How to Test and Tune App Infrastructure to Prepare for the Unexpected

Performance testing verifies that an infrastructure can manage the demands of its users by testing it under a particular workload. It helps identify bottlenecks that could cause problems down the line. By conducting performance testing early, you can prepare the infrastructure for almost anything, be it a steady increase in the number of users or a sudden massive traffic surge.

This article will explore the role performance testing plays in strategic infrastructure resource planning to help you manage traffic during peak periods. You'll also take a look at some best practices for conducting these tests to ensure system scalability and reliability.

The Role of Performance Testing in Effective Resource Planning

Performance testing helps organizations plan their infrastructure resources effectively. In particular, it can help identify how much computing power, memory and storage an organization needs to handle its operations in any circumstance. This is achieved by testing how the system performs under different conditions, which allows you to identify the size and type of resources that are required for efficient operations. An effective performance testing process can usually be divided into three steps: capacity assessment, stability and reliability and data handling.

Capacity Assessment

This assessment includes load testing, scalability testing and stress testing. While load testing checks how well the system can handle expected user numbers, scalability testing shows how well the system can adapt to increasing workloads without making compromises in performance. Stress testing pushes the system to its limits to see how much it can handle before it completely breaks down. Stress testing measures several metrics to evaluate how the system behaves under extreme conditions. These metrics include things like response time, throughput, CPU and memory utilization and error rates.

Stability and Reliability

After you've assessed your application's capacity, you should test whether the system can remain stable under different conditions by performing soak testing and spike testing.

During soak testing, you run the system at high loads for a longer period to identify potential problems that could occur over time, such as memory leaks or slowdowns. In contrast, spike testing simulates sudden increases in the load on the system to see if the system can withstand a surge in traffic.

Data Handling

Lastly, examine the data-handling capabilities of the infrastructure through volume testing and concurrency testing. Volume testing helps you examine how the system operates when it must handle large amounts of data. In particular, it identifies how well the processing capacity of the system can be scaled with increasing data volumes to uncover possible performance degradation. Concurrency testing tests how well the infrastructure can handle large numbers of users or processes at the same time. It helps testers determine how well the system handles simultaneous access to the same resources, helping prevent problems such as data corruption, deadlocks and slow response times.

Best Practices for Assessing Infrastructure Resources with Performance Testing

Now that you know why performance testing is so integral to effective resource planning, let's take a look at a few best practices for implementing it. These strategies will help organizations ensure that their systems are reliable, scalable and capable of handling real-world demands.

Define Clear Performance Goals

Before you run any tests, you need to clarify what exactly you want to measure. Predefined test goals and metrics, such as response time and throughput, ensure that tests are performed in a targeted manner and meet specific performance criteria.

Defining clear key performance indicators (KPIs) is a good place to start. For instance, you could define a KPI for latency to focus on minimizing response times. Similarly, you could set throughput goals to assess the capacity of the system to handle large volumes of data. Other important KPIs in performance testing could involve resource usage metrics, such as CPU and memory utilization and error rates, which help identify the reliability of the infrastructure under stress.

Design Realistic Test Scenarios

When you design test scenarios for performance testing, you should incorporate real-world data and user behavior patterns. This could involve simulating common user actions, such as logging in, searching for products and making purchases. This helps you create test conditions that closely resemble actual user interactions.

For example, consider an e-commerce platform preparing for Black Friday sales. In this scenario, a tester could implement a test scenario that includes data from previous years, such as a surge in user logins, multiple simultaneous searches and high-volume transactions. By using this real-world data, the tester could observe how the system can handle increased loads and many concurrent users.

Replicate Production-Like Environments

To make tests more effective, you should try to replicate a production-like environment. This means you set up the testing infrastructure in a way that matches the production environment in terms of hardware and software as closely as possible. With this setup, you should get the same results from testing that are also expected in a real-world setting.

Run Performance Baselines

During load and performance testing, you need to have a standard for how well your system should perform under different conditions. A performance baseline helps identify these standards.

In the context of performance testing, a performance baseline could be the initial set of performance metrics (such as response time, throughput and resource usage) that are collected from a system under typical load conditions. You might begin by testing the response of your system to typical user activities, like loading pages or completing transactions. These recorded results can serve as your baseline metric and a reference point for future tests.

For example, running a baseline test on an e-commerce site during a normal traffic period helps you gather insights into how long it takes for the homepage to load, how quickly product images load and the response time for checkout processes. These initial metrics show how the system performs under normal conditions.

In the following steps, as you make upgrades or changes to the system, you should conduct the same tests and compare the new data with your baseline. This comparison shows how these changes affect the performance of the system.

Automate Testing Using CI/CD Pipelines

Another best practice in performance and load testing is to automate tests. In particular, you can integrate tests directly into CI/CD processes to ensure that the infrastructure capacity is validated with every code change.

Imagine you're launching a new service or a major overhaul of your platform. You must ensure that the infrastructure can handle the changes. Instead of deploying all the testing infrastructure from scratch, it's recommended that you have a proper Terraform plan or similar infrastructure as code (IaC). For example, you can set up a CI/CD pipeline to deploy the test environment using IaC tools like Terraform to check if the infrastructure capacity is sufficient before pushing a significant code change. With this setup, the testing environment can mirror the production environment as closely as possible and account for any changes made in production. After the testing infrastructure is deployed, various predefined tests, such as load tests, soak tests and stress tests, can be automatically executed.

Apply Varying Load Levels

To find out how much traffic your system can handle, apply different load levels when testing. You can simulate different traffic scenarios to see if the infrastructure can handle high demands and scale during peak times.

Begin by setting up tests that simulate low, medium and high user traffic and recording how the system behaves under these conditions. Does it remain stable, or does it slow down? This helps you identify the maximum capacity of the current setup and the point at which performance begins to degrade.

Next, try to increase the complexity of your tests. Introduce sudden spikes in traffic to see how the system can cope with quick changes in load and traffic. This is especially recommended for applications that expect to experience bursts of traffic, like during sales or special events. In addition, gradually increase the load and observe how the infrastructure responds to test its scalability. Does it scale up without intervention, or do issues arise?

Use Distributed Load Testing

When you perform load testing, you want to see how the system behaves when the traffic comes from different geographic locations, as is typically the case in the real world. This type of testing simulates traffic coming from various geographic areas and helps you identify traffic patterns and latency issues depending on the region where the traffic originates.

Start distributed load testing by choosing different regions where your users come from (or might in the future). Next, set up nodes in these areas or use cloud services that can generate traffic from multiple locations. Once you've done that, start the test and observe how your system responds to these different sources of traffic. Does the system perform well for some regions but not others? This kind of testing helps identify potential problems that are caused by distance and network variability. Use the obtained information to make improvements in the system, such as adjusting the infrastructure to better serve areas with higher latency.

Implement Observability

The main purpose of load and performance testing is to identify the infrastructure that can manage high traffic and effectively scale during peak times. To achieve this, you should implement observability, which consists of three components: monitoring, tracing and logs.

Begin by implementing monitoring. Set up tools that continuously check the health and performance metrics of the system, like response times, server load and error rates. With this real-time data, you can identify issues as they arise and prevent potential downtime.

In the next step, add tracing. Tracing allows you to follow the path of a request through the entire system to see where delays or problems occur. This is especially handy for complex systems where it's difficult to pinpoint the exact source of an issue. Finally, you should maintain logs of system activity. These logs will provide a detailed record of events and allow you to look back at what happened before and during an issue.

Analyze Test Results

After you've implemented observability, take that data and analyze the test results. These insights can help you establish capacity margins, define service-level agreements (SLAs), identify bottlenecks, manage peak load handling and ensure system stability.

Take a look at the collected data to determine the capacity margins of the system. It's important to understand how close the system is to reaching the limits of the current setup during peak loads. This tells you whether you need more resources or if you can optimize the ones that already exist.

Use the insights obtained to define SLAs. Based on the analyzed performance data, make sure that the SLAs accurately reflect what the infrastructure can realistically support. This helps to define clear performance targets for your team. For example, if the data reveals that the response time of the system averages 200 ms during peak load but can spike up to 500 ms, set an SLA that targets a maximum response time of 400 ms. This takes into account occasional spikes while setting a clear, achievable performance target that aligns with current system capabilities.

In addition, trace the paths of the requests that experience delays to identify bottlenecks. Observe exactly where these delays occur to pinpoint specific areas that need optimization, like database queries, server configurations or code optimizations. You should also regularly review the performance trends to ensure that the system remains stable. Look for any irregularities or patterns that could signal potential issues.

Fine-Tune and Iterate Based on Feedback

Keep in mind that assessing infrastructure capacity through performance testing is a closed-loop cycle. This means that once you have analyzed the results, you should make the optimizations and test the infrastructure again. Retesting the system is necessary to see how the change affects the overall performance. This should confirm the improvements and also ensure that the modifications haven't introduced new issues or bugs elsewhere in the system.

The process of testing, problem identification and optimization should be continuously iterated. In each iteration, use new data from the current iteration to refine the system further. Each cycle of testing and tuning should get you closer to an optimally performing infrastructure.

Document the Entire Process

Document the entire performance testing process in detail—from the environment setup and configurations to the test results and optimizations. Include details like the date, purpose of the test, environment specifics, methodology, observed results and any follow-up actions. This documentation is a valuable resource for your team, especially when troubleshooting issues, planning future tests or onboarding new team members.

Make sure you keep your documentation clear and accessible. The documentation should help anyone on your team understand what was tested and how. If similar issues arise in the future, your team can refer to these documents to see previous solutions and understand the context of past decisions.

Conclusion

Performance testing is integral to determining the readiness and scalability of infrastructure resources under various operational loads. In this article, you learned about several best practices for effective testing, including setting clear performance goals and creating realistic test scenarios. These practices ensure that testing reflects real-world conditions and challenges, providing reliable data on system behavior and performance limits.

If you are building a highly distributed application with a specific network configuration, high-performance compute and low latency requirements, Equinix dedicated cloud gives you the control to quickly create exactly the infrastructure you need for testing and taking it to production at global scale later. The dedicated cloud is global, automated and can be managed via a powerful API using your favorite Infrastructure as Code tools.