🔧 Planned Improvements and Enhancements
1. Database Management
Implement robust tooling to deploy, manage, and back up multi-vendor databases (e.g., PostgreSQL, MySQL) across clusters and cloud providers. This includes automated provisioning, policy-driven lifecycle management, and seamless integration with existing infrastructure.
Optimize performance across all critical system layers:
- Watchers: Improve event-driven data synchronization and reduce latency.
- Metadata Storage: Introduce optimized metadata storage mechanisms to improve system performance and responsiveness.
- Table Partitions: Enable partitioning and sharding strategies to accelerate large-scale data operations.
- Garbage Collection: Shift Kubernetes garbage collection logic to the database layer to enable more efficient cleanup of orphaned or expired resources, reduce API server load, and improve overall system performance.
3. Database Support Expansion
Extend backend compatibility to support a broader range of relational and NoSQL databases. This enables users to choose the most suitable storage technology for their use case, while maintaining the same unified API and operational model.
4. High Availability (HA)
Introduce high-availability features for the middleware layer, including:
- Leader election and failover mechanisms
- Stateful load balancing
- Redundancy and replication strategies to prevent single points of failure
5. Encryption Support
Support multiple encryption strategies for data in transit and at rest. This includes:
- Implement support for Kubernetes Envelope Encryption to secure sensitive data at rest using external Key Management Systems (KMS)
- Integration with 3rd party KMS solutions (e.g., Vault, AWS, GCP, Azure)
6. Flexible API Server Deployment
In addition to the built-in API server, support the ability to deploy dedicated API servers per resource type or resource group. This enables isolated scaling, improved fault tolerance, and performance optimization for high-traffic or latency-sensitive resources.