What Is API Load Testing?

API Testing,

Open Source Automation

Load testing is an important practice for APIs and applications that have lots of users. In this blog, you'll get an overview of API load testing and how to do it.

What Is API Load Testing?
Which Tool Is Best For API Load Testing?
Configuring an API Load Test
API Load Test Creation

What Is API Load Testing?

API load testing refers to checking your application backend to see if it can handle multiple, simultaneous user requests to your server, giving teams a better understanding of their API's overall performance. Developers can choose between many popular open-source tools for this process, including Gatling.

This blog post will explain how to execute an API load test with Gatling for a REST API endpoint that uses the GET method.

Which Tool Is Best For API Load Testing?

Both Gatling and JMeter are very powerful load testing tools. But one of the advantages Gatling offers Java developers, is the Scala development language for test creation, which is nicer to use in my opinion.

If you want to practice yourself, you can find the complete source with all the configs and codes on GitHub. This article will show code fragments.

The environment we will be using is Java and Spring Boot API (an open source API framework), together with the Gradle build tool, as usual in my articles. Don’t forget to install Gatling. Let’s get started.

Configuring an API Load Test

1. First, like in any library, we need to add the dependencies to our build.gradle file.

testCompile group: 'io.gatling.highcharts', name: 'gatling-charts-highcharts', version: '2.2.5'

2. As mentioned before, Gatling uses Scala for test configuration. Therefore, we have to add plugin support to build.gradle. Run the following command:

apply plugin: 'scala'

3. Next we need to add a task for Gradle, so that we can execute our load tests from the command line. To do that, add the following code to the build.gradle file:

task loadTest(type: JavaExec) {
   dependsOn testClasses
   description = "Load Test With Gatling"
   group = "Load Test"
   classpath = sourceSets.test.runtimeClasspath
   jvmArgs = [
        "-Dgatling.core.directory.binaries=${sourceSets.test.output.classesDir.toString()}"
   ]
   main = "io.gatling.app.Gatling"
   args = [
           "--simulation", "BlazeMeterGatlingTest",
           "--results-folder", "${buildDir}/gatling-results",
           "--binaries-folder", sourceSets.test.output.classesDir.toString(),
           "--bodies-folder", sourceSets.test.resources.srcDirs.toList().first().toString() + "/gatling/bodies",
   ]
}

This is the default way for creating custom tasks for Gradle. It is a JavaExec type task, with test class dependencies. This means it has nothing to do with main classes. The most important part here is the configuration, where we let Gatling know which simulation (basically that’s a test) to execute, where to store reports and where to get body files (body files are for requests that require having a body).

API Load Test Creation

4. That’s all the basic configuration we have to do for executing a Gatling test. Now let’s create the actual test (simulation). For that, create a BlazeMeterGatlingTest file in src/test/scala folder with the following content:

import io.gatling.core.Predef._
import io.gatling.core.structure.ScenarioBuilder
import io.gatling.http.Predef._
import io.gatling.http.protocol.HttpProtocolBuilder

class BlazeMeterGatlingTest extends Simulation {
   private val baseUrl = "http://localhost:16666"
   private val basicAuthHeader = "Basic YmxhemU6UTF3MmUzcjQ="
   private val authPass = "Q1w2e3r4"
   private val uri = "http://localhost:16666/api/1.0/arrival/all"
   private val contentType = "application/json"
   private val endpoint = "/api/1.0/arrival/all"
   private val authUser= "blaze"
   private val requestCount = 1000


 val httpProtocol: HttpProtocolBuilder = http
   .baseURL(baseUrl)
   .inferHtmlResources()
   .acceptHeader("*/*")
   .authorizationHeader(basicAuthHeader)
   .contentTypeHeader(contentType)
   .userAgentHeader("curl/7.54.0")

 val headers_0 = Map("Expect" -> "100-continue")

 val scn: ScenarioBuilder = scenario("RecordedSimulation")
   .exec(http("request_0")
     .get(endpoint)
     .headers(headers_0)
     .basicAuth(authUser, authPass)
     .check(status.is(200)))

 setUp(scn.inject(atOnceUsers(requestCount))).protocols(httpProtocol)
}

This our basic load test, which will do following:

It will call http://localhost:16666/api/1.0/arrival/all 1000 times and make sure that we have status 200, meaning that it is fine. You can change this to your own API config and the number of requests, i.e the load.

5. Compared to JMeter, Gatling has an advantage in reporting. After each execution you can see the path to the latest generated report in the command line. In my case, it is following part of the command line output:

Reports generated in 0s.
Please open the following file: /Users/avagyang/Projects/blazedemo/build/gatling-results/blazemetergatlingtest-1503222349931/index.html

BUILD SUCCESSFUL

And when I point my browser to that path I can see the report shown in the pictures below. In the reports we have lots of useful information, the most useful part is on the first page directly where we can see request count (1000) , passed requests count (OK) and failed request count (KO).

This is the minimum that we need, but for our API improvements, we can take a deeper look into the details. For example, in case of errors we will have this: