MonetDB/X100 - A DBMS in the CPU cache

X100 is a new execution engine for the MonetDB system, that improves execution speed and overcomes its main memory limitation. It introduces t

Vectorwise: Beyond Column Stores

textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution of its technical architecture, customer reactions to the product and its future research and development roadmap. One take-away from this story is that the novelty in Vectorwise is much more than just column-storage: it boasts many query processing innovations in its vectorized execution model, and an adaptive mixed row/column data storage model with indexing support tailored to analytical workloads. Another one is that there is a long road from research prototype to commercial product, though database research continues to achieve a strong innovative influence on product development

Improving I/O Bandwidth for Data-Intensive Applications

High disk bandwidth in data-intensive applications is usually achieved with expensive hardware solutions consisting of a large number of disks. In this article we present our current work on software methods for improving disk bandwidth in ColumnBM, a new storage system for MonetDB/X100 query execution engine. Two novel techniques are discussed: superscalar compression for standalone queries and cooperative scans for multi-query optimization

Efficient Cross-Device Query Processing

The increasing diversity of hardware within a single system promises large performance gains but also poses a challenge for data management systems. Strategies for the efficient use of hardware with large performance differences are still lacking. For example, existing research on GPU supported data management largely handles the GPU in isolation from the system’s CPU — The GPU is considered the central processor and the CPU used only to mitigate the GPU’s weaknesses where necessary. To make efficient use of all available devices, we developed a processing strategy that lets unequal devices like GPU and CPU combine their strengths rather than work in isolation. To this end, we decompose relational data into individual bits and place the resulting partitions on the appropriate devices. Operations are processed in phases, each phase executed on one device. This way, we achieve significant performance gains and good load distribution among the available devices in a limited real-life use case. To grow this idea into a generic system, we identify challenges as well as potential hardware configurations and applications that can benefit from this approach

Runtime Optimizations for Prediction with Tree-Based Models

Tree-based models have proven to be an effective solution for web ranking as well as other problems in diverse domains. This paper focuses on optimizing the runtime performance of applying such models to make predictions, given an already-trained model. Although exceedingly simple conceptually, most implementations of tree-based models do not efficiently utilize modern superscalar processor architectures. By laying out data structures in memory in a more cache-conscious fashion, removing branches from the execution flow using a technique called predication, and micro-batching predictions using a technique called vectorization, we are able to better exploit modern processor architectures and significantly improve the speed of tree-based models over hard-coded if-else blocks. Our work contributes to the exploration of architecture-conscious runtime implementations of machine learning algorithms

Optymalizacja zapytań w środowisku heterogenicznym CPU/GPU dla baz danych szeregów czasowych

In recent years, processing and exploration of time series has experienced a noticeable interest. Growing volumes of data and needs of efficient processing pushed the research in new directions, including hardware based solutions. Graphics Processing Units (GPU) have significantly more applications than just rendering images. They are also used in general purpose computing to solve problems that can benefit from massive parallel processing. There are numerous reports confirming the effectiveness of GPU in science and industrial applications. However, there are several issues related with GPU usage as a databases coprocessor that must be considered. First, all computations on the GPU are preceded by time consuming memory transfers. In this thesis we present a study on lossless lightweight compression algorithms in the context of GPU computations and time series database systems. We discuss the algorithms, their application and implementation details on GPU. We analyse their influence on the data processing efficiency, taking into account both the data transfer time and decompression time. Moreover, we propose a data adaptive compression planner based on those algorithms, which uses hierarchy of multiple compression algorithms in order to further reduce the data size. Secondly, there are tasks that either hardly suit GPU or fit GPU only partially. This may be related to the size or type of the task. We elaborate on heterogeneous CPU/GPU computation environment and optimization method that seeks equilibrium between these two computation platforms. This method is based on heuristic search for bi-objective optimal execution plans. The underlying model mimics the commodity market, where devices are producers and queries are consumers. The value of resources of computing devices is controlled by supply-and-demand laws. Our model of the optimization criteria allows finding solutions for heterogeneous query processing problems where existing methods have been ineffective. Furthermore, it also offers lower time complexity and higher accuracy than other methods. The dissertation also discusses an exemplary application of time series databases: the analysis of zebra mussel (Dreissena polymorpha) behaviour based on observations of the change of the gap between the valves, collected as a time series. We propose a new algorithm based on wavelets and kernel methods that detects relevant events in the collected data. This algorithm allows us to extract elementary behaviour events from the observations. Moreover, we propose an efficient framework for automatic classification to separate the control and stressful conditions. Since zebra mussels are well-known bioindicators this is an important step towards the creation of an advanced environmental biomonitoring system.W ostatnich latach przetwarzanie i badanie szeregów czasowych zyskało spore zainteresowanie. Rosnące ilości danych i potrzeba ich sprawnego przetwarzania nadały nowe kierunki prowadzonym badaniom, które uwzględniają również wykorzystanie rozwiązań sprzętowych. Procesory graficzne (GPU) mają znacznie więcej zastosowań niż tylko wyświetlanie obrazów. Coraz częściej są wykorzystywane przy rozwiązywaniu problemów obliczeniowych ogólnego zastosowania, które mogą spożytkować możliwości przetwarzania masywnie równoległego. Wiele źródeł potwierdza skuteczność GPU zarówno w nauce, jak i w zastosowaniach w przemyśle. Jest jednak kilka kwestii związanych z użyciem GPU jako koprocesora w bazach danych, które trzeba mieć na uwadze. Po pierwsze, wszystkie obliczenia na GPU są poprzedzone czasochłonnym transferem danych. W pracy zaprezentowano rezultaty badań dotyczących lekkich i bezstratnych algorytmów kompresji w kontekście obliczeń GPU i systemów baz danych dla szeregów czasowych. Omówione zostały algorytmy, ich zastosowanie oraz szczegóły implementacyjne na GPU. Rozważono wpływ algorytmów na wydajność przetwarzania danych z uwzględnieniem czasu transferu i dekompresji danych. Ponadto, zaproponowany został adaptacyjny planer kompresji danych, który wykorzystuje różne algorytmy lekkiej kompresji w celu dalszego zmniejszenia rozmiaru skompresowanych danych. Kolejnym problemem są zadania, które źle (lub tylko częściowo) wpisują się w architekturę GPU. Może być to związane z rozmiarem lub rodzajem zadania. W pracy zaproponowany został model heterogenicznych obliczeń na CPU/GPU. Przedstawiono metody optymalizacji, poszukujące równowagi między różnymi platformami obliczeniowymi. Opierają się one na heurystycznym poszukiwaniu planów wykonania uwzględniających wiele celów optymalizacyjnych. Model leżący u podstaw tego podejścia naśladuje rynki towarowe, gdzie urządzenia są traktowane jako producenci, konsumentami są natomiast plany zapytań. Wartość zasobów urządzeń komputerowych jest kontrolowana przez prawa popytu i podaży. Zastosowanie różnych kryteriów optymalizacji pozwala rozwiązać problemy z zakresu heterogenicznego przetwarzania zapytań, dla których dotychczasowe metody były nieskuteczne. Ponadto proponowane rozwiązania wyróżnia mniejsza złożoność czasowa i lepsza dokładność. W rozprawie omówiono przykładowe zastosowanie baz danych szeregów czasowych: analizę zachowań racicznicy zmiennej (Dreissena polymorpha) opartą na obserwacji rozchyleń muszli zapisanej w postaci szeregów czasowych. Proponowany jest nowy algorytm oparty na falkach i funkcjach jądrowych (ang. kernel functions), który wykrywa odpowiednie zdarzenia w zebranych danych. Algorytm ten pozwala wyodrębnić zdarzenia elementarne z zapisanych obserwacji. Ponadto proponowany jest zarys systemu do automatycznego oddzielenia pomiarów kontrolnych i tych dokonanych w stresujących warunkach. Jako że małże z gatunku Dreissena polymorpha są znanymi wskaźnikami biologicznymi, jest to istotny krok w kierunku biologicznych systemów wczesnego ostrzegania

Functional pearl: a SQL to C compiler in 500 lines of code

We present the design and implementation of a SQL query processor that outperforms existing database systems and is written in just about 500 lines of Scala code - a convincing case study that high-level functional programming can handily beat C for systems-level programming where the last drop of performance matters. The key enabler is a shift in perspective towards generative programming. The core of the query engine is an interpreter for relational algebra operations, written in Scala. Using the open-source LMS Framework (Lightweight Modular Staging), we turn this interpreter into a query compiler with very low effort. To do so, we capitalize on an old and widely known result from partial evaluation known as Futamura projections, which state that a program that can specialize an interpreter to any given input program is equivalent to a compiler. In this pearl, we discuss LMS programming patterns such as mixed-stage data structures (e.g. data records with static schema and dynamic field components) and techniques to generate low-level C code, including specialized data structures and data loading primitives

Query processing of pre-partitioned data using Sandwich Operators

textabstractIn this paper we present the Sandwich Operators, an elegant approach to exploit pre-sorting or pre-grouping from clustered storage schemes in operators such as Aggregation/Grouping, HashJoin, and Sort of a database management system. Thereby, each of these operator types is "sandwiched" by two new operators, namely PartitionSplit and PartitionRestart. PartitionSplit splits the input relation into its smaller independent groups on which the sandwiched operator is executed. After a group is processed PartitionRestart is used to trigger the execution on the following group. Executing one of these operator types with the help of the Sandwich Operators introduces minimal overhead and does not penalty performance of the sandwiched operator as its implementation remains unchanged. On the contrary, we show that sandwiched execution of an operator results in lower memory consumption and faster execution time. PartitionSplit and PartitionRestart replace special implementations of partitioned versions of these operator. Sandwich Operators also turn blocking operators in streaming operators, resulting in faster response times for the first query results

Accelerating Foreign-Key Joins using Asymmetric Memory Channels

Indexed Foreign-Key Joins expose a very asymmetric access pattern: the Foreign-Key Index is sequentially scanned whilst the Primary-Key table is target of many quasi-random lookups which is the dominant cost factor. To reduce the costs of the random lookups the fact-table can be (re-) partitioned at runtime to increase access locality on the dimension table, and thus limit the random memory access to inside the CPU's cache. However, this is very hard to optimize and the performance impact on recent architectures is limited because the partitioning costs consume most of the achievable join improvement. GPGPUs on the other hand have an architecture that is well suited for this operation: a relatively slow connection to the large system memory and a very fast connection to the smaller internal device memory. We show how to accelerate Foreign-Key Joins by executing the random table lookups on the GPU's VRAM while sequentially streaming the Foreign- Key-Index through the PCI-E Bus. We also experimentally study the memory access costs on GPU and CPU to provide estimations of the benefit of this technique

From Cooperative Scans to Predictive Buffer Management

In analytical applications, database systems often need to sustain workloads with multiple concurrent scans hitting the same table. The Cooperative Scans (CScans) framework, which introduces an Active Buffer Manager (ABM) component into the database architecture, has been the most effective and elaborate response to this problem, and was initially developed in the X100 research prototype. We now report on the the experiences of integrating Cooperative Scans into its industrial-strength successor, the Vectorwise database product. During this implementation we invented a simpler optimization of concurrent scan buffer management, called Predictive Buffer Management (PBM). PBM is based on the observation that in a workload with long-running scans, the buffer manager has quite a bit of information on the workload in the immediate future, such that an approximation of the ideal OPT algorithm becomes feasible. In the evaluation on both synthetic benchmarks as well as a TPC-H throughput run we compare the benefits of naive buffer management (LRU) versus CScans, PBM and OPT; showing that PBM achieves benefits close to Cooperative Scans, while incurring much lower architectural impact.Comment: VLDB201