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Spike testing is a way to prepare systems for a sudden rush in activity, for instance, instantaneous, massive web traffic that may cause a system to crash.

It allows you to discover the system’s behavior. This includes whether it’ll crash, slow down, and the time taken to get back to normal. Companies are incorporating spike tests into their routines for application testing to enumerate weak areas in production environments.

By accumulating results, developers acquire insights into where and when the application fails and the tools needed to optimize performance.

As a part of the software development life cycle (SDLC), spike testing screens out important software components such as speed, dependability, and scalability. This piece is about spike testing, why you need it, how it works, and its benefits. You’ll also discover a few tools to get started with spike testing seamlessly.

What is Spike Testing?

Spike testing is one of the performance testing methods that entails bombarding an application or website with surges in load. For example, you can increase the number of requests to stressful levels, decrease it, and continuously test to evaluate the system’s performance.

Unlike load testing, where uniform loads are applied to the system, spike testing is flexible. You can have a thousand requests in a minute, and in the next, have double, and so on. However, spike testing is only done in test environments, keeping the main application running and untouched.

Since real-world applications do not get traffic at a steady rate, spike testing unravels bottlenecks, scalability issues, and how your system recovers. In a business setup, spike testing allows you to prepare for events like large flash sales, high-profile registrations for public setups, influencer collaborations like marketing campaigns, and handling limited edition products.

It asserts that your system can seamlessly handle traffic surges and is especially helpful for companies running high-traffic processes like sales and registrations. It’s a way to maintain a positive user experience while reinforcing system reliability.

Spike testing bears several advantages, including determining the effects of unexpected spikes in users, discovering the extremes an application can handle past its designated load, and allowing developers to eradicate software breaking at user loads greater than maximum levels.

Spike Testing vs. Other Performance Testing

In the context of performance testing, the spike is a subset and a sibling to other assessments like load, stress, and endurance testing. For load testing, the system’s performance is evaluated at a pre-specified load.

To check for linear scalability via load tests, an application’s performance should exhibit zero variance with increasing users. Scalability is assessed by checking server responses at browser form submissions; pinpointing performance changes over time, testing for functions that fail at high loads, and identifying network latency issues on web application functions.

Or consider stress testing, which uses high loads to determine application response, show web application features that fail, track forms and links operating differently, and observe how the application functions after a system crash or failure of components.

So, stress testing evaluates the system’s response to near-breaking point conditions. And with soak testing (or endurance testing), you’re observing a system’s performance over time. Soak tests will look for parameters like memory utilization to reveal memory leaks and other performance issues.

How Spike Testing Works

The spike testing process can be summarized into three significant steps. First is load simulation, where surges in user activity are enacted by ramping activity or number of requests. Data collection follows next, and performance metrics like response times, load resources utilization, and error rates are collected. Lastly, an analysis is due to reveal how well the system copes with load increase.

Since the process is procedural, there’s a need to walk through all steps to achieve a quality test process. Start by setting up a test environment based on your specific business needs. It also ensures that no one is engaged in the live environment while executing the spike test.

Next, determine the extreme load – the maximum number of users supported concurrently in an application. Abruptly increase the load to peak. This can be done using web performance tools. With max load applied at peak point, screen the system performance, monitoring whether it crashes or survives.

Moving forward, decrease the load to zero or minimum rapidly. Analyze the system’s behavior at its zero load, observing whether it crashes (or not). As the last step, you need to analyze the performance graph.

By instantaneously increasing and decreasing the system load, a spike is formed. The spike is now analyzed to track metrics, including failures, time taken, and virtual users. By following these steps, test engineers can detect an application’s errors and report them back to developers, who, in turn, address the issues.

Types of Spike Testing

Spike tests will take different forms. Here’s an overview.

  1. Positive Spike Test – Tests how your system handles abrupt traffic, for instance, due to a successful marketing campaign.
  2. Negative Spike Test – Asses a system’s resilience to sudden adverse events, like the case of DDoS and smurfing attacks.
  3. Constant Spike Test – In this case, a server is subjected to a high-volume load for a short duration at fixed intervals. Spikes for this test will have an identical height; loads are constant.
  4. Step-up Spike Test – Server load is gradually incremented after small intervals. The response time quantifies each spike, analyzing its deviation from the baseload response time.
  5. Random Spike Test – Load spikes and their intervals are kept random; this is best for applications that get frequent spikes in production environments.

Spike Testing Tools

Most performance testing tools could be used for spike testing. However, let’s brush over the most common ones. They are preferred for their task specificity, but I haven’t ranked them in any order.

#1. BlazeMeter

BlazeMeter is an inclusive continuous testing platform for various scenarios, such as spike testing, API monitoring, functional testing, mock services, and data.

BlazeMeter gives you a massive test directly from your IDE for load and performance testing. For this reason, it has been used globally to prepare sports streaming applications to handle millions of people worldwide.

Regarding API monitoring, BlazeMeter can create and execute tests in minutes without compromising quality. It also notifies you of issues with API traffic before it has a significant impact on end users.

Among its latest features, Blaze is leveraging artificial intelligence, which, in turn, accelerates testing. You can request a demo to get started with new features like AI data-driven profiler, which allows you to robustly identify hard-coded data and automatically generate data from predefined lists, AI-driven test data creation that lets you streamline test data generation by converting text to test data functions, and chaos testing where you bolden system resilience by using AI-driven test data to challenge systems and pinpoint vulnerabilities.

#2. Apache JMeter

Apache JMeter is an open-source automation testing software. It was initially designed to test the functional behavior and quantify the performance of web applications. The framework can be used to test static and dynamic applications.

You can simulate heavy server loads (and groups) and test objects/networks under different loads. Although it looks like a browser, it’s not one. Meaning it does not perform browser tasks like rendering HTML or executing JavaScript. If you want to extend its functionality to render HTML output, you can use the JSR223 PostProcessor or JSSR Sampler to execute custom JavaScript code to be run after requests.

JMeter has multiple features, including testing the load and performance of multiple applications, servers, and protocol types like Web – HTTP, SOAP/REST services, databases, emails, and Java objects. It’s highly extensible and works well with the command line for all Java-compatible operating systems.

One superpower I’ve found in JMeter is its easy correlation through data extraction from popular formats like JSON, XML, HTML, and other textual formats. Here’s a quick start on using Apache JMeter.

#3. Locust

Locust is an open-source load-testing tool. It is scalable and scriptable. Unlike other tools that constrain you on a user interface (UI), a domain-specific language, or bloated XMLs, Locust uses plain code; you use regular Python programming constructs.

By running every user inside its greenlet (a lightweight process/coroutine), Locust allows you to write tests similar to writing block code instead of using callbacks or other mechanisms.

Additionally, Locust is events-based (employing gevent) and allows for a single process to handle thousands of concurrent users. As a result, this makes it easy to run load tests spanning multiple machines.

Its web-based UI is available for optional use, easing its use in the CI/CD pipeline. You can use it to show the progress of the tests with load variations implemented. While Locust natively works with websites and services, it can be used on any protocol. You only need to write a client for your specific use case or explore some created by the community.

Benefits of Spike Testing

Spike testing has multiple benefits. By proactively identifying issues, you can address all performance impediments before they evolve into critical issues. Considering aspects like software reliability, spike testing ensures your system is stabilized through unpredictable events.

From the user experience perspective, spike testing avoids downtimes and associated financial and reputation costs. Users expect your site and applications to be fully functional during traffic spikes, whether running campaigns or launching black Friday sales.

Spike testing evaluates your software strength, preparing it for real-world use cases and protecting it from crashing. Software sustainability can be achieved using spike testing.

Successful spike tests will reveal worst-case scenarios which aren’t targeted at standard testing procedures. Spike testing regulates software performance by addressing all performance issues and tuning it into a quality product with seamless experiences.

Limitations of Spike Testing

It’s worth mentioning the drawbacks of spike testing. The need to run spike tests in a unique (test) environment requires setting special test conditions, making the process a bit more expensive. This is a result of the complexity and intensity of resources. Handling complex procedures calls for expertise, in this case, software test engineers.

While running tests, there’s a possibility that your application could slow down, aggravate, or stop entirely. Unlike other performance tests, the spike method is time-consuming. Again, simulating accurate real-world spikes can be challenging.

Best Practices for Spike Testing

As you have seen, spike testing is a key player when testing performance, enhancing the resilience and reliability of web applications under heavy load conditions. Its use of abrupt, unexpected traffic spikes helps developers identify and mitigate performance issues, ensuring a positive user experience and system stability.

If your organization needs a spike test, you must set clear testing objectives and benchmarks. They need to be realistic. Why? No web application can handle unlimited traffic, auto-scale instantly or infinitely, and recover immediately. Having objectives will determine the right tracking metrics, which could include:

  • Response time – How long an application takes to send requests.
  • Error responses – The count of responses producing errors.
  • Throughput – A record or transaction per second.
  • Resource utilization – How software uses the central processing unit (CPU) and memory.

Should the above metrics be in your tracking list, you could have several questions. They include:

  • How many users should my application handle?
  • What levels of latency should I expect for my users?
  • To what extent is the CPU/memory utilized?
  • How many errors occur?
  • How long does the application take to recover from the spike?

Back to the objectives, constrain your test within realistic limits; it is cheaper. Handling massive traffic at high speed takes lots of work and is costly. Occasionally, you may need to adjust your architecture, replace or toggle data models, or tweak core business logic and operation models.

You may also consider putting research into understanding the user’s journey. There’s a big difference between having thousands of users on your landing page and the same number of users browsing through an e-commerce site as they purchase items. Understanding how users interact with your software will direct the queries to and from the server, consequently guiding the spike testing process. This means pointing you to the right spike testing tools, running the tests, removing performance bottlenecks, and repeating/iterating the whole process to meet specified needs.

Explore more software testing tools and take your testing skills to the next level.

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  • John Walter
    John Walter is an Electrical and Electronics Engineer with deep passion for software development, and blockchain technology. He loves to learn new technologies and educate the online community about them. He is also a classical organist.
  • Rashmi Sharma

    Rashmi is a highly experienced content manager, SEO specialist, and data analyst with over 7 years of expertise. She has a solid academic background in computer applications and a keen interest in data analysis.

    Rashmi is…

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