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29th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Shah, Bhavik Padiya, Trupti Bhise, Minal DA IICT Gandhinagar India

ISBN: (纸本)9781467376846

The paper explores use of various partitioning methods to store RDF data effectively, to meet the needs of extensively growing highly interactive semantic web applications. It proposes a combinational approach of structure index partitioning and vertical partitioning -SIVP and demonstrates the implementation of SIVP. The paper presents five metrics to measure and analyze performance of SIVP store. SIVP is experimented on FOAF and SwetoDBLP datasets. SIVP store have shown an average of 34% gain over vertical partitioning for FOAF dataset. For SwetoDBLP dataset, SIVP have shown an average of 26% gain over VP. SIVP is better than vertical partitioning provided extra time needed in SIVP, which consists of lookup time and merge time, is compensated by frequency of a query higher than breakeven point for that query.

关键词： Structure Indexed Vertical Partitioning Vertical Partitioning Structure Index Partitioning RDF Semantic Web query execution

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query execution Optimization for Clients of Triple Pattern Fragments 12

Query Execution Optimization for Clients of Triple Pattern F...

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12th European Semantic Web Conference (ESWC)

作者： Van Herwegen, Joachim Verborgh, Ruben Mannens, Erik Van de Walle, Rik Univ Ghent Multimedia Lab iMinds B-9050 Ledeberg Ghent Belgium

ISBN: (纸本)9783319188188;9783319188171

In order to reduce the server-side cost of publishing queryable Linked Data, Triple Pattern Fragments (TPF) were introduced as a simple interface to RDF triples. They allow for SPARQL query execution at low server cost, by partially shifting the load from servers to clients. The previously proposed query execution algorithm uses more HTTP requests than necessary, and only makes partial use of the available metadata. In this paper, we propose a new query execution algorithm for a client communicating with a TPF server. In contrast to a greedy solution, we maintain an overview of the entire query to find the optimal steps for solving a given query. We show multiple cases in which our algorithm reaches solutions with far fewer HTTP requests, without significantly increasing the cost in other cases. This improves the efficiency of common SPARQL queries against TPF interfaces, augmenting their viability compared to the more powerful, but more costly, sparql interface.

关键词： Linked data sparql query execution query optimization

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query execution for RDF Data on Row and Column Store 11

Query Execution for RDF Data on Row and Column Store

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11th International Conference on Distributed Computing and Internet Technology (ICDCIT)

作者： Padiya, Trupti Bhise, Minal Vasani, Sandeep Pandey, Mohit DA IICT Gandhinagar Gandhinagar India

ISBN: (纸本)9783319149769;9783319149776

This paper shows experimental comparison between various data storage techniques to manage RDF data. The work represents evaluation of query performance in terms of query execution time and data scalability, using row and column store for various data storage techniques. To demonstrate these ideas FOAF (Friend Of A Friend) data is used. The paper contributes experimental and analytical study for application of partitioning techniques on FOAF data which makes queries 168 times faster compared to traditional triples table. Materialized views over vertically partitioned data show an additional 8 times improvement in query performance against partitioned data for the frequently occurring queries. Vertical partitioning is executed on column store also, and as FOAF data size scales, an order of magnitude improved performance is observed over row store execution.

关键词： Data Partitioning Data Scalability FOAF Materialized Views query execution Semantic Web

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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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Load balancing for parallel query execution on NUMA multiprocessors

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DISTRIBUTED AND PARALLEL DATABASES 1999年第1期7卷 99-121页

作者： Bouganim, L Florescu, D Valduriez, P INRIA Rocquencourt France

To scale up to high-end configurations, shared-memory multiprocessors are evolving towards Non Uniform Memory Access (NUMA) architectures. In this paper, we address the central problem of load balancing during parallel query execution in NUMA multiprocessors. We first show that an execution model for NUMA should not use data partitioning (as shared-nothing systems do) but should strive to exploit efficient shared-memory strategies like Synchronous Pipelining (SP). However, SP has problems in NUMA, especially with skewed data. Thus, we propose a new execution strategy which solves these problems. The basic idea is to allow partial materialization of intermediate results and to make them progressivly public, i.e., able to be processed by any processor, as needed to avoid processor idle times. Hence, we call this strategy Progressive Sharing (PS). We conducted a performance comparison using an implementation of SP and PS on a 72-processor KSR1 computer, with many queries and large relations. With no skew, SP and PS have both linear speed-up. However, the impact of skew is very severe on SP performance while it is insignificant on PS. Finally, we show that, in NUMA, PS can also be beneficial in executing several pipeline chains concurrently.

关键词： parallel databases query execution load balancing NUMA synchronous pipeline execution engines

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ENCAPSULATION OF PARALLELISM AND ARCHITECTURE-INDEPENDENCE IN EXTENSIBLE DATABASE query execution

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1993年第8期19卷 749-764页

作者： GRAEFE, G DAVISON, DL UNIV COLORADO DEPT COMP SCI BOULDER CO 80309 USA

Emerging database application domains demand not only high functionality but also high performance. To satisfy these two requirements, the Volcano query execution engine combines the efficient use of parallelism on a wide variety of computer architectures with an extensible set of query processing operators that can be nested into arbitrarily complex query evaluation plans. Volcano's novel exchange operator permits designing, developing, debugging, and tuning data manipulation operators in single-process environments but executing them in various forms of parallelism. The exchange operator shields the data manipulation operators from all parallelism issues, including process allocation, inter-process communication, and flow control. It also hides the machine's architecture, which can be a single-processor, shared-memory, or distributed-memory design. It can even be hierarchical, i.e., an interconnected group of shared-memory machines. In this paper, we detail design and implementation of the recently generalized exchange operator, justify our decision to support hierarchical architectures, and argue that the exchange operator offers a significant advantage for development and maintenance of database query processing software. We also discuss the integration of bit vector filtering into the exchange operator paradigm with only minor modifications.

关键词： ARCHITECTURE-INDEPENDENCE DISTRIBUTED MEMORY ENCAPSULATION OF PARALLELISM EXTENSIBLE DATABASE SYSTEMS HIERARCHICAL MEMORY ITERATORS query execution SHARED MEMORY

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Combining User Interaction, Speculative query execution and Sampling in the DICE System

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第13期7卷 1697-1700页

作者： Jayachandran, Prasanth Tunga, Karthik Kamat, Niranjan Nandi, Arnab Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA

The interactive exploration of data cubes has become a popular application, especially over large datasets. In this paper, we present DICE, a combination of a novel frontend query interface and distributed aggregation backend that enables interactive cube exploration. DICE provides a convenient, practical alternative to the typical offline cube materialization strategy by allowing the user to explore facets of the data cube, trading off accuracy for interactive response-times, by sampling the data. We consider the time spent by the user perusing the results of their current query as an opportunity to execute and cache the most likely followup queries. The frontend presents a novel intuitive interface that allows for sampling-aware aggregations, and encourages interaction via our proposed faceted model. The design of our backend is tailored towards the low-latency user interaction at the frontend, and viceversa. We discuss the synergistic design behind both the frontend user experience and the backend architecture of DICE;and, present a demonstration that allows the user to fluidly interact with billiontuple datasets within sub-second interactive response times.

关键词： User interfaces Geometry Interactive exploration Intuitive interfaces Large datasets Most likely query execution query interfaces User experience User interaction

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Optimizing query execution for Variable-Aligned Length Compression of Bitmap Indices 14

Optimizing Query Execution for Variable-Aligned Length Compr...

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18th International Database Engineering and Applications Symposium (IDEAS)

作者： Slechta, Ryan Sawin, Jason McCamish, Ben Chiu, David Canahuate, Guadalupe Univ St Thomas St Paul MN 55105 USA Washington State Univ Pullman WA 99164 USA Univ Puget Sound Tacoma WA 98416 USA Univ Iowa Iowa City IA 52242 USA

ISBN: (纸本)9781450326278

Indexing is a fundamental mechanism for efficient data access. Recently, we proposed the Variable-Aligned Length (VAL) bitmap index encoding framework, which generalizes the commonly used word-aligned compression techniques. VAL presented a variable-aligned compression framework, which allows columns of a bitmap to be compressed using different encoding lengths. This flexibility creates a tunable compression that balances the trade-off between space and query processing time. The variable format of VAL presents several unique opportunities for query optimization. In this paper we explore multiple algorithms to optimize both point queries and range queries in VAL. In particular, we propose a dynamic encoding-length translation heuristic to process point queries. For range queries, we propose several column orderings based on the bitmap's metadata: largest segment length first (lsf), column size (size), and weighted size (ws). In our empirical study over both real and synthetic data sets, we show that our dynamic translation selection scheme produces query execution times only 3:5% below the optimal. We also found that the weighted size column ordering significantly and consistently out-performs other ordering techniques. Finally, we show that algorithms scale to data sets that are row-ordered.

关键词： bitmap indices bitmap compression query execution

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SINGLE vs. MapReduce vs. Relational: Predicting query execution Time 14th

SINGLE vs. MapReduce vs. Relational: Predicting Query Execut...

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14th International Scientific Conference on Beyond Databases, Architectures, and Structures (BDAS) Held at the 24th IFIP World Computer Congress (IFIP WCC)

作者： Abbasi, Maryam Martins, Pedro Cecilio, Jose Costa, Joao Furtado, Pedro Univ Coimbra Dept Comp Sci Coimbra Portugal Polytech Inst Viseu Viseu Portugal Polytech Inst Coimbra Coimbra Portugal

ISBN: (纸本)9783319999876

Over the past decade's several new concepts emerged to organize and query data over large Data Warehouse (DW) system with the same primary objective, that is, optimize processing speed. More recently, with the rise of BigData concept, storage cost lowered significantly, and performance (random accesses) increased, particularly with modern SSD disks. This paper introduces and tested a storage alternative which goes against current data normalization premises, where storage space is no longer a concern. By de-normalizing the entire data schema (transparent to the user) it is proposed a new concept system where query execution time must be entirely predictable, independently of its complexity, called, SINGLE. The proposed data model also allows easy partitioning and distributed processing to enable execution parallelism, boosting performance, as happens in MapReduce. TPC-H benchmark is used to evaluate storage space and query performance. Results show predictable performance when comparing with approaches based on a normalized relational schema, and MapReduce oriented.

关键词： Predictable query execution Data warehouse MapReduce Normalization De-normalization Distributed Relational

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GUESS: MonitorinG Join query execution in Serverless and Serverful Spark 29th

GUESS: MonitorinG Join qUery Execution in Serverless and Ser...

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29th International Conference on Database Systems for Advanced Applications (DASFAA)

作者： An-Truong Tran Phan D'orazio, Laurent Thuong-Cang Phan Le Gruenwald Vinh Long Univ Technol Educ Vinh Long Vietnam Univ Rennes IRISA CNRS Rennes France Can Tho Univ Can Tho Vietnam Univ Oklahoma Sch Comp Sci Norman OK 73019 USA

ISBN: (纸本)9789819755745;9789819755752

This paper proposes a monitoring system called GUESS to compare the performance and energy consumption of join query processing on Spark in serverless and serverful environments. The system collects metrics on resource utilization, query execution times, and power usage through Prometheus, Grafana, Spark History Server, and Open-Manage Enterprise Power Manager. These metrics are visualized through an intuitive web dashboard to easily compare the serverless and serverful Spark workloads. Experimental results using the TPC-H benchmark show that the serverless environment consumes less energy than the serverful environment due to on-demand resource allocation. However, the serverful environment exhibits better query performance, especially for workloads with known resource requirements. GUESS provides insights into optimizing resource efficiency and query performance when deploying Spark analytic workloads.

关键词： serverless computing serverful computing Cold-start Warm-start query execution Spark

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