Load Testing on Web Application API Using Locust

Load testing is a non-functional software testing approach in which a software application’s output is checked under a particular planned load. It specifies how the software application acts when several users access it simultaneously. Load Testing aims to improve bottlenecks in performance and to ensure a software application’s reliability and smooth running before deployment. Sometimes load testing happens when the software is already deployed to the production environment and tries to prepare for an important event.

It tests the system or component’s speed or capability via transaction response time. The system is likely to have reached its full operating ability when the system components extend response times significantly or become dysfunctional. When this occurs, it is important to recognize bottlenecks and provide solutions.

Yes, how come?

Stress Testing is a method of software testing that verifies software application stability & reliability. Stress testing aims at evaluating software under extremely heavy load conditions for its robustness and error handling capabilities and ensuring the software does not crash in crunch situations. It also checks beyond standard operating points and measures how software functions under severe conditions.

The purpose of stress testing is to evaluate the system’s actions after a failure. A device should show a suitable error message for stress testing to be effective when it is under extreme conditions. Often, large data sets that could get lost during stress testing can be used to perform stress testing. While doing stress testing, testers should not lose these security-related data. The main goal of stress testing is to ensure that, after failure, the device recovers, which is called recoverability.

Load testing is a subset of output tests at performance testing. It is used to validate how the devices perform under regular or peak load conditions. Load testing will bring the output test and can be measured by the number of data that will be provided. Load testing measures some points such as response time metrics, concurrent user, per unit over time, etc. When the program reaches performance, availability, scalability, reliability, and security it will reflect the measurement of those components.

The purpose of load testing is before moving the application to market or output, loading tests recognize some problems are for each transaction, response time, device component output under multiple loads, database part output under various loads, network delay between the server and the client, issues with device design, configuration problems with the server, problems with hardware limitations, memory limitations, the bottleneck of the network. Load testing can decide whether it is appropriate to fine-tune the device or change hardware and software to enhance performance.

Let’s compare the main point of the differences.

First thing you need to install Python and install the Locust package library after that. How to install Python won’t describe here, I assume that you already install Python on your PC or notebook.

Installing the Locust package.

Then you can check your Locust version.

Create your first load testing code using Locust.

Basically, we need to create a subclass and the main class. The subclass to define the task needs to execute and the main class to call the task that has been defined at the subclass.

After trying to code it, let’s run the code to get the result.

The first screen will show is like this.

Need to fill in the number of users to simulate, user spawn rate per second, and the host. Just press the Start button and the load testing is begun. From the terminal, you can see the progress of load testing depend on the total user to simulate.

After finished load testing, the report can be downloaded.

The first thing we need to understand the flow of the business process from the app we’ll test. Basically for the web apps need to validate the user to get the authentication token and need the token to continue to call the other API.

Here is the example code using Locust to test the web app.

We try to execute a task from the code to validate and retrieve the data based on the authentication code. The next move is to call the API after having the authentication code, including the authentication code itself. We may obtain the response code from the Locust script to verify that the response code is expected. We may also set the status of success and failure from the response code that we expected. To do that, a catch response argument must be added to the request function we call.

The second thing is to create the load testing code and try to simulate how many users and spawn users we need to set.

The content of the dashboard consists of the main navigation and the statistics. The dashboard navigation menu contains the host address of API, status testing (you can also re-set the load test), RPS (Request Per Second), Failures, Stop, and Reset Stats on the top right. The dashboard statistics contain chart metric live load, failure statistic, exceptions, and download report.

The dashboard of metrics will running and won’t stop if we don’t stop the load testing process. From the dashboard and report, we can also see the request statistics, response statistics, and failure statistics. From fixture 4, we can see the three charts of metrics there are.

Each chart metric will give different information that needs to analyze after the load testing is already done. Locust also provides a report that can be generated like fixture 5 and can be downloaded also.

We learned a lot about load testing, how to create the code with Locust, and understand the readiness of the system itself

There are the benefits of using Locust to do load testing after that.

The challenge of doing the job of using Locust to conduct load testing is to use load testing that requires programming skills.

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