Grimoire Lab basic infrastructure
This image provides basic infrastructure to set up a Grimorie Lab based analysis.
By basic I mean, it just gets the data from data repositories described in a yaml file, it stores them in a running Elasticsearch indicated in the same yaml file.
Once there, you can use Kibana to visualize and play with the data. If you need to update the data, just run the container again with the same configuration file.
data-sources.yml file similar to existing one to define the repositories to analyze and the elasticsearch host to store the data.
Remember: You need an Elasticsearch up and listening in the host and port you have defined in
es_host item. It might be a
user:password secured Elasticsearch, so host should be written as
Run the GrimoireLab Basic Infra as:
$ docker run -v /absolute-path-to/data-sources.yml:/settings/data-sources.yml --net=host -ti jsmanrique/grimoirelab-basic-infra
Supported data sources
- git (commits)
- GitHub repositories (commits, github issues and pull requests)
- GitHub organizations (git and github issues and pull requests). GitHub users repositories analysis is not supported, yet (issue)
- Meetup (groups activities)
- Discourse (forums questions)
What is missing?
Some features from the whole Grimoire Lab environment are not provided by this basic infraestructure:
- Sorting Hat related features: merging people multiple identities to unify people profiles and managing people affiliation information to show activity by organization.
- Some backends might be missing. Check Perceval for supported backends in GrimoireLab
- No data auto-update.
I am not a 100% technical person, so I am self-learning Python, Docker and many other things to create this. The aim for this project is to have a simple way to set up and run a simple Grimoire Lab analysis for some demo purposes. I am sure you can find better ways to do this, so any help is welcome. There are bugs, and many improvements can be done. Feel free to submit them in its GitHub repository.
Bitergia folks are working on training materials for Grimoire Lab. Worth reading. Contributions are welcome too ;-)