Peer Data Management Systems (PDMS) are a natural extension of heterogeneous database systems. One of the main tasks in such systems is efficient query processing. Insisting on complete answers, however, leads to asking almost every peer in the network. Relaxing these completeness requirements by applying approximate query answering techniques can significantly reduce costs. Since most users are not interested in the exact answers to their queries, rank-aware query operators like top-k or skyline play an important role in query processing. In this paper, we present the novel concept of relaxed skylines that combines the advantages of both rank-aware query operators and approximate query processing techniques. Furthermore, we propose a strategy for processing relaxed skylines in distributed environments that allows for giving guarantees for the completeness of the result using distributed data summaries as routing indexes.

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Non-Volatile RAM (NVRAM) is a novel class of hardware technology which is an interesting blend of two storage paradigms: byte-addressable DRAM and block-addressable storage (e.g. HDD/SSD). Most of the existing enterprise relational data management systems such as SAP HANA have their internal architecture based on the inherent assumption that memory is volatile and base their persistence on explicit handling of block-oriented storage devices. In this paper, we present the early adoption of Non-Volatile Memory within the SAP HANA Database, from the architectural and technical angles. We discuss our architectural choices, dive deeper into a few challenges of the NVRAM integration and their solutions, and share our experimental results. As we present our solutions for the NVRAM integration, we also give, as a basis, a detailed description of the relevant HANA internals.

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We present an overview of SAP HANA's Native Store Extension (NSE). This extension substantially increases database capacity, allowing to scale far beyond available system memory. NSE is based on a hybrid in-memory and paged column store architecture composed from data access primitives. These primitives enable the processing of hybrid columns using the same algorithms optimized for traditional HANA's in-memory columns. Using only three key primitives, we fabricated byte-compatible counterparts for complex memory resident data structures (e.g. dictionary and hash-index), compressed schemes (e.g. sparse and run-length encoding), and exotic data types (e.g. geo-spatial). We developed a new buffer cache which optimizes the management of paged resources by smart strategies sensitive to page type and access patterns. The buffer cache integrates with HANA's new execution engine that issues pipelined prefetch requests to improve disk access patterns. A novel load unit configuration, along with a unified persistence format, allows the hybrid column store to dynamically switch between inmemory and paged data access to balance performance and storage economy according to application demands while reducing Total Cost of Ownership (TCO). A new partitioning scheme supports load unit specification at table, partition, and column level. Finally, a new advisor recommends optimal load unit configurations. Our experiments illustrate the performance and memory footprint improvements on typical customer scenarios.

Software Testing is an established activity in the software development process to ensure and improve the quality of a software. Consequently, there exists a wide range of literature, popular information, and even multiple ISO standards covering this topic. However, we found that testing very large database management systems (DBMS) requires special adaptations of the generally available guidance for software testing and requires to solve specific challenges that may not be relevant for other areas or smaller software projects. We therefore discuss the testing of SAP HANA, a very large software project with millions of lines of code, to share insights about our approach, best practices, and unsolved challenges that are open for further research.

Query processing in large-scale unstructured P2P networks is a crucial part of operating such systems. In order to avoid expensive flooding of the network during query processing so-called routing indexes are used. Each peer maintains such an index for its neighbors. It provides a compact representation (data summary) of data accessible via each neighboring peer. An important problem in this context is to keep these data summaries up-to-date without paying high maintenance costs. In this paper, we investigate the problem of maintaining distributed data summaries in P2P-based environments without global knowledge and central instances. Based on a classification of update propagation strategies, we discuss several approaches to reduce maintenance costs and present results from an experimental evaluation.