To maximize the performance and value of your software apps, networks and systems, it’s critical to eliminate performance bottlenecks. Performance testing has become critical in every organization to reveal and fix performance bottlenecks, ensuring the best experience to end users.
This article explains what performance testing is, its importance, and the various types of performance testing. It also delves into the metrics used in performance testing, automated performance testing tools, and the benefits of leveraging performance testing.
Let’s dive in!
What is performance testing?
Performance testing evaluates the behavior of a software application, network, or system under specific loads to evaluate its stability, responsiveness, robustness, and speed. Performance testing aims to identify the performance bottlenecks of the target system and fix them before they lead to system unavailability.
Performance testing can be conducted in two different scenarios:
- On software applications upon the completion of all the functional tests.
- In production environments under controlled conditions.
The importance of performance testing
Performance testing is critical for offering a consistent user experience regardless of the load the system is experiencing. The turnover rate of users can increase dramatically if they encounter:
- Intermittent issues
- Application outages due to performance issues
Each of these adversely impacts the progress of your business. Performance tests help mitigate such circumstances by revealing areas where performance fails to meet the expected service level agreements (SLAs).
Performance testing also plays a key role in identifying the computing resources required to effectively scale your software applications in production environments. Additionally, these tests provide insights into how your applications, networks, and systems perform compared to similar components. (We call this observability.) These insights enable you to assess your preparation and readiness for upcoming events and to make well-informed decisions regarding your businesses.
Types of performance testing
Different types of performance testing use various methodologies to assess performance. The following are some of the commonly used types of performance testing.
Load testing evaluates the behavior of systems when they experience a high volume of concurrent users and transactions at a specific time. During a load test, engineers gradually increase the number of virtual users and transactions. They observe the defined performance metrics such as:
- Response time
- Error rates
- CPU and memory usage of the server
- Database errors
Then, the collected measures are analyzed to identify transactions that have violated the expected SLA values. To identify performance bottlenecks, yeams can integrate load tests for CI/CD processes, pre-production environments, or production environments in controlled settings. .
For example, there might be queries with slow performance under high load that cannot be identified during functional or single-user performance testing. Additionally, load testing helps identify if there is a need to increase or decrease the system resources.
Stress testing is conducted by stressing the system with an extremely high load, surpassing the usually expected peak load. The aim is to identify the point at which the system breaks down under such extreme loads. Stress tests place pressure on underlying system resources like the CPU, memory, and storage revealing issues related to existing resources, such as data transfer issues and memory shortages. These tests also provide insights into the recovery time of a system to regain its normal state after a significant event.
Endurance testing & soak testing
The goal of endurance testing is to assess the endurance of a system under higher loads over time. The period can span hours or even days. The team monitors the performance and stability of the system during the sustainable load period.
Stress testing can reveal potential performance bottlenecks, resource utilization issues (ex: memory leaks), and system errors when the system is under a higher load for a long period. Soak testing is critical for databases and web applications that must run continuously.
Spike testing is another type of stress testing. It involves observing the system's behavior during a sudden surge in user loads. Unlike stress testing, spike testing considers the number of users over a short period. Spike testing helps determine whether the system can withstand unexpected and sudden user spikes.
Teams usually perform spike tests before a major event that is likely to attract many users to the applications or systems. Some examples are product launches or marketing campaigns that will generate many new users. Spike testing helps increase confidence in systems and eliminate any application downtime due to sudden surges in user loads.
This is another form of stress testing that evaluates the system behavior under the expected number of users or maximum load conditions. It focuses on the number of users and the amount of transactions the system can handle. Understanding the capacity limits of the system helps organizations improve the capacity limits of the existing system or introduce other alternatives.
For instance, suppose you expect your system to handle 1,000 users at a time. However, the capacity testing revealed that it can only handle 500 users before it starts to slow down. In that case, the organization may need to add more servers or upgrade the existing ones to handle the expected load.
Scalability testing allows users to understand the performance of the system with increased or decreased workloads. The goal of scalability testing is to determine the capability of the system to scale up or down resources like the CPU, memory, or network to accommodate varying demands without compromising performance or stability.
Scalability tests can be performed in an upward or downward manner:
- Downward scalability testing increases the number of users checking the breakpoints.
- Upward testing decreases the number of users to identify the performance issue.
What to measure in performance testing
Performance testing teams measure the following key performance metrics during the various types of performances described above.
- Response time: The time it takes to get a response after sending a particular request. Higher response times indicate poor performance.
- Average response time: The average of all the response times. This metric shows the overall response time the user experiences across the application. It is calculated as:.
- Average response time = Total response time / Number of requests
- Throughput: The number of transactions the system can process within a specific period. Higher throughput means the system can handle many users within a given period.
- Error rate: The percentage of failed requests during a performance test. It is typically calculated like so:
- (Number of failed requests/ Total number of requests) x 100
- A higher error rate than usual indicates that the system is poor in handling errors.
- Requests per second: The number of requests the system can handle per second.
- Resource utilization: The amount of resources being used during the test. For example, the percentage of CPU and memory usage and network bandwidth.
- 90th percentile response time: This value represents the response time that is slower than 90% of the measured response times. This measurement helps identify the performance experienced by the majority of users.
- Network latency: The delay in data transmission through a network. Higher latency indicates issues with network bandwidth or other performance issues within the network.
Apart from the above server-side performance metrics, performance tests also help assess client-side metrics such as the following.
- Page load time: The amount of time it takes to completely load a particular web page in the browser.
- Time To First Byte (TTFB): Time spent to receive the first response byte since the HTTP request is sent to the server.
- Time to Interact (TTI): The time it takes for a web page to become completely interactive to the user.
- Time to Render: The time it takes for a web browser to render a web page.
(These client-side metrics often overlap with common web analytics.)
Benefits of performance testing
Performance testing provides numerous benefits to organizations. Some significant benefits can be listed below.
- Improved user experience. Performance testing helps reveal potential issues impacting the user experience — ensuring a consistent user experience of an application or system.
- Optimized costs. Performance testing reveals bottlenecks that can lead to more heavy resource usage than necessary, such as CPU, memory, or network bandwidth. Performance optimization enables us to reduce costs associated with such excessive resource usage.
- Improved scalability of the application. Performance tests allow systems to optimize the resources according to the increased user loads. It increases the ability of the system to handle varying user loads.
- The ability to make informed decisions. Performance testing provides valuable insights into the resources and configuration changes required to run production systems smoothly. It allows stakeholders to make informed decisions on budgets and resource allocation, reducing costly re-planning and estimations.
Performance testing tools
Today, various performance testing tools have emerged to automate performance testing. Many companies worldwide use popular performance testing tools to increase the effectiveness of performance testing.
A widely-used performance testing tool for load and website performance testing. It is a completely free tool based on Java. This tool can conduct performance tests on both static and dynamic websites, even though it can’t perform all browser actions. JMeter can be used in two modes: GUI mode and CLI mode.
A load, stress, and soak testing tool for testing back-end services. It can also be used for synthetic monitoring to ensure all key workflows are working properly. Artillery comes in two versions:
- Artillery Core, the free and open-source version
- Artillery Pro, the paid, cloud-native version.
This tool is designed for load testing, simulating thousands of users. Having been developed by Micro Focus, LoadRunner supports many protocols, such as HTTP, web services, and databases, This extensive support for protocols allows you to use it for testing various applications.
A tool developed by Google for testing the performance of web applications. This open-source tool can be used in different ways as an integrated part of the Chrome developer tools, as a node module, as a web app, or through the CLI.
This is another load-testing tool for web, mobile applications, and enterprise systems. It has four editions: free, pro, enterprise, and SP. StressStimulus is a beginner-friendly tool that can also be used through a GUI.
Splunk Observability Cloud
Last but certainly not least, we have Splunk.
Splunk Observability Cloud is a comprehensive observability solution, going beyond just performance to give you visibility into your entire operation. It offers real-time visibility, allowing organizations to instantly see how every code change affects the user experience and performance of the application. With its powerful analytics capabilities, Splunk allows organizations to detect and fix potential problems, and perform root-cause analysis.
Splunk’s Observability products bundle synthetic testing, real-user monitoring, and application performance management with profiling support all in one place, making it easy to investigate your application’s performance from various angles.
Summing up performance testing
Performance testing is essential to identifying performance bottlenecks in software applications, networks, and systems. There are various types of performance tests, such as load tests, stress tests, endurance testing, spike testing, capacity testing, and scalability testing. Automated tools like Apache JMeter, Artillery, LoadRunner, Google Lighthouse, and StresStimulus have simplified performance testing.
Performance testing offers numerous benefits to organizations and end users, such as ensuring a consistent user experience, optimizing costs, enhancing scalability, and the ability to make informed decisions.
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This posting does not necessarily represent Splunk's position, strategies or opinion.