locust VS gatling.io

Finally, let's compare the response time of the requests. For that, we will use Locust , an open source load testing tool. The tests will run for 5 minutes, and will increase 4 requests per second every second until they reach 1000 requests per second. - Source: dev.to / about 1 month ago

Locust: Another open-source tool, Locust is particularly flexible due to its support for Python scripts. It can conduct load tests across multiple machines, making it possible to simulate millions of users simultaneously. An exceptional feature of Locust is its web-based UI, which allows real-time tracking of performance metrics during test execution. - Source: dev.to / about 1 month ago

Locust is a perfect tool to use on such occasion:. - Source: dev.to / about 2 months ago

So, in theory, we can handle 300 requests per minute on a single server which was the assumption we started with. After this, I decided to play with this configuration and see what we could achieve. But, to go ahead I need a system to measure the metrics of our load testing. So I quickly set up Locust on my system. Locust is an open-source easy to setup load-testing framework. - Source: dev.to / about 2 months ago

The OpenTelemetry Demo is composed of microservices written in different programming languages that talk to each other over gRPC and HTTP; and a load generator which uses Locust to fake user traffic. - Source: dev.to / 5 months ago

Gatling: An open-source load and performance testing tool primarily designed for web applications, Gatling utilizes a simple domain-specific language (DSL) for creating and maintaining test scripts. It supports HTTP/2 and allows recording and generation of scenarios directly from a browser. The tool also provides detailed performance reports that are easy to analyze. - Source: dev.to / about 1 month ago

Performance and load testing are essential parts of GraphQL API testing. It ensures APIs can handle expected traffic volumes and respond within acceptable timeframes. You can use tools like Apache JMeter or Gatling to generate realistic loads and evaluate the API's performance under different scenarios. Techniques like batched queries and caching can help mitigate this issue. - Source: dev.to / 10 months ago

New to the .NET community and trying to learn! I have used tools such as Apache JMeter (Java), gatling.io (Java) and Locust (Python) that are decent full featured web perf frameworks. Typically these integrate well with your code, and can be run as part of your unit/integration tests and produce offline reports. Source: about 1 year ago

Gatling , this is what we tested concurrency with. Setting up might take a while depending on your exp. But the tool is solid. Source: about 1 year ago

I used SpringBoot 3.0.2, GraalVM 22 (JVM mode), a MacOS 2,6 GHz 6-Core Intel Core i7, running 1000 users for 5 minutes. The idea was to test how memory consumption and CPU usage evolve. Below, I compared the footprint of these three solutions. I collected the total count of requests, throughput, memory consumption, and CPU usage using VisualVM and Gatling. - Source: dev.to / about 1 year ago