SQLScript: Efficiently Analyzing Big Enterprise Data in SAP HANA
Carsten Binnig, DHBW Mannheim
Norman May, SAP AG Walldorf
Tobias Mindnich, SAP AG Walldorf
Abstract: Today, not only Internet companies such as Google, Facebook or Twitter do have Big Data but also Enterprise Information Systems store an ever growing amount of data (called Big Enterprise Data in this paper). In a classical SAP system landscape a central data warehouse (SAP BW) is used to integrate and analyze all enterprise data. In SAP BW most of the business logic required for complex analytical tasks (e.g., a complex currency conversion) is implemented in the application layer on top of a stan- dard relational database. While being independent from the underlying database when using such an architecture, this architecture has two major drawbacks when analyzing Big Enterprise Data: (1) algorithms in ABAP do not scale with the amount of data and (2) data shipping is required.
To this end, we present a novel programming language called SQLScript to efficiently support complex and scalable analytical tasks inside SAP’s new main-memory database HANA. SQLScript provides two major extensions to the SQL dialect of SAP HANA: A functional and a procedural extension. While the functional extension al- lows the definition of scalable analytical tasks on Big Enterprise Data, the procedural extension provides imperative constructs to orchestrate the analytical tasks. The major contributions of this paper are two novel functional extensions: First, an extended ver- sion of the MapReduce programming model for supporting parallelizable user-defined functions (UDFs). Second, compared to recursion in the SQL standard, a generalized version of recursion to support graph analytics as well as machine learning tasks.
Published in the Proceedings of the 15. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web, 11. März – 15. März 2013
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