Distributed Testing

Distributed Testing is a testing approach where test execution is distributed across multiple machines or environments, often geographically dispersed, to simulate real-world user load and network conditions.

Detailed explanation

Distributed testing is a powerful technique used to evaluate the performance, scalability, and reliability of software applications, particularly those designed to operate in distributed environments. Unlike traditional testing methods that rely on a single machine or a limited set of resources, distributed testing leverages a network of computers to simulate a large number of concurrent users or complex network topologies. This approach is crucial for identifying bottlenecks, performance degradation, and other issues that may not be apparent in smaller-scale testing scenarios.

One of the primary motivations for adopting distributed testing is to accurately replicate real-world user behavior. Modern applications, especially web-based and cloud-native systems, are often accessed by users from diverse geographical locations with varying network conditions. By distributing the test load across multiple machines, testers can simulate this variability and gain a more realistic understanding of how the application will perform under real-world conditions. This is particularly important for applications that rely on geographically distributed data centers or content delivery networks (CDNs).

Another key benefit of distributed testing is its ability to scale the test load beyond the limitations of a single machine. When testing applications that are expected to handle a large number of concurrent users, it may be impossible to generate sufficient load from a single machine without overwhelming its resources. Distributed testing allows testers to distribute the load across multiple machines, effectively multiplying the available resources and enabling them to simulate a much larger user base.

Several tools and frameworks are available to support distributed testing. Some popular options include:

  • Apache JMeter: A widely used open-source load testing tool that can be configured to run in distributed mode. JMeter allows testers to define test plans that specify the number of users, the types of requests to be sent, and the duration of the test. These test plans can then be distributed across multiple JMeter instances, each running on a separate machine.

    <hashTree>
  <HTTPSamplerProxy guiclass="HttpTestSampleGui" testclass="HTTPSamplerProxy" testname="HTTP Request" enabled="true">
    <elementProp name="HTTPsampler.Arguments" elementType="Arguments" guiclass="HTTPArgumentsPanel" testclass="Arguments" testname="User Defined Variables" enabled="true">
      <collectionProp name="Arguments.arguments"/>
    </elementProp>
    <stringProp name="HTTPSampler.domain">example.com</stringProp>
    <stringProp name="HTTPSampler.port">80</stringProp>
    <stringProp name="HTTPSampler.path">/</stringProp>
    <stringProp name="HTTPSampler.method">GET</stringProp>
    <boolProp name="HTTPSampler.follow_redirects">true</boolProp>
    <boolProp name="HTTPSampler.auto_redirects">false</boolProp>
    <boolProp name="HTTPSampler.use_keepalive">true</boolProp>
    <boolProp name="HTTPSampler.DO_MULTIPART_POST">false</boolProp>
    <boolProp name="HTTPSampler.BROWSER_COMPATIBLE_MULTIPART">false</boolProp>
    <boolProp name="HTTPSampler.image_parser">false</boolProp>
    <boolProp name="HTTPSampler.concurrentDwn">false</boolProp>
    <stringProp name="HTTPSampler.concurrentPool">6</stringProp>
    <boolProp name="HTTPSampler.md5">false</boolProp>
    <intProp name="HTTPSampler.connect_timeout">0</intProp>
    <stringProp name="HTTPSampler.embedded_url_re"></stringProp>
    <intProp name="HTTPSampler.response_timeout">0</intProp>
  </HTTPSamplerProxy>
  <hashTree/>
</hashTree>

  • Gatling: Another open-source load testing tool that is known for its high performance and scalability. Gatling uses a Scala-based DSL to define test scenarios, which can then be executed in a distributed manner. Gatling is particularly well-suited for testing applications that use asynchronous communication protocols, such as WebSockets and Server-Sent Events.

    import io.gatling.core.Predef._
import io.gatling.http.Predef._
import scala.concurrent.duration._
 
class BasicSimulation extends Simulation {
 
  val httpProtocol = http
    .baseUrl("http://example.com")
    .acceptHeader("text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8")
    .doNotTrackHeader("1")
    .acceptLanguageHeader("en-US,en;q=0.5")
    .acceptEncodingHeader("gzip, deflate")
    .userAgentHeader("Mozilla/5.0 (Windows NT 5.1; rv:31.0) Gecko/20100101 Firefox/31.0")
 
  val scn = scenario("Basic Simulation")
    .exec(http("request_1")
      .get("/"))
 
  setUp(scn.inject(atOnceUsers(1))).protocols(httpProtocol)
}

  • Locust: An open-source load testing tool that allows testers to define user behavior using Python code. Locust is designed to be easy to use and highly scalable, making it a good choice for testing applications of all sizes. Locust also provides a web-based interface for monitoring the test execution and analyzing the results.

    from locust import HttpUser, task, between
 
class WebsiteUser(HttpUser):
    wait_time = between(5, 15)
 
    @task
    def index(self):
        self.client.get("/")
 
    @task
    def about(self):
        self.client.get("/about/")

  • Cloud-based testing platforms: Several cloud-based platforms, such as BlazeMeter and LoadView, provide distributed testing capabilities as a service. These platforms allow testers to easily provision and manage a network of test machines in the cloud, without having to worry about the underlying infrastructure.

When implementing distributed testing, it is important to consider several best practices:

  • Properly configure the test environment: Ensure that all test machines are properly configured and synchronized. This includes installing the necessary software, configuring network settings, and synchronizing clocks.
  • Monitor the test execution: Monitor the performance of the test machines and the application under test. This will help identify any bottlenecks or issues that may arise during the test.
  • Analyze the results: Carefully analyze the test results to identify areas for improvement. This includes identifying performance bottlenecks, scalability limitations, and other issues that may impact the user experience.
  • Use realistic test data: Use realistic test data that accurately reflects the types of data that will be used in the production environment. This will help ensure that the test results are representative of real-world performance.
  • Automate the testing process: Automate the testing process as much as possible. This will help reduce the risk of human error and ensure that the tests are executed consistently.

In conclusion, distributed testing is an essential technique for evaluating the performance, scalability, and reliability of modern software applications. By leveraging a network of computers to simulate real-world user load and network conditions, testers can gain a more realistic understanding of how the application will perform in production and identify potential issues before they impact users. By following best practices and utilizing appropriate tools, organizations can effectively implement distributed testing and ensure the quality of their software applications.

Further reading

Related Terms

A/B Testing

Acceptance Testing

Accessibility Tester