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- ZeitschriftenartikelDissertationen(Datenbank-Spektrum: Vol. 14, No. 3, 2014)
- ZeitschriftenartikelDie Abteilung Informationssysteme der Universität Oldenburg(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Appelrath, H.-Jürgen; Grawunder, Marco
- ZeitschriftenartikelEditorial(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Härder, Theo; Teubner, Jens
- ZeitschriftenartikelThe Design and Implementation of CoGaDB: A Column-oriented GPU-accelerated DBMS(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Breß, SebastianNowadays, the performance of processors is primarily bound by a fixed energy budget, the power wall. This forces hardware vendors to optimize processors for specific tasks, which leads to an increasingly heterogeneous hardware landscape. Although efficient algorithms for modern processors such as GPUs are heavily investigated, we also need to prepare the database optimizer to handle computations on heterogeneous processors. GPUs are an interesting base for case studies, because they already offer many difficulties we will face tomorrow.In this paper, we present CoGaDB, a main-memory DBMS with built-in GPU acceleration, which is optimized for OLAP workloads. CoGaDB uses the self-tuning optimizer framework HyPE to build a hardware-oblivious optimizer, which learns cost models for database operators and efficiently distributes a workload on available processors. Furthermore, CoGaDB implements efficient algorithms on CPU and GPU and efficiently supports star joins. We show in this paper, how these novel techniques interact with each other in a single system. Our evaluation shows that CoGaDB quickly adapts to the underlying hardware by increasing the accuracy of its cost models at runtime.
- ZeitschriftenartikelHyPer Beyond Software: Exploiting Modern Hardware for Main-Memory Database Systems(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Funke, Florian; Kemper, Alfons; Mühlbauer, Tobias; Neumann, Thomas; Leis, ViktorIn this paper, we survey the use of advanced hardware features for optimizing main-memory database systems in the context of our HyPer project. We exploit the virtual memory management for snapshotting the transactional data in order to separate OLAP queries from parallel OLTP transactions. The access behavior of database objects from simultaneous OLTP transactions is monitored using the virtual memory management component in order to compact the database into hot and cold partitions. Utilizing many-core NUMA-organized database servers is facilitated by the morsel-driven adaptive parallelization and partitioning that guarantees data locality w.r.t. the processing core. The most recent Hardware Transactional Memory support of, e.g., Intel’s Haswell processor, can be used as the basis for a lock-free concurrency control scheme for OLTP transactions. Finally, we show how heterogeneous processors of “wimpy” devices such as tablets can be utilized for high-performance and energy-efficient query processing.
- ZeitschriftenartikelWattDB - A Journey towards Energy Efficiency(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Schall, Daniel; Härder, TheoDue to their narrow power spectrum between idle and full utilization [2], satisfactory energy efficiency of servers can only be reached in the peak-performance range, whereas energy efficiency obtained for lower activity levels is far from being optimal. Hence, this hardware property obviates a desired energy proportionality or minimal energy use for the entire range of system utilization. To approximate energy proportionality for all activity levels, we developed various versions of WattDB, a distributed DBMS, which runs on a dynamic cluster of wimpy computing nodes. In this survey, we sketch important design decisions and implementation steps towards the final state of WattDB. For these reasons, we discuss our findings on a cluster with dedicated storage nodes and static data allocation, on dynamic data repartitioning and allocation, and on a dynamic cluster where each node can serve as storage and processing node in a symmetric way. Our experiments show that WattDB dynamically adjusts to the workload present and reconfigures itself to satisfy performance demands while keeping its energy consumption at a minimum. Finally, we compare the performance and energy results of the WattDB software running on the cluster of wimpy nodes with that of a brawny server.
- ZeitschriftenartikelNews(Datenbank-Spektrum: Vol. 14, No. 3, 2014)
- ZeitschriftenartikelHeterogeneity-Aware Operator Placement in Column-Store DBMS(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Karnagel, Tomas; Habich, Dirk; Schlegel, Benjamin; Lehner, WolfgangDue to the tremendous increase in the amount of data efficiently managed by current database systems, optimization is still one of the most challenging issues in database research. Today’s query optimizer determine the most efficient composition of physical operators to execute a given SQL query, whereas the underlying hardware consists of a multi-core CPU. However, hardware systems are more and more shifting towards heterogeneity, combining a multi-core CPU with various computing units, e.g., GPU or FPGA cores. In order to efficiently utilize the provided performance capability of such heterogeneous hardware, the assignment of physical operators to computing units gains importance. In this paper, we propose a heterogeneity-aware physical operator placement strategy (HOP) for in-memory columnar database systems in a heterogeneous environment. Our placement approach takes operators from the physical query execution plan as an input and assigns them to computing units using a cost model at runtime. To enable this runtime decision, our cost model uses the characteristics of the computing units, execution properties of the operators, as well as runtime data to estimate execution costs for each unit. We evaluated our approach on full TPC-H queries within a prototype database engine. As we are going to show, the placement in a heterogeneous hardware system has a high influence on query performance.
- ZeitschriftenartikelEine Erweiterung des Relationalen Modells zur Repräsentation räumlichen Wissens(Datenbank-Spektrum: Vol. 14, No. 3, 2014) Paul, Norbert; Bradley, Patrick E.Das Relationale Modell der Datenhaltung beruht auf der Mengenlehre und steht damit auf dem gleichen mathematischen Fundament wie die Topologie, eine wichtige mathematische Disziplin und gleichzeitig wesentliche Grundlage der räumlichen Datenmodellierung. Die enge Verwandtschaft von Topologie und Relationalem Modell kann genutzt werden, um topologische Konzepte in das Relationale Modell einzuführen: Jede Topologie für eine endliche Menge, etwa eine Datenstruktur oder eine Tabelle einer Datenbank, kann durch eine Relation dargestellt werden. Damit kann eine Tabelle zu einem topologischen Raum werden, und auf derartigen Räumen operieren die relationalen Anfrageoperatoren als topologische Fundamentalkonstruktionen, die wiederum Räume erzeugen. Der relationalen Abgeschlossenheit der Relationalen Algebra entspricht also eine Art „räumlicher Abgeschlossenheit“ in der Topologie. Die relationale Darstellung von Topologien ist nachweisbar effizient und hat für beliebige Topologien zu einer gegebenen Menge optimalen Speicherbedarf. Dieser ist auch im Wesentlichen unabhängig von der Dimension des modellierten Objekts.Eine erste prototypische Implementierung dieser topologisch-Relationalen Algebra illustriert, wie Relationen zu topologischen Räumen werden können und wie die entsprechend erweiterte Relationale Algebra auf diesen Räumen operiert. Zudem gibt es dediziert topologische Anfragen, wie Inneres, Rand oder Abschluss von Mengen in Räumen. An einem Beispiel aus der räumlichen Wissensverarbeitung, dem Region-Connection-Calculus (RCC-8), wird der Nutzen dieses generischen Ansatzes deutlich: Mit räumlichen Datenbankanfragen lassen sich die topologisch definierten RCC-8-Prädikate realisieren und deren Eigenschaften genauer untersuchen.