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On applying hash filters to improving the execution of multi-join queries

VLDB Journal 1997年第2期6卷 121-131页

作者： Chen, Ming-Syan Hsiao, Hui-I Yu, Philip S. Electrical Engineering Department National Taiwan University Taipei Taiwan IBM T.J. Watson Research Center Yorktown NY 10598 P.O.Box 704 United States

In this paper, we explore an approach of inter-leaving a bushy execution tree with hash filters to improve the execution of multi-join queries. Similar to semi-joins in distributed query processing, hash filters can be applied to eliminate non-matching tuples from joining relations before the execution of a join, thus reducing the join cost. Note that hash filters built in different execution stages of a bushy tree can have different costs and effects. The effect of hash filters is evaluated first. Then, an efficient scheme to determine an effective sequence of hash filters for a bushy execution tree is developed, where hash filters are built and applied based on the join sequence specified in the bushy tree so that not only is the reduction effect optimized but also the cost associated is minimized. Various schemes using hash filters are implemented and evaluated via simulation. It is experimentally shown that the application of hash filters is in general a very powerful means to improve the execution of multi-join queries, and the improvement becomes more prominent as the number of relations in a query increases.

关键词： Bushy trees Hash filters parallel query processing Sort-merge joins

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parallelizing Federated SPARQL Queries in Presence of Replicated Data 14th

Parallelizing Federated SPARQL Queries in Presence of Replic...

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14th International Semantic Web Conference (ESWC)

作者： Minier, Thomas Montoya, Gabriela Skaf-Molli, Hala Molli, Pascal Nantes Univ LS2N Nantes France Aalborg Univ Dept Comp Sci Aalborg Denmark

ISBN: (纸本)9783319704074;9783319704067

Federated query engines have been enhanced to exploit new data localities created by replicated data, e.g., FEDRA. However, existing replication aware federated query engines mainly focus on pruning sources during the source selection and query decomposition in order to reduce intermediate results thanks to data locality. In this paper, we implement a replication-aware parallel join operator: Pen. This operator can be used to exploit replicated data during query execution. For existing replication-aware federated query engines, this operator exploits replicated data to parallelize the execution of joins and reduce execution time. For Triple Pattern Fragment (TPF) clients, this operator exploits the availability of several TPF servers exposing the same dataset to share the load among the servers. We implemented Pen in the federated query engine FEDX with the replicated-aware source selection FEDRA and in the reference TPF client. We empirically evaluated the performance of engines extended with the Pen operator and the experimental results suggest that our extensions outperform the existing approaches in terms of execution time and balance of load among the servers, respectively.

关键词： Linked Data parallel query processing Fragment replication Federated SPARQL Queries processing Triple Pattern Fragment Load balancing

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OTPM: Failure Handling in Data-intensive Analytical processing

OTPM: Failure Handling in Data-intensive Analytical Processi...

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7th International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Han, Binh Omiecinski, Edward Mark, Leo Liu, Ling Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA

ISBN: (纸本)9781936968367

parallel processing is the key to speedup performance and to achieve high throughput in processing large scale data analytical workloads. However, failures of nodes involved in the analytical query can interrupt the whole process, resulting in the complete restart of the query if the system does not have query fault-tolerance. Complete restart might be too costly for processing query on very large databases and might not be able to meet the time constraints in decision support systems. In this paper, we present an approach to resume query processing after failure by keeping track of the point at which data has been processed by an operator, called operator tracking. We also consider saving intermediate results using partial materialization. We look at several fundamental parallel database techniques which are widely used today and analyze the performance cost of query processing and recovery using those techniques with our OTPM fault-tolerance approach. We perform simulation-based experiments which show that our approach incurs only a small resume overhead compared to complete pipelining and complete materialization of intermediate results. Also, the combination of our approach with vertically partitioned database in a sharednothing environment yields the best performance among different settings for parallel processing of data intensive analytical workloads.

关键词： Failure handling fault tolerance analytical processing parallel query processing

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HYPERSONIC: A Hybrid parallelization Approach for Scalable Complex Event processing 22

HYPERSONIC: A Hybrid Parallelization Approach for Scalable C...

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International Conference on Management of Data (SIGMOD)

作者： Yankovitch, Maor Kolchinsky, Ilya Schuster, Assaf Technion Israel Inst Technol Haifa Israel

ISBN: (纸本)9781450392495

The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time data streams is a crucial requirement in many modern applications. The ever-growing scale of these applications and the sophistication of the patterns involved makes it imperative to employ advanced solutions that can optimize pattern detection. One of the most prominent and well-established ways to achieve the above goal is to apply complex event processing (CEP) in a parallel manner, using a multi-core machine and/or a distributed environment. However, the inherent tightly coupled nature of CEP severely limits the scalability of the parallelization methods currently available. In this paper, we introduce a novel parallelization mechanism for efficient complex event processing over data streams. This mechanism is based on a hybrid two-tier model combining multiple layers of parallelism. By employing a fine-grained load balancing model, this multi-layered approach leads to a substantial increase in event detection throughput, while at the same time reducing the latency and the memory consumption. An extensive experimental evaluation on multiple real-life datasets shows that our approach consistently outperforms state-of-the-art CEP parallelization methods by a factor of two to three orders of magnitude.

关键词： Complex Event processing parallel query processing Distributed query processing query Optimization

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Semantic parallelism for documentary database systems

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International Conference and Exhibition on High-Performance Computing and Networking

作者： Biscondi, N Inst Natl Sci Appl LISI F-69621 Villeurbanne France

ISBN: (纸本)3540644431

As data volume and query processing loads increase, companies that provide information retrieval services are turning to highperformance parallel computing, storage and searching. In this paper we present a new paradigm of semantic parallelism dedicated to documentary databases. Based on existing parallel database techniques, our approach uses particular features of documentary databases to retrieve semantically relevant information in a more rapid and efficient way. Thus it can greatly alleviate both information overload and vocabulary problem of information retrieval. Extensive simulation results confirm the efficiency of our approach.

关键词： parallel query processing parallel algorithms information retrieval semantic analysis

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A Demonstration of Lusail - querying Linked Data at Scale 17

A Demonstration of Lusail - Querying Linked Data at Scale

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ACM International Conference on Management of Data

作者： Mansour, Essam Abdelaziz, Ibrahim Ouzzani, Mourad Aboulnaga, Ashraf Kalnis, Panos HBKU Qatar Comp Res Inst Doha Qatar King Abdullah Univ Sci & Technol Thuwal Saudi Arabia

ISBN: (纸本)9781450341974

There has been a proliferation of datasets available as interlinked RDF data accessible through SPARQL endpoints. This has led to the emergence of various applications in life science, distributed social networks, and Internet of Things that need to integrate data from multiple endpoints. We will demonstrate Lusail;a system that supports the need of emerging applications to access tens to hundreds of geo-distributed datasets. Lusail is a geo-distributed graph engine for querying linked RDF data. Lusail delivers outstanding performance using (i) a novel locality-aware query decomposition technique that minimizes the intermediate data to be accessed by the subqueries, and (ii) selectivity-awareness and parallel query execution to reduce network latency and to increase parallelism. During the demo, the audience will be able to query actually deployed RDF endpoints as well as large synthetic and real benchmarks that we have deployed in the public cloud. The demo will also show that Lusail outperforms state-of-the-art systems by orders of magnitude in terms of scalability and response time.

关键词： sparql rdf data query processing parallel query processing linked data federated sparql queries decentralized rdf graphs

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Cost Effective Load-Balancing Approach for Range-Partitioned Main-Memory Resident Data 29th

Cost Effective Load-Balancing Approach for Range-Partitioned...

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29th International Conference on Database and Expert Systems Applications (DEXA)

作者： Belayadi, Djahida Hidouci, Khaled-Walid Bellatreche, Ladjel Ordonez, Carlos Ecole Natl Super Informat LCSI Lab Algiers Algeria LIAS ISAE ENSMA F-86960 Futuroscope France Univ Houston Houston TX USA

ISBN: (纸本)9783319988122;9783319988115

Due to the availability of larger RAM capacity, there is a new trend bringing parallel main memory database systems with higher performance, compared to traditional DBMSs. In parallel database systems the most critical aspect is data partitioning, which significantly impacts query processing time. Specifically, unbalanced data partitioning introduces data skew, which ends up decreasing query performance if not managed. In this work, we focus on optimizing range queries, widely used in P2P, decision support systems and spatio-temporal databases. We improve the communication complexity of the state-of-the-art previous algorithm based on skip graphs, which required O(log p) messages between 2 nodes to rebalance load, resulting in a high complexity O(p log p) to rebalance load on the p nodes. With such high cost in mind, we propose to create a global view of data distribution among all processing nodes and database clients. Our main contribution is the Approximate Partitioning Vector (APV), which provides a global approximate view of data distribution to both processing nodes and database clients. A new data balancing algorithm, following a ring topology, reduces communication to 2 messages per node pair, resulting in O(1) communication complexity per node pair and O(p) globally among the p nodes. Experiments analyze the tradeoff between adjusting load balance and query performance.

关键词： Data skew Load-balancing parallel database Range-partitioning Main memory databases P2P databases parallel query processing

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Join Decomposition Based on Fragmented Column Indices

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2016年第3期37卷 255-260页

作者： Ivanova, E. Sokolinsky, L. B. Natl Res Univ South Ural State Univ Prospekt Lenina 76 Chelyabinsk 454080 Russia

The paper is devoted to the issue of decomposition of the join relational operator with the aid of distributed column indices. Such decomposition allows one to utilize the modern manycore accelerators (GPU or Intel Xeon Phi) to speed up the query execution for very large databases. Column indices are the new kind of index structures, which exploits "key-value" technics. The paper describes the methods of column index fragmentation based on domain intervals. This technic allows organizing the parallel query processing without exchanges. All column index fragments are stored in main memory in compressed form to conserve space. This approach can be implemented as a coprocessor for relational database systems. The database coprocessor is able to perform resource-intensive operations much more faster than a conventional DBMS.

关键词： Very large databases parallel query processing column indices domain-interval fragmentation

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DATA-FLOW query EXECUTION IN A parallel MAIN-MEMORY ENVIRONMENT

DATA-FLOW QUERY EXECUTION IN A PARALLEL MAIN-MEMORY ENVIRONM...

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1991 International Conference on parallel and Distributed Information Systems

作者： WILSCHUT, AN APERS, PMG

ISBN: (纸本)0818622954

In this paper, the performance and characteristics of the execution of various join-trees on a parallel DBMS are studied. The results of this study are a step into the direction of the design of a query optimization strategy that is fit for parallel execution of complex queries. Among others, synchronization issues are identified to limit the performance gain from parallelism. A new hash-join algorithm is introduced that has fewer synchronization constraints than the known hash-join algorithms. Also, the behavior of individual join operations in a join-tree is studied in a simulation experiment. The results show that the introduced Pipelining hash-join algorithm yields a better performance for multi-join queries. The format of the optimal join-tree appears to depend on the size of the operands of the join: A multi-join between small operands performs best with a bushy schedule;larger operands are better off with a linear schedule. The results from the simulation study are confirmed with an analytic model for dataflow query execution.

关键词： parallel query processing MULTI-JOIN QUERIES SIMULATION ANALYTICAL MODELING

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Modeling performance-driven workload characterization of web search systems 06

Modeling performance-driven workload characterization of web...

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15th ACM Conference on Information and Knowledge Management, CIKM 2006

作者： Badue, Claudine Baeza-Yates, Ricardo Ribeiro-Neto, Berthier Ziviani, Artur Ziviani, Nivio Computer Science Dept. Federal University of Minas Gerais Belo Horizonte Brazil Petrópolis Brazil Yahoo Research Barcelona Spain Google Engineering Belo Horizonte Brazil

No abstract available

ISBN: (纸本)1595934332

No abstract available

关键词： performance analysis parallel query processing search engines workload characterization

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