We present an overview of our work on the SAP HANA Scale-out Extension, a novel distributed database architecture designed to support large scale analytics over real-time data. This platform permits high performance OLAP with massive scale-out capabilities, while concurrently allowing OLTP workloads. This dual capability enables analytics over real-time changing data and allows fine grained user-specified service level agreements (SLAs) on data freshness. We advocate the decoupling of core database components such as query processing, concurrency control, and persistence, a design choice made possible by advances in high-throughput low-latency networks and storage devices. We provide full ACID guarantees and build on a logical timestamp mechanism to provide MVCC-based snapshot isolation, while not requiring synchronous updates of replicas. Instead, we use asynchronous update propagation guaranteeing consistency with timestamp validation. We provide a view into the design and development of a large scale data management platform for real-time analytics, driven by the needs of modern enterprise customers.

We present some architectural and technological insights on SAP's HANA database and derive research challenges for future enterprise application development.

XTCcmp, the XQuery Compiler of a native XML database system, extends Starburst's well-known Query Graph Model to serve as an internal representation and basis for query restructuring of XQuery expressions. Furthermore, XTCcmp is able to generate execution plans supporting a wide range of both well-known and newly developed variants of core XML processing algorithms and indexes. Our demo visualizes all rule-based transformation stages, i. e., simplification, algebraic rewriting, and plan generation. Furthermore, via an interface to the extensible rule configuration, it allows interaction with the query compiler to vary configuration parameters and to control the compilation outcome.

Because XML documents tend to be very large, are accessed by declarative and navigational languages, and often are processed in a collaborative way using read/write transactions, their fine-grained storage and management in XML DBMSs is a must for which, in turn, a flexible and space-economic tree representation is mandatory. In this paper, we explore a variety of options to natively store, encode, and compress XML documents thereby preserving the full DBMS processing flexibility on the documents required by the various language models and usage characteristics. Important issues of our empirical study are related to node labeling, document container layout, indexing, as well as structure and content compression. Encoding and compression of XML documents with their complete structure leads to a space consumption of ~40% to ~60% compared to their plain representation, whereas structure virtualization (elementless storage) saves in the average more than 10%, in addition.

As observed in many publications so far, the matching of twig pattern queries (i.e., queries that contain only the child and the descendant axis) is a core operation in XML database management systems (XDBMSs) for which the structural join and the holistic twig join algorithms were proposed. In a single-user environment, especially the latter algorithm provides a good evaluation strategy. However, when it comes to multi-user access to a single XML document, it may lead to extensive blocking situations: The XDBMS has to ensure data consistency and, therefore, has to prevent concurrent modification operations from changing elements in the input sequences, a holistic twig algorithm accesses while operating. To circumvent this problem, we propose a set of new locking-aware operators for twig pattern query evaluation that rely on stable path labels (SPLIDs) as well as document and element set indexes. Furthermore, by running extensive tests on our own XDBMS, we show that their performance is comparable to existing approaches in a single-user environment, and leads to higher throughput rates in the case of multi-user access.