Orientdb is an open source graph/document database. It supports various language bindings and plug-ins.
On top of that, it supports TinkerPop Blueprints and Java Data Objects(JDO). The TinkerPop Blueprints are particularly interesting as they provide a graph database Domain Specific Language (DSL) to operate directly on the graph structure.
This all looks like a solid foundation for a Configuration Management Data Base (CMDB), starting with capturing infrastructure information (server connections, service dependencies) in a graph, using a standardized information model structure, and storing configuration item (CI) details as documents.
Goal for the week is to have all the classes of the current CIM model reflected in the database.
This project is part of:
Hack Week 10
Activity
Comments
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about 11 years ago by kwk | Reply
This plugin connects OrientDB with Pacer for easy graph manipulation and traversal in Ruby
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about 11 years ago by kwk | Reply
OrientDB supports a graph and a database model. When setting up the database, one has to choose one model. This presentation recommends to use the graph model for our purpose.
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about 11 years ago by kwk | Reply
Install OrientDB from here. Then (as root) edit /usr/share/orientdb/config/orientdb-server-config.xml and insert
between the tags. Then (as root) systemctl start orientdb.service
Run the console with orientdb
Connect to database with connect remote:localhost MyUser MyPassword
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about 11 years ago by kwk | Reply
Install OrientDB from here. Then (as root) edit /usr/share/orientdb/config/orientdb-server-config.xml and insert
<user name="MyUser" password="MyPassword" resources="*"/>
between the <users> tags.Then (as root)
systemctl start orientdb.service
Run the console with
orientdb
Connect to database with
connect remote:localhost MyUser MyPassword
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about 11 years ago by kwk | Reply
I consider this project done. The importer code is not perfect but creates classes and parent relationships.
Next step: Class properties, then instances, then instance associations
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about 11 years ago by bmaryniuk | Reply
Most of the time I was looking how to store the actual instances into the Graph database and recursively traverse a CIM instance. The current SrMF project (https://github.com/isbm/srmf) already can clone the system with KIWI as well as it can describe the whole service, as long all XML definitions and XSL transformations are there to the basic CIM providers.
The extension from the Hackweek now also allows to store the data into Object Database (Orient DB in this case) and manipulate it there. The next step for SrMF would be to distribute the database into a grid, where each local node describes only itself but the client tool queries the entire grid. There are plans to support Titan distributed graph database for really big scales (http://thinkaurelius.github.io/titan/) which is running on top of Hadoop. However, the primary focus is to support CMDB in smaller cases within the Orient DB cluster, which is already capable to hold billions of vertexes anyway. :)
Please note, that an alternative attempt to re-implement SrMF (https://github.com/isbm/srmf) once again is "Project Alfred".
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Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!
Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.
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- Bonus point: Java part for product identification, and monitoring enablement
- Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
- Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
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- If you don't have knowledge about some of the steps: ask the team
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This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)
Pending
FUSS
FUSS is a complete GNU/Linux solution (server, client and desktop/standalone) based on Debian for managing an educational network.
https://fuss.bz.it/
Seems to be a Debian 12 derivative, so adding it could be quite easy.
[W]
Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)[W]
Onboarding (salt minion from UI, salt minion from bootstrap script, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator) --> Working for all 3 options (salt minion UI, salt minion bootstrap script and salt-ssh minion from the UI).[W]
Package management (install, remove, update...) --> Installing a new package works, needs to test the rest.[I]
Patching (if patch information is available, could require writing some code to parse it, but IIRC we have support for Ubuntu already). No patches detected. Do we support patches for Debian at all?[W]
Applying any basic salt state (including a formula)[W]
Salt remote commands[ ]
Bonus point: Java part for product identification, and monitoring enablement
SUSE AI Meets the Game Board by moio
Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
Results: Infrastructure Achievements
We successfully built and automated a containerized stack to support our AI experiments. This included:
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./deploy.sh
and voilà - Kubernetes running PyTAG (k9s
, above) with GPU acceleration (nvtop
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Results: Game Design Insights
Our project focused on modeling and analyzing two card games of our own design within the TAG framework:
- Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
- AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
- Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site
A family picture of our card games in progress. From the top: Bamboo, Totoro, R3
Results: Learning, Collaboration, and Innovation
Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:
- "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
- AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
- GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
- Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.
Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!
The Context: AI + Board Games