5 Essential Chaos Engineering Experiments to Run Before Black Friday
When Black Friday hits, your system needs to perform flawlessly under immense pressure. Chaos Engineering is the proactive approach that ensures your infrastructure isn’t just hoping for the best—it’s prepared for the worst. Here are five essential chaos experiments every e-commerce business should run before Black Friday to identify weak points and strengthen their system.
Experiment 1: Auto-Scaling Response to Resource Stress
During Black Friday, your infrastructure’s ability to scale efficiently is critical. Instead of relying on traditional load tests, you can trigger your auto-scaler by directly stressing specific metrics like CPU or memory usage. This approach allows you to see how well your deployments handle increased demand in a targeted and controlled way.
Objective: Test how effectively your auto-scaling mechanisms respond to resource stress.
Key Learnings: Identify bottlenecks in scaling and load distribution.
How to Perform:
- Stress Key Metrics: Use Steadybit’s Stress CPU or Stress Memory attacks to increase resource usage. You can utilize the Kubernetes HPA CPU Stress template from Steadybit’s library here.
- Monitor Scaling: Observe how your auto-scaling groups react—do they add instances quickly and efficiently to manage the increased resource demand?
- Evaluate Load Balancers: Check if traffic remains evenly distributed across servers as the auto-scaling kicks in.
This method allows you to test your system’s scalability without requiring a full-scale load test, making it a practical approach for pre-Black Friday preparation.
Experiment 2: Server Failure Simulation
When a critical server goes down during peak traffic, your system’s ability to handle the failover is essential. Chaos Engineering allows you to simulate server failures in a way that’s specific to your infrastructure, helping you evaluate how quickly your system recovers and reroutes traffic.
Objective: Test your system’s failover mechanisms under stress.
Key Learnings: Evaluate how well traffic reroutes during server failures.
How to Perform:
- Identify Critical Servers: Determine which servers are most crucial for your operations.
- Simulate Failure: Depending on your infrastructure, use the appropriate Steadybit attack:
- For AWS EC2 instances, trigger a state change using this action.
- For GCP Virtual Machines, utilize this action.
- For Azure Virtual Machines, simulate a failure with this action.
- For Linux hosts, you can perform a shutdown using this general-purpose action.
- Monitor Failover: Analyze the speed and effectiveness of your failover mechanisms to ensure minimal disruption.
By tailoring your server failure simulation to your specific infrastructure, you gain more precise insights into your system’s resilience and ability to maintain operations during critical incidents.
Experiment 3: Database Failover and Availability Testing
Your database is the backbone of your e-commerce transactions. Rather than focusing solely on query loads, it’s crucial to test how your application responds when your database experiences a failover or becomes temporarily unavailable. Understanding your system’s behavior in these scenarios can help you ensure that it recovers quickly and maintains performance.
Objective: Simulate database failover and test your system’s ability to handle temporary database outages.
Key Learnings: Evaluate your system’s resilience and recovery mechanisms during database disruptions.
How to Perform:
- Simulate Database Failover: If you’re using AWS RDS, initiate a cluster failover using this action to observe how your application handles the transition.
- Test Database Unavailability: Use Steadybit templates to simulate a database outage for systems like Microsoft SQL Server, Oracle, or Postgres. You can explore the relevant templates here.
- Monitor Recovery: Assess whether your application can automatically reconnect to the database once it’s back online, and analyze any delays or errors in the recovery process.
By focusing on failover scenarios and database availability, you’ll gain insights into potential vulnerabilities in your system’s data handling and improve your infrastructure’s resilience before the peak demands of Black Friday.
Experiment 4: Latency Injection for Critical Processes
Latency can slowly degrade the user experience, causing customers to abandon their carts. To better understand your system’s tolerance for delays, it’s essential to simulate increased latency in key processes, such as checkout or search. This experiment allows you to identify areas where you can optimize performance before it impacts the user experience.
Objective: Test how system delays affect key user interactions.
Key Learnings: Optimize network routing to minimize latency.
How to Perform:
- Inject Latency Based on Infrastructure: Use the appropriate Steadybit attack depending on your setup:
- For containerized environments, introduce network delays using this action.
- For AWS Lambda functions, add latency with this action.
- For Linux hosts, simulate traffic delays using this action.
- Monitor User Impact: Track metrics related to user behavior, such as page load times or cart abandonment rates, to see how latency affects the overall experience.
- Optimize Routing Paths: Adjust network configurations or server locations based on findings to reduce latency and improve response times.
By injecting latency directly into your system, you can better understand its impact on the user experience and make targeted improvements to enhance performance during peak traffic periods.
Experiment 5: Third-Party Service Failure Simulation
Many e-commerce platforms rely on third-party services, like payment processors or shipping APIs. Testing your system’s resilience to third-party service failures is crucial. Instead of shutting down the service itself, it’s more effective to block traffic on the client side. This way, you can simulate outages even when you don’t have direct access to the third-party service, ensuring that only your application is affected.
Objective: Test how your system handles third-party service failures.
Key Learnings: Ensure clear error handling and fallback mechanisms.
How to Perform:
- Block Traffic to Third-Party Services: Use the appropriate Steadybit attack based on your infrastructure:
- For containerized environments, block network traffic using this action.
- For Linux hosts, create a network blackhole with this action.
- For AWS Lambda functions, denylist specific traffic using this action.
- Assess Error Handling: Check if proper error messages are displayed and if fallback protocols are triggered effectively.
- Monitor Recovery: Observe how quickly your system recovers and re-establishes connections once the service becomes available again.
Simulating service failures by blocking traffic on the client side gives you greater control and insight into how your application responds to disruptions, allowing you to strengthen your system’s resilience before Black Friday.
Let’s Get Started
Black Friday puts your e-commerce system to the test, and these chaos engineering experiments—auto-scaling checks, server and database failovers, latency simulations, and third-party service outages—are crucial steps to prepare for peak performance. Chaos engineering helps you find and fix potential problems before they impact your customers, making sure your infrastructure is as solid as it can be when traffic surges.
With this approach, you’re not just hoping your system will handle the pressure; you’re making sure it will. Start your preparation now by taking advantage of Steadybit’s two-week trial and see how chaos engineering can make all the difference.