Gatling implemented a fully new architecture for a performance testing tool, in order to be more resource efficient. Gatling calls 20 requests per second, while our service can process 10 requests every 1 second so we accumulate 10 requests in the Tomcat requests queue every second. I am not sure why this would be the case. The original request was redirected and Gatling followed the redirection and sent a second request. That test lasted for 6 min and generated 1.5 billion requests in total. Miscellaneous Private bytes, page faults, disk queue length, maximum active sessions, network bytes total per second. //Magnus. We ran this test for the duration of 3 hours. time. Also this had to be done by invoking a few of APIs during the load to simulate varying requests per second (For example: One api is invoked every 5 seconds while another is done every 10 seconds). CPU interrupts per second: The average number of hardware interrupts that a process receives. The problem you encounter is not the number of requests per second being unreachable by Gatling, it is the number of scenarios started in one second (via the ramp keyword). With instantaneous values, we can find out what time during the test, there were more failures and vice versa. (iv) Responses per second: Similar to the requests per second, this represents the number of responses that were received per second. It is important to put holdFor() method, otherwise, Gatling goes to unlimited requests per second and can crash the server. format. Dealing with a component that can actually handle 200 requests per second is noteworthy if production actually requires 10,000 requests per second. where beforeRequest is measured before the request is passed to AHC (the async http client atop netty). I am not sure why this would be the case. I have very heterogeneous use cases. Gatling is a performance scala library that facilitates running performance tests on your web services/applications. From what I see in the docs, constantUsersPerSec accepts double but it's rounded while reachRps in throttling deals only with seconds. In this case, they are more interested in the rate of requests per second than the number of users. In our test, the final reports produced by Gatling looks somewhat like this: Picture 1: Gatling reports – endpoint list Gatling default reports also produce a few very useful charts, like the ones below: Picture 2: Gatling reports – overall requests/responses per second The above examples are “real-life” results from iFood platform load tests. Each of those requests must contain data for the game (game ID, game name etc. By default it is not possible without creating a custom Gatling Action. When we only get 14 requests per minute; Gatling reports that the max response time was 1 second. The number of requests per second is 236 – over twice the result we saw with non-persistentsdf connections. The report printed to the terminal is not bad, but where Gatling really shines, in my opinion, is the graphical reports it produces. Latency against Global RPS – distribution of current request’s latency (process time) related to total request per second of the simulation. Since this feature was proposed clj-gatling has got quite a lot of other new features. Having a central data feeder was a problem. This will launch 10 virtual users that will do 20 requests each, to the specified URL. Components Recorder. We will use a custom feeder to create that data. It's supposed to issue some requests occasionally, e.g. The number of requests per second. Number of requests per second; There are also the charts that do not aggregate information for all requests. max: This performs the assertions on the maximum time of the metric. Final Conclusion. gatling. More is possible, but we didn't push further. slightly amended to make the maths easier... the ^Z is for 5 seconds out of the 10 seconds test Selecting the metrics. Under the gatling-results directory, go to simulation folder and open ‘index.html’ file. Create a new Scala class in the simulations folder, give it a name of CustomFeeder. At the end of your test, Gatling automatically generates an exhaustive, dynamic and colorful report. Source: I'm one of the guys behind k6. Gatling data in simulation.log file. It generates various types of reports i.e active users along the simulation, response time distribution, response time percentiles over time(OK), number of requests per second & number of responses per second. Magnus Jensen: 9/22/15 5:41 AM: I have tweaked alle the parameters in gatling.conf that comes with default 60.000 ms to 90.000 but still I get timeout after 60.000. Latency against Global RPS – distribution of current request’s latency (process time) related to total request per second of the simulation. is it possible to get Gatling to wait more than 60 seconds for a request? The total number of request that we are sending is 5*4*60 = 1200 requests. Conclusion. So if new :request-per-second option is used there should be a similar :requests-per-second-distribution option. DateTimeFormatter import io. Cheers, This comment has been minimized. Showing 1-13 of 13 messages . Gatling comes up with a recorder to … The most RPS I achieved with k6 was 4 791 928 (~4.8 million requests per second). Sign in to view. More than 100 requests per second (3) “Less than 1% failed requests” was in fact the only criterion that properly represents a system under load between the three. is it possible to get Gatling to wait more than 60 seconds for a request? What do you try to do exactly? How to stress Kafka with thousands of request per second using a Gatling Scala codebase. LocalDate import java. It makes it possible to simulate a high number of requests per second with a single machine. So here I’m trying to maintain 5 users constantly per second for the duration of 4 minutes. Add in the following code: package simulations import java. Gatling API provides a wide range of functionalities to support this … ), in the form of either JSON or XML. From simple performance regression tests that are executed from a Jenkins node to eventual large-ish stress tests that run with over 100K requests per second and > 100 load generators. min: This performs the assertions on the minimum of the metric. Gatling gives a complete analysis of each and every request with graphical representation (charts) in HTML files. We could have a second API, along the current user oriented one, that would let the users configure user launch rate. It follows our second Gatling Simulation scripts parameterization article. There’s nothing much that can be inferred from this graph but is useful to find out in case there are failure responses. So it looks like Gatling hangs for a long time between making requests. requestsPerSec: This will target the rate of request per second. Let's be clear that the real number of requests per sec would depend on the time it takes for a user to complete the scenario. We will continue to load test a fake e-commerce, and so we are going to improve our Virtual User to make it browse the store in a more humanly way. There are different parameters that we call our metrics and are relevant to response time only. The quick command supports some other flags to control how the load will be distributed, like rate, which can be used to define the number of new users per second, or the duration, which defines a fixed time for the test. Gatling data in simulation.log file. We ramped up from 1 to 25 requests per second. Gatling is a powerful tool: with only a few machines, you can simulate hundreds of thousands of requests per second on your web application and get high-precision metrics. good and not so good news - the good news is that under moderate load (ie. I need to write Gatling scenario that will mimic real users interaction. It turns out that Add comment was one that was very slow (in my try): Response Time Percentiles over Time (OK) for “Add comment” requests. They allow us to view details of a specific request. 10 per hour per user (total 20 users). Most problems only arise with higher loads. So they should be adapted too. Latency: Amount of time between a user request and the start of a system’s response. What it allows you to do is to simulate thousands of requests per second on your web application and get a complete report of the execution, with just a few load generators. On the `k6 cloud` side, we have executed 500k+ VUs. Set the number of req/s to a known number (to have a given charge in terms of req/s) or something else? Performance figures are only valuable if they are compared to requirements of the real world. Response time 50 th percentile is at 506 milliseconds. By default Gatling works with HTTP Rest requests. More details on simulation setup can be found on Gatling Simulation setup page. One of those features is :concurrency-distribution. Gatling Reports are well described in the graphical & table format as shown below. Graph the requests per second and the responses per second, including the comparison of the quantity of active users. We ran the same SSL simulation with JMeter with 10 concurrent users and got 10000 requests per minute, so we are pretty confident that Apache isn't the bottleneck. Selecting the metric. Hence, they are portable and can be viewed on a web browser of any device. So, not way to have less than 1 rps. 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