Will your software perform well during peak hours? Can it handle the load during sales periods? Is your infrastructure valid if you double your sales by the end of the year? These are just some of the questions that scalability testing can help you answer.
Scalability testing evaluates the activity level of your application by upscaling and downscaling the number of users. With testing, you’re able to check if your otherwise healthy app can handle your customer’s requests in production at any given time.
You may want to validate your hypothesis as to how many concurrent users your application can handle before launching your software as well as validate any auto scaling solution that you’ve put into place to allocate resources on demand. Scalability tests allow you to prepare and monitor for these types of issues and prevent you from seeing overload during deployment.
In this article, you’ll learn how and when to apply scalability tests and strategies for designing and running tests effectively.
The Importance of Scalability Tests
Scalability tests are part of the performance test family. They help you evaluate the limits of your application and anticipate the amount of required resources as you increase the load.
Let’s talk a little more about areas that scalability tests are especially important for including user experience and infrastructure.
Improves User Experience
No one wants to use an application that takes a long time to load. It’s vital for your application to run efficiently to ensure a positive user experience. Performing scalability tests enables you to graph your performance (response time, for instance) as a function of the number of concurrent users.
Reduces Infrastructure
Identifying critical points where performance becomes unacceptable is essential for architects to start thinking about the evolution toward a more scalable system. However, scalable infrastructure is challenging because it requires a more complex infrastructure that can increase costs.
For example, using scalability tests you may find that one million users per day is possible based on your minimum viable product (MVP) but discover that two million causes severe performance issues. As a result, if your goal was to double sales by the end of the year, you know that you need to refactor your infrastructure by then.
Cost Efficient
If you want to be cost-efficient, you need to provide the right amount of resources (server, CPU, RAM, disk), given the number of users you have, so that you’re not paying for a server you aren’t using. Scalability testing can help you know what metric you should rely on to scale your infrastructure.
After testing, you can create a scaling plan that defines the resources you need at a given time. You can also design an auto-scaling solution where resources are automatically adjusted based on the metrics you selected. They can also be used to validate and adapt your auto-scaling strategy to optimize costs.
Even if you’re implementing a good scaling plan that includes load balancing and a replicated server, your application inevitably has bottlenecks, typically at the database or caching layer. Without a doubt, the data layer is the hardest and the most expensive to scale, so identifying those bottlenecks and having a system to test your improvements is essential for your business to scale efficiently.
How Scalability Testing Ensures Your Software Is Ready for Production
When you’re using scalability testing, there are several steps you should employ to ensure your software is ready for production. Remember your goals are:
- To validate that you can handle your target number of concurrent users
- To identify critical points where user experience becomes degradation
- To create a scaling plan to adapt your resource to the number of users
Set Up Your Target
Before you begin, you need to define your goals and decide what metric represents the best user experience. It could be the loading time of the front page, the time required to generate a report, or the time for a user to receive a purchase confirmation. Clear definitions and documentation can help with the company vision and marketing goals.
Based on your goals, you can define your target(s). When performing a scalability test, it’s essential to scale up in increments, test different aspects of your system, and have input to help create your scaling plan. At a minimum, you should have small, medium, and high load targets to make sure your software is ready. It would also be helpful to include next year’s marketing goals in order to prepare for potential growth.
Estimate the Potential and Thresholds of the System
Scalability tests essentially follow the scientific method of setting your goal and target which can tell you if you’re ready for production, and making hypotheses is critical for engineering teams. By estimating potential thresholds you can test processes and validate your solution.
Developing hypotheses might lead to preliminary explanations of load testing, which is one component to extrapolate the potential thresholds. You can perform a load test and find out how many users one instance of your application can handle. Or you can estimate how many “n” applications can handle the information in hand. It’s easier to build small and straightforward hypotheses to validate experiments, and it can ultimately save you time and effort if you identify bottlenecks early.
Once you have listed your hypothesis and validated some preliminary load tests, you must create a test plan and implement the scalability test experiments. You’ll also need to instrument your experiments to collect metrics that answer your hypothesis.
Execute Test Plans and Record Results
If you’ve been implementing the previous steps, this point should be fairly straightforward. After you’ve developed a hypothesis and estimated your thresholds, you need to execute the different scenarios to make sure you’re collecting your data and that everything is going according to plan (no significant application failure).
Once all the experiments are complete, aggregate the data in a presentable manner using graphs and charts.
Analyze the Data
To ensure you understand what changes need to be implemented, you need to report what resulted from each test. This is a step that software engineers can easily forget.
Every test should have a report that gives evidence of the outcome. The most important thing is to see if your targets are met. If so, then you are ready for production. But don’t forget that all hypotheses should be validated; if the result of your estimate is wrong, you should be able to understand why.
If your targets are not met, more work needs to be done. Often, you’ll find that infrastructure needs to be redesigned or scaling plans need to be updated.
Best Practices for Scalability Testing
Before you begin implementing scalability tests, keep these best practices in mind.
Keep It as Scientific as Possible
You should only change one variable at a time and observe its impact on the outcome. Modifying multiple parameters like the quantity of RAM and the number of servers at the same time would not give conclusive results as to what the best scaling strategy is.
Don’t Let Tests Influence Each Other
When running a scalability test, reset as much of the system as possible between separate test runs.
Systems will accumulate data during scalability tests and can affect future tests. If you create millions of entries in the database after the first run and don’t clean it up, you’ll start the second test iteration with that many database entries, affecting the end result.
When you reset your agents daily, for instance, you’ll be able to review the results of your tests frequently and have a set timestamp to compare the conditions of your tests, making it easier to optimize the parameters of your test agents. If you make this an automated task, you can ensure your labor isn’t affected.
Test and Repeat
Do not rely on the first test results to make your changes. It’s always essential to compare tests and see what influences the results. The dependencies of your application might change weekly or even daily.
For example, NumPy, a Python package used to manage data types and arrays, has been releasing updates weekly. If you set up your app like that, you need to ensure a test is done at least weekly to check if the dependencies are still working correctly.
Your target and goal must be updated based on the company vision and potential for growth and then you need to revisit that test plan periodically.
Are You Ready for Production?
How do you know when your application is ready for production?
Your data is ready when it shows you that you can achieve some of the key targets you defined at the beginning of the scalability test process.
Here are some key metrics you should review when trying to determine if you’re ready:
- Response Time: Is the period between a user request and the application’s response. Your application is scalable when it can deliver the same response time even as the number of users increases.
- Throughput: It’s important to check the throughput on the system and database levels. If the number of requests handled over time is maintained as user numbers increase, your application is ready.
- CPU Load: The less CPU utilization you have, the more efficient your tool is. If you want to have more throughput and less CPU load, you should optimize your code. CPU is a resource that has a cost, so if code optimization is not possible you’ll have to find what amount of CPU is required for a given load.
- Memory Load: The less memory your tool uses, the more scalable it is. Similar to CPU, you may have to scale the amount of memory based on the number of concurrent users. In the case of a database, the memory required only increases overtime since you usually keep all your data. In that case, the scalability test can measure the increase.
- Network Load: During each test, you’re checking to see how much bandwidth your application requires. The less congestion or traffic, the better experience for the user.
Performing Scalability test inevitably increases you readiness for production by:
- validating your infrastructure
- creating automated tests and a testing environment
- ensuring you’re able monitor and gather key metrics about your business
- analyzing which factors impact the scale of your application
Conclusion
Scalability tests ensure that as the load on your application increases, your servers, nodes, and other system resources scale properly to handle it. It might feel like a lot of extra work to run these tests, but if your goal is to increase the user experience and avoid problems on the frontend, employing scalability tests is essential.
In this article, you learned about scalability tests, their benefits, and how to use their results in an advantageous way. But imagine if there was already a tool that can automatically design scalability tests specifically for your needs.
Speedscale offers just that and more by providing a service that helps you test your APIs with real-life scenarios using data replay capabilities. If you want to implement scalability testing, Speedscale is an incredible resource for you as you get started.