{"title":"基于弹性状态表的细粒度状态数据分析方法","authors":"Jike Ge, Wenbo He, Zuqin Chen, Can Liu, Jun Peng, Guorong Chen","doi":"10.4018/IJSSCI.2018040105","DOIUrl":null,"url":null,"abstract":"Thisarticledescribeshowstatefuldataanalyticframeworkshaveemergedtoprovidefreshandlowlatencyresultsforbigdataprocessing.Atpresent,itisdesiredtoachievethefine-graineddatamodel inSparkdataprocessingframework.However,Sparkadoptscoarse-graineddatamodelinorderto facilitateparallelization,itischallengingindealingwiththefine-graineddataaccessinstatefuldata analytics.Inthispaper,theauthorsintroduceafine-grainedstatefuldatacomponent,ResilientState Table(RST),toSparkframework.Forfillingthegapbetweenthecoarse-graineddatamodelinSpark andthefine-graineddataaccessrequirementsinstatefuldataanalytics,theydevisetheprogramming model of RST which interacts with Spark’s coarse-grained memory representation seamlessly, andenableuserstoquery/updatethestateentriesinfinegranularitywithSpark-likeprogramming interfaces.Performanceevaluationexperimentsinvariousapplicationfieldsdemonstratethattheir proposedsolutionachievestheimprovementsinlatency,fault-tolerance,aswellasscalability. KeywoRDS Big Data, Resilient Distributed Dataset, Resilient State Table, Spark, Stateful Data Analytics","PeriodicalId":432255,"journal":{"name":"Int. J. Softw. Sci. Comput. Intell.","volume":"18 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Fine-Grained Stateful Data Analytics Method Based on Resilient State Table\",\"authors\":\"Jike Ge, Wenbo He, Zuqin Chen, Can Liu, Jun Peng, Guorong Chen\",\"doi\":\"10.4018/IJSSCI.2018040105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Thisarticledescribeshowstatefuldataanalyticframeworkshaveemergedtoprovidefreshandlowlatencyresultsforbigdataprocessing.Atpresent,itisdesiredtoachievethefine-graineddatamodel inSparkdataprocessingframework.However,Sparkadoptscoarse-graineddatamodelinorderto facilitateparallelization,itischallengingindealingwiththefine-graineddataaccessinstatefuldata analytics.Inthispaper,theauthorsintroduceafine-grainedstatefuldatacomponent,ResilientState Table(RST),toSparkframework.Forfillingthegapbetweenthecoarse-graineddatamodelinSpark andthefine-graineddataaccessrequirementsinstatefuldataanalytics,theydevisetheprogramming model of RST which interacts with Spark’s coarse-grained memory representation seamlessly, andenableuserstoquery/updatethestateentriesinfinegranularitywithSpark-likeprogramming interfaces.Performanceevaluationexperimentsinvariousapplicationfieldsdemonstratethattheir proposedsolutionachievestheimprovementsinlatency,fault-tolerance,aswellasscalability. KeywoRDS Big Data, Resilient Distributed Dataset, Resilient State Table, Spark, Stateful Data Analytics\",\"PeriodicalId\":432255,\"journal\":{\"name\":\"Int. J. Softw. Sci. Comput. Intell.\",\"volume\":\"18 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Int. J. Softw. Sci. Comput. Intell.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4018/IJSSCI.2018040105\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Softw. Sci. Comput. Intell.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/IJSSCI.2018040105","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
A Fine-Grained Stateful Data Analytics Method Based on Resilient State Table
Thisarticledescribeshowstatefuldataanalyticframeworkshaveemergedtoprovidefreshandlowlatencyresultsforbigdataprocessing.Atpresent,itisdesiredtoachievethefine-graineddatamodel inSparkdataprocessingframework.However,Sparkadoptscoarse-graineddatamodelinorderto facilitateparallelization,itischallengingindealingwiththefine-graineddataaccessinstatefuldata analytics.Inthispaper,theauthorsintroduceafine-grainedstatefuldatacomponent,ResilientState Table(RST),toSparkframework.Forfillingthegapbetweenthecoarse-graineddatamodelinSpark andthefine-graineddataaccessrequirementsinstatefuldataanalytics,theydevisetheprogramming model of RST which interacts with Spark’s coarse-grained memory representation seamlessly, andenableuserstoquery/updatethestateentriesinfinegranularitywithSpark-likeprogramming interfaces.Performanceevaluationexperimentsinvariousapplicationfieldsdemonstratethattheir proposedsolutionachievestheimprovementsinlatency,fault-tolerance,aswellasscalability. KeywoRDS Big Data, Resilient Distributed Dataset, Resilient State Table, Spark, Stateful Data Analytics
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