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1、Dremel:InteractiveAnalysisofWeb-ScaleDatasetsSergeyMelnik,AndreyGubarev,JingJingLong,GeoffreyRomer,ShivaShivakumar,MattTolton,TheoVassilakisGoogle,Inc.fmelnik,andrey,jlong,gromer,shiva,mtolton,theovg@google.comABSTRACTexchangedbydistributedsystems,structured
2、documents,etc.lendthemselvesnaturallytoanestedrepresentation.NormalizingandDremelisascalable,interactivead-hocquerysystemforanaly-recombiningsuchdataatwebscaleisusuallyprohibitive.Anestedsisofread-onlynesteddata.Bycombiningmulti-levelexecutiondatamodelunderl
3、iesmostofstructureddataprocessingatGoogletreesandcolumnardatalayout,itiscapableofrunningaggrega-[21]andreportedlyatothermajorwebcompanies.tionqueriesovertrillion-rowtablesinseconds.Thesystemscales1ThispaperdescribesasystemcalledDremelthatsupportsinter-tothou
4、sandsofCPUsandpetabytesofdata,andhasthousandsactiveanalysisofverylargedatasetsoversharedclustersofcom-ofusersatGoogle.Inthispaper,wedescribethearchitecturemoditymachines.Unliketraditionaldatabases,itiscapableofop-andimplementationofDremel,andexplainhowitcomp
5、lementseratingoninsitunesteddata.InsitureferstotheabilitytoaccessMapReduce-basedcomputing.Wepresentanovelcolumnarstor-data‘inplace’,e.g.,inadistributedfilesystem(likeGFS[14])oragerepresentationfornestedrecordsanddiscussexperimentsonanotherstoragelayer(e.g.,Bi
6、gtable[8]).Dremelcanexecutemanyfew-thousandnodeinstancesofthesystem.queriesoversuchdatathatwouldordinarilyrequireasequenceofMapReduce(MR[12])jobs,butatafractionoftheexecutiontime.1.INTRODUCTIONDremelisnotintendedasareplacementforMRandisoftenusedLarge-scalean
7、alyticaldataprocessinghasbecomewidespreadininconjunctionwithittoanalyzeoutputsofMRpipelinesorrapidlywebcompaniesandacrossindustries,notleastduetolow-costprototypelargercomputations.storagethatenabledcollectingvastamountsofbusiness-criticalDremelhasbeeninprod
8、uctionsince2006andhasthousandsofdata.PuttingthisdataatthefingertipsofanalystsandengineersuserswithinGoogle.MultipleinstancesofDremelaredeployedinhasgrownincreasinglyimportant;interactiveresponset
