SAP HANA is an in-memory, columnar-oriented database management system developed by SAP. The key differentiators of HANA are its tight integration into the SAP application stack coupled with its columnar store, the latter of which is intended to enable customers to fold both OLTP and OLAP operations into a single database entity. In practical terms, this means an SAP HANA database stores both analytical and transactional data. Analysis of the data combination is possible on-the-fly, helping businesses make real-time analytical decisions.

SAP HANA has progressed on a rapid trajectory of software development and enhancement. Key among recent changes has been the movement from a Single Database Container (SDC) to Multi-Tenant Database Containers (MDC), an architecture that is enforced on all HANA 2.0 SPS 01 (Revision 10) releases.

Implications of the HANA column store

In a row-based relational database, data is stored by row, usu­ally in large files. Data is then accessed from the database by reading and returning full rows of data.


Country Product Sales
1   US Alpha   3000
2   US Beta    1250 <-----storing this line across------>
3   JP Alpha   400
4   UK Alpha   700


Country Product Sales    ^
US      Alpha 3000     |
US      Beta 1250    storing each column up / down
JP      Alpha 400     |
UK      Alpha 700     v


In a columnar database, the entire row does not need to be read, only the columns associated with the query. This columnar structure improves (read) ef­ficiency and therefore query performance, particularly for very large data sets.

Columnar data storage allows highly efficient compression because the majority of the columns contain only a few distinct values (compared to the number of rows). The data, therefore, has a smaller memory footprint and takes fewer processing cycles to interrogate.

Better parallel Processing: in a column store, data is already vertically partitioned. This means that operations on different columns can easily be processed in parallel. If multiple columns need to be searched or aggregated, each of these operations can be assigned to a different processor core.

Insert / Update

Storing data in columns instead of rows is challenging for workloads with many data modifying operations. Therefore, the concept of a differential buffer was introduced, where new entries are written to a differential buffer first. In SAP HANA this is referred to as the delta store. In contrast to the main store, the delta store is optimized for inserts. At a later point in time and depending on thresholds, e.g. the frequency of changes and new entries, the data in the delta store is merged into the main store. This process is referred to as delta merge.

The delta merge process has important implications for filesystem-based table modifications. Delta merge on any table will reorganize all data in that table and as a corollary will adjust all filesystem blocks related to that table. The space savings Delphix can offer by tracking block changes between snapshots become far less effective or predictable when entire tables are reorganized as a function of delta merge.