Step-by-Step Soak Testing and Spike Testing | Loadium

Step-by-Step Explanation of Soak Testing and Spike Testing

Let’s assume that you are working on a web application or an API. Let’s say that you have finished all of the functional testing and even…

Step-by-Step Explanation of Soak Testing and Spike Testing

Let’s assume that you are working on a web application or an API. Let’s say that you have finished all of the functional testing and even some performance tests. The product release went off without a hitch. However, after a while, your application’s performance suffers, and API response times escalate. Perhaps you would wonder about what happened and go wrong. Soak testing and spike testing are two different types of performance testing that might assist you in preventing such problems. Initially, we’ll look into stress testing because soak and spike testing are interrelated; later we’ll look at the other types of tests in more detail, including when they’re useful and how to design and implement them.

Stress Testing: A Second Look

To put soak testing and spike testing into focus, let’s revisit the description of stress testing. A stress test‘s goal is to determine the load capacity that causes a system to fail. This is accomplished by increasing the amount of users and requests affecting the software to a level that is higher than expected in real-world scenarios (ex. two three times more). You will reach a state where your software will no longer function as planned. To put it another way, you’re stressing your tests to see how they perform so you can determine which sections need to be fixed or develop a contingency plan in case you reach these figures in the real life.

Two forms of stress tests are soak testing and spike testing and the way they go up and down is what distinguishes them.

How Does Soak Testing Work?

The time length of a soak test is its distinguishing feature. Unlike stress tests, you don’t increase traffic to the failure point; instead, you stop when the projected average load is reached. Even so, there’s a ramp-up period, and you usually don’t start with the desired load. However, once you have attained your goal, you keep this burden on for a longer time. This time frame can range from a few hours to several days.

Soak testing is also known as endurance testing, capacity testing, and longevity testing, and these terms accurately define the goal of the test. Let’s have a look at why soak tests are performed.

Why Is Soak Testing Necessary?

Although it’s obvious that an application requires CPU, memory, and bandwidth to handle requests, the system doesn’t always clear up resources effectively after each request. The filesystem can become crowded if each request sends to log files or creates temporary folders that are required to finish the interaction but are not deleted. Network connections to backend elements such as databases, which may start but not stop on time, are another widespread issue.

All of the concerns discussed share a commonality. They will have no immediate impact on the system’s performance or accessibility. Consider the following scenario. Assume you perform a lot of log writing. Every request gradually consumes disk space until it is used up. You may have already enabled log rotation to erase logs after 30 days if you’ve been cautious.

The drive will never be filled with a month’s load of traffic from a few users. But how about a month’s traffic building up from thousands of users? Soak testing can allow you uncover potential leaks like these in your software so you can make sure they don’t affect performance negatively.

What Is a Soak Test and How to Execute it?

A soak test, as previously stated, is a long-running test with the goal of identifying issues that might otherwise go undetected in shorter tests. This need can make certain tests more difficult to execute and demand a thorough planning.

Soak tests are used to identify problems that are caused by limits, such as memory. It’s not possible to get correct results if the staging environment differs from the production environment. The optimum approach to run tests is to create an exact replica of the actual system, but this may not be possible. However, you never know when you’ll get lucky. Assume you have an application that does not receive traffic 24 hours a day, seven days a week, such as sector-specific software that is only utilized during business hours or a system that is only used during special events. If this is the case, you can test your system with no or limited actual production traffic during outages.

Testing over the weekend is a frequent strategy: you can start your test on Friday, run it for two days, and get your findings Monday morning. Although a soak test should be done with real-world traffic, it is feasible to compress the load slightly. As an example, you may run the same amount of requests over two days of the weekend identical to the number of requests you expect over five days from Monday to Friday.

You need to keep track of metrics like CPU, memory, network, and disk utilization when your soak test is underway since they will help you uncover the problematic bottlenecks. Metrics should mostly remain consistent if traffic holds steady, with relatively small variances in both directions. If a measure increases over time without finally falling back to normal, something is incorrect, and you’ve discovered a leak. You can u se the findings of the tests to enhance infrastructure design, such as adding RAM or configuring concurrent data structures.

Peak testing…

Peak testing is an alternative option to long-running soak tests that provides even greater compression. It involves simply running a test for a few sessions with the demand that you expect during peak times, such as Black Friday and Cyber Monday. Peak testing won’t discover problems that appear after days of constant load, but it can help you troubleshoot certain performance issues. The system’s behavior as it transitions from average load to peaks and return is an interesting topic. This brings us to the second subject: spike testing.

How Does Spike Testing Work?

Most stress tests simply raise load until they fail, and soak tests increase traffic and then keep it steady. Spike testing on the other hand necessitates ramping up and down. Testers rapidly raise the amount of requests to the point of stress, then reduce it and repeat the test in a spike test. To look at it another way, the test load may be thought of as a mostly straight line with a few spikes, and that’s where the name comes from.

Traffic spikes can occur at any time or at regular periods. For example, a spike test may run for eight minutes at normal load, then spike up to twice the request count for couple of minutes, return to prior levels, and repeat 5–7 times, resulting in an hour-long test. You might also run a spike test by “step up” in which the intensity of each spike is gradually increased.

Different Use Cases for Spike Testing

Here are couple of cases when spike testing is beneficial:

1. Spike Testing When Recovery Fails

Spike tests are different from other performance tests in that they examine how the software responds as well as how it recovers from failure. Let’s continue with our basic example and consider various behaviors to highlight possible test outcomes and their effects for your software system. It would be great if the couple minutes of increased load have no effect on performance. It’s also uninformative, therefore we should definitely put more load on the system to force it to discover its limits.

During the bigger spike, two different scenarios can happen. REesponse times might rise while the system remains functional, or you might face with greater number of errors. You might wish to enhance the program in both circumstances, although the first scenario is still preferable to the latter because there are no more defects. In addition, spike testing can assist in finding an answer to a new question: “What occurs when the peak has passed and traffic goes back to regular levels?” In the best possible situation, errors are minimized and response times are reduced.

Based on the underlying cause of the issues, this might happen quickly, however it’s possible that the system will take a few minutes longer to recover. Worse yet, mistake rates and reaction times might continue to be high. The worst-case scenario, on the other hand, is that the system never returns back to normal.

Proper behavior during a peak may be less important than quick recovery. Spikes may be brief, and performance decreases might be inevitable, but they can have a long-term impact on your overall performance indicators and crucial KPIs like accessibility. Spike tests should be included in your overall testing approach if these are significant to your test strategy.

2. Auto-Scaling Spike Testing

Various systems have some form of auto-scaling. Consider cloud settings, where an application creates additional servers as the load grows and then terminates them once the peak has passed. Furthermore, only a single process on a single system might depend on trash collection or other related frequent cleanup activities.

The typical load pace of a stress test allows such systems plenty of time to supply new resources and conduct the internal maintenance procedures needed to keep them running. Spike testing, on the other hand, cause traffic to fluctuate suddenly. They occur at a faster rate than the system normally responds. They occur at a faster rate than the software normally responds. To achieve effective scalability in these circumstances, it’s crucial to know the behavior of the system and its supporting infrastructure. You may notice that reaction times are delayed when the spike begins at a certain minute. The system then tries to catch up by increasing its resources to accommodate the increased load. If that’s the case, you can be certain that any performance issues will be momentary.

There’s a clear need for enhancement if nothing changes during the peak since the scale-up takes more than couple of minutes. The time limits of couple of minutes are optional, as they are determined by your objectives and expectations. Don’t forget to look at the “decrease scale” option as well. You’ll wind up with underused servers and extra cloud computing fees from your host if you provide more cloud resources when a peak begins but never cease them when it stops. It’s essential to mention that traffic patterns shift in both ways.

3. Product Launches: Spike Testing

Many items and websites have consistent demand and traffic conditions, or they increase gradually and consistently. For ex, after being live on a well-known website, or being promoted by a prominent audience, you might become viral. Spike testing are required if you expect this to happen on your website in order to avoid dissatisfied users.

Strategy for Performance Testing

Performance tests follow functional testing, while stress tests follow the initial round of basic load testing. When briefly looking at the soak testing and spike testing; soak testing is beneficial for any application with consistent traffic patterns, while spike testing is required for any application that expects heavy load that must be handled without damaged performance. Soak testing and spike testing can be configured in open source tools like JMeter, and they can be initiated from the cloud using Loadium.