| Server Component | Description |
| Index server | The index server contains the actual data stores and the engines for processing the data. |
| Name server | The name server owns the information about the topology of the SAP HANA system. In a distributed system with instances of the SAP HANA database on multiple hosts, the name server knows where the components are running and which data is located on which server. |
| XS advanced runtime | As of SAP HANA 1.0 SPS 11, SAP HANA includes an additional run-time environment for application development: SAP HANA extended application services (XS), advanced model. SAP HANA XS advanced model represents an evolution of the application server architecture within SAP HANA by building upon the strengths (and expanding the scope) of SAP HANA extended application services (XS), classic model. |
| The SAP HANA XS advanced runtime consists of several processes for platform services and for executing applications. For more information about the individual services, see the table below. | |
| The SAP HANA XS Advanced runtime runs either on dedicated hosts or together with other SAP HANA components on the same host. | |
| SAP HANA Deployment Infrastructure (HDI) server | HDI handles the deployment of design-time artifacts into SAP HANA. |
| XS classic server | SAP HANA Extended Application Services (SAP HANA XS) is the application server for native SAP HANA-based web applications. It is installed with the SAP HANA system and allows developers to write and run SAP HANA-based applications without the need to run an additional application server. SAP HANA XS is also used to run web-based tools that come with SAP HANA, for instance for administration, lifecycle management and development. |
| SAP HANA XS classic is the original implementation of SAP HANA XS. | |
| The XS classic server can run as a separate server process or embedded within the index server. | |
| Extended store server | The extended store server is part of the SAP HANA dynamic tiering option for SAP HANA. It provides a high-performance disk-based column store for very big data up to the petabyte range. |
| For more information about SAP HANA dynamic tiering, see SAP HANA Options and Capabilities on SAP Help Portal. | |
| Data provisioning server | The data provisioning server is part of the SAP HANA smart data integration option for SAP HANA. It provides capabilities such as data provisioning in real time and batch mode, real-time data transformations, data quality functions, adapters for various types of remote sources, and an adapter SDK for developing additional adapters. |
| For more information about SAP HANA smart data integration, see SAP HANA Options and Capabilities on SAP Help Portal. | |
| Streaming cluster | The streaming cluster is part of the SAP HANA smart data streaming option for SAP HANA. Smart data streaming extends SAP HANA with capabilities of SAP Event Stream Processor for consuming data streams and complex event processing. |
| For more information about SAP HANA smart data streaming, see SAP HANA Options and Capabilities on SAP Help Portal. | |
| Accelerator for SAP ASE | The SAP ASE server is part of the SAP HANA Accelerator for SAP ASE option for SAP HANA. It provides SAP Adaptive Server Enterprise (ASE) users the ability to use SAP HANA on SAP ASE data, for real-time analytics, |
| SAP HANA remote data sync | The remote data sync server is part of the SAP HANA Real-Time Replication option for SAP HANA. SAP HANA remote data sync is a session-based synchronization technology designed to synchronize SAP SQL Anywhere remote databases with a consolidated database. |
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.
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