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.serviceRun the console with
orientdbConnect 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
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Pending
FUSS
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https://fuss.bz.it/
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Results: Infrastructure Achievements
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./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)
Results: Game Design Insights
Our project focused on modeling and analyzing two card games of our own design within the TAG framework:
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- 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