Enterprise AI Platform on Kubernetes

What is Continuous Load Testing?

Load testing isn’t something that can “shift left” or “shift right.” Load testing should be integrated at all phases within the software development lifecycle. Load testing can be defined as putting a demand on a system and measuring for performance. The word continue means to carry on or resume.Continuous testing is a procedure for executing automated tests as part of the entire delivery pipeline. Continuous Load testing is a risk management activity. Application risks generally transpose to much more significant business risks, be it financial or face value of the company. This is especially true for performance-related risks, which often has a wide surface area in terms of impact.

The core idea is simple – as developers continuously develop new features and make changes to an application, wouldn’t it be amazing to know, immediately and correctly, what kind of effect each change would have on the performance?

The new application build must be tested for performance under stress — after the build process is complete, but before the build is released to production. Since most development teams have a functioning CI process, performance testing turns into a stage in the CI pipeline — mostly performed after regression testing but before production deployment.

The goal of Continuous Load Testing

Goals of continuous load testing are below –

Certify that the application meets performance targets. Load testing goals must correlate to business needs and represent real-world business scenarios concerning real customers. Each company has different critical performance constraints.

Plan capacity and manage growth. Even excellent performance from the applications currently have in production won’t necessarily protect from crashes if the number of customers, requests or data sets suddenly increases. Preparation for growth is a dangerous challenge. Performance testing helps build a capacity planning model.

Track useful performance metrics. Business users receive ‘green’ reports that tell them performance is excellent or panic reports about failures. A well-designed performance testing strategy can incorporate many key metrics so business users can see trends and ready. These metrics could be identified with user experience, limits, and business areas (e.g., response time, maximum throughput, resources usage, etc.).

Recognize load-related weaknesses. The knowledge of risks and exposures is an essential element of reasonable risk management strategies. Performance stress testing is how to discover these areas and give all necessary information in advance to make informed risk management decisions.

The challenge lies in the practical application of the ideas. Making a fully-automated performance regression and stress testing system is non-trivial, and for some development teams still falls into the class of “rocket science.”


Why Continuous Load Testing

Performance

Performance is a term for an entire scope of measurable application measurements. These metrics can begin with page load time and span to numbers of concurrent users, the source of page components, browser caching, amount of page views per minute, and more.

Availability

Availability is the infamous HTTP 503 page. These issues are among the bad dimension to experience as a customer, and since are a high priority in load testing.

Reliability

Reliability measurements are essentially asking whether the application returns the expected result. Reliability issues are long and wide and can be challenging to reveal in scripted situations, making exploratory load testing a great way to discover into reliability issues.

Scalability

Scalability covers viewpoints ranging from scaling up or down infrastructure because of unexpectedly increased workload, to just knowing if the infrastructure is correctly scaled for day-to-day operations. Long hold up times, reduced services, incomplete transactions all have the knack of driving up customer angst.


When to do Continuous Load Testing?

Performance in an application is seen as a “system issue.” But leaving load testing until production is as risky as leaving functional testing until creation. By load testing PRIOR, we can understand performance at the component level, which is a more relaxed, highly repeatable approach to test. It’s the similar concept as unit testing and API testing. The prior find the problem, the easier it is to debug.

Later Load testing in the DevOps cycle is similarly essential. This is when tackling production-sized problems around availability, scalability, and reliability. This provides critical insight into production behavior and can help validate the risk mitigation load testing did prior during development.

Features of Continuous Load Testing

Flexibility

Performance tests are, almost always automated. They have to be because it is tough to push massive levels of load using manual testing methods. Pressing the “submit” button on mouse ten thousand times is far more complicated.Because of the high level of automation inherent in performance tests, they can be executed whenever as required, including off days and ends of the week.

Coverage

Performance Tests Quickly Cover Broad Areas of Functionality

Performance tests provide “good enough” coverage of significant functions without going deep into the functionality. If a functional bug is in a significant feature, it frequently gets captured in the net of a performance test. Performance tests do, inherently, add a measure of functional validation.

You’ll want to be cautious not to allow performance tests to turn into the accepted functional tests, as doing so can make the team to lose focus on discovering performance issues. When utilized together, however, functional and performance tests become capable partners in finding significant bugs.

Effectiveness

Performance Tests Catch Hard-to-Pinpoint Defects Immediately

The majority of performance-related bugs occur in the weak code. A defective change has a minor performance impact when the lines of code are executed once, but when executed thousands or a large number of times, they have a significant combined slowing effect.

The little performance delay is now causing a major decrease in the number of transactions our system can process per second.

The key is that functional changes are commonly prescriptive,a functional code change influences the system to behave differently by design and by expectation. Performance changes, in any case, especially negative performance changes, are less willing to be prescriptive and bound to be an unintentional side effect of an otherwise well-intended change.

Getting the types of performance issues rapidly is key to giving developers the opportunity with regards to checking the source of the bug and fixing it. Developers and testers will be able to invest less time looking for the famous needle and more time concentrating on preparing the product ready for a quality release.


How to boost Continuous Load Testing

Choose the right tools

Choose a CI-friendly tool that allows to compare versions and detect differences with Git repository manager quickly. I’m a proponent of Apache JMeter. It’s great for load testing, but tests are put away as XML.

For CI purposes, it is smarter to utilize something dependent on code or simple text; that makes it simpler to contrast results and to detect differences. The open-source tools Gatling and Taurus are perfect for this sort of test.

Consider test levels

Load simulations are end to end, affecting the actions that the browser does (the user interactions). These tests are not that simple to maintain, because they’re sensitive to changes in these HTTP cooperations (when dealing with web-based systems). For CI, a better strategy is to automate the API layer, affecting the REST calls.

Those tests are simpler and less expensive to plan and maintain, yet obtain valuable information from them faster than by doing load simulations. This is a compliment.

Build the correct test infrastructure

As in any load test, infrastructure should be entirely for that test; otherwise, the results won’t be reproducible, and it will be more challenging to discover false positives. The more like the test infrastructure is to that in production, the more accurate the outcomes.

But if you don’t have such a test infrastructure for continuous load tests, don’t worry. It could be better to run tests on a scaled-down infrastructure. By doing so, won’t need as many machines to generate a load that comes close to the breaking point and it’s easier to learn about the system when it is running under its limits.

Get the frequency and timing down

Test the most critical things first and more frequently. You cannot test everything since that’s costly to create and maintain. The key is to organize and keep a decreased number of tests. From all out of tests, select the most important ones and put them in a different stage, earlier in the pipeline, and run that for each build. Then let the full regression test suite run once a day.

Test the most critical things prior and all the more every now and again. You can’t test everything since that is costly to make and keep up. The key is to organize and keep a decreased number of tests. From the all-out gathering of tests, select the most basic ones and place them in an alternate stage, prior in your pipeline, and run that for each form. At that point let the full relapse test suite run once per day.

Create the load scenario and assertions

Which performance tests will run continuously? When talking about load simulations, must think about how people will use the system and try to match. In this case, do something similar by considering how the API is going to be used according to user interactions. You can get data about that with the help of developers, by detecting the logs.

Another methodology is to try to hit a fixed load that is close to the breaking point of the infrastructure. Then define assertions according to the results get from the underlying executions. With this methodology, it can be sure that CI will tell you which change generated a degradation as soon as it occurs.


Techniques for Continuous Load testing

Initially, Fundamental DevOps standards should be set up. If QA and performance teams are siloed, they should be rearranged and moved up under tasks. Operations team members should then be coordinated with development teams. These ops engineers become the automation experts at isolating, replicating, and describing the environmental variables causing issues and assuring that test harnesses are continuously improving. The pipeline becomes more efficient, giving developers, QA engineers, and managers direct understanding of what is happening in each stage leading up to production.

Second, if tests are extensive, need to start breaking them up. The objective is to componentize the tests and run as many tests as can in a half hour to an hour. This has been done at the API layer so that other services are tested at the same time but independently of one another. Each test has an underpinning goal and should provide an answer to a specific what-if scenario.

Third, want to replace downstream services with mocks wherever possible. This allows to more quickly test what-if scenarios for dependent services without depending on them to be up or stable.


Tools or Platforms for Continuous Load Testing

There are many tools/platforms for continuous load testing –

  • Neoload
  • Load Impact
  • Apache JMeter
  • LoadNinja
  • Web load

A Comprehensive Approach

A Continuous Load Testing approach can assist your team in detecting code bugs quicker.To Facilitate Continuous Testing as a Testing Approach we advise taking the following steps –



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