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2 SAP HANA
SAP HANA is an in-memory data platform that is deployable as an appliance or in the cloud. At its core, it is an
innovative in-memory relational database management system that makes full use of the capabilities of current
hardware to increase application performance, to reduce cost of ownership, and to enable new scenarios and
applications that were not possible before.
With SAP HANA, you have the opportunity to build applications that integrate the business control logic and the
database layer with unprecedented performance. As a developer, one of the key questions is how you can
minimize data movements. The more you can do directly on the data in memory next to the CPUs, the better the
application will perform.
2.1 The SAP HANA In-Memory Database
SAP HANA was designed to run on modern, distributed computers built out of multi-core CPUs (multiple CPUs on
one chip) with fast communication between processor cores, and containing terabytes of main memory. With
SAP HANA, all data is available in main memory, which completely avoids the performance penalty of disk I/O.
Either disk or solid-state drives are still required for permanent persistency in the event of a power failure or some
other catastrophe. This does not slow down performance, however, because the required backup operations to
disk can take place asynchronously as a background task.
2.1.1 Columnar Data Storage
A database table is conceptually a two-dimensional data structure organized in rows and columns. Computer
memory, in contrast, is organized as a linear structure. A table can be represented in row-order or column-order.
A row-oriented organization stores a table as a sequence of records. Conversely, in column storage the entries of
a column are stored in contiguous memory locations. SAP HANA supports both, but is particularly optimized for
column-order storage.
Columnar data storage allows highly efficient compression. Especially if a column is sorted, there will normally be
repeated adjacent values. SAP HANA employs highly efficient compression methods, such as run-length
encoding, cluster coding and dictionary coding. With dictionary encoding, columns are stored as sequences of bitcoded
integers. That means that a check for equality can be executed on the integers (for example during scans
or join operations). This is much faster than comparing, for example, string values.
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