- OLTP - Online Transaction Processing
- OLTP System deals with operational data. Operational data are those data involved in the operation of a particular system.
- OLAP - Online Analytical Processing
- OLAP deals with Historical Data or Archival Data. Historical data are those data that are archived over a long period of time. Data from OLTP are collected over a period of time and store it in a very large database called Data warehouse.
Historically, column-based storage was mainly used for analytics and data warehousing, where aggregate functions play an important role.
Row-based store: If you want to report on all the columns of a table, then the row store is more suitable because reconstructing the complete row is one the most expensive operations for a column-based table.
Column-based store: If you want to store in a table huge amounts of data that should be aggregated and analyzed, then a column-based storage is more suitable.
When a SAP system is migrated to SAP HANA, the SAP tables are automatically migrated into the storage type suited best. This logic is defined by SAP.
The majority of tables are held in the Column Store.
This information can be accessed in SAP HANA studio (Catalog > Open Definition) or in the technical settings of each table in the SAP dictionary (transaction SE13).
With column-based storage, data is only partially blocked. Therefore, individual columns can be processed at the same time by different cores.
Apart from performance reasons, column store offers much more potential leverage state-of-the-art data compression concepts.
For example, SAP HANA works with bit encoded values and compresses repeated values, which results in much less memory requirements than for a classical row store table.
Row storage is used to store the data in the tabular form. In row storage, data is inserted in form of tuple. Each tuple is nothing but a row which is unique identification of each record.
Data in column tables can have a two-fold compression:
- Dictionary compression: This default method of compression is applied to all columns.
- It involves the mapping of distinct column values to consecutive numbers, so that instead of the actual value being stored, the typically much smaller consecutive number is stored.
- Advanced compression: Each column can be further compressed using different compression methods, namely prefix encoding, run length encoding (RLE), cluster encoding, sparse encoding, and indirect encoding.
- The SAP HANA database uses compression algorithms to determine which type of compression is most appropriate for a column.
- Columns with the PAGE LOADABLE attribute are compressed with the NBit algorithm only.
- Advanced compression is applied only to the main storage of column tables. As the delta storage is optimized for write operations, it has only dictionary compression applied.
98. What are the different compression methods?
- prefix encoding,
- run length encoding (RLE),
- cluster encoding,
- sparse encoding, and
- indirect encoding.
99. what is a Compression Factor?
The compression factor refers to the ratio of the uncompressed data size to the compressed data size in SAP HANA.
The uncompressed data volume is a database-independent value that is defined as follows: the nominal record size multiplied by the number of records in the table. The nominal record size is the sum of the sizes of the data types of all columns.
The compressed data volume in SAP HANA is the total size that the table occupies in the main memory of SAP HANA.
100. How SAP HANA handles Partitioning?
- spreads table contents across the blades
- work on smaller sets of data in parallel
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