wrk VS Grafana

> My initial requirement was to send requests with unique parameters. To the best of my knowledge, no tool could do this.

wrk does this with lua. https://github.com/wg/wrk/blob/master/src/wrk.lua

Also even things like the venerable jmeter supported pulling parameters from a csv file.

We used wrk2, a fantastic tool to generate stable load on the server. Wrk2 improves on wrk and adds the ability to generate sustained load at a specific rate (-R parameter).

I am using a tool called wrk to make a bunch of requests to my website, and changing the concurrency of the requests drastically changes the result, with concurrency set to 1, I get 894 requests made in 10 seconds, and when I set concurrency to 10, I get 8549 requests. This linear scaling stops when increasing the concurrency more drastically (18805 requests for 100 and 19814 for 500 concurrency).

Im creating multithreaded async http server for learning purposes and i'm facing problem when benchmarking with wrk. I get socket read errors on every connection:

We use locust at work but I HIGHLY recommend wrk for a very robust yet simple load testing tool.

https://github.com/wg/wrk

And of course, this talk by Gil Tene is fantastic if you're interested in load testing stats https://www.youtube.com/watch?v=lJ8ydIuPFeU

Good morning what tool do you use to test your infra in terms of http load ? A tool that works, I tested : - https://github.com/tsenart/vegeta but it returns 0 errors or a http_net error from Golang - LOIC (Low Orbit Ion Canon) https://github.com/NewEraCracker/LOIC but the requests do not appear in my nginx logs and I feel no slowdown - Apache Jmeter https://jmeter.apache.org/ but I can't drop my infra and I have Java socket closed errors - K6 https://k6.io/ but I can't bring down my infra with - wrk https://github.com/wg/wrk no matter what parameter I put it doesn't make enough requests per second, I put the same parameters as on a tutorial and I don't get the same result...

You could try to just blast it with wrk or bombardier. Can easily get around 50k requests on consumer machine.

Sure, first I did the load testing on the same machine. The same machine ran web servers, php-fpm and ab / wrk programs.

There are http benchmarking tools like wrk [0]. You don't need a ddos service for that.

[0] https://github.com/wg/wrk

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.