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Leverage real traffic patterns to better understand your app’s breaking point and verify load capacity in a safe environment.
Today’s fast-paced and complex development cycles need new approaches to load testing to achieve peak Kubernetes performance. With Speedscale, automatically generate load tests with real-world scenarios. Use your application’s actual traffic to measure latency, throughput, saturation, and error rate—quickly, reliably, and cost-effectively.
Verify that your load tests can handle your actual, expected traffic and any increases in traffic, so you aren’t misled by false confidence.
Scripting is slow and unrealistic. Capture traffic and multiply it–even replay it in lower environments. Autogenerate load tests from real traffic.
All traffic is invoked natively in your Kubernetes cluster so you can factor out external variables while testing.
Simulate third party systems and internal backends for more reliable, affordable testing. No need to spin up costly, end-to-end environments that take days to deploy.
"chaos": {
"choasPercent":25,
"badStatusCodes": true,
"intermittentResponses": true,
"randomLatency":true,
"randomHighLatencyMS":5000
}
Introduce new variables that put your app’s integrity to the test. Simulate errors, latency, and non-responsive third-party applications—all in an automated fashion.
Speedscale works with any CI/CD provider to help you understand your app’s limitations when you make code changes. See how updates impact metrics like peak transactions per second, latency, and autoscaling thresholds.
Redact sensitive data and personally-identifiable information (PII) on the fly. Anonymize traffic while keeping data structure intact for more accurate and useful testing.
Learn the key differences between Speedscale and popular load testing tools on the market, and make an informed decision when it’s time to select the right solution for your business.
“Speedscale gave us clear insight on the upper load limit of our software. Whereas with K6, it was more binary, like a PASS/FAIL grade. Speedscale took it one step further to help us understand, based on transactions per second, exactly where our breakpoints were. We felt more confident in where our service could be.”
– Jeff Kwan, Principal Software Engineer, Cimpress
Replay past traffic, gain confidence in optimizations, and elevate performance.
Load testing is a great way to verify the stability and scalability of your application by generating load towards it. Imagine you’re going to launch a new e-commerce website or a highly anticipated new product.
After implementing unit tests for every possible use case and performing integration and end-to-end testing, you’re confident that all the website features are working perfectly. Then, on the launch day, the site crashes because of large amounts of traffic.
In this scenario, load testing would’ve identified the limitations of the website in terms of how much traffic it could handle. By identifying these bottlenecks before the launch, developers could’ve ensured stability throughout the launch, preventing frustrated users and a loss in revenue.
This post covers the basics of load testing, including its definition, purpose, and how to get started. By the end, you should have a comprehensive understanding of load testing and be well-prepared to build reliable and scalable systems.
Load testing is a critical part of determining the resiliency and scalability of an application under various forms of load. It involves simulating real-world usage conditions to measure an application’s performance and stability. In general, you want to measure any metric that can identify bottlenecks, like response times, response codes, resource usage, etc.
Let’s revisit the example of an e-commerce application launching a new product. In that scenario, you could generate a large number of users and validate the stability by measuring transactions per second.
But perhaps the issue doesn’t stem entirely from the application itself. It’s not uncommon that engineers are surprised by the amount of time it takes for an application to scale. Load testing can be used to increase the load gradually, letting you verify exactly how your application scales.
That said, your application may have had faulty error handling. By combining load testing with mock servers, you can introduce chaos—like bad requests or latency spikes—to verify how your application handles unexpected scenarios.
Whatever the case, load testing is an important tool in any modern agile environment because it enables teams to deploy changes efficiently. Load testing provides additional testing and insights that reduce the number of surprises when deploying into production.
Done properly with the right tools, it can lead to a quicker development process and less technical debt. Some legacy load testing methodologies however, can be cumbersome and slow.
Now that you understand load testing and how it works, let’s take a look at its benefits.
With load testing, you can simulate real-world scenarios even before merging your new code. Different developer workflows exist, but most of them involve at least the creation of a new branch. If you have to create a new branch for something, it increases the cognitive load and reduces the likelihood of it being done.
Since load testing provides insights about performance and reliability before code is merged, it’s much easier to implement performance fixes immediately.
As performance and reliability increases, users will encounter fewer issues, smoother interactions, and be met with a better overall experience using your product.
Focusing on an enhanced user experience helps build strong customer relationships, which in most cases is the key to a successful business.
Some organizations have a rule of not deploying on Fridays because nobody wants to spend Friday night troubleshooting. Load testing may not remove this rule. But it will greatly decrease the likelihood of any deployments resulting in anyone working overtime because load testing would’ve caught most issues already.
As mentioned previously, load testing can verify scenarios that’ll occur in production, like scaling rules. However, incorporating realistic data can take testing production to the next level. With concepts like traffic replay, you can fully replicate the behavior your application experiences in production within the confines of a secure testing environment.
As you catch performance issues early, you’ll spend less time “fighting fires” and subsequently have more time to develop features.
Since teams can simulate expected loads, it’s unnecessary to over-provision resources. Instead, spin up your application and generate the load you expect to see in the future. Then note the amount of resources you’re using.
Although not a distinct benefit, it deserves a mention. All the above benefits come together to increase the competitiveness of your organization.
While load testing comes with several advantages, there are some possible drawbacks to consider. They can be:
It’s also important to consider that it’s difficult to obtain realistic data—you need to capture, sanitize, store, and then finally replay the data. How you approach it depends on your infrastructure. But if you’re using Kubernetes, Speedscale has solved this issue using sidecars.
The drawbacks above are valid concerns that you should address. However, consider how the benefits outweigh the drawbacks. For example, while load testing can be costly, it’s anecdotally been used to compare cloud providers, resulting in seven-figure savings.
At this point, you should have a clear idea about load testing and why you should implement it. Before looking for load testing tools, understand what to expect in terms of planning and preparation.
As part of getting ready for load testing, you need to:
Whether it’s as simple as keeping services under 80% CPU usage or something more advanced metrics like X transactions per second at Y amount of RAM usage, identifying and setting specific performance goals should be one of the first steps you take.
Remember that these goals evolve with time. But if you don’t have goals, you can’t define success.
No matter the testing methodology, it’s necessary to define a set of test scenarios. In the case of load testing, ensure your test scenarios align with the performance goals set by the previous step.
For example, if you want to verify the stability of your application according to the current load, you’ll likely want to verify a certain number of transactions per second when replicating peak traffic.
Part of selecting the right tools is determining whether you’re building it yourself or using a third-party tool. Regardless, determine a set of criteria.
For example, if you want to perform load testing in a continuous manner, you need a tool that can easily configure mock servers.
By now, you must have completed all the necessary planning steps. Although you may find more steps when looking at your infrastructure, you can start preparing the test data. This may include traffic capture from your production environment or preparing test databases.
Establishing the test environment can be a surprisingly complex task. It’s not just about spinning up a development version of your application, you also need to consider how you configure dependencies, how you seed data, etc.
Depending on your environment, you may need to warn other team members. Or, depending on the size of the load you’re generating, you may want to optimize for low resource costs, like using spot instances.
Once your load testing is complete, look at the metrics you collected. Then analyze the metrics to determine whether you met your performance goals.
Load testing, performance testing, and stress testing all belong to the category of non-functional tests—a type of test for evaluating the performance and behavior of applications rather than its feature sets, such as unit tests.
Performance testing is a broad category with the general goal of testing and validating the performance of your application. Performance testing includes load testing and stress testing.
Load testing, as you’ve seen throughout this post, focuses on testing various parts of your application using specific amounts of load. Stress testing, on the other hand, has one specific purpose—to test the limits of your application.
All the testing types have one similarity—the general execution is the same. You can also generate traffic, capture metrics, and analyze the results. The biggest difference is how you generate the traffic.
Once you’re ready to load tests in your environment, there are some best practices to follow.
While it’s possible to execute a load test in production, which can be useful in some cases, it’s generally recommended to run these tests in a separate defined test environment.
It’s important to consider the scope of your tests. For example, if you want to keep tests in-scope, consider using mock APIs. Mocks have the added benefit of helping you avoid unnecessary resource usage and rate-limiting.
Because the purpose of executing load tests is gaining realistic insights into your application’s behavior, it’s important to use realistic data. This means the load generated towards your application should accurately reflect the behavior of your users.
To determine whether you’ve met your performance goals, consider the metrics you want to collect and how you’ll collect them. Using an agent in your testing environment may produce unrealistic results if you’re using an agentless solution to collect metrics in your production environment, because agents may affect your application’s performance.
Load testing will likely benefit everyone including developers and, in some capacity, users. However, there are some groups where the benefit is explicitly clear.
Developers benefit from the ability to identify bottlenecks without relying on real-user reports. They can then fix these bottlenecks preemptively rather than adding them as a backlog item for fixing at a later date.
A good load testing strategy and implementation helps developers spend less time troubleshooting and debugging, and helps them spend more time developing new features. As a result, it leads to more satisfied developers, and subsequently, happier and more efficient teams.
Business owners will also see a clear benefit from load testing because it provides valuable insight into the performance of applications. For example, being able to say “we can handle X amount of load” can be a compelling argument during sales meetings.
The insight provided by testing also allows improved infrastructure changes and more efficient investment decisions. Additionally, the extra focus on testing and fixing bugs before deployment will ultimately lead to a better UX and likely a revenue increase.
Despite several use cases of load testing, it may not be the right call in some situations. For example, if you’re a small startup still trying to find product-market fit, load testing may not be a top priority. In addition, if your application is experiencing low traffic or serves mainly static content, the benefits of load testing may not be as great.
Also, if you have limited budgets, the cost savings or increased revenue doesn’t appear fast enough to justify the up-front costs—and these are just a few examples.
But to make the best decision for your business, consider the benefits, drawbacks, and tradeoffs of load testing before implementing it.
Looking back on the example from the introduction, you now know exactly how to prepare for a big new product launch. From the planning phase, where you identify performance goals and define test scenarios, to the execution phase, where you collect and analyze metrics, you should now have a strong understanding of how to get the most out of load testing.
Whether you’re a developer or a business owner, or perhaps someone else entirely, you’ve also seen that proper load testing implementation affects different groups. In case you’re still not entirely sold on the idea of load testing, you may want to experience how it feels to work with it. In that case, you should check out the post on how to load test Kubernetes.
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