Maintaining and querying hierarchical data in a relational database system is an important task in many business applications. This task is especially challenging when considering dynamic use cases with a high rate of complex, possibly skewed structural updates. Labeling schemes are widely considered the indexing technique of choice for hierarchical data, and many different schemes have been proposed. However, they cannot handle dynamic use cases well due to various problems, which we investigate in this paper. We therefore propose Order Indexes-a dynamic representation of the nested intervals encoding-which offer competitive query performance, unprecedented update efficiency, and robustness for highly dynamic workloads.

Databases commonly use multi-column indices for composite keys that concatenate attribute values for fast entity retrieval. For real-world applications, such concatenated composite keys contribute significantly to the overall space consumption, which is particularly expensive for main memory-resident databases. We present an integer-based hash representation of the actual values for the purpose of reducing the overall memory footprint of a system while maintaining the level of performance. We analyzed the performance impact as well as the memory footprint reduction of hash-based indices in SAP HANA in a real-world enterprise database setting. For a live production SAP ERP system, the introduction of hash-based primary key indices alone reduces the entire memory footprint by 10% with comparable performance.

Managing hierarchies is an ever-recurring challenge for relational database systems. Through investigations of customer scenarios at SAP we found that today's RDBMSs still leave a lot to be desired in order to meet the requirements of typical applications. Our research puts a new twist on handling hierarchies in SQL-based systems. We present an approach for modeling hierarchical data natively, and we extend the SQL language with expressive constructs for creating, manipulating, and querying a hierarchy. The constructs can be evaluated efficiently by leveraging existing indexing and query processing techniques. We demonstrate the feasibility of our concepts with initial measurements on a HANA-based prototype.

For decades researchers have studied the duality of hashing and sorting for the implementation of the relational operators, especially for efficient aggregation. Depending on the underlying hardware and software architecture, the specifically implemented algorithms, and the data sets used in the experiments, different authors came to different conclusions about which is the better approach. In this paper we argue that in terms of cache efficiency, the two paradigms are actually the same. We support our claim by showing that the complexity of hashing is the same as the complexity of sorting in the external memory model. Furthermore we make the similarity of the two approaches obvious by designing an algorithmic framework that allows to switch seamlessly between hashing and sorting during execution. The fact that we mix hashing and sorting routines in the same algorithmic framework allows us to leverage the advantages of both approaches and makes their similarity obvious. On a more practical note, we also show how to achieve very low constant factors by tuning both the hashing and the sorting routines to modern hardware. Since we observe a complementary dependency of the constant factors of the two routines to the locality of the input, we exploit our framework to switch to the faster routine where appropriate. The result is a novel relational aggregation algorithm that is cache-efficient---independently and without prior knowledge of input skew and output cardinality---, highly parallelizable on modern multi-core systems, and operating at a speed close to the memory bandwidth, thus outperforming the state-of-the-art by up to 3.7x.

Maintaining and querying hierarchical data in a relational database system is an important task in many business applications. This task is especially challenging when considering dynamic use cases with a high rate of complex, possibly skewed structural updates. Labeling schemes are widely considered the indexing technique of choice for hierarchical data, and many different schemes have been proposed. However, they cannot handle dynamic use cases well due to various problems which we investigate in this paper. We therefore propose our dynamic Order Indexes , which offer competitive query performance, unprecedented update efficiency, and robustness for highly dynamic workloads.

Over the years, data management has diversified and moved into multiple directions, mainly caused by a significant growth in the application space with different usage patterns, a massive change in the underlying hardware characteristics, and-last but not least-growing data volumes to be processed. A solution matching these constraints has to cope with a multidimensional problem space including techniques dealing with a large number of domain-specific data types, data and consistency models, deployment scenarios, and processing, storage, and communication infrastructures on a hardware level. Specialized database engines are available and are positioned in the market optimizing a particular dimension on the one hand while relaxing other aspects (e.g. web-scale deployment with relaxed consistency).Today it is common sense, that there is no single engine which can handle all the different dimensions equally well and therefore we have very good reasons to tackle this problem and optimize the dimensions with specialized approaches in a first step. However, we argue for a second step (reflecting in our opinion on the even harder problem) of a deep integration of individual engines into a single coherent and consistent data management ecosystem providing not only shared components but also a common understanding of the overall business semantics. More specifically, a data management ecosystem provides common "infrastructure" for software and data life cycle management, backup/recovery, replication and high availability, accounting and monitoring, and many other operational topics, where administrators and users expect a harmonized experience. More importantly from an application perspective however, customer experience teaches us to provide a consistent business view across all different components and the ability to seamlessly combine different capabilities. For example, within recent customer-based Internet of Things scenarios, a huge potential exists in combining graph-processing functionality with temporal and geospatial information and keywords extracted from high-throughput twitter streams. Using SAP HANA as the running example, we want to demonstrate what moving a set of individual engines and infra-structural components towards a holistic but also flexible data management ecosystem could look like. Although there are some solutions for some problems already visible on the horizon, we encourage the database research community in general to focus more on the Big Picture providing a holistic/integrated approach to efficiently deal with different types of data, with different access methods, and different consistency requirements-research in this field would push the envelope far beyond the traditional notion of data management.

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.

Proteomes are characterized by large protein-abundance differences, cell-type- and time-dependent expression patterns and post-translational modifications, all of which carry biological information that is not accessible by genomics or transcriptomics. Here we present a mass-spectrometry-based draft of the human proteome and a public, high-performance, in-memory database for real-time analysis of terabytes of big data, called ProteomicsDB. The information assembled from human tissues, cell lines and body fluids enabled estimation of the size of the protein-coding genome, and identified organ-specific proteins and a large number of translated lincRNAs (long intergenic non-coding RNAs). Analysis of messenger RNA and protein-expression profiles of human tissues revealed conserved control of protein abundance, and integration of drug-sensitivity data enabled the identification of proteins predicting resistance or sensitivity. The proteome profiles also hold considerable promise for analysing the composition and stoichiometry of protein complexes. ProteomicsDB thus enables navigation of proteomes, provides biological insight and fosters the development of proteomic technology.

Domain encoding is a common technique to compress the columns of a column store and to accelerate many types of queries at the same time. It is based on the assumption that most columns contain a relatively small set of distinct values, in particular string columns. In this paper, we argue that domain encoding is not the end of the story. In real world systems, we observe that a substantial amount of the columns are of string types. Moreover, most of the memory space is consumed by only a small fraction of these columns. To address this issue, we make three main contributions: First we survey several approaches and variants for dictionary compression, i. e., data structures that store the dictionary of domain encoding in a compressed way. As expected, there is a trade-off between size of the data structure and its access performance. This observation can be used to compress rarely accessed data more than frequently accessed data. Furthermore the question which approach has the best compression ratio for a certain column heavily depends on specific characteristics of its content. Consequently, as a second contribution, we present non-trivial sampling schemes for all our dictionary formats, enabling us to estimate their size for a given column. This way it is possible to identify compression schemes specialized for the content of a specific column. Third, we draft how to fully automate the decision of the dictionary format. We sketch a compression manager that selects the most appropriate dictionary format based on column access and update patterns, characteristics of the underlying data, and costs for set-up and access of the different data structures. We evaluate an off-line prototype of a compression manager using a variation of the TPC-H benchmark [15]. The compression manager can configure the database system to be anywhere in a large range of the space / time trade-off with a fine granularity, providing significantly better trade-offs than any fixed dictionary format.

Modern database systems employ Snapshot Isolation to implement concurrency control and isolationbecause it promises superior query performance compared to lock-based alternatives. Furthermore, Snapshot Isolation never blocks readers, which is an important property for modern information systems, which have mixed workloads of heavy OLAP queries and short update transactions. This paper revisits the problem of implementing Snapshot Isolation in a distributed database system and makes three important contributions. First, a complete definition of Distributed Snapshot Isolation is given, thereby extending existing definitions from the literature. Based on this definition, a set of criteria is proposed to efficiently implement Snapshot Isolation in a distributed system. Second, the design space of alternative methods to implement Distributed Snapshot Isolation is presented based on this set of criteria. Third, a new approach to implement Distributed Snapshot Isolation is devised; we refer to this approach as Incremental. The results of comprehensive performance experiments with the TPC-C benchmark show that the Incremental approach significantly outperforms any other known method from the literature. Furthermore, the Incremental approach requires no a priori knowledge of which nodes of a distributed system are involved in executing a transaction. Also, the Incremental approach can execute transactions that involve data from a single node only with the same efficiency as a centralized database system. This way, the Incremental approach takes advantage of sharding or other ways to improve data locality. The cost for synchronizing transactions in a distributed system is only paid by transactions that actually involve data from several nodes. All these properties make the Incremental approach more practical than related methods proposed in the literature.