-release.

Key Statistics & Figures

Maximum Koi per School
5,000
Koi are spun up in Kubernetes pods, referred to as Schools, with a maximum of 5,000 koi per School.
API success rate threshold
95%
If the web API success rate is sustained below 95% for five minutes, the Automatic Shutdown service will halt active load tests.

Technologies & Tools

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Orchestration
Kubernetes
Used to manage the deployment of Koi Pond pods for load testing.
Database
AWS Dynamodb
Provides a NoSQL database backend for Koi Pond to persist load test data.
Monitoring
Prometheus
Used for querying metrics to support the Automatic Shutdown service.

Key Actionable Insights

1
Implement continuous load testing to proactively identify performance issues before they reach production.
By continuously running load tests, teams can catch performance regressions early, which reduces the risk of negative impacts on user experience during high-traffic events.
2
Utilize the Automatic Shutdown service to safeguard production environments during load testing.
This service helps prevent load tests from causing disruptions by automatically stopping tests if performance metrics drop below acceptable levels, thus maintaining system integrity.
3
Leverage historical data from continuous load testing to validate significant changes in the system.
With a robust dataset reflecting the usage of large customers, teams can confidently deploy changes, knowing they have tested against realistic load scenarios.

Common Pitfalls

1
Failing to account for shared infrastructure during load testing can lead to unintended consequences.
Since some parts of the load test environment are shared with production, it's crucial to implement safety features to prevent load tests from affecting live services.

Related Concepts

Load Testing
Performance Engineering
Continuous Integration/Continuous Deployment (ci/Cd)