Hello good friends.
We’re currently managing a large-scale ontology implementation in Foundry, and we’ve identified that indexed storage is becoming a significant cost factor. I’m seeking detailed technical information about property storage settings and their impact on both performance and costs.
Specific Areas of Interest
- Property Storage Settings:
- Searchable
- Sortable
- Keywords
- Low Cardinality
We primarily consume these ontologies through Workshop applications, and I need to understand both the performance implications and potential cost trade-offs of these settings.
Technical Questions
- What are the underlying data structures and indexing methods used for ontology storage (specifically “highbury storage” as shown in Resource Management)?
- How do different property settings affect storage patterns and associated costs?
- Are there any performance trade-offs to consider when optimizing these settings?
Current Context
- We have a large number of object types in our ontology
- Storage costs are becoming a significant concern
- Need to balance functionality with cost optimization
Has anyone successfully optimized their ontology storage costs while maintaining necessary functionality? Any insights into the technical details of these storage settings would be greatly appreciated.
