With the exponential growth of data showing no signs of slowing, customers looking to lower their total cost of ownership (TCO), while still leveraging their vast reservoirs of data to maximum effect, must continuously manage their data storage costs without forfeiting the high performance they’ve come to expect from SAP HANA. A few data tiering options with different price/performance characteristics are already available, but soon SAP customers will have another option in their data tiering arsenal: a native solution for warm data tiering—the SAP HANA Native Storage Extension.
A quick data-tiering refresher—mission-critical hot data is retained in-memory on the SAP HANA database for real-time processing and analysis. Less frequently used warm data is stored in a lower cost tier, but still managed as a unified part of the SAP HANA database. Rarely used, voluminous cold data is located on the lowest cost storage. Regardless of location, all data remains accessible at any time.
The new SAP HANA Native Storage Extension (NSE) adds a native warm data tier to the SAP HANA database. Any customer-built or SAP-built HANA application that is challenged by ballooning data volumes can leverage this deeply integrated warm data tier. NSE increases SAP HANA data capacity at a low TCO through a simple, scalable landscape that offers great performance. Supporting full SAP HANA functionality and all SAP HANA data types and data models, NSE complements—without replacing—the Extension Node and Dynamic Tiering warm data options. NSE is supported for both on-premise SAP HANA systems, and SAP Cloud Platform, SAP HANA Service.
While hot data is 'column loadable', residing completely in-memory for fast processing and loaded from disk into SAP HANA memory in columns, the SAP HANA Native Storage Extension allows a user to specify certain warm data as 'page loadable', which is then loaded into memory page by page as required for query processing. Page loadable data does not need to reside completely in-memory, like column loadable data.
NSE reduces the memory footprint of the SAP HANA database with expanded disk capacity and an intelligent buffer cache that transfers pages of data between memory and disk. Query performance differences may be noticeable between warm data and hot data.
SAP HANA scale up systems are supported with this upcoming initial NSE release and scale out support will follow in a later release. With NSE, you will be able to expand SAP HANA database capacity with warm data on disk up to about 4x the size of hot data in memory. A relatively small amount of SAP HANA memory for the NSE buffer cache will be needed for paging operations, as the buffer cache can handle 8x its size of warm data on disk. As an example, a 2TB SAP HANA system without NSE equates to a 1TB database in memory. With NSE and the addition of a 500GB buffer cache, you can expand your 1TB database to a 5TB database: 1TB of hot data, 4TB of warm data, and a 500GB buffer cache to page data between memory and disk.
We are excited to roll out this native SAP HANA warm data tiering solution that boasts full SAP HANA functionality at a lower TCO for customers. Want to learn more about SAP HANA Native Storage Extensions? Find out more here or drop me a question in the comments section below.
A Quick Data Tiering Refresher
A quick data-tiering refresher—mission-critical hot data is retained in-memory on the SAP HANA database for real-time processing and analysis. Less frequently used warm data is stored in a lower cost tier, but still managed as a unified part of the SAP HANA database. Rarely used, voluminous cold data is located on the lowest cost storage. Regardless of location, all data remains accessible at any time.
The new SAP HANA Native Storage Extension (NSE) adds a native warm data tier to the SAP HANA database. Any customer-built or SAP-built HANA application that is challenged by ballooning data volumes can leverage this deeply integrated warm data tier. NSE increases SAP HANA data capacity at a low TCO through a simple, scalable landscape that offers great performance. Supporting full SAP HANA functionality and all SAP HANA data types and data models, NSE complements—without replacing—the Extension Node and Dynamic Tiering warm data options. NSE is supported for both on-premise SAP HANA systems, and SAP Cloud Platform, SAP HANA Service.
How Does SAP HANA Native Storage Extension Work?
While hot data is 'column loadable', residing completely in-memory for fast processing and loaded from disk into SAP HANA memory in columns, the SAP HANA Native Storage Extension allows a user to specify certain warm data as 'page loadable', which is then loaded into memory page by page as required for query processing. Page loadable data does not need to reside completely in-memory, like column loadable data.
NSE reduces the memory footprint of the SAP HANA database with expanded disk capacity and an intelligent buffer cache that transfers pages of data between memory and disk. Query performance differences may be noticeable between warm data and hot data.
NSE Warm Data Tiering
Additional Database Capacity with SAP HANA Native Storage Extension
SAP HANA scale up systems are supported with this upcoming initial NSE release and scale out support will follow in a later release. With NSE, you will be able to expand SAP HANA database capacity with warm data on disk up to about 4x the size of hot data in memory. A relatively small amount of SAP HANA memory for the NSE buffer cache will be needed for paging operations, as the buffer cache can handle 8x its size of warm data on disk. As an example, a 2TB SAP HANA system without NSE equates to a 1TB database in memory. With NSE and the addition of a 500GB buffer cache, you can expand your 1TB database to a 5TB database: 1TB of hot data, 4TB of warm data, and a 500GB buffer cache to page data between memory and disk.
Next Steps
We are excited to roll out this native SAP HANA warm data tiering solution that boasts full SAP HANA functionality at a lower TCO for customers. Want to learn more about SAP HANA Native Storage Extensions? Find out more here or drop me a question in the comments section below.
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