Gatling - Stress test tool
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Gatling is a high-performance load testing tool designed for analyzing and measuring the performance of web applications, utilizing asynchronous concurrency and I/O through a Scala-based DSL. It enables efficient simulations of user behavior with simplified scenario scripting, allowing for substantial scaling in load testing performance compared to traditional tools like JMeter. Gatling reports provide clear graphical visualizations of test results and can be easily configured and run within a SBT environment.
Gatling - Stress test tool
- 1. Gatling Load / Stress Test Tool Ruchi Agarwal Knoldus Software LLP
- 2. Introduction: - Gatling is a project that can be used as a load testing tool for analyzing and measuring the performance of a variety of services, with a focus on web applications - It is Scala-based, high performance load and stress test tool - Generates graphical reports with pretty graphs to analyze test report - Concepts behind Gatling that make it really efficient and easy-to-use: ● Asynchronous concurrency - Actor model ● Asynchronous IOs - Async HTTP Client with Netty ● Scenarios written as DSL - Scala code
- 3. Asynchronous concurrency - Actor model: - Opposed to the "One Thread = One User" model - Actor model consists in small entities named actors communicating via messages (asynchronous concurrency). - Actors are composed of: ● a Mailbox which stores all the messages received ● a Computation Unit which executes a predefined action for each received message - This allows the Gatling engine to be more efficient and reduces drastically the number of threads needed to run lots of users (~50 threads for 2000 users in Gatling's case). - To use actors, Gatling rely on the Akka actor library, which is much efficient and part of the Typesafe Stack.
- 4. Asynchronous IOs - Async HTTP Client with Netty: - To benefit fully from the actor model, Gatling also used asynchronous IOs - This allows actors to send a request to the tested application, and pass to the next message they received instead of waiting for a response. This saves a lot of time. - To send these asynchronous requests, used the Async HTTP Client library with Netty as the underlying HTTP library - Async HTTP Client, purpose is to easily execute HTTP requests and asynchronously process the HTTP responses - Netty, is an asynchronous event-driven network application framework for rapid development of maintainable high performance protocol servers & clients
- 5. Scenarios written as DSL - Scala code: - Used a Domain Specific Language (DSL) to describe scenarios in Gatling - Scenarios are written in Scala code - Example: scenario("Standard User") .exec( http("Access Github").get("http://github.com") .check(status.is(200))) .pause(2, 3) .exec( http("Search for gatling'") .get("http://github.com/search").queryParam("q", "gatling") .check(status.is(200))) .pause(2) 1. Access Github is a GET request pointing at http://github.com 2. Search for 'gatling' is a GET request pointing at http://github.com/search?q=gatling
- 6. Simulations: - A simulation is a load test. It is made of different scenarios, each representing a typical user behavior - At the last of the file contains the simulation definition, where defines the load want to inject to the server. Eg: setUp(scn.inject(rampUsers(10) over (1 seconds))).protocols(httpConf) setUp(scn.inject(atOnceUsers(10))).protocols(httpConf)
- 7. Gatling-sbt: - Sbt- version: 0.13.5 - plugins.sbt: resolvers += "Sonatype OSS Snapshots" at "https://oss.sonatype.org/content/repositories/snapshots" addSbtPlugin("io.gatling" % "sbt-plugin" % "1.0-SNAPSHOT") - build.sbt: "io.gatling.highcharts" % "gatling-charts-highcharts" % "2.0.0-SNAPSHOT" % "test" "io.gatling" % "test-framework" % "1.0-SNAPSHOT" % "test" val test = project.in(file(".")) .enablePlugins(GatlingPlugin) .settings(resolvers += "Sonatype OSS Snapshots" at "https://oss.sonatype.org/content/repositories/snapshots") .settings(libraryDependencies ++= /* Gatling dependencies */)
- 8. Run Gatling simulations: - gatling:test, runs the all gatling test files found in src/test/scala ● By default, Gatling simulations must be in src/test/scala, configurable using the scalaSource in Gatling setting ● By default, Gatling reports are written to target/gatling, configurable using the target in Gatling setting - gatling:testOnly - gatling:testQuick - gatling:startRecorder, launches the Recorder pre-configured to write recorded simulations to your tests sources folder - gatling:lastReport, opens the last generated report in your default web browser
- 9. Gatling over JMetre: - Gatling’s clean DSL API (written in Scala) as opposed to the JMeter misuse of XML. XML is not a programming language and should not be used that way. Using DSL is a great advantage since we want to treat performance tests as a code. - High performance - gatling uses asynchronous concurrency (Scala, Akka, Netty) and asynchronous IO. This approach scales a lot better than the standard stress tools using one thread per user. - Gatling by comparison has a very light profile in terms of JVM performance. - Generated more clean and understable graphical reports
- 10. References: ● http://gatling-tool.org/ ● https://github.com/excilys/gatling/wiki/Concepts ● https://github.com/gatling/gatling-sbt ● http://de.slideshare.net/slandelle/gatling-j-duchess-20121017-14830793 ● https://github.com/excilys/gatling/wiki/First-Steps-with-Gatling ● https://blog.bluepyth.fr/gatling-stress-tool-made-efficient.html