Automated Configuration Parameter Classfication Model for Hive Query Plan on the Apache Yarn

2019 ◽  
Author(s):  
Jongyeop Kim ◽  
Seongsoo Kim ◽  
Donghoon Kim ◽  
Hong Liu
Keyword(s):  
Query Plan ◽  
Configuration Parameter

scholarly journals An empirical evaluation of cost-based federated SPARQL query processing engines

Semantic Web ◽  
2021 ◽  
pp. 1-26
Author(s):  
Umair Qudus ◽  
Muhammad Saleem ◽  
Axel-Cyrille Ngonga Ngomo ◽  
Young-Koo Lee
Keyword(s):  
Query Processing ◽  
Detailed Analysis ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Sparql Query ◽  
Evaluation Metrics ◽  
Future Cost ◽  
Query Plan ◽  
Fine Grained ◽  
Runtime Performance

Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.

Query Plan

2018 ◽  
pp. 3020-3020
Author(s):  
Evaggelia Pitoura
Keyword(s):  
Query Plan

scholarly journals The Effect of Anisotropy on the Creep of Polycrystalline Ice

1978 ◽  
Vol 21 (85) ◽  
pp. 475-483 ◽  
Author(s):  
R. C. Lile
Keyword(s):  
Quantitative Measure ◽  
Dissipative Process ◽  
Shear Stresses ◽  
Creep Tests ◽  
First Approximation ◽  
Configuration Parameter ◽  
Polycrystalline Ice ◽  
The Mean ◽  
Basal Shear

AbstractQuantitative effects of crystallographic orientation fabrics are incorporated into the flow law for isotropic polycrystalline ice by the introduction of an enhancement factor applied to the isotropic fluidity. An aggregate is viewed to a first approximation as a collection of grains deforming independently by basal glide. The influence of preferred orientations on the mean intragranular rate of strain is treated in terms of a redistribution of the magnitude and orientation of resolved basal shear stress. A quantitative measure of this effect on the fluidity of the aggregate is provided through the development of a geometric tensor and a stress configuration parameter. Intergranular interference is then considered as a dissipative process modifying the fluidity of the aggregate.Empirical justification for the model at low octahedral shear stresses is provided by several laboratory creep tests on naturally anisotropic bore-hole specimens under both in situ and anomalous stress situations. Predicted enhancement factors ranged from approximately 0.2 to 2.8 and agree well with measured values. The tests were carried out in uniaxial compression and simple shear.

Dynamic routing of data stream tuples among parallel query plan running on multi-core processors

2012 ◽  
Vol 30 (2) ◽  
pp. 145-176 ◽  
Author(s):  
Ali A. Safaei ◽  
Ali Sharifrazavian ◽  
Mohsen Sharifi ◽  
Mostafa S. Haghjoo
Keyword(s):  
Data Stream ◽  
Dynamic Routing ◽  
Query Plan ◽  
Parallel Query

scholarly journals A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ramalingam Gomathi ◽  
Dhandapani Sharmila
Keyword(s):  
Semantic Web ◽  
Query Optimization ◽  
Execution Time ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Web Data ◽  
Query Plan ◽  
Plan Generation ◽  
Description Framework ◽  
Rdf Graphs

The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

scholarly journals Hadoop Configuration Tuning for Performance Optimization

2017 ◽  
Vol 4 (1) ◽  
pp. 31-40
Author(s):  
Christian Christian ◽  
Kho I Eng ◽  
Heru Purnomo Ipung
Keyword(s):  
Performance Optimization ◽  
Parameter Tuning ◽  
Performance Gain ◽  
Performance Improvements ◽  
Configuration Parameter ◽  
Cpu Utilization ◽  
Cluster Configuration ◽  
Significant Performance ◽  
Hadoop Clusters ◽  
The Impact

Configuration parameter tuning is an essential part of the implementation of Hadoop clusters. Each parameter in a configuration plays a role that impacts the ov erall performance of the cluster. Therefore, we need to learn the characteristics of said parameter and understand the impact in hardware utilization in order to achieve optimal configuration. In this paper, we conducted experiments that includes modifying configuration and performed benchmark to find out if there is any performance gain. TeraSort is the program that runs the benchmark, we measure the time needed to complete the sort of the set of data and the CPU utilization during the benchmark. We conclu de that from our experiments we can see significant performance improvements by tuning with the configurations. However, the results may vary between different cluster configuration.

Map-Side Join Processing of SPARQL Queries Based on Abstract RDF Data Filtering

2019 ◽  
Vol 30 (1) ◽  
pp. 22-40 ◽  
Author(s):  
Minjae Song ◽  
Hyunsuk Oh ◽  
Seungmin Seo ◽  
Kyong-Ho Lee
Keyword(s):  
Query Processing ◽  
Input Data ◽  
General Trend ◽  
Processing System ◽  
Sparql Query ◽  
Data Filtering ◽  
Mapreduce Framework ◽  
Query Plan ◽  
Network Bandwidth ◽  
Rdf Data

The amount of RDF data being published on the Web is increasing at a massive rate. MapReduce-based distributed frameworks have become the general trend in processing SPARQL queries against RDF data. Currently, query processing systems that use MapReduce have not been able to keep up with the increase of semantic annotated data, resulting in non-interactive SPARQL query processing. The principal reason is that intermediate query results from join operations in a MapReduce framework are so massive that they consume all available network bandwidth. In this article, the authors present an efficient SPARQL processing system that uses MapReduce and HBase. The system runs a job optimized query plan using their proposed abstract RDF data to decrease the number of jobs and also decrease the amount of input data. The authors also present an efficient algorithm of using Map-side joins while also using the abstract RDF data to filter out unneeded RDF data. Experimental results show that the proposed approach demonstrates better performance when processing queries with a large amount of input data than those found in previous works.

Sign in / Sign up

Export Citation Format

Share Document