Experimentswith

HadoopandSpark,

discussion

AndréSchaaff,François-XavierPineau,NoémieWali

IVOAmeeFng,Sydney2015

AroundBigData…

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AnongoingexploraFon(startedlastmonth)of

emerging(ormaturing)«BigData»

technologieswiththeXMatchasmainusecase

Emerging/maturingtechnologies

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Credits:ApachefundaFon

Hadoop?

•  High-availabilitydistributedobject-orientedpla]orm

– Frameworkforthedistributedprocessingoflarge

datasets(HDFS(distributedfilesystem),

MapReduce)

– Scalablefromasingleservertothousandsof

machines

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HDFS:moredetails

•  AbstracFonofthestorage– Asetofdistributedhardisksisseenasonehardisk

– NameNode•  namespace,filetree,metadata

•  locaFonofthedatablocks– DataNodes•  wherethedatablocksare•  theDataNodesinformtheNameNodeoftheircontent

(datablocks)

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HDFSArchitecture

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Credits:ApachefundaFon

Spark?

•  «Fastandgeneralenginefordataprocessing»

•  RunsonHadoop(HDFS),Mesos,standaloneor

inthecloud

=>CompaFblewithHadoopdata

•  ApplicaFonscanbewrideninJava,Scala,PythonorR

•  NotonlyMapReducedriven

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CDSXMatch

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•  TheCDSXMatchisanefficientservicebased

onopFmizeddevelopmentsand

implementedonawellshapedhardware

CDSXMatch(2)

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•  Thedataisnotdistributedonafewservers:allthecataloguesarelocatedononeRAIDsystem

•  But…

CDSXMatch(3)

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•  …but«organized»

DistributedXMatch

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•  InthecaseofHadoop/Spark,the

dataisdistributedon

clustersofservers

•  Pointsofinterest:howisthedata

distributed?,howto

opFmizethis

distribuFon?

Whythisstudy?...

•  WeareevaluaFngwhatHadoop/Sparkcould

bring

– Whatitcouldreplaceorimprove,especiallyinthe

frameofscalability(largerdatasets,hardware

faciliFes,deployments,etc.)

– Andforwhichcost(money,manpower,

performancesup?/down?)

•  LearntouseHadoop/Spark

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Discussion

•  Whois(orhastested)usingHadoop,Spark?

– Whichusecase?

–  InproducFon?– Feedback?

•  Intheframeof«bringingthecodetothe

data»?

– Example:icansendajarfiletothemasternode

tobeexecuted«nearthedata»

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