Дисертації з теми "Query Executor"
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Zeuch, Steffen. "Query Execution on Modern CPUs." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19296.
Повний текст джерелаOver the last decades, database systems have been migrated from disk to memory architectures such as RAM, Flash, or NVRAM. Research has shown that this migration fundamentally shifts the performance bottleneck upwards in the memory hierarchy. Whereas disk-based database systems were largely dominated by disk bandwidth and latency, in-memory database systems mainly depend on the efficiency of faster memory components, e.g., RAM, caches, and registers. To encounter these challenges and enable the full potential of the available processing power of modern CPUs for database systems, this thesis proposes four approaches to reduce the impact of the Memory Wall. First, SIMD instructions increase the cache line utilization and decrease the number of executed instructions if they operate on an appropriate data layout. Thus, we adapt tree structures for processing with SIMD instructions to reduce demands on the memory bus and processing units are decreased. Second, by modeling and executing queries following a unified model, we are able to achieve high resource utilization. Therefore, we propose a unified model that enables us to utilize knowledge about the query plan and the underlying hardware to optimize query execution. Third, we need a fundamental knowledge about the individual database operators and their behavior and requirements to optimally distribute the resources among available computing units. We conduct an in-depth analysis of different workloads using performance counters create these insights. Fourth, we propose a non-invasive progressive optimization approach based on in-depth knowledge of individual operators that is able to optimize query execution during run-time. In sum, using additional run-time statistics gathered by performance counters, a unified model, and SIMD instructions, this thesis improves query execution on modern CPUs.
AYRES, FAUSTO VERAS MARANHAO. "QEEF: AN EXTENSIBLE QUERY EXECUTION ENGINE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5110@1.
Повний текст джерелаO processamento de consultas em Sistemas de Gerência de Banco de Dados tradicionais tem sido largamente estudado na literatura e utilizado comercialmente com enorme sucesso. Isso é devido, em parte, à eficiência das Máquinas de Execução de Consultas (MEC) no suporte ao modelo de execução tradicional. Porém, o surgimento de novos cenários de aplicação, principalmente em conseqüência do modelo computacional da web, motivou a pesquisa de novos modelos de execução, tais como: modelo adaptável e modelo contínuo, além da pesquisa de modelos de dados semi-estruturados, tal como o XML, ambos não suportados pelas MEC tradicionais. O objetivo desta tese consiste no desenvolvimento de uma MEC extensível frente a diferentes modelos de execução e de dados. Adicionalmente, esta proposta trata de maneira ortogonal o modelo de execução e o modelo de dados, o que permite a avaliação de planos de execução de consultas (PEC) com fragmentos em diferentes modelos. Utilizou-se a técnica de framework de software para a especificação da MEC extensível, produzindo o framework QEEF (Query Execution Engine Framework). A extensibilidade da solução reflete-se em um meta-modelo, denominado QUEM (QUery Execution Meta-model), capaz de exprimir diferentes modelos em um meta-PEC. O framework QEEF pré-processa um meta-PEC e produz um PEC final a ser avaliado pela MEC instanciada. Como parte da validação desta proposta, instanciou-se o QEEF para diferentes modelos de execução e de dados.
Querying processing in traditional Database Management Systems (DBMS) has been extensively studied in the literature and adopted in industry. Such success is, in part, due to the performance of their Query Execution Engines (QEE) for supporting the traditional query execution model. The advent of new query scenarios, mainly due to the web computational model, has motivate the research on new execution models such as: adaptive and continuous, and on semistructured data models, such as XML, both not natively supported by traditional query engines. This thesis proposes the development of an extensible QEE adapted to the new execution and data models. Achieving this goal, we use a software design approach based on framework technique to produce the Query Execution Engine Framework (QEEF). Moreover, we address the question of the orthogonality between execution and data models, witch allows for executing query execution plans (QEP) with fragments in different models. The extensibility of our solution is specified by in a QEP by an execution meta- model named QUEM (QUery Execution Meta-model) used to express different models in a meta-QEP. During query evaluation, the latter is pre-processed by the QEEF producing a final QEP to be evaluated by the running QEE. The QEEF is instantiated for different execution and data models as part of the validation of this proposal.
Lundquist, Andreas. "Combining Result Size Calculation and Query Execution for the GraphQL Query Language." Thesis, Linköpings universitet, Databas och informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-167086.
Повний текст джерелаAbadi, Daniel J. "Query execution in column-oriented database systems." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43043.
Повний текст джерелаIncludes bibliographical references (p. 145-148).
There are two obvious ways to map a two-dimension relational database table onto a one-dimensional storage interface: store the table row-by-row, or store the table column-by-column. Historically, database system implementations and research have focused on the row-by row data layout, since it performs best on the most common application for database systems: business transactional data processing. However, there are a set of emerging applications for database systems for which the row-by-row layout performs poorly. These applications are more analytical in nature, whose goal is to read through the data to gain new insight and use it to drive decision making and planning. In this dissertation, we study the problem of poor performance of row-by-row data layout for these emerging applications, and evaluate the column-by-column data layout opportunity as a solution to this problem. There have been a variety of proposals in the literature for how to build a database system on top of column-by-column layout. These proposals have different levels of implementation effort, and have different performance characteristics. If one wanted to build a new database system that utilizes the column-by-column data layout, it is unclear which proposal to follow. This dissertation provides (to the best of our knowledge) the only detailed study of multiple implementation approaches of such systems, categorizing the different approaches into three broad categories, and evaluating the tradeoffs between approaches. We conclude that building a query executer specifically designed for the column-by-column query layout is essential to archive good performance. Consequently, we describe the implementation of C-Store, a new database system with a storage layer and query executer built for column-by-column data layout. We introduce three new query execution techniques that significantly improve performance. First, we look at the problem of integrating compression and execution so that the query executer is capable of directly operating on compressed data. This improves performance by improving I/O (less data needs to be read off disk), and CPU (the data need not be decompressed). We describe our solution to the problem of executer extensibility - how can new compression techniques be added to the system without having to rewrite the operator code? Second, we analyze the problem of tuple construction (stitching together attributes from multiple columns into a row-oriented "tuple").
(cont.) Tuple construction is required when operators need to access multiple attributes from the same tuple; however, if done at the wrong point in a query plan, a significant performance penalty is paid. We introduce an analytical model and some heuristics to use that help decide when in a query plan tuple construction should occur. Third, we introduce a new join technique, the "invisible join" that improves performance of a specific type of join that is common in the applications for which column-by-column data layout is a good idea. Finally, we benchmark performance of the complete C-Store database system against other column-oriented database system implementation approaches, and against row-oriented databases. We benchmark two applications. The first application is a typical analytical application for which column-by-column data layout is known to outperform row-by-row data layout. The second application is another emerging application, the Semantic Web, for which column-oriented database systems are not currently used. We find that on the first application, the complete C-Store system performed 10 to 18 times faster than alternative column-store implementation approaches, and 6 to 12 times faster than a commercial database system that uses a row-by-row data layout. On the Semantic Web application, we find that C-Store outperforms other state-of-the-art data management techniques by an order of magnitude, and outperforms other common data management techniques by almost two orders of magnitude. Benchmark queries, which used to take multiple minutes to execute, can now be answered in several seconds.
by Daniel J. Abadi.
Ph.D.
Fomkin, Ruslan. "Optimization and Execution of Complex Scientific Queries." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9514.
Повний текст джерелаLiu, Feilong. "Accelerating Analytical Query Processing with Data Placement Conscious Optimization and RDMA-aware Query Execution." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543532295915722.
Повний текст джерелаFerreira, Miguel C. (Miguel Cacela Rosa Lopes Ferreira). "Compression and query execution within column oriented databases." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33150.
Повний текст джерелаIncludes bibliographical references (p. 65-66).
Compression is a known technique used by many database management systems ("DBMS") to increase performance[4, 5, 14]. However, not much research has been done in how compression can be used within column oriented architectures. Storing data in column increases the similarity between adjacent records, thus increase the compressibility of the data. In addition, compression schemes not traditionally used in row-oriented DBMSs can be applied to column-oriented systems. This thesis presents a column-oriented query executor designed to operate directly on compressed data. 'We show that operating directly on compressed data can improve query performance. Additionally, the choice of compression scheme depends on the expected query workload, suggesting that for ad-hoc queries we may wish to store a column redundantly under different coding schemes. Furthermore, the executor is designed to be extensible so that the addition of new compression schemes does not impact operator implementation. The executor is part of a larger database system, known as CStore [10].
by Miguel C. Ferreira.
M.Eng.
Gupta, Ankush M. "Cross-engine query execution in federated database systems." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106013.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 47-48).
Duggan et al.have created a reference implementation of the BigDAWG system: a new architecture for future Big Data applications, guided by the philosophy that "one size does not fit all." Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. The importance and effectiveness of such a system has been demonstrated in a hospital application using data from an intensive care unit (ICU). In this report, we implement and evaluate a concrete version of a cross-system Query Executor and its interface with a cross-system Query Planner. In particular, we focus on cross-engine shuffle joins within the BigDAWG system.
by Ankush M. Gupta.
M. Eng.
Neumann, Thomas. "Efficient generation and execution of DAG-structured query graphs." [S.l.] : [s.n.], 2005. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11947805.
Повний текст джерелаNarula, Neha. "Distributed query execution on a replicated and partitioned database." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62436.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (p. 63-64).
Web application developers partition and replicate their data amongst a set of SQL databases to achieve higher throughput. Given multiple copies of tables partioned different ways, developers must manually select different replicas in their application code. This work presents Dixie, a query planner and executor which automatically executes queries over replicas of partitioned data stored in a set of relational databases, and optimizes for high throughput. The challenge in choosing a good query plan lies in predicting query cost, which Dixie does by balancing row retrieval costs with the overhead of contacting many servers to execute a query. For web workloads, per-query overhead in the servers is a large part of the overall cost of execution. Dixie's cost calculation tends to minimize the number of servers used to satisfy a query, which is essential for minimizing this query overhead and obtaining high throughput; this is in direct contrast to optimizers over large data sets that try to maximize parallelism by parallelizing the execution of a query over all the servers. Dixie automatically takes advantage of the addition or removal of replicas without requiring changes in the application code. We show that Dixie sometimes chooses plans that existing parallel database query optimizers might not consider. For certain queries, Dixie chooses a plan that gives a 2.3x improvement in overall system throughput over a plan which does not take into account perserver query overhead costs. Using table replicas, Dixie provides a throughput improvement of 35% over a naive execution without replicas on an artificial workload generated by Pinax, an open source social web site.
by Neha Narula.
S.M.
Ling, Daniel Hiak Ong. "Query execution and temporal support in a distributed database system." Thesis, University of Ulster, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.328221.
Повний текст джерелаDu, Chu-Ming. "A practical approach to set orientated query execution in semistructured databases." Thesis, University of Birmingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.412622.
Повний текст джерелаApaydin, Tan. "Query Support for Multi-Dimensional and Dynamic Databases." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1221842826.
Повний текст джерелаZeuch, Steffen [Verfasser], Johann-Christoph [Gutachter] Freytag, Wolfgang [Gutachter] Lehner, and Stefan [Gutachter] Manegold. "Query Execution on Modern CPUs / Steffen Zeuch ; Gutachter: Johann-Christoph Freytag, Wolfgang Lehner, Stefan Manegold." Berlin : Humboldt-Universität zu Berlin, 2018. http://d-nb.info/1182541461/34.
Повний текст джерелаRaghavan, Venkatesh. "VAMANA -- A high performance, scalable and cost driven XPath engine." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0505104-185545/.
Повний текст джерелаCeran, Erhan. "A C++ Distributed Database Select-project-join Queryprocessor On A Hpc Cluster." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614311/index.pdf.
Повний текст джерелаhowever distributed DBMS are still not used widely because of the design difficulties. In this study, we aim to help overcome these difficulties by implementing a simulation testbed for a distributed query plan processor. This testbed works on our departmental HPC cluster machine and is able to perform select, project and join operations. A data generation module has also been implemented which preserves the foreign key and primary key constraints in the database schema. The testbed has capability to measure, simulate and estimate the response time of a given query execution plan using specified communication network parameters. Extensive experimental work is performed to show the correctness of the produced results. The estimated execution time costs are also compared with the actual run-times obtained from the testbed to verify the proposed estimation functions. Thus, we make sure that these estimation iv functions can be used in distributed database query optimization and distributed database design tools.
Hahn, Florian [Verfasser], and J. [Akademischer Betreuer] Müller-Quade. "Practical yet Provably Secure: Complex Database Query Execution over Encrypted Data / Florian Hahn ; Betreuer: J. Müller-Quade." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/117852809X/34.
Повний текст джерелаOnder, Ibrahim Seckin. "Execution Of Distributed Database Queries On A Hpc System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611524/index.pdf.
Повний текст джерелаMuller, Leslie. "'n Ondersoek na en bydraes tot navraaghantering en -optimering deur databasisbestuurstelsels / L. Muller." Thesis, North-West University, 2006. http://hdl.handle.net/10394/1181.
Повний текст джерелаThesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2007.
Idris, Muhammad. "Real-time Business Intelligence through Compact and Efficient Query Processing Under Updates." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/284705/5/contratMI.pdf.
Повний текст джерелаDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Huber, Frank. "Anfragebearbeitung auf Mehrkern-Rechnerarchitekturen." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16512.
Повний текст джерелаThe upcoming generation of many-core architectures poses several new challenges for software development: Software design and software implementation has to change from sequential execution to a highly parallel execution, such that it takes full advantage of the steadily growing number of cores on a single processor. With this thesis, we investigate such highly parallel program execution in the context of relational database management systems (RDBMSs). We consider the complete process of query processing and identify four problem areas which are crucial for efficient parallel query processing on many-core architectures. These four areas are: Hardware, physical data model, query execution, and query optimization. Furthermore, we present a framework which covers all four parts, one after another. First, we give a detailed survey of computer hardware with a special focus on memory and processors. Based on this survey we propose a hardware model. Our abstraction aims to simplify the task of software development on many-core hardware. Based on the hardware model, we investigate physical data models and evaluate how the physical data model may support optimal query execution by providing efficient and parallelizable data structures. Additionally, we design a new index structure that utilizes data parallel execution by using SIMD operations. The next layer within our framework is query execution, for which we present a new task based query execution model. Our query execution model allows for a lightweight parallelism. Finally, we cover query optimization by explaining approaches for optimizing resource utilization on a query local point of view as well as query global point of view.
Kamat, Niranjan Ganesh. "Sampling-based Techniques for Interactive Exploration of Large Datasets." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523552932728325.
Повний текст джерелаLarsson, Markus, and David Ångström. "A Performance Comparison of Auto-Generated GraphQL Server Implementations." Thesis, Linköpings universitet, Tekniska fakulteten, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170825.
Повний текст джерелаLinköping GraphQL Benchmark (LinGBM)
Verlaine, Lionel. "Optimisation des requêtes dans une machine bases de données." Paris 6, 1986. http://www.theses.fr/1986PA066532.
Повний текст джерелаScarlato, Michele. "Sicurezza di rete, analisi del traffico e monitoraggio." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3223/.
Повний текст джерелаTabbara, Hiba. "Native Language OLAP Query Execution." Thesis, 2012. http://spectrum.library.concordia.ca/974715/1/Tabbara_PhD__F2012.pdf.
Повний текст джерела"Rxqee - relational-xml query execution engine." Tese, MAXWELL, 2004. http://www.maxwell.lambda.ele.puc-rio.br/cgi-bin/db2www/PRG_0991.D2W/SHOW?Cont=5925:pt&Mat=&Sys=&Nr=&Fun=&CdLinPrg=pt.
Повний текст джерелаTaleb, Ahmad. "Query Optimization and Execution for Multi-Dimensional OLAP." Thesis, 2011. http://spectrum.library.concordia.ca/7388/1/Taleb_PhD_S2011.pdf.
Повний текст джерелаYu, Chia-Hao, and 余家豪. "A Distributed Query Execution Protocol in Sensor Networks." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/20156514028609322851.
Повний текст джерела國立中央大學
資訊工程研究所
92
In the wireless sensor networks, query execution over a specific geographical region is an essential function for collecting sensed data or detecting unusual event. However, sensor nodes deployed in the sensor networks have limited battery power. Hence, how to find a minimum number of connected sensor nodes that are sufficient to cover the queried region is an important issue in the sensor networks. This paper proposes an efficient distributed protocol to find a subset of connected sensor nodes to cover the queried region. Each sensor node in the sensor network determines whether to sense the queried region according to its priority value, which is determined by the remaining power or sensing area within the queried region. The proposed protocol can efficiently construct a subset of connected sensing nodes and fast response the query request in the sensed region. Simulation results show that the proposed protocol is more efficient and consumes less communication overhead than other existing protocol.
Juliana, Hsieh. "An Optimization Strategy for Efficient Query Execution over Streaming Sources." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0016-1303200709285431.
Повний текст джерелаPolychroniou, Orestis. "Analytical Query Execution Optimized for all Layers of Modern Hardware." Thesis, 2018. https://doi.org/10.7916/D8Q25G8H.
Повний текст джерелаHsieh, Juliana, and 薛佩如. "An Optimization Strategy for Efficient Query Execution over Streaming Sources." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/70766095466901570073.
Повний текст джерела國立清華大學
資訊工程學系
94
Continuous queries over data streams, particularly the joins of streams, have gained popularity as the scope of their applications has increased in the past years. Applications range from network monitoring to sensor processing for environmental monitoring or inventory tracking. The cost of evaluating such queries over streaming sources may vary according to the order in which the joins of streams are processed. In order to lower the cost of executing a query, the query optimizer needs to generate an execution plan that better fits the current conditions of the environment. Existing optimizers try to resolve the above problem by finding a better probing order for multi-way join operators or choosing a better sequence for the binary join operators. However, there are cases where the performance of a hybrid plan (query plan containing both types of operators) exceeds the performance of query plans composed of a single multi-way operator or trees binary join operators. We address the problem of finding a low-cost execution plan in order to execute continuous multi-way join queries over infinite data streams. The search space encompasses plans consisting of a single multi-way operator, plans composed of binary join operators, and hybrid plans. We propose heuristics with a partial cost-based optimization technique to address the three main components of a query optimizer, namely the search space, the cost model and the search strategy. The cost model is used to evaluate all feasible query plans and the heuristics are used to prune the candidates that cannot lead us to good plans. In our work, we evaluate the performance of the proposed approach by comparing the time needed to produce the low-cost query execution plan and quality of our result with the optimum solution and the single multi-way operator with probing order. The result shows that our methodology can find a better plan for the current environment and that is close to the optimal query plan.
Meng, Yabin. "SQL Query Disassembler: An Approach to Managing the Execution of Large SQL Queries." Thesis, 2007. http://hdl.handle.net/1974/701.
Повний текст джерелаThesis (Master, Computing) -- Queen's University, 2007-09-17 22:05:05.304
Neumann, Thomas [Verfasser]. "Efficient generation and execution of DAG-structured query graphs / vorgelegt von Thomas Neumann." 2005. http://d-nb.info/975790420/34.
Повний текст джерелаAbdul, Khalek Shadi. "Systematic testing using test summaries : effective and efficient testing of relational applications." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4574.
Повний текст джерелаtext
Liu, Kevin H. "Skew characteristics and their effects on parallel relational query processing." Thesis, 1997. https://vuir.vu.edu.au/30101/.
Повний текст джерела