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adaptive query processing on RAW Data

PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第12期7卷 1119-1130页

作者： Karpathiotakis, Manos Branco, Miguel Alagiannis, Ioannis Ailamaki, Anastasia Ecole Polytech Fed Lausanne Lausanne Switzerland

Database systems deliver impressive performance for large classes of workloads as the result of decades of research into optimizing database engines. High performance, however, is achieved at the cost of versatility. In particular, database systems only operate efficiently over loaded data, i.e., data converted from its original raw format into the system's internal data format. At the same time, data volume continues to increase exponentially and data varies increasingly, with an escalating number of new formats. The consequence is a growing impedance mismatch between the original structures holding the data in the raw files and the structures used by query engines for efficient processing. In an ideal scenario, the query engine would seamlessly adapt itself to the data and ensure efficient query processing regardless of the input data formats, optimizing itself to each instance of a file and of a query by leveraging information available at query time. Today's systems, however, force data to adapt to the query engine during data loading. This paper proposes adapting the query engine to the formats of raw data. It presents RAW, a prototype query engine which enables querying heterogeneous data sources transparently. RAW employs Just-In-Time access paths, which efficiently couple heterogeneous raw files to the query engine and reduce the overheads of traditional general-purpose scan operators. There are, however, inherent overheads with accessing raw data directly that cannot be eliminated, such as converting the raw values. Therefore, RAW also uses column shreds, ensuring that we pay these costs only for the subsets of raw data strictly needed by a query. We use RAWin a real-world scenario and achieve a two-order of magnitude speedup against the existing hand-written solution.

关键词： Search engines Data handling Database systems Engines query processing adaptive query processing Database engine Heterogeneous data sources Impedance mismatch Just in time Original structures query engines Real world scenario

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adaptive query processing in Cloud Database Systems

Adaptive Query Processing in Cloud Database Systems

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3rd IEEE International Conference on Cloud and Green Computing (CGC)

作者： Costa, Clayton Maciel Sousa, Antonio Luis Inst Fed Rio Grande Norte HASLab Ipanguacu Brazil Univ Minho INESC TEC Braga Portugal Univ Minho HASLab Braga Portugal

ISBN: (纸本)9780769551142

In cloud environments, resources should be acquired and released automatically and quickly at runtime. Thereby, the implementation of traditional query optimization strategies in cloud platforms can have a poor performance, because they cannot predict future availability and/or release of resources. In such scenarios, adaptive query processing can adapt itself to the available resources to run queries and, consequently, present an acceptable performance in response to a query. However, traditional and adaptive query optimizers main objective is to reduce response time. Moreover, in the context of cloud computing, users and providers of services expect to get answers in time to guarantee the SLA. Therefore, we propose a framework that uses adaptive query processing based on heuristic rules and cost of failing the SLA. It will be implemented on structured data, considering that some cloud computing platforms support SQL queries directly or indirectly, which makes this problem relevant.

关键词： cloud computing database systems adaptive query processing

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Self-monitoring query execution for adaptive query processing

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DATA & KNOWLEDGE ENGINEERING 2004年第3期51卷 325-348页

作者： Gounaris, A Paton, NW Fernandes, AAA Sakellariou, R Univ Manchester Dept Comp Sci Manchester M13 9PL Lancs England

adaptive query processing generally involves a feedback loop comprising monitoring, assessment and response. So far, individual proposals have tended to group together an approach to monitoring, a means of assessment, and a form of response. However, there are many benefits in decoupling these three phases, and in constructing generic frameworks for each of them. To this end, this paper discusses monitoring of query plan execution as a topic in its own right, and advocates an approach based on self-monitoring algebraic operators. This approach is shown to be generic and independent of any specific adaptation mechanism, easily implementable and portable, sufficiently comprehensive, appropriate for heterogeneous distributed environments, and more importantly, capable of driving on-the-fly adaptations of query plan execution. An experimental evaluation of the overheads and of the quality of the results obtained by monitoring is also presented. (C) 2004 Elsevier B.V. All rights reserved.

关键词： query monitoring adaptive query processing query execution operators

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Incorporating change detection in the monitoring phase of adaptive query processing

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JOURNAL OF INTERNET SERVICES AND APPLICATIONS 2016年第1期7卷 1-18页

作者： Tsamoura, Efthymia Gounaris, Anastasios Manolopoulos, Yannis Univ Oxford Parks Rd Oxford England Aristotle Univ Thessaloniki Thessaloniki Greece

Recent Big Data research typically emphasizes on the need to address the challenges stemming from the volume, velocity, variety and veracity aspects. However, another cross-cutting property of Big Data is volatility. In database technology, volatility is addressed with the help of adaptive query processing (AQP), which has become the dominant paradigm for executing queries in dynamic and/or streaming environments. As the characteristics of the runtime environment may vary significantly along time, AQP techniques employ a three-phase adaptivity loop to process the input queries, comprising feedback collection, analysis and re-optimization. In the monitoring phase, the standard approach is to collect feedback in a fixed-size sliding window. However, several problems arise when the techniques adopt a fixed-size sliding window for maintaining runtime collected feedback. In this work, we tackle this limitation and we propose a novel monitoring phase, which assesses the collected feedback rendering an AQP technique capable of taking more informed decisions during the subsequent phases. The proposed approach is non-intrusive to the state-of-the-art adaptivity loop and can adopt any state-of-the-art online change detection algorithm through its plug-and-play abstraction. Another contribution of this work is a novel algorithm for detecting changes in a filter's drop probability, called beta-CUSUM. The potential of the novel monitoring phase and of beta-CUSUM is experimentally evaluated using both real-world and synthetic datasets.

关键词： adaptive query processing Stream data processing Stream change detection

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adaptive query processing in Cloud Database Systems

Adaptive Query Processing in Cloud Database Systems

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International Conference on Cloud and Green Computing

作者： Clayton Maciel Costa Antonio Luis Sousa HASLab/INESC TEC Instituto Federal do Rio Grande do Norte/Univ. do Minho HASLab/INESC TEC Universidade do Minho

ISBN: (纸本)9781479913626

关键词： Cloud computing Database systems adaptive query processing

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adaptive query processing in E-commerce environment

Adaptive query processing in E-commerce environment

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ACS/IEEE International Conference on Computer Systems and Applications

作者： Ying Wah Teh Abu Bakar Zaitun Sai Peck Lee Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (纸本)0769511651

In the E-Commerce environment, the response time of query processing is very critical. Database Management Systems (DBMSs) processes the query in the Wide Area Network (WAN) in contrast with Local Area Network (LAN). The processing speed in WAN is slow with large data volume. Given this situation, how can we achieve a reasonable response time? Response time relies heavily on the server to process the relevant tuples and pass them back to the client side. Therefore, the different types of improve techniques are built in the E-Commerce to meet the critical response time required. In this paper, we discuss the threshold values, which can improve the response time of a query processing.

关键词： query processing electronic commerce Internet relational databases information resources adaptive query processing e-commerce response time database management systems wide area network local area network tuples threshold values Web server Internet relational database

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adaptive spatial window aggregation query processing algorithm in wireless sensor networks

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2014年第3期16卷 127-147页

作者： Liu, Liang Qin, Xiaolin Zheng, Guineng Li, Bohan Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China

Users of wireless sensor networks (WSNs) often submit spatial window aggregation queries to obtain the summary information about a local area in the monitored region. We propose an adaptive spatial window aggregation query processing algorithm called ASWAP in this paper. It divides the query area into several disjoint sub-regions. A cluster head node is elected in each sub-region which collects the sensory data of sensor nodes in it, uses the data collected to calculate the partial query result and sends it to the cluster head node of the next sub-region. While a cluster head node fails during the above process, a recovery area can be found in its corresponding sub-region in which any node can substitute the cluster head node to resume the process of query processing. Experimental results show that in most cases ASWAP outperforms the existing IWQE algorithm in terms of energy consumption and query success rate.

关键词： WSNs wireless sensor networks adaptive query processing spatial window query node failure tolerance

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adaptive workload allocation in query processing in autonomous heterogeneous environments

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DISTRIBUTED AND PARALLEL DATABASES 2009年第3期25卷 125-164页

作者： Gounaris, Anastasios Smith, Jim Paton, Norman W. Sakellariou, Rizos Fernandes, Alvaro A. A. Watson, Paul Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece Univ Newcastle Tyne Sch Comp Sci Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England

The increasing prevalence of networked storage and computational resources, along with middleware for managing resource access and sharing, raises the prospect that queries can be run over resources obtained on demand, rather than on dedicated infrastructures. However, the movement of query processing into non-dedicated environments means that it is necessary to take account of the partial information and unstable conditions that characterise autonomous, shared, distributed settings. Thus, query processing on grid platforms needs to be adaptive, revising evaluation strategies at query runtime in response to the evolving environment, such as changes to machine load and availability. To address this challenge, adaptive techniques are described that: (i) balance load across plan partitions supporting intra-operator parallelism;(ii) remove bottlenecks in pipelined plans supporting inter-operator parallelism;and (iii) combine the two aforementioned techniques. The approach has been empirically evaluated in a grid-enabled adaptive query processor.

关键词： adaptive query processing Dynamic resource allocation Load balancing Distributed query processing query optimization Grid computing

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Optimizing adaptive multi-route query processing via time-partitioned indices

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2013年第3期79卷 330-348页

作者： Works, Karen Rundensteiner, Elke A. Agu, Emmanuel Worcester Polytech Inst Worcester MA 01609 USA

adaptive multi-route query processing (AMR) is an emerging paradigm for processing stream queries in highly fluctuating environments. The. content of stream data can be unpredictable. Thus, instead of selecting a fixed plan;AMR dynamically routes batches of tuples to operators in the query network based on up-to-date system statistics. The workload of query access patterns in AMR systems is ever changing. Selecting a single best index may not efficiently support all query access patterns at all times. While maintaining multiple indices to match a variety of query access patterns increases overhead and decreases throughput. Index design, while paramount for efficient query execution, is particularly challenging in AMR systems as the indices must serve the continuously evolving query access patterns. Our proposed adaptive Multi-Route Index (AMRI) employs a bitmap time-partitioned design that serves a diverse ever changing workload of query access patterns and remains lightweight in terms of maintenance and storage requirements. We propose a high quality yet efficient assessment method modeled after hierarchical heavy hitters that exploits route relationships by modeling the frequency of the search access patterns used as nodes in a lattice. We also design assessment scheduling methods for AMRI based upon detecting changes in the search access patterns used. Our AMRI incorporates migration strategies that seek to meet the needs of both old partially serviced and new incoming search requests. Our experimental study using both synthetic and real data streams demonstrates that AMRI strikes a balance between effectively supporting dynamic stream environments while keeping the index overhead to a minimum. Using an environmental data set collected in the Intel Berkeley Research lab, our AMRI produced on average 68% more cumulative throughput than the state-of-the-art approach. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Data stream database systems Indexing adaptive query processing

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Grizzly: Efficient Stream processing Through adaptive query Compilation 20

Grizzly: Efficient Stream Processing Through Adaptive Query ...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Grulich, Philipp M. Bress, Sebastian Zeuch, Steffen Traub, Jonas von Bleichert, Janis Chen, Zongxiong Rabl, Tilmann Markl, Volker Tech Univ Berlin Berlin Germany DFKI GmbH Berlin Germany Univ Potsdam HPI Potsdam Germany

ISBN: (纸本)9781450367356

Stream processing Engines (SPEs) execute long-running queries on unbounded data streams. They follow an interpretation-based processing model and do not perform runtime optimizations. This limits the utilization of modern hardware and neglects changing data characteristics at runtime. In this paper, we present Grizzly, a novel adaptive query compilation-based SPE, to enable highly efficient query execution. We extend query compilation and task-based parallelization for the unique requirements of stream processing and apply adaptive compilation to enable runtime reoptimizations. The combination of light-weight statistic gathering with just-in-time compilation enables Grizzly to adjust to changing data-characteristics dynamically at runtime. Our experiments show that Grizzly outperforms state-of-the-art SPEs by up to an order of magnitude in throughput.

关键词： query compilation mutli-core adaptive query processing hardware code generation stream processing

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