Giter Club home page Giter Club logo

Comments (1)

DifferentialOrange avatar DifferentialOrange commented on September 25, 2024 1

Thank you for your comments!

The example cluster was meant to be run with docker. There is a README section with short description https://github.com/tarantool/grafana-dashboard#experimental-cluster . If you use docker-compose up, it will start up containers and a network which will make up configured and fully functional monitoring stack (even two of them) connected to a Tarantool example app. Tarantool container is a container which runs luatest that starts up a cluster, configures it and send some requests to make up some load for graphs (https://github.com/tarantool/grafana-dashboard/tree/master/example/project/cluster). Metrics output is configured with clusterwide configuration with luatest, and that is the reason why we don't have metrics.set_export code for now. The other reason is that this cluster was introduced before set_export and there wasn't any obvious reasons why we should upgrade. The cluster runs with luatest instead of cartridge-cli because it was built before introduction of replicasets.yml and it was not convenient to works with cluster as I wanted to (for example, set up roles and replicasets automatically).

So if you want to use this example cluster to test only Grafana-related functionality, you can simply run docker-compose up and go to localhost:3000 to set up some boards. You do not need to set up Prometheus or Tarantool metrics output. If you want to change example app code, you can change example app code in folders and, if needed, changed enabled roles or cluster topology here if you're familiar with luatest https://github.com/tarantool/grafana-dashboard/tree/master/example/project/cluster (you may need to run docker-compose build --no-cache to reload code if you're have started a cluster before). If you want to monitor some app that already runs and have exposed metrics on your localhost, you may also use docker containers (Prometheus + Grafana) but configure your instances host:port in Prometheus config.

The reason that configuration uses example_project: is also so we could run it with docker-compose (which build a network for containers) and it will set up without any manual configuration.

It was planned to migrate to using cartridge-cli instead of luatest (#34) someday to set up example cluster. Maybe it is the time. But I still plan to run in inside docker container for now. The main reason for example cluster is to create an environment in which it is convenient to develop a dashboard. You can call a single docker-compose up and then go to localhost:3000 to check up your new panel. So we won't change target urls until something changes.

Finally, we have a documentation page on how to import a dashboard and set up parameters https://www.tarantool.io/en/doc/latest/book/monitoring/grafana_dashboard/ . I have a link in README, but it seems like it's easy to miss. And looks like it is still not obvious how to find a job from here.

I hope that I can use this issue to build up such a README so its crucial points will be hard to miss.

from grafana-dashboard.

Related Issues (20)

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.