Load testing plays a significant role in ensuring that enterprise software performs well under realistic load conditions — uncovering any potential bottlenecks and performance issues that may arise in real-world scenarios.
This article describes what load testing is, the load testing process (in 5 simple steps!), the importance of load testing, and best of all, commonly used load testing tools and essential metrics for load test success.
What is load testing?
Load testing is the process that assesses whether a production software system can effectively handle real-life load conditions without compromising its expected performance. One type of performance testing, load testing aids in identifying potential bottlenecks within applications by simulating concurrent users and transactions under various load conditions.
During load testing, performance engineers measure parameters such as:
- Application response time
- Error rates
- Server resource utilization
You can configure tests for various system transactions, focusing on different parts of your application — be it a sign-in page, a purchasing page, etc.
Load testing can be carried out in an environment that stimulates the production environment or in the production environment itself under defined settings. The load can be specified in different ways, such as the number of requests and concurrent users. Ultimately, load tests help improve the performance of the system before deploying the code in production environments.
Importance of load testing
Functional testing alone is insufficient to ensure that production systems will work as expected, as user load can be very high in real-world scenarios. Issues in typical testing scenarios can remain undetected in:
- Application code
For example, there can be SQL queries with larger query costs or running in loops. These queries can significantly slow down the application, negatively impacting user experience. To address this issue, Service Level Agreements (SLAs) are established for critical transactions, specifying certain performance criteria that need to be met. Load tests help determine if the application violates these predefined performance values when subjected to different loads.
At its core, performing load tests allows you to identify the hardware, network, and infrastructure required to handle your system usage, and assess the scalability and reliability of applications.
How to load test in 5 steps
A load test involves a series of tests, starting from requirement analysis to the execution of the tests.
Step 1. Requirement analysis
In many software development organizations, a dedicated performance engineering team is responsible for conducting performance tests for various products. The engineering team discussed with them their requirements for the performance test. Initially, the team identifies specific requirements, such as:
- User journeys to cover
- Expected SLA values
- Environment configurations
Step 2. Test script creation
The performance team then creates the scripts mapping relevant user journeys. They also set up the necessary test data and environments to perform the tests. Currently, there are several load-testing tools enterprises leverage at this stage.
Step 3. Load test execution
Load test executes in either a production or a production-simulated environment. In a load test with user loads, the performance engineering team gradually increases the load generated through a load generator. Then, they observe if there are any deviations in the metrics, including:
- Error rates
- Response times
Step 4. Results analysis
At the end of the execution, the load ramps down to the normal state. Then, the performance team gathers the automatically generated results through the load testing tool and presents their observations, recommendations, and decisions to the engineering team for further analysis.
Step 5. Re-execution
If the test fails, the team analyzes the related user journeys for the root cause and fixes the exact issue. Then, they perform another load test to validate that their fix has optimized the application.
Load testing metrics
Various load-testing metrics can be defined based on the requirements specific to your organization and goals. Generally, the following are the metrics used to form the baseline performance measurements of applications under different load conditions.
Most load tests measure the system’s response time to a specific user request. If the response time goes significantly higher than the defined SLA, it shows that load time compromises the user experience. Usually, the average response time is taken as the final value — typically defined in seconds or milliseconds
This metric defines the percentage of failed requests compared to the sum of all requests. This error rate increases when the application cannot handle it within a specific period. A high error rate indicates bottlenecks in application resources and poor error-handling techniques.
Throughput refers to the number of transactions processed within a unit of time. A high throughput indicates that the application is performing well under high load. Load testing helps determine the maximum throughput the system can achieve and identifies potential bottlenecks that may limit it.
CPU and memory usage
The behavior of these two attributes is monitored before, during, and at the end of the load test, enabling teams to identify abnormal spikes or behaviors in system resources. High CPU or memory usage indicates bottlenecks in code and inadequate hardware resources. CPU and memory usage is primarily measured in percentages.
Number of requests per second
How many requests are received by the application per second? Requests include:
- API requests
- Web page loads
- Document loads
- Image loads
Many requests for this metric indicate the systems’ ability to handle and scale for larger user loads.
This metric defines the time it takes for the data to travel between the client and the server. The higher the latency, the lower the system performance would become. For example, networks with high latency take more time to retrieve data from a server. Load testing helps uncover this type of network latency issue under varying load conditions.
Benefits of load testing
Identifying performance issues
Load tests help reveal performance bottlenecks in your applications before deploying them into production. A few examples are slow database queries, loop conditions that can seriously impact performance under load, and infrastructure issues.
Load testing allows developers to make enhancements before deploying the code to production, eliminating future issues.
The load increases gradually during a load test. This process helps identify the point where the application behaves abnormally or deviates from the expected behavior. It enables the team to test the scalability efforts by adding more resources with increasing loads.
Improves the user experience
Load tests uncover application performance issues before the features are deployed to production, delivering users the most optimized versions of the application. Error rates and response times can be significantly reduced if issues related to them are identified and fixed beforehand. Thus, users can enjoy the features of your application without any performance issues.
Reduce unnecessary costs
Sometimes, there can be application outages or the application can become unresponsive with unexpectedly high user loads. Such outages can cause significant financial losses for the organization. Load tests help the team prepare the production environments for such issues by fine-tuning the code, networking, and infrastructure for optimized performance.
Load testing tools
Several tools have emerged to automate the load-testing process. The following are some of the top load testing tools in use today:
A widely used open-source load-testing tool based on Java, Apache JMeter uses several protocols and supports testing various technologies, including REST, SOAP, FTP, and databases. This tool includes a user-friendly IDE where you can:
- Create test scripts
- Execute load tests
- Record load tests
- Debug load test executions
JMeter can also be used to test the performance of mobile apps and perform distributed testing.
Another continuous open-source tool that supports a variety of performance tests like functional tests, API testing, performance tests, and load testing. Currently, it can be used for both web and mobile load testing. BlazeMeter also integrates with other popular testing tools like JMeter, Gatling, and Selenium.
This tool offers scalability and real-time reporting while providing on-demand load generators from various geographical locations.
This tool is based on the Python programming language, allowing you to define your load tests with Python. It is also an open-source tool that supports distributed load tests executing on multiple machines. Hence, Locust enables scaling loads to millions of concurrent users. Additionally, Locus provides a web-based user interface for real-time monitoring and visualization of test results.
This tool allows users to create and execute scalable load tests in real browsers. You can replay the test scripts immediately after recording them. This feature enables performance teams to quickly debug and identify performance issues. This cloud-based testing tool eliminates the need for manual correlations and simplifies complex load-testing scenarios in web applications.
This open-source load-testing tool can be used for performing end-to-end performance tests, including load tests to identify bottlenecks in an application. It has a simple user interface with a test recording feature. Gatling is capable of load-testing web applications. It also comes with features such as reporting and continuous integration capability.
Importance of load testing
Load testing is critical for ensuring the performance and reliability of production software systems. It helps identify bottlenecks and weaknesses impacting the expected performance of the system. Many load-testing tools, such as Apache JMeter, BlazeMeter, Locust, LoadNinja, and Gatling, have been introduced to automate and streamline the load-testing process.
Load testing provides valuable insights into application performance by analyzing metrics such as response time, error rate, throughput, CPU and memory usage, number of requests per second, and network latency. Load testing offers numerous benefits, such as identifying and resolving performance issues, improving scalability, enhancing the user experience, and minimizing unnecessary costs.
What is Splunk?
This posting does not necessarily represent Splunk's position, strategies or opinion.