Cooperative scans

Data mining, information retrieval and other application areas exhibit a query load with multiple concurrent queries touching a large fraction of a relation. This leads to individual query plans based on a table scan or large index scan. The implementation of this access path in most database systems is straightforward. The Scan operator issues next page requests to the buffer manager without concern for the system state. Conversely, the buffer manager is not aware of the work ahead and it focuses on keeping the most-recently-used pages in the buffer pool. This paper introduces cooperative scans -- a new algorithm, based on a better sharing of knowledge and responsibility between the Scan operator and the buffer manager, which significantly improves performance of concurrent scan queries. In this approach, queries share the buffer content, and progress of the scans is optimized by the buffer manager by minimizing the number of disk transfers in light of the total workload ahead. The experimental results are based on a simulation of the various disk-access scheduling policies, and implementation of the cooperative scans within PostgreSQL and MonetDB/X100. These real-life experiments show that with a little effort the performance of existing database systems on concurrent scan queries can be strongly improve

Benchmarking insider threat intrusion detection systems

viii, 97 leaves : ill. ; 29 cm.Includes abstract.Includes bibliographical references (leaves 88-97).An intrusion detection system generally detects unwanted manipulations to computer systems. In recent years, this technology has been used to protect personal information after it has been collected by an organization. Selecting an appropriate IDS is an important decision for system security administrators, to keep authorized employees from abusing their access to the system to exploit sensitive information. To date, little work has been done to create a benchmark for small and mid-size organizations to measure and compare the capability of different insider threat IDSs which are based on user profiling. It motivates us to create a benchmark which enables organizations to compare these different IDSs. The benchmark is used to produce useful comparisons of the accuracy and overhead of two key research implementations of future insider threat intrusion algorithms, which are based on user behavior

Prefetching techniques for client server object-oriented database systems

The performance of many object-oriented database applications suffers from the page fetch latency which is determined by the expense of disk access. In this work we suggest several prefetching techniques to avoid, or at least to reduce, page fetch latency. In practice no prediction technique is perfect and no prefetching technique can entirely eliminate delay due to page fetch latency. Therefore we are interested in the trade-off between the level of accuracy required for obtaining good results in terms of elapsed time reduction and the processing overhead needed to achieve this level of accuracy. If prefetching accuracy is high then the total elapsed time of an application can be reduced significantly otherwise if the prefetching accuracy is low, many incorrect pages are prefetched and the extra load on the client, network, server and disks decreases the whole system performance. Access pattern of object-oriented databases are often complex and usually hard to predict accurately. The ..

Efficient Reorganisation of Hybrid Index Structures Supporting Multimedia Search Criteria

This thesis describes the development and setup of hybrid index structures. They are access methods for retrieval techniques in hybrid data spaces which are formed by one or more relational or normalised columns in conjunction with one non-relational or non-normalised column. Examples for these hybrid data spaces are, among others, textual data combined with geographical ones or data from enterprise content management systems. However, all non-relational data types may be stored as well as image feature vectors or comparable types. Hybrid index structures are known to function efficiently regarding retrieval operations. Unfortunately, little information is available about reorganisation operations which insert or update the row tuples. The fundamental research is mainly executed in simulation based environments. This work is written ensuing from a previous thesis that implements hybrid access structures in realistic database surroundings. During this implementation it has become obvious that retrieval works efficiently. Yet, the restructuring approaches require too much effort to be set up, e.g., in web search engine environments where several thousands of documents are inserted or modified every day. These search engines rely on relational database systems as storage backends. Hence, the setup of these access methods for hybrid data spaces is required in real world database management systems. This thesis tries to apply a systematic approach for the optimisation of the rearrangement algorithms inside realistic scenarios. Thus, a measurement and evaluation scheme is created which is repeatedly deployed to an evolving state and a model of hybrid index structures in order to optimise the regrouping algorithms to make a setup of hybrid index structures in real world information systems possible. Thus, a set of input corpora is selected which is applied to the test suite as well as an evaluation scheme. To sum up, it can be said that this thesis describes input sets, a test suite including an evaluation scheme as well as optimisation iterations on reorganisation algorithms reflecting a theoretical model framework to provide efficient reorganisations of hybrid index structures supporting multimedia search criteria

Effiziente Laufzeitsysteme für Datenlager

Aktuelle DBMS sind für OLTP-Anwendungen optimiert. Die Anforderungen von OLAP- und OLTP-Anwendungen an das DBMS unterscheiden sich erheblich. Wir habe einige dieser Unterschiede identifiziert und ein Laufzeitsystem entwickelt, das diese Unterschiede ausnutzt, um die Leistung für OLAP-Anwendungen zu verbessern. Die entwickelten Techniken beinhalten (1) die Verwendung einer virtuellen Maschine zur Auswertung von Ausdrücken, (2) die effiziente Integration von Kompression und (3) spezifische algebraische Operatoren. Unsere Evaluierung hat ergeben, daß die Verwendung dieser Techniken signifikante (Faktor 2 oder mehr) Leistungssteigerungen ermöglicht

Adaptive Database Systems Based On Query Feedback and Cached Results

This dissertation explores the query optimization technique of using cached results and feedback for improving performance of database systems. Cached results and experience obtained by running queries are used to save execution time for follow–up queries, adapt data and system parameters, and improve overall system performance. First, we develop a framework which integrates query optimization and cache management. The optimizer is capable of generating efficient query plans using previous query results cached on the disk. Alternative methods to access and update the caches are considered by the optimizer based on cost estimation. Different cache management strategies are also included in this framework for comparison. Empirical performance study verifies the advantage and practicality of this framework. To help the optimizer in selecting the best plan, we propose a novel approach for providing accurate but cost-effective selectivity estimation. Distribution of attribute values is regressed in real time, using actual query result sizes obtained as feedback, to make accurate selectivity estimation. This method avoids the expensive off-line database access overhead required by the conventional methods and adapts fairly well to updates and query locality. This is verified empirically. To execute a query plan more efficiently, a buffer pool is usually provided for caching data pages in memory to reduce disk accesses. We enhance buffer utilization by devising a buffer allocation scheme for recurring queries using page fault feedback obtained from previous executions. Performance improvement of this scheme is shown by empirical examples and a systematic simulation

Query Interactions in Database Systems

The typical workload in a database system consists of a mix of multiple queries of different types, running concurrently and interacting with each other. The same query may have different performance in different mixes. Hence, optimizing performance requires reasoning about query mixes and their interactions, rather than considering individual queries or query types. In this dissertation, we demonstrate how queries affect each other when they are executing concurrently in different mixes. We show the significant impact that query interactions can have on the end-to-end workload performance. A major hurdle in the understanding of query interactions in database systems is that there is a large spectrum of possible causes of interactions. For example, query interactions can happen because of any of the resource-related, data-related or configuration-related dependencies that exist in the system. This variation in underlying causes makes it very difficult to come up with robust analytical performance models to capture and model query interactions. We present a new approach for modeling performance in the presence of interactions, based on conducting experiments to measure the effect of query interactions and fitting statistical models to the data collected in these experiments to capture the impact of query interactions. The experiments collect samples of the different possible query mixes, and measure the performance metrics of interest for the different queries in these sample mixes. Statistical models such as simple regression and instance-based learning techniques are used to train models from these sample mixes. This approach requires no prior assumptions about the internal workings of the database system or the nature or cause of the interactions, making it portable across systems. We demonstrate the potential of capturing, modeling, and exploiting query interactions by developing techniques to help in two database performance related tasks: workload scheduling and estimating the completion time of a workload. These are important workload management problems that database administrators have to deal with routinely. We consider the problem of scheduling a workload of report-generation queries. Our scheduling algorithms employ statistical performance models to schedule appropriate query mixes for the given workload. Our experimental evaluation demonstrates that our interaction-aware scheduling algorithms outperform scheduling policies that are typically used in database systems. The problem of estimating the completion time of a workload is an important problem, and the state of the art does not offer any systematic solution. Typically database administrators rely on heuristics or observations of past behavior to solve this problem. We propose a more rigorous solution to this problem, based on a workload simulator that employs performance models to simulate the execution of the different mixes that make up a workload. This mix-based simulator provides a systematic tool that can help database administrators in estimating workload completion time. Our experimental evaluation shows that our approach can estimate the workload completion times with a high degree of accuracy. Overall, this dissertation demonstrates that reasoning about query interactions holds significant potential for realizing performance improvements in database systems. The techniques developed in this work can be viewed as initial steps in this interesting area of research, with lots of potential for future work

Mapper: an efficient data transformation operator

Tese de doutoramento em Informática (Engenharia Informática), apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Data transformations are fundamental operations in legacy data migration, data integration, data cleaning, and data warehousing. These operations are often implemented as relational queries that aim at leveraging the optimization capabilities of most DBMSs. However, relational query languages like SQL are not expressive enough to specify one-to-many data transformations, an important class of data transformations that produce several output tuples for a single input tuple. These transformations are required for solving several types of data heterogeneities, like those that occur when the source data represents aggregations of the target data. This thesis proposes a new relational operator, named data mapper, as an extension to the relational algebra to address one-to-many data transformations and focus on its optimization. It also provides algebraic rewriting rules and execution algorithms for the logical and physical optimization, respectively. As a result, queries may be expressed as a combination of standard relational operators and mappers. The proposed optimizations have been experimentally validated and the key factors that influence the obtained performance gains identified. Keywords: Relational Algebra, Data Transformation, Data Integration, Data Cleaning, Data WarehousingAs transformações de dados são operações fundamentais em processos de migração de dados de sistemas legados, integração de dados, limpeza de dados e ao refrescamento de Data Warehouses. Usualmente, estas operações são implementadas através de interrogações relacionais por forma a explorar as optimizações proporcionadas pela maioria dos SGBDs. No entanto, as linguagens de interrogação relacionais, como o SQL, não são suficientemente expressivas para especificar as transformações de dados do tipo um-para-muitos. Esta importante classe de transformações é necessária para resolver de forma adequada diversos tipos de heterogeneidades de dados tais como as que decorrem de situações em que os dados do esquema origem representam uma agregação dos dados do sistema destino. Esta tese propõe a extensão da álgebra relacional com um novo operador relacional denominado data mapper, por forma a permitir a especificação e optimização de transformações de dados um-para-muitos. O trabalho apresenta regras de reescrita algébrica juntamente com diversos algoritmos de execução que proporcionam, respectivamente, a optimização lógica e física de transformações de dados um-para-muitos. Como resultado, é possivel optimizar transformações de dados que combinem operadores relacionais comuns com data mappers. As optimizações propostas foram validadas experimentalmente e identificados os factores que influênciam os seus respectivos ganhos

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Today, an ever-increasing number of researchers, businesses, and data scientists collect and analyze massive amounts of data in database systems. The database system needs to process the resulting highly concurrent analytical workloads by exploiting modern multi-socket multi-core processor systems with non-uniform memory access (NUMA) architectures and increasing memory sizes. Conventional execution engines, however, are not designed for many cores, and neither scale nor perform efficiently on modern multi-core NUMA architectures. Firstly, their query-centric approach, where each query is optimized and evaluated independently, can result in unnecessary contention for hardware resources due to redundant work found across queries in highly concurrent workloads. Secondly, they are unaware of the non-uniform memory access costs and the underlying hardware topology, incurring unnecessarily expensive memory accesses and bandwidth saturation. In this thesis, we show how these scalability and performance impediments can be solved by exploiting sharing among concurrent queries and incorporating NUMA-aware adaptive task scheduling and data placement strategies in the execution engine. Regarding sharing, we identify and categorize state-of-the-art techniques for sharing data and work across concurrent queries at run-time into two categories: reactive sharing, which shares intermediate results across common query sub-plans, and proactive sharing, which builds a global query plan with shared operators to evaluate queries. We integrate the original research prototypes that introduce reactive and proactive sharing, perform a sensitivity analysis, and show how and when each technique benefits performance. Our most significant finding is that reactive and proactive sharing can be combined to exploit the advantages of both sharing techniques for highly concurrent analytical workloads. Regarding NUMA-awareness, we identify, implement, and compare various combinations of task scheduling and data placement strategies under a diverse set of highly concurrent analytical workloads. We develop a prototype based on a commercial main-memory column-store database system. Our most significant finding is that there is no single strategy for task scheduling and data placement that is best for all workloads. In specific, inter-socket stealing of memory-intensive tasks can hurt overall performance, and unnecessary partitioning of data across sockets involves an overhead. For this reason, we implement algorithms that adapt task scheduling and data placement to the workload at run-time. Our experiments show that both sharing and NUMA-awareness can significantly improve the performance and scalability of highly concurrent analytical workloads on modern multi-core servers. Thus, we argue that sharing and NUMA-awareness are key factors for supporting faster processing of big data analytical applications, fully exploiting the hardware resources of modern multi-core servers, and for more responsive user experience