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Статті в журналах з теми "Query Executor"
Huang, Silu, Erkang Zhu, Surajit Chaudhuri, and Leonhard Spiegelberg. "T-Rex: Optimizing Pattern Search on Time Series." Proceedings of the ACM on Management of Data 1, no. 2 (June 13, 2023): 1–26. http://dx.doi.org/10.1145/3589275.
Повний текст джерелаYogatama, Bobbi W., Weiwei Gong, and Xiangyao Yu. "Orchestrating data placement and query execution in heterogeneous CPU-GPU DBMS." Proceedings of the VLDB Endowment 15, no. 11 (July 2022): 2491–503. http://dx.doi.org/10.14778/3551793.3551809.
Повний текст джерелаBarish, G., and C. A. Knoblock. "An Expressive Language and Efficient Execution System for Software Agents." Journal of Artificial Intelligence Research 23 (June 1, 2005): 625–66. http://dx.doi.org/10.1613/jair.1548.
Повний текст джерелаYang, Yifei, Matt Youill, Matthew Woicik, Yizhou Liu, Xiangyao Yu, Marco Serafini, Ashraf Aboulnaga, and Michael Stonebraker. "FlexPushdownDB." Proceedings of the VLDB Endowment 14, no. 11 (July 2021): 2101–13. http://dx.doi.org/10.14778/3476249.3476265.
Повний текст джерелаDAS, ARIYAM, and CARLO ZANIOLO. "A Case for Stale Synchronous Distributed Model for Declarative Recursive Computation." Theory and Practice of Logic Programming 19, no. 5-6 (September 2019): 1056–72. http://dx.doi.org/10.1017/s1471068419000358.
Повний текст джерелаPaudel, Nawaraj, and Jagdish Bhatta. "Cost-Based Query Optimization in Centralized Relational Databases." Journal of Institute of Science and Technology 24, no. 1 (June 26, 2019): 42–46. http://dx.doi.org/10.3126/jist.v24i1.24627.
Повний текст джерелаWang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio, and Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning." International Journal of Database Management Systems 13, no. 1 (February 28, 2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.
Повний текст джерелаSen, Rathijit, Abhishek Roy, Alekh Jindal, Rui Fang, Jeff Zheng, Xiaolei Liu, and Ruiping Li. "AutoExecutor." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2855–58. http://dx.doi.org/10.14778/3476311.3476362.
Повний текст джерелаBeedkar, Kaustubh, David Brekardin, Jorge-Anulfo Quiané-Ruiz, and Volker Markl. "Compliant geo-distributed data processing in action." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2843–46. http://dx.doi.org/10.14778/3476311.3476359.
Повний текст джерелаAzhir, Elham, Mehdi Hosseinzadeh, Faheem Khan, and Amir Mosavi. "Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark." Mathematics 10, no. 19 (September 26, 2022): 3517. http://dx.doi.org/10.3390/math10193517.
Повний текст джерелаДисертації з теми "Query Executor"
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.
Книги з теми "Query Executor"
Ling, Daniel Hiak Ong. Query execution and temporal support in a distributed database system. [s.l: The Author], 1988.
Знайти повний текст джерелаPolychroniou, Orestis. Analytical Query Execution Optimized for all Layers of Modern Hardware. [New York, N.Y.?]: [publisher not identified], 2018.
Знайти повний текст джерелаKrogh, Jesper Wisborg. MySQL 8 Query Performance Tuning: A Systematic Method for Improving Execution Speeds. Apress L. P., 2020.
Знайти повний текст джерелаMARROW, Jack. As Melhores Ideias de NegÓcios Da Classe: Ideias de Pequenos NegÓcios para Quem Quer Executar Seu PrÓprio NegÓcio. Independently Published, 2022.
Знайти повний текст джерелаЧастини книг з теми "Query Executor"
Sharygin, Eugene, Ruben Buchatskiy, Roman Zhuykov, and Arseny Sher. "Runtime Specialization of PostgreSQL Query Executor." In Lecture Notes in Computer Science, 375–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74313-4_27.
Повний текст джерелаDombrovskaya, Henrietta, Boris Novikov, and Anna Bailliekova. "Understanding Execution Plans." In PostgreSQL Query Optimization, 43–55. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6885-8_4.
Повний текст джерелаBell, Charles. "Query Execution." In Expert MySQL, 543–85. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4660-2_14.
Повний текст джерелаKrogh, Jesper Wisborg. "Basic Query Execution." In MySQL Connector/Python Revealed, 83–132. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3694-9_3.
Повний текст джерелаKrogh, Jesper Wisborg. "Advanced Query Execution." In MySQL Connector/Python Revealed, 133–221. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3694-9_4.
Повний текст джерелаL’Esteve, Ron. "Adaptive Query Execution." In The Azure Data Lakehouse Toolkit, 327–38. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8233-5_14.
Повний текст джерелаFritchey, Grant. "Execution Plan Generation." In SQL Server Query Performance Tuning, 269–81. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_14.
Повний текст джерелаFritchey, Grant. "Execution Plan Cache Behavior." In SQL Server Query Performance Tuning, 283–309. Berkeley, CA: Apress, 2014. http://dx.doi.org/10.1007/978-1-4302-6742-3_15.
Повний текст джерелаFritchey, Grant. "Execution Plan Generation." In SQL Server 2017 Query Performance Tuning, 451–70. Berkeley, CA: Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3888-2_15.
Повний текст джерелаKorotkevitch, Dmitri. "Query Optimization and Execution." In Pro SQL Server Internals, 463–89. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-1964-5_25.
Повний текст джерелаТези доповідей конференцій з теми "Query Executor"
Gurumurthy, Bala, David Broneske, Gabriel Campero Durand, Thilo Pionteck, and Gunter Saake. "ADAMANT: A Query Executor with Plug-In Interfaces for Easy Co-processor Integration." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00093.
Повний текст джерелаSong, Chunyao, Zheng Li, Tingjian Ge, and Jie Wang. "Query execution timing." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505736.
Повний текст джерелаPomares, Alexandra. "Distributed query execution adaptation." In 2011 6th Colombian Computing Congress (CCC). IEEE, 2011. http://dx.doi.org/10.1109/colomcc.2011.5936301.
Повний текст джерелаKyu, Khin Myat, and Aung Nway Oo. "Enhancement of Query Execution Time in SPARQL Query Processing." In 2020 International Conference on Advanced Information Technologies (ICAIT). IEEE, 2020. http://dx.doi.org/10.1109/icait51105.2020.9261805.
Повний текст джерелаAllenstein, Brett, Andrew Yost, Paul Wagner, and Joline Morrison. "A query simulation system to illustrate database query execution." In the 39th SIGCSE technical symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1352135.1352301.
Повний текст джерелаMühlbauer, Tobias, Wolf Rödiger, Robert Seilbeck, Alfons Kemper, and Thomas Neumann. "Heterogeneity-conscious parallel query execution." In the Tenth International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2619228.2619230.
Повний текст джерелаTang, Dixin, Zechao Shang, Aaron J. Elmore, Sanjay Krishnan, and Michael J. Franklin. "Thrifty Query Execution via Incrementability." In SIGMOD/PODS '20: International Conference on Management of Data. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3318464.3389756.
Повний текст джерелаKumar, Raju Ranjan, and Muzzammil Hussain. "Query Execution over Encrypted Database." In 2015 Second International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2015. http://dx.doi.org/10.1109/icacce.2015.13.
Повний текст джерелаGanguly, Sumit, Waqar Hasan, and Ravi Krishnamurthy. "Query optimization for parallel execution." In the 1992 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/130283.130291.
Повний текст джерелаVerma, Pulkit. "Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/693.
Повний текст джерелаЗвіти організацій з теми "Query Executor"
Koopmann, Patrick. Actions with Conjunctive Queries: Projection, Conflict Detection and Verification. Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.243.
Повний текст джерелаHarkema, Marcel, Dick Quartel, Rob van der Mei, and Bart Gijsen. JPMT: A Java Performance Monitoring Tool. Centre for Telematics and Information Technology (CTIT), 2003. http://dx.doi.org/10.3990/1.5152400.
Повний текст джерела