Multitenant Database Containers (MDC) | Multiple Components One System (MCOS) |
All the tenant DB sharing the same system HANA revision | Independent HANA revision of each HANA instance |
No additional SAP HANA License | Separate SAP HANA License |
No additional hypervisor virtualization license and hardware independent | No additional hypervisor virtualization license and hardware independent |
No additional machine for hardware/vm management console | No additional machine for hardware/vm management console |
No support on storage snap shot backup | Support on storage snap shot backup |
Shared SAP HANA Binaries - Yes | Shared SAP HANA Binaries - No |
1 Linux license | 1 Linux license |
Support > 4 socket hardware | Support > 4 socket hardware |
Support > 1TB memory | Support > 1TB memory |
Tenant DB only can restore to tenant DB | No dependency of tenant DB backup/restore |
Multiple BW on HANA - Yes | Multiple BW on HANA - No |
No Performance degrade | No Performance degrade |
No additional maintenance required for vsphere patch, Lpar patching | No additional maintenance required for vsphere patch, Lpar patching |
Hardware Resource Management – SAP HANA internal | Hardware Resource Management - SAP HANA internal |
Vector Databases Usage: Typically used for vector search use cases such as visual, semantic, and multimodal search. More recently, they are paired with generative AI text models for conversational search experiences. Development Process: Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors. The data import process is referred to as data hydration. Application Development: Application developers utilize the database to search for similar products. This involves encoding a product image and using the vector to query for similar images. k-Nearest Neighbor (k-NN) Indexes: Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors. A distance function like cosine is applied to rank results by similarity.
Comments