Gatling is a leading open-source load-testing solution with a powerful load generator. This tool generates static reports and ensures web application performance and scalability. Gatling is more than just a testing framework. It serves as a comprehensive solution for load and performance testing. Built on Scala, Gatling offers a scalable and expressive Domain Specific Language (DSL) for crafting intricate test scenarios. Further, it provides extensibility through plugins, enabling users to customize and enhance their testing capabilities according to specific requirements. This extensibility allows integration with third-party tools and services, further enriching the testing ecosystem.
The blog explores Gatling’s capabilities, emphasizing its relevance in modern software development. Let us dive in and understand why Gatling has become a preferred choice for performance testing.
Why Use Gatling for Load Testing?
High Performance
Gatling leverages the power of Akka and simulates thousands of virtual users efficiently with minimal resource usage, making it ideal for testing high-performance systems. Akka is a powerful toolkit and runtime for building highly concurrent, distributed, and resilient applications in Scala. Its asynchronous, non-blocking architecture ensures effective resource utilization, enabling testers to generate substantial loads without significant hardware requirements.
Scala-based DSL
The Scala-based DSL helps to prepare complex test scenarios in a breeze, offering readability and expressiveness. The DSL simplifies the creation of realistic user behaviors such as navigating through web pages, submitting forms, and handling dynamic data.
Detailed Metrics Dashboards
Gatling provides detailed, out-of-the-box metrics dashboards, allowing you to analyze test results comprehensively, without additional plugins.
Integration with CI/CD Pipelines
Gatling integrates with TeamCity, Travis CI, CircleCI, Bamboo, Azure DevOps, GitLab CI/CD, and AWS Code Pipeline for automated performance testing in CD pipelines, showcasing flexibility and adaptability across various environments.
Gatling Capable Assertions
Integrated assertions API enables functional checks alongside performance testing. Gatling assertions enable testers to verify system behavior during load tests. These assertions include response time, status code, response body, error rate, concurrency, throughput, session data, and custom assertions.
Performance Criteria Tested Using Gatling
Gatling is a versatile tool capable of testing various performance criteria including:
- Response Time
- Throughput
- Concurrency
- Stability
- Error Rate
- Resource Utilization
- Latency
- Transactions per Second (TPS)
- Peak Load Handling
- Scalability upon increasing load
Applications Suitable for Gatling Testing
Gatling is not limited to testing the performance and scalability of web applications. It is suitable for testing a variety of applications and systems including:
- Web applications including e-commerce websites, social media platforms, content management systems, and online banking portals.
- APIs (RESTful, SOAP, GraphQL) simulate numerous API requests with diverse parameters to evaluate response times, throughput, and error rates.
- The responsiveness and scalability of mobile applications under load cover native, hybrid, and web-based mobile applications.
- ERP (Enterprise Resource Planning) software, CRM (Customer Relationship Management) systems, and HR management platforms.
How to quickly start the Gatling setup in IntelliJ
Installing Gatling in IntelliJ is straightforward:
- Install OpenJDK.
- Set up Maven.
- Install IntelliJ Community Edition.
- Create a Gatling Project using Maven.
- Import the Gatling project in IntelliJ.
- Install Scala Plugin from settings.
- Configure Scala SDK.
- Verify Development Environment.
Project Structure
- build.sbt: This is the build configuration file for your project using sbt. It specifies the project dependencies and other settings.
- conf/: This directory contains Gatling’s runtime configurations such as Gatling. conf for Gatling settings and logback.xml for logging configuration.
- project/: This directory holds sbt configuration files including plugins.sbt, which defines the plugins used by the project.
- src/: This is the source code directory. The test/ directory contains Gatling test scripts. Within test/, resources/ holds any additional resources required for testing, and scala/ contains the Scala source files for Gatling simulations.
- target/: This directory stores compiled classes and generated reports.
- user-files/: This directory is used by Gatling to store user-specific files required for testing such as data files (data/) and request bodies (bodies/).
This structure provides a clear organization for Gatling projects, separating source code, configuration, resources, and output files. It helps in maintaining and managing Gatling tests effectively.
Scripting in Gatling
Complex test scenarios can be created using Gatling’s scripting capabilities. Here is a glimpse into the scripting functionality.
- Protocol Definition
- Scenario Design
- Setup for Load Simulation
Generating Reports with Gatling
Gatling’s reporting features offer insights into test results including:
- Global Statistics
- Response Time Distribution
- Active Users and Simulation Timeline
- Request and Response Metrics
Source: Gatling Load Testing | 8 Reasons to Use It | Blazemeter by Perforce
Benefits of Using Scala in Gatling
Scala provides a solid foundation for Gatling, offering expressive syntax, functional programming capabilities, strong static typing, seamless integration with Gatling DSL, concurrency, and asynchronicity for realistic simulations, and scalability. By harnessing the power of Scala, Gatling empowers developers to build robust and efficient performance tests that meet the demands of modern software applications.
Comparison between Gatling and JMeter
Gatling and JMeter are two popular tools for load and performance testing, each with its own strengths and weaknesses. Gatling, built on Scala, offers a high-performance, expressive DSL that excels in simulating heavy loads and complex scenarios. Its scalability and efficiency, leveraging Akka’s concurrency model, makes it suitable for testing high-performance systems. Additionally, Gatling’s integration with CI/CD pipelines and detailed metrics dashboards streamlines the testing process, enabling automation and comprehensive result analysis. On the other hand, JMeter with its GUI-based interface, provides user-friendliness and ease of use, particularly for non-technical users.
Conclusion
To sum it up, Gatling stands out as a powerful solution for load and performance testing, offering a combination of high performance, scalability, expressive scripting capabilities, and seamless integration with modern development workflows. Its Scala-based DSL provides developers the flexibility to craft intricate test scenarios while maintaining readability and expressiveness. With detailed metrics dashboards, automated testing capabilities, and support for various performance criteria, Gatling empowers teams to identify and address performance issues early in the development lifecycle, ensuring the delivery of robust and high-performing software applications.
If you have any questions, feel free to write to us at services@suyati.com.
About the Authors:
Jeena Manuel
Jeena Manuel is a Senior Engineer at Suyati Technologies, a Milestone Company. As a QA team member, she dedicates herself to delivering high-quality assignments. An avid technology enthusiast, Jeena is keen on learning about emerging technologies.
Jina Najeeb
Jina Najeeb is an Associate Quality Assurance Lead Engineer at Suyati Technologies, a Milestone Company. With a firm dedication to upholding outstanding software quality standards and a passion for exploring and learning cutting-edge test automation tools and methodologies, she plays a pivotal role in ensuring timely project delivery by collaborating closely with cross-functional teams. Jina’s commitment and expertise in the quality assurance domain establishes her as a critical asset in the field.