Spike: Supabase Database Access Evaluation

[m0nq]

Objective: Determine if database access should use vector operations exclusively and evaluate saving graph data via GraphQL calls.

Duration: 3-5 days

Steps:

1. Research Supabase and Vector Operations
   • Study Supabase documentation and capabilities
   • Investigate vector operations in database contexts
   • Understand pros and cons of vector-based access
2. Evaluate GraphQL Integration
   • Review GraphQL implementation with Supabase
   • Assess methods for saving graph data via GraphQL calls
3. Prototype Development
   • Create a small proof-of-concept application
   • Implement vector operations for data access
   • Test GraphQL calls for saving graph data
4. Performance Testing
   • Benchmark vector operations vs. traditional queries
   • Measure GraphQL call efficiency for graph data storage
5. Analysis and Recommendation
   • Compile findings from research and prototyping
   • Develop a recommendation for the optimal approach

Deliverables:

• Technical summary of findings
• Prototype demonstrating vector operations and GraphQL integration
• Performance comparison results
• Recommendation report for database access strategy

Key Questions to Answer:

1. What advantages do vector operations offer for our specific use case?
2. How does vector-based access impact query performance and scalability?
3. What are the trade-offs between vector-only and hybrid access approaches?
4. How efficiently can we save and retrieve graph data using GraphQL calls?
5. What are the implications for our existing data models and access patterns?

This spike will provide valuable insights to inform the decision on whether to adopt a vector-only approach for database access and how to effectively integrate GraphQL for graph data management.

[linear[bot]]

DEV-70 Spike: Supabase Database Access Evaluation