HANA NSE (Warm storage)
let's delve a bit deeper into the key concepts and details mentioned in the article about SAP HANA Cloud Native Storage Extension (NSE):
Monitoring Data Growth:
Customers implementing NSE should closely monitor their data growth. This involves keeping track of how much data is being stored and used within their SAP HANA system.
To make informed decisions, they need to compare the total memory size of their system with the actual memory being utilized.
Hybrid Column Store:
NSE offers a hybrid approach to data storage. It allows data to be stored in two different ways: in a disk-based column store and in an in-memory column store.
This hybrid approach is valuable because it provides flexibility in managing data, allowing organizations to balance performance and storage requirements based on their needs.
Integration:
NSE is tightly integrated into the SAP HANA system, particularly with the Hana Persistence layer. This integration ensures that data management and access are seamless.
It also closely interacts with Page Access and Resource Manager, which are essential components for efficiently accessing and managing data.
Buffer Cache:
Buffer Cache (BC) plays a crucial role in performance optimization. It helps improve access to data stored on disk.
BC reduces redundant input/output (I/O) operations by keeping frequently accessed data pages in memory rather than repeatedly reading them from disk.
It uses strategies like Last Recently Used (LRU) and Hot Buffer List (HBL) to prioritize data pages for caching, enhancing overall system performance.
NSE Advisor:
The NSE Advisor is a tool designed to assist organizations in making decisions about data storage.
It identifies objects within the system (such as tables, partitions, or columns) that can be converted to page loadable or column loadable formats.
This conversion helps save memory space and improve system performance.
Functional Restrictions:
Organizations must consider certain factors when using NSE, especially when dealing with large datasets and limited memory:
Queries in HANA are executed in-memory, so managing transient data and interim results in memory is crucial.
Low hit rates in the buffer cache can lead to performance issues due to excessive disk reads.
SAP provides guidelines for buffer cache sizing, but deviations may require application-specific sizing or testing.
NSE is intended for scale-up systems; it may not fully support scale-out systems or certain partitioning schemes.
Hana Data Tiering Options:
NSE is one of SAP's data tiering options, along with Hot Store (using Persistent Memory) and Warm Store (comprising NSE, Extension Node, Dynamic Tiering), as well as Cold Store (using Spark Controller).
Value Proposition and Use Cases:
NSE aims to increase data capacity while keeping costs low. It offers full HANA functionality and is suitable for various data types and models.
It's applicable to a wide range of HANA applications and can be used alongside other warm data tiering solutions.
Use cases include managing data growth in SAP applications, such as S/4HANA and BW/4HANA.
Technical Overview:
NSE employs buffer caches for efficiently loading data into memory, either as column loadable or page loadable data.
Data is written from the main store to disk during save point operations, and write-optimized data is not paged.
Tooling:
SAP provides tools like HANA Cockpit to configure buffer cache size, monitor usage, and offer recommendations based on data access patterns.
Data Lifecycle Manager (DLM) allows users to convert tables, columns, and partitions between loadable formats.
Seamless Integration:
NSE seamlessly integrates into SAP HANA, making it easy for organizations to implement and use. It harmoniously coexists with existing HANA components, and the NSE Advisor assists in optimizing the setup.
In essence, NSE is a versatile solution for managing data growth within SAP HANA systems, offering a combination of storage options and tools for efficient memory usage and enhanced performance. It's a valuable asset for organizations facing increasing data volumes in their SAP applications.
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