Karate Gatling

API Perf-Testing Made Simple.



  • Re-use Karate tests as performance tests executed by Gatling
  • Use Gatling (and Scala) only for defining the load-model, everything else can be in Karate
  • Karate assertion failures appear in Gatling report, along with the line-numbers that failed
  • Leverage Karate’s powerful assertion capabilities to check that server responses are as expected under load - which is much harder to do in Gatling and other performance testing tools
  • API invocation sequences that represent end-user workflows are much easier to express in Karate
  • Anything that can be written in Java can be performance tested !
  • Option to scale out by distributing a test over multiple hardware nodes or Docker containers

Demo Video

Refer to this webinar recording.


also see using a Maven Profile.


You will also need the Gatling Maven Plugin and the Scala Maven Pugin. Refer to the sample project for how to use this for a typical Karate project where feature files are in src/test/java. For convenience we recommend you keep even the Gatling simulation files in the same folder hierarchy, even though they are technically files with a *.scala extension.


To run the Gatling test:

mvn clean test-compile gatling:test

And in case you have multiple Gatling simulation files and you want to choose only one to run:

mvn clean test-compile gatling:test -Dgatling.simulationClass=mock.CatsKarateSimulation

It is worth calling out that in the sample project, we are perf-testing Karate test-doubles ! A truly self-contained demo.

Using a Maven Profile

Mixing karate-gatling into a project that already has other frameworks in test scope can cause problems such as library version conflicts or just slowing down your normal unit tests. You can use Maven profiles to keep the Gatling dependencies and executions in a separate scope. Your “primary” Maven <dependencies> section can depend on karate-core or karate-junit5 like normal.

Here is an example:


To use the gatling Maven profile and run the performance-test simulation:

mvn clean test -P gatling

Note that because the <execution> phase is defined for test, just running mvn clean test will work. If you don’t want to run Gatling tests as part of the normal Maven test lifecycle, you can avoid the <executions> section as described previously.


For those who use Gradle, this sample build.gradle provides a gatlingRun task that executes the Gatling test of the karate-netty project - which you can use as a reference. The approach is fairly simple, and does not require the use of any Gradle Gatling plugins.

Most problems when using Karate with Gradle occur when “test-resources” are not configured properly. So make sure that all your *.js and *.feature files are copied to the “resources” folder - when you build the project.


Karate introduces a different threading model for the HTTP Client, so if you have to attain a high RPS (Requests Per Second) value, you need to introduce a config-file that is normally not required for “vanilla” Gatling. We have found that by default an RPS of around 30 is suppported, but to go higher - please see Increasing Thread Pool Size.

As of now the Gatling concept of “throttle” and related syntax is not supported. Most teams don’t need this, but you can declare “pause” times in Karate, see pauseFor().


Once you have your performance tests working, you may want to tune the logging config. Note that there are options to reduce or completely suppress the console logging.

Also note that the logback-test.xml in the examples project uses <immediateFlush>false</immediateFlush>.


Let’s look at an example:

package mock

import com.intuit.karate.gatling.PreDef._
import io.gatling.core.Predef._
import scala.concurrent.duration._

class CatsSimulation extends Simulation {

  val protocol = karateProtocol(
    "/cats/{id}" -> Nil,
    "/cats" -> pauseFor("get" -> 15, "post" -> 25)

  protocol.nameResolver = (req, ctx) => req.getHeader("karate-name")

  val create = scenario("create").exec(karateFeature("classpath:mock/cats-create.feature"))
  val delete = scenario("delete").exec(karateFeature("classpath:mock/cats-delete.feature@name=delete"))

    create.inject(rampUsers(10) during (5 seconds)).protocols(protocol),
    delete.inject(rampUsers(5) during (5 seconds)).protocols(protocol)



This piece is needed because Karate is responsible for making HTTP requests while Gatling is only measuring the timings and managing threads. In order for HTTP requests to “aggregate” correctly in the Gatling report, you need to declare the URL patterns involved in your test. For example, in the example above, the {id} would be random - and Gatling would by default report each one as a different request.


This is optional, and is useful for teams that need more control over the “segregation” of requests described above. This is especially needed for GraphQL and SOAP - where the URI and request-paths remain constant and only the payload changes. You can supply a function that takes 2 Karate core-objects as arguments. The first argument HttpRequestBuilder is all you would typically need, and gives you ways to access the HTTP request such as getUrlAndPath(), getHeader(name) and getParameter(name). The example below over-rides the “request name” with the value of a custom-header:

 protocol.nameResolver = (req, ctx) => req.getHeader("karate-name")

For convenience, if the nameResolver returns null, Karate will fall-back to the default strategy. And HttpRequestBuilder.getHeader(name) happens to return null if the header does not exist.

So any HTTP request where a karate-name header is present can be “collected” in the Gatling report under a different name. This is how it could look like in a Karate feature (example):

Given path id
And header karate-name = 'cats-get-404'
When method get


You can also set pause times (in milliseconds) per URL pattern and HTTP method (get, post etc.) if needed (see limitations). If non-zero, this pause will be applied before the invocation of the matching HTTP request.

We recommend you set that to 0 for everything unless you really need to artifically limit the requests per second. Note how you can use Nil to default to 0 for all HTTP methods for a URL pattern. Make sure you wire up the protocol in the Gatling setUp. If you use a nameResolver, even those names can be used in the pauseFor lookup (instead of a URL pattern).

Also see how to pause() without blocking threads if you really need to do it within a Karate feature, for e.g. to simulate user “think time” - in more detail.


Which feature to call and what tags to use are driven by the karateFeature() syntax as described in later sections. Most of the time this would be sufficient. But in cases where you have custom configuration, you will need a way to replicate what you may be doing using the Runner.Builder methods. Most of the time this would be setting the karate.env.

To enable this, a Runner.Builder instance is made available on the protocol in a variable called runner and all the builder methods such as karateEnv(), configDir() and systemProperty() can be configured.

Note that tags are typically set by the use of karateFeature(). If you call tags() on the runner instance, they will be inherited by all karateFeature() calls, where you can add more tags, but you can’t remove any that were set on the runner.

Here is an example of setting the karate.env to perf which means that karate-config-perf.js will be used in addition to karate-config.js for bootstrapping the config for each Scenario.


But the alternate mechanism of setting a Java system-property karate.env via the command-line is always an option, so using the runner can be avoided in most cases.


This declares a whole Karate feature as a “flow”. Note how you can have concurrent flows in the same Gatling simulation.

Tag Selector

In the code above, note how a single Scenario (or multiple) can be “chosen” by appending the tag name to the Feature path. This allows you to re-use only selected tests out of your existing functional or regression test suites for composing a performance test-suite.

If multiple Scenario-s have the tag on them, they will all be executed. The order of execution will be the order in which they appear in the Feature.

The tag does not need to be in the @key=value form and you can use the plain “@foo” form if you want to. But using the pattern @name=someName is arguably more readable when it comes to giving multiple Scenario-s meaningful names.

Ignore Tags

The above Tag Selector approach is designed for simple cases where you have to pick and run only one Scenario out of many. Sometimes you will need the full flexibility of tag combinations and “ignore”. The karateFeature() method takes an optional (vararg) set of Strings after the first feature-path argument. For example you can do this:

  val delete = scenario("delete").exec(karateFeature("classpath:mock/cats-delete.feature", "@name=delete"))

To exclude (note that @ignore is skipped by default):

  val delete = scenario("delete").exec(karateFeature("classpath:mock/cats-delete.feature", "~@skipme"))

To run scenarios tagged foo OR bar

  val delete = scenario("delete").exec(karateFeature("classpath:mock/cats-delete.feature", "@foo,@bar"))

And to run scenarios tagged foo AND bar

  val delete = scenario("delete").exec(karateFeature("classpath:mock/cats-delete.feature", "@foo", "@bar"))

Karate Variables

On the Scala side, after a scenario involving a karateFeature() completes, the Karate variables that were part of the feature will be added to the Gatling session.

This is rarely needed - but useful when you want to pass data across feature files or do some assertions on the Gatling side. Here is an example:

val create = scenario("create").exec(karateFeature("classpath:mock/cats-create.feature")).exec(session => {
    println("*** id in gatling: " + session("id").as[String])
    println("*** session status in gatling: " + session.status)

Here above, the variable id that was defined (using def) in the Karate feature - is being retrieved on the Gatling side using the Scala API.

On the Karate side, after a scenario involving a karateFeature() completes, the variables are passed onto any other karateFeature() invocations as shown in this example. Note how the Gatling scenario called read which uses a Karate Scenario (cats-chained.feature@name=read) depends on the id variable mentioned above.

Also see chaining - where Karate variables created in one Scenario can flow into others in advanced Gatling set-ups.

Gatling Session

The Gatling session attributes and userId would be available in a Karate variable under the name-space __gatling. So you can refer to the user-id for the thread as follows:

* print 'gatling userId:', __gatling.userId

This is useful as an alternative to using a random UUID where you want to create unique users, and makes it easy to co-relate values to your test-run in some situations.

For advanced Gatling simulations - the state of Karate variables at the end of a Feature execution will be automatically injected into the Gatling session and available to other karateFeature() executions within the same Gatling “scenario” (note that the terminology “scenario” here is specific to Gatling). See Chaining for more.

So there are two ways to pass data from Gatling to a Karate Feature. The first is the use of the __gatling “special” variable. But when you want to minimize conditional logic in your Karate scripts, you can use karateSet() to set-up variables directly. So prefer the second approach, as the goal should be to re-use Karate functional-tests as performance-tests without making any changes to the Feature files.


Because of the above mechanism which allows Karate to “see” Gatling session data, you can use feeders effectively. For example:

val feeder = Iterator.continually(Map("catName" -> MockUtils.getNextCatName, "someKey" -> "someValue"))

val create = scenario("create").feed(feeder).exec(karateFeature("classpath:mock/cats-create.feature"))

There is some Java code behind the scenes that takes care of dispensing a new catName every time getNextCatName() is invoked:

private static final AtomicInteger counter = new AtomicInteger();

public static String getNextCatName() {
    return catNames.get(counter.getAndIncrement() % catNames.size());

The List of catNames above is actually initialized (only once) by a Java API call to another Karate feature (see below). If you use true instead of false, the karate-config.js will be honored. You could also pass custom config via the second Map argument to Runner.runFeature(). This is just to demonstrate some possibilities, and you can use any combination of Java or Scala (even without Karate) - to set up feeders.

List<String> catNames = (List) Runner.runFeature("classpath:mock/feeder.feature", null, false).get("names");

And now in the feature file you can do this:

* print __gatling.catName


Normally we recommend that Scenario-s should be self-contained and that you should model any “flows” of calls made to API-s or code within a single Scenario itself or by using call.

But for advanced load-modelling, you may want to compose Scenario-s along with Gatling feeders and have variables “flow” from one Scenario into another. This way you will be able to use all of Gatling’s features such as grouping, assertions and control over each sub-execution.

So the rule is that any variable created within the exec() of a karateFeature() would return back to the Gatling session, and be automatically injected into any subsequent Karate feature within the same Gatling scenario.

And if you have a need to create new variables on the “Gatling side” and inject them into Karate features, you can use karateSet(), explained below.


This example shows how you can use the karateSet() Gatling action to pipe data from the Gatling session (typically a feeder) into variables that Karate can access.


A common need is to run a routine, typically a sign-in and setting up of an Authorization header only once - for all Feature invocations. Keep in mind that when you use Gatling, what used to be a single Feature in “normal” Karate will now be multiplied by the number of users you define. But callonce is designed so that it will be “once for everything” when Karate is running in Gatling “perf” mode.

You can also use karate.callSingle() in these situations. Ideally you should use Feeders since callonce and karate.callSingle() will lock all threads - which may not play very well with Gatling. But when you want to quickly re-use existing Karate tests as performance tests, this will work nicely.

Detecting Gatling At Run Time

You would typically want your feature file to be usable when not being run via Gatling, so you can use this pattern, since karate.get() has an optional second argument to use as a “default” value if the variable does not exist or is null.

* def name = karate.get('__gatling.catName', 'Billie')

For a full, working, stand-alone example, refer to the karate-gatling-demo.

Think Time

Gatling provides a way to pause() between HTTP requests, to simulate user “think time”. But when you have all your requests in a Karate feature file, this can be difficult to simulate - and you may think that adding java.lang.Thread.sleep() here and there will do the trick. But no, what a Thread.sleep() will do is block threads - which is a very bad thing in a load simulation. This will get in the way of Gatling, which is specialized to generate load in a non-blocking fashion.

The karate.pause() function is specially designed to use the Gatling session if applicable - or do nothing.

* karate.pause(5000)

You normally don’t want to slow down your functional tests. But you can use the configure pauseIfNotPerf flag (default false) to have karate.pause() work even in “normal” mode.

configure localAddress

Gatling has a way to bind the HTTP “protocol” to use a specific “local address”, which is useful when you want to use an IP range to avoid triggering rate-limiting on the server under test etc. But since Karate makes the HTTP requests, you can use the configure keyword, and this can actually be done any time within a Karate script or *.feature file. Note that the IP address needs to be physically assigned to the local machine.

* configure localAddress = ''

One easy way to achieve a “round-robin” effect is to write a simple Java static method that will return a random IP out of a pool. See feeders for example code. Note that you can “conditionally” perform a configure by using the JavaScript API on the karate object:

* if (__gatling) karate.configure('localAddress', MyUtil.getIp())

Since you can use Java code, any kind of logic or strategy should be possible, and you can refer to config or variables if needed.


You can even include any custom code you write in Java into a performance test, complete with full Gatling reporting.

What this means is that you can easily script performance tests for database-access, gRPC, proprietary non-HTTP protocols or pretty much anything, really.

Just use a single Karate interface called PerfContext. Here is an example:

public static Map<String, Object> myRpc(Map<String, Object> map, PerfContext context) {
    long startTime = System.currentTimeMillis();
    // this is just an example, you can put any kind of code here
    int sleepTime = (Integer) map.get("sleep");
    try {
    } catch (Exception e) {
        throw new RuntimeException(e);
    long endTime = System.currentTimeMillis();
    // and here is where you send the performance data to the reporting engine
    context.capturePerfEvent("myRpc-" + sleepTime, startTime, endTime);
    return Collections.singletonMap("success", true);


The PerfContext.capturePerfEvent() method takes these arguments:

  • eventName - string, which will show up in the Gatling report
  • startTime - long
  • endTime - long


To get a reference to the current PerfContext, just pass the built-in karate JavaScript object from the “Karate side” to the “Java side”. For example:

  * def Utils = Java.type('mock.MockUtils')

Scenario: fifty
  * def payload = { sleep: 50 }
  * def response = Utils.myRpc(payload, karate)
  * match response == { success: true }

The karate object happens to implement the PerfContext interface and keeps your code simple. Note how the myRpc method has been implemented to accept a Map (auto-converted from JSON) and the PerfContext as arguments.

Like the built-in HTTP support, any test failures are automatically linked to the previous “perf event” captured.

Increasing Thread Pool Size

The defaults should suffice most of the time, but if you see odd behavior such as freezing of a test, you can change the settings for the underlying Akka engine. A typical situation is when one of your responses takes a very long time to respond (30-60 seconds) and the system is stuck waiting for threads to be freed.

Add a file called gatling-akka.conf to the root of the classpath (typically src/test/resources). Here is an example:

akka {
  actor {
    default-dispatcher {
      type = Dispatcher
      executor = "thread-pool-executor"
      thread-pool-executor {
        fixed-pool-size = 100
      throughput = 1

So now the system can go up to 100 threads waiting for responses. You can experiment with more settings as described here. Of course a lot will depend on the compute resources (CPU, RAM) available on the machine on which you are running a test.

Distributed Testing

See wiki: Distributed Testing