Gatling data in simulation.log file. I am not sure why this would be the case. 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. requestsPerSec: This will target the rate of request per second. The problem I have then is that I have one request per user, but still the requests per second falls dramatically when max times significantly increase. Add in the following code: package simulations import java. From what I see in the docs, constantUsersPerSec accepts double but it's rounded while reachRps in throttling deals only with seconds. Graph the requests per second and the responses per second, including the comparison of the quantity of active users. Showing 1-13 of 13 messages . At the end of your test, Gatling automatically generates an exhaustive, dynamic and colorful report. 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. The number of requests per second is 236 – over twice the result we saw with non-persistentsdf connections. Load Simulation Graphical Report. Number of requests per second; There are also the charts that do not aggregate information for all requests. So it looks like Gatling hangs for a long time between making requests. This will launch 10 virtual users that will do 20 requests each, to the specified URL. With instantaneous values, we can find out what time during the test, there were more failures and vice versa. 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. 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. Gatling implemented a fully new architecture for a performance testing tool, in order to be more resource efficient. That test lasted for 6 min and generated 1.5 billion requests in total. Gatling comes up with a recorder to … gatling. How to stress Kafka with thousands of request per second using a Gatling Scala codebase. good and not so good news - the good news is that under moderate load (ie. The number of responses per second. 10 per hour per user (total 20 users). 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. Sign in to view. So they should be adapted too. 50 requests per second) on my MBP the test roughly passes. When we only get 14 requests per minute; Gatling reports that the max response time was 1 second. Each of those requests must contain data for the game (game ID, game name etc. 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). Gatling Reports are well described in the graphical & table format as shown below. I hope all of you readers find this article useful! Under the gatling-results directory, go to simulation folder and open ‘index.html’ file. We could have a second API, along the current user oriented one, that would let the users configure user launch rate. So, not way to have less than 1 rps. Performance figures are only valuable if they are compared to requirements of the real world. 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. 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. Latency against Global RPS – distribution of current request’s latency (process time) related to total request per second of the simulation. Cheers, This comment has been minimized. So here I’m trying to maintain 5 users constantly per second for the duration of 4 minutes. CPU interrupts per second: The average number of hardware interrupts that a process receives. ), in the form of either JSON or XML. There’s nothing much that can be inferred from this graph but is useful to find out in case there are failure responses. format. is it possible to get Gatling to wait more than 60 seconds for a request? Set the number of req/s to a known number (to have a given charge in terms of req/s) or something else? Gatling is a performance scala library that facilitates running performance tests on your web services/applications. Keeping Gatling code maintainable and reusable is a good practice to create complex performance scenarios. The total number of request that we are sending is 5*4*60 = 1200 requests. It's supposed to issue some requests occasionally, e.g. Conclusion. We ran this test for the duration of 3 hours. DateTimeFormatter import io. It makes it possible to simulate a high number of requests per second with a single machine. Most problems only arise with higher loads. I have very heterogeneous use cases. I need to write Gatling scenario that will mimic real users interaction. 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. By default Gatling works with HTTP Rest requests. min: This performs the assertions on the minimum of the metric. 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. is it possible to get Gatling to wait more than 60 seconds for a request? Miscellaneous Private bytes, page faults, disk queue length, maximum active sessions, network bytes total per second. max: This performs the assertions on the maximum time of the metric. So if new :request-per-second option is used there should be a similar :requests-per-second-distribution option. Components Recorder. In this case, they are more interested in the rate of requests per second than the number of users. Selecting the metric. LocalDate import java. 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. slightly amended to make the maths easier... the ^Z is for 5 seconds out of the 10 seconds test On the `k6 cloud` side, we have executed 500k+ VUs. I am not sure why this would be the case. It is important to put holdFor() method, otherwise, Gatling goes to unlimited requests per second and can crash the server. Since this feature was proposed clj-gatling has got quite a lot of other new features. Latency against Global RPS – distribution of current request’s latency (process time) related to total request per second of the simulation. Having a central data feeder was a problem. The most RPS I achieved with k6 was 4 791 928 (~4.8 million requests per second). Source: I'm one of the guys behind k6. where beforeRequest is measured before the request is passed to AHC (the async http client atop netty). By default it is not possible without creating a custom Gatling Action. Hence, they are portable and can be viewed on a web browser of any device. 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. The number of requests per second. 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. Selecting the metrics. //Magnus. More details on simulation setup can be found on Gatling Simulation setup page. Response time 50 th percentile is at 506 milliseconds. Gatling API provides a wide range of functionalities to support this … We will use a custom feeder to create that data. More is possible, but we didn't push further. time. Final Conclusion. One of those features is :concurrency-distribution. Dealing with a component that can actually handle 200 requests per second is noteworthy if production actually requires 10,000 requests per second. 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). Latency: Amount of time between a user request and the start of a system’s response. 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. Gatling gives a complete analysis of each and every request with graphical representation (charts) in HTML files. (iv) Responses per second: Similar to the requests per second, this represents the number of responses that were received per second. The original request was redirected and Gatling followed the redirection and sent a second request. We ramped up from 1 to 25 requests per second. Gatling data in simulation.log file. They allow us to view details of a specific request. There are different parameters that we call our metrics and are relevant to response time only. What do you try to do exactly? Create a new Scala class in the simulations folder, give it a name of CustomFeeder. time. It follows our second Gatling Simulation scripts parameterization article. We ran this test for the duration of 3 hours. Let's do it. The report printed to the terminal is not bad, but where Gatling really shines, in my opinion, is the graphical reports it produces. Practice to create that data, game name etc of request per second can! The end of your test, there were more failures and vice versa a new! Graph but is useful to find out in case there are also the charts that do aggregate! 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