big data architectures - inria · (9)

BigDataArchitectures

IoanaManolescuINRIASaclay&EcolePolytechnique

[email protected]://pages.saclay.inria.fr/ioana.manolescu/

M2DataandKnowledgeUniversitédeParisSaclay

BigDataArchitectures2019-2020 IoanaManolescu 1

Dimensionsofdistributeddatamanagementsystems

•  Datamodel:–  Relations,trees(XML,JSON),graphs(RDF,others…),nestedrelations

–  Querylanguage•  Heterogeneity(DM,QL):none,some,alot•  Scale:small(~10-20sites)orlarge(~10.000sites)•  ACIDproperties•  Control:

–  Singlemasterw/completecontroloverNslaves(Hadoop/HDFS)–  Sitespublishindependentlyandprocessqueriesasdirectedbysinglemaster/mediator

–  Many-mediatorsystems,orpeer-to-peer(P2P)withsuper-peers–  Sitescompletelyindependent(P2P)

IoanaManolescu 2BigDataArchitectures2019-2020

Today'slecture

•  P2Parchitectures:– highestdegreeofpeerautonomy– highdegreeofdistribution

•  CloudBigDatamanagementarchitectures– Cloudcomputing– Structureddatamanagementontopofcloudservices


PEER-TO-PEERDATAMANAGEMENTARCHITECTURES


Peer-to-peerarchitectures•  Idea:easy,large-scalesharingofdatawithnocentralpointof

control•  Allthepeersplayidenticalroles•  Peersmayjointhepeernetworkorleaveitatanytime

•  Advantages:–  Distributework;preservepeerindependence

•  Disadvantages:–  Lackofcontroloverpeerswhichmayleaveorfailàneedformechanismstocopewithpeersjoiningorleaving(churn)

–  Schemaunknowninadvance;needfordatadiscovery


Peer-to-peerarchitectures•  Large-scalesharingofdatawithnocentralpointofcontrol•  TwomainfamiliesofP2Parchitectures:

–  UnstructuredP2Pnetworks•  Eachpeerisfreetoconnecttootherpeers;•  Variant:super-peernetworks

–  StructuredP2Pnetworks•  Eachpeerisconnectedtoasetofotherpeersdeterminedbythesystem

•  Also:hybridP2Parchitectures–  A"central"subsetofthenetworkisstructured,therestisunstructured


UnstructuredP2PnetworksApeerjoinsthenetworkbyconnectingtoanotherpeer(«gettingintroduced»)Eachpeermayadvertisedatathatitpublishesàpeers«knowtheirneighbors»uptosomelevelBigDataArchitectures2019-2020 IoanaManolescu 7

UnstructuredP2Pnetworks

Apeerjoinsthenetworkbyconnectingtoanotherpeer(«gettingintroduced»)


I’vegot:«GoTseason1»«Mr.Robot»

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR») (p1,«GoT»)

(p1,«MR»)(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

UnstructuredP2Pnetworks

Apeerjoinsthenetworkbyconnectingtoanotherpeer(«gettingintroduced»)Eachpeermayadvertisedatathatitpublishesàpeers«knowtheirneighbors»uptosomelevelBigDataArchitectures2019-2020 IoanaManolescu 9

I’vegot:«GoTseason1»«Mr.Robot»

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR») (p1,«GoT»)

(p1,«MR»)(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

(p1,«GoT»)(p1,«MR»)

UnstructuredP2PnetworksQueriesareevaluatedbypropagationfromthequerypeertoitsneighborsandsoonrecursively(flooding)Toavoidsaturatingthenetwork,querieshaveTTL(time-to-live)Thismayleadtomissinganswersàa.replication;b.superpeers


Q?

Q?Q?

Q?

Q?Q?

Q?

HybridP2Pnetwork

•  Smallsubsetofsuperpeersallconnectedtoeachother•  Specializedbydatadomain,e.g.[Aa—Bw],[Ca—Dw],…orbyaddressspace•  Eachpeerisconnectedatleasttoasuperpeer,whichroutesthepeer’squeries


Q?

Q?

Q?

Q?

StructuredP2Pnetwork•  Peersformalogicaladdressspace0…2k-1

–  Somepositionsmaybeempty–  Thepeerpositionisobtainedwiththehelpofahashfunction

–  e.g.,H(peerIPaddress)=n,0<=n<=2k-1–  Thisalsoleadstothename:distributedhashtable,DHT

•  Theglobaldatacatalogiscreatedanddistributedacrossthepeers,usingthesamehashfunction(seenext)


Catalogconstruction(indexing)instructuredP2Pnetworks

•  Thecatalogisbuiltasasetofkey-valuepairs–  Key:expectedtooccurinsearchqueries,e.g.«GoT»,«MrRobot»

–  Value:theaddressofcontentinthenetworkmatchingthekey,e.g.«peer5/Users/a/movies/GoT»

•  Ahashfunctionisusedtomapeverykeyintotheaddressspace;thisdistributes(key,value)pairs–  H(key)=nàthe(key,value)pairissenttopeern–  Ifnisempty,thenextpeerinlogicalorderischosen




12

SearchinginstructuredP2Pnetworks

Locateallitemscharacterizedby?Hash()=6Peer6knowsallthelocationsLocateallitemscharacterizedby?Hash()=14Peer15knowsallthelocationsHowdowefindpeers6and15?


ConnectionsbetweenpeersinstructuredP2Pnetworks

Apeer'sconnectionsaredictatedbythenetworkorganizationandthelogicaladdressofeachpeerinthespace0…2k-1Example:Chord(MIT,mostpopular)Eachpeernisconnectedto•  n+1,n+2,…,n+2k-1,ortothefirstpeerfollowingthatposition

intheaddressspace;•  ThepredecessorofnTheconnectionsarecalledfingers


ConnectionsbetweenpeersinChord


SearchinginstructuredP2PnetworksLocateallitemscharacterizedby?•  Hash()=6•  Peer6knowsallthelocationsHowdoespeer1findpeer6?•  6-1=5;2<=log2(5)<=3,thus6isafter1+22•  Redirectthequestiontothe2ndfingerof1.Howdoespeer10findpeer3?•  (3-modulo1610)=9;3<=log2(9)<=4,thus3isafter10+23

•  Redirectthequestiontothe3rdfingerof10.

Quicktraversalsofthering(log2(N)hops)


PeersjoininginChordTojoin,apeernmustknow(any)peern'alreadyinthenetworkProceduren.join(n'):

s=n'.findSuccessor(n);buildFingers(s); successor=s;



s=n'.findSuccessor(n);buildFingers(s); successor=s;



s=n'.findSuccessor(n);buildFingers(s);successor=s;

If3hadsomekey-valuepairsforthekey2,3givesthemoverto2Thenetworkisnotstabilizedyet…


NetworkstabilizationinChordEachpeerperiodicallyrunsstabilize()n.stabilize():

x=n.succ().pred()if(n<x<succ)thensucc=x;

succ.notify(n)

n.notify(p):if(pred<p<n)thenpred=p


NetworkstabilizationinChordFirststabilize()of2:3learnsitsnewpredecessorn.stabilize():


succ.notify(n)



NetworkstabilizationinChordFirststabilize()of1:1and2connectn.stabilize():


succ.notify(n)



Peerleavingthenetwork

•  Thepeerleaves(withsomeadvancenotice,«ingoodorder»)

•  Networkadaptationtopeerleave:–  (key,value)pairs:thoseoftheleavingpeeraremovedtoitssuccessor

– Routing:Pnotifiessuccessorandpredecessor,whichreconnect"overP"


Peerfailure•  Withoutwarning•  Intheabsenceofreplication,the(key,value)pairsheldonP

arelost–  Peersmayalsore-publishperiodically

ExampleRunningstab(),6notices9isdown6replaces9withitsnextfinger10àallnodeshavecorrectsuccessors,butfingersarewrongRoutingstillworks,evenifalittlesloweddownFingersmustberecomputed


Peerfailure

Chordusessuccessorstoadjusttoanychange•  Adjustmentmay«slowlypropagate»alongthering,sinceitisrelativelyrare

Topreventerroneousroutingduetosuccessorfailure,eachpeermaintainsalistofitsrdirectsuccessors(2log2N)•  Whenthefirstonefails,thenextoneisused...•  Allrsuccessorsmustfailsimultaneouslyinordertodisruptsearch


GossipinP2Parchitectures•  Constant,«background»communicationbetweenpeers•  Structuredorunstructurednetworks•  Disseminatesinformationaboutpeernetwork,peerdata


Peer-to-peernetworks:wrap-up

•  Datamodel:– Catalogandsearchatasimplekeylevel

•  Querylanguage:keys•  Heterogeneity:notthemainissue•  Control:

– peersareautonomousinstoringandpublishing– queryprocessingthroughsymetricalgorithm(exceptforsuperpeers)


Peer-to-peerdatamanagement•  Extractkey-valuepairsfromthedata&indexthem•  Toprocessqueries:

–  LookuprelevantdatafragmentsintheP2Pnetwork

–  Rundistributedqueryplan

Peer1

PeeriLocaldataLocalqueryprocessorIndexing&look-up

Peern

PeermPeerp

…


Example:storingrelationaldatainP2Pdatamanagementplatform

•  Eachpeerstoresahorizontalsliceofatable•  Catalogatthegranularityofthetable:

–  Keys:tablenames,e.g.Singer,Song–  Value:fragmentdescription,e.g.,peer1:postgres:sch1/Singer&u=u1&p=p1,

–  Query:selectSinger.birthdayfromSinger,SongwhereSong.title=«ComeAway»andSinger.sID=Song.singer

– Whatcanhappen?•  Tryothergranularities


ModernP2Pdatamanagementsystem:Cassandra

–  Partitionedrowstore,fullysymetricstructuredP2Parchitecture–  BasedontheDynamoK-Vsystem[CHG+07]–  Somenesting;indexes.Queries:select,project.Tablesongs:ALTERTABLEsongsADDtagsset<text>;UPDATEsongsSETtags=tags+{'2007'}WHEREid=8a172618…;UPDATEsongsSETtags=tags+{'covers'}WHEREid=8a172618…;UPDATEsongsSETtags=tags+{'1973'}WHEREid=a3e64f8f-…;SELECTid,tagsfromsongs;

33BigDataArchitectures2019-2020 IoanaManolescu

LargeCassandradeployments:•  Apple:over75,000nodesstoringover10PBofdata•  Netflix:2,500nodes,420TB,over1trillionrequestsperdayCAPtrade-off:timeoutfordecidingwhenanodeisdead«Duringgossipexchanges,everynodemaintainsaslidingwindowofinter-arrivaltimesofgossipmessagesfromothernodesinthecluster.Configuringthephi_convict_thresholdpropertyadjuststhesensitivityofthefailuredetector.Lowervaluesincreasethelikelihoodthatanunresponsivenodewillbemarkedasdown,whilehighervaluesdecreasethelikelihoodthattransientfailurescausingnodefailure.Usethedefaultvalueformostsituations,butincreaseitto10or12forAmazonEC2(duetofrequentlyencounterednetworkcongestion)tohelppreventfalsefailures.Valueshigherthan12andlowerthan5arenotrecommended.»


ModernP2Pdatamanagementsystem:Cassandra

STRUCTUREDDATAMANAGEMENTINCLOUDENVIRONMENTS


Cloudcomputing

•  Idea:delegatelarge-scalestorageandlarge-scalecomputingtoremotecenters– Runbythe(only)enterpriseusingthem:"privateclouds"

•  Largecompaniescanaffordthecosttoownandoperateacloudservice:LaPoste,Orange,...

– Runbyacompanywhorentsoutstorageandcomputingservices:"commercialclouds"

•  Mainplayers:Amazon(hasbasicallycreatedtheindustry),Google,Microsoft


Advantagesofcloudcomputing•  AllowcompaniestofocusontheirmainbusinessnotonIT

•  Allowscalingtheresourceusageupanddownaccordingtotheneeds

•  ComesatacostExamples:•  Satelliteimagedataprocessingcompanywhichneedssignificantcomputingresources(only)whenithasanorderfromaclient

•  ShopswithmoreclientsasChristmasapproaches


https://www.wired.com/2015/03/orbital-insight/

Howcloudserviceswork(1/3)•  Storage

–  Usershostfilesontrustedservers–  TheserviceispaidbytheGBandday

•  Totalcost=sum(filesizexfilestoragetime)•  Computing

–  Usersbuyvirtualcomputers("virtualmachines")–  ServicepaidbythedurageofuseoftheVM–  Eachvirtualcomputerishostedbysomephysicalcomputerinthecloudprovider'scluster

–  Ifaphysicalmachinefails,thevirtualmachinewillberecreatedelsewhereandtheworkwillrestart


Howcloudserviceswork(2/3)•  Computing(continued)

–  Therearetypicallydifferentsizes(capacities)ofvirtualmachines•  Small(S),Medium(M),Large(L),Extra-Large(XL)•  Thedifferenceisinthecomputingspeed

•  Faststorageofsmall-granularitydata,typicallyinmemoryinthecloud–  For:metadata(catalog,usermanagement,...)–  Key-valuestores,documentstores–  Payperoperation(put,get)

•  Otherservices–  E.g.messagingqueuestosynchronizedifferentapplications


Howcloudserviceswork(3/3):cloudcomputingmodels

•  Infrastructure-as-a-service–  Thevendorprovidesaccesstocomputingresourcessuchasservers,storageandnetworking.

–  Clientsusetheirownplatformsandapplicationswithinaserviceprovider’sinfrastructure.Theydonothostbuttheydevelop,deployandadministerinthecloud.

•  Platform-as-a-service–  Thevendorprovides:storageandothercomputingresources,prebuilttoolstodevelop,customizeandtesttheirownapplications.

–  Clientsdonothostandmostlydonotadministereither.Theystilldevelopanddeployinthecloud.


Howcloudserviceswork(3/3):cloudcomputingmodels

•  Software-as-a-service– Thevendorprovides:storageandothercomputingresources;softwareandapplicationsviaasubscriptionmodel(orpay-per-use...)

– Clientsaccesstheapplicationsremotely.Theydonotstore,host,developnoradminister.


Cloudservices

Fine-granularity data store

Virtual machines

Queue service

File storage service

Amazon Scalable

Storage Service (S3)

Google Cloud Storage

Windows Azure BLOB Storage

Amazon Elastic Compute Cloud

(EC2)

Google Compute Engine

Windows Azure Virtual Machines

Amazon DynamoDB

Google High Replication Datastore

Windows Azure Tables

Amazon Simple Queue Service

(SQS)

Google Task Queues

Windows Azure Queues

Cloud Platform

BigDataArchitectures2019-2020 42IoanaManolescu

Performanceinlarge-scaleclusters

•  On-site(withinacompanyororganization)oroff-site(cloud)

•  Theremaybevariablelatencyacrossthecluster,i.e.somemachine(s)maytemporarilybeslow,dueto–  Sharedresources(CPU,cache,memory,network)– Maintenance,softwareupgrade–  Globalresourcesharing,e.g.,networkswitches,distributedfilesystems

–  Garbagecollection–  Energymanagement

•  Thevariabilitygetsamplifiedbyscale(seenext)


Latencyvariationsinlarge-scaleclusters


Considerasettingwhereeachserverresponds•  in10ms,99%ofthetime•  in1s,1%ofthetime(1in100,bluecurve)

Ifaclientneedstotalkto100servers,theprobabilityof>1slatencyis63%!

Source:DeanandBarroso,"TheTailatScale",CommunicationsofACM,2013

Structureddatamanagementincloudplatforms

•  Thecloudprovides:–  Distributedfilesystem;Virtualmachines;Fine-granularity(e.g.,key-value

ordocument)store;Distributedmessagequeues

•  Basedonthis,needtoproposearchitecturesfor:–  Storingverylargevolumesoffine-granularitydata(relational,XML,JSON,

graphs...)andqueryingit–  Transactions–  Concurrencycontrol

Cloud services provider


AMADA:XMLdatamanagementwithintheAmazoncloud[CCM13]

Fine-granularity data store

Virtual machines

Queue service


Amazon Scalable

Storage Service (S3)

Google Cloud Storage

Windows Azure BLOB Storage

Amazon Elastic Compute Cloud

(EC2)

Google Compute Engine

Windows Azure Virtual Machines

Amazon DynamoDB

Google High Replication Datastore

Windows Azure Tables

Amazon Simple Queue Service

(SQS)

Google Task Queues

Windows Azure Queues

Cloud Platform


•  Functionality:•  XMLandRDFstorageandfine-grainindexinginthecloud

•  Datastorage:1.  Blobstorageincloudfilesystem(i.e.,S3)2.  Fine-granularityindexingink-vstore

•  Dataquerying:1.  Consulttheindextodelimitthedocuments(graphs)

whichmustbeaccessed2.  Evaluatequeryoverrelevantdocuments(graphs)




Front-end

① 

⑤ 

② 

④ 

Indexing module

③ 

Indexing service

Virtual machines

Queue service


Cloud warehouse


AMADAarchitecture

Front-end

Query processor

① 

⑤ 

② 

④ 

①  ② 

③  ④  ⑤ 

⑥  ⑦ 

⑧ 

Indexing module

③ 

Fine-granularity fast store

Virtual machines

Queue service


Cloud warehouse


Cloud warehouse

Indexingandstoragecosts

Front-end

① 

⑤ 

② 

④ 

Indexing module

③ 

Indexing cost depends on: -  Documents set (D) -  Indexing strategy (I)


Virtual machines

Queue service



Cloud warehouse

Indexingandstoragecosts

Front-end

Indexing module

$r

$r

$putidx

$h

$put

$get

$r


Virtual machines

Queue service


Storage cost


Cloud warehouse

Queryingcost

Front-end

Query processor

①  ② 

③  ④  ⑤ 

⑥  ⑦ 

⑧ 

Querying cost depends on: -  Query (q) -  Documents set (D) -  Indexing strategy (I)


Virtual machines

Queue service



table+ item+

key

attribute+

name

value

Fine-granularityfaststoreinAmazoncloud:DynamoDB


Indexingfine-granularitydataintheAmazoncloud

Indexing strategy I: Function associating (key,(name,value)+)+ to a document

BigDataArchitectures2019-2020 54

DynamoDB data model

IoanaManolescu

table+ item+

key

attribute+

name

value

Indexingfine-granularitydataintheAmazoncloud

•  Fourindexingstrategies- Label-URI(LU)- Label-URI-Path(LUP)- Label-URI-ID(LUI)- Label-URI-Path/Label-URI-ID(2LUPI)


Indexing strategy I: Function associating (key,(name,value)+)+ to a document

IoanaManolescu

table+ item+

key

attribute+

name

value

XMLindexing:example

name

family

‘Smith’

name

brand

car

‘Ford’

‘Audi’

family

model

‘A3’

family

brand ‘Lee’

‘Ford’

model

’330d’

name car car

brand

‘BMW’

name

brand

car

‘Martin’

‘Ford’

family

model

‘Mustang’

doc1.xml doc2.xml doc3.xml doc4.xml

name

brand

car

‘Ford’

family

model

q


XMLindexing:example

doc4.xml Martin Mustang

Result:


name

brand

car

‘Ford’

family

model

q

name

family

‘Smith’

name

brand

car

‘Ford’

‘Audi’

family

model

‘A3’

family

brand ‘Lee’

‘Ford’

model

’330d’

name car car

brand

‘BMW’

name

brand

car

‘Martin’

‘Ford’

family

model

‘Mustang’

doc1.xml doc2.xml doc3.xml doc4.xml

IoanaManolescu

Label-URI(LU)strategy

efamily doc1.xml doc2.xml doc3.xml doc4.xml

Ø Ø Ø Ø

wFord doc2.xml doc3.xml doc4.xml

Ø Ø Ø

ename doc1.xml doc2.xml doc3.xml doc4.xml

Ø Ø Ø Ø

…

Look-up:IntersectionofURIsetsassociatedtoeachquerynode

Index: (key, (name, value))


name

family

‘Smith’

name

brand

car

‘Ford’

‘Audi’

family

model

‘A3’

family

brand ‘Lee’

‘Ford’

model

’330d’

name car car

brand

‘BMW’

name

brand

car

‘Martin’

‘Ford’

family

model

‘Mustang’

doc1.xml doc2.xml doc3.xml doc4.xml ✔ ✖ ✔ ✔

name

brand

car

‘Ford’

family

model

q

IoanaManolescu

Label-URI-ID(LUI)strategy

efamily doc1.xml doc2.xml doc3.xml doc4.xml

[1 3 0] [1 8 0] [1 11 0] [1 8 0]

wFord doc2.xml doc3.xml doc4.xml

[3 1 2] [6 3 3] [6 3 2]

ename doc1.xml doc2.xml doc3.xml doc4.xml

[2 2 1] [2 2 1] [2 2 1] [2 2 1]

…

Look-up:StructuraljoinoverIDsassociatedtoeachquerynode

Index: (key, (name, value))


name

brand

car

‘Ford’

family

model

q

name

family

‘Smith’

name

brand

car

‘Ford’

‘Audi’

family

model

‘A3’

family

brand ‘Lee’

‘Ford’

model

’330d’

name car car

brand

‘BMW’

name

brand

car

‘Martin’

‘Ford’

family

model

‘Mustang’

doc1.xml doc2.xml doc3.xml doc4.xml ✖ ✔ ✖ ✖

IoanaManolescu

Queryanswering(eightruns)

0

6000

12000

18000

24000

30000

36000

LU LUP LUI 2LUPI LU LUP LUI 2LUPIL XL

Res

pons

e Ti

me

(s)

1 instance8 instances


Queryansweringtime

0,1

1

10

100

1000

10000

L XL

Res

pons

e Ti

me

(s)

No Index LU LUP LUI 2LUPI No Index – 20000 docs LU – 3 docs LUP – 2 docs LUI – 1 doc 2LUPI – 1 doc

Q1 Log scale


Queryansweringcost

0

0,2

0,4

0,6

0,8

1

L XL

Cos

t ($)

No Index LU LUP LUI 2LUPI


Queryansweringcostdetail(XL)


Indexcostamortization

-100

-75

-50

-25

0

25

50

75

100

125

0 2 4 6 8 10 12 14 16 18 20

#run

s x

bene

fit(I,

W) -

bu

ildin

gCos

t(I)

# runs

LU LUP LUI 2LUPI


CloudDataWarehouseServices

•  Software-as-a-Service(SaaS)solutions•  SnowflakeintheAmazonCloud•  GoogleBigQuery(https://cloud.google.com/bigquery/)

•  AmazonRedshift(https://aws.amazon.com/redshift/)

•  MicrosoftAzureSQLDataWarehouse(https://azure.microsoft.com/)


CloudDataWarehouseServicesTheneed:efficientdataprocessingatverylargescaleàdistributedsystemPrevioussolution:share-nothingarchitectures(MapReduceorSparkclusters)•  Eachnodestoressomedataandcomputesonit•  StorageandcomputationsaredistributedatthesametimeLimitations:1.  Heterogeneousworkload,e.g.bulkloading(highI/O,littletono

CPU)...intensivecomputing(littletonoI/O,highCPU)àhardtochosemachines!

2.  Membershipchangesarefrequentinthecloud,leadtodatashufflesandnegativelyimpactperformance

3.  Hardtodoonlineupgradeinsymetric,homogeneousarchitecture


Snowflake:separatingstoragefromcomputationsinthecloud[DGZ+16]

•  StoredataintheAmazonS3service.•  Storemetadata(catalog,usermanagement,...)inhigh-performance,transactionalkey-valuestore

•  Performcomputationsinvirtualwarehouses(Snowflakeproprietarysoftware)– Avirtualwarehouse(VW)hasasetofworkers.ItiscreatedondemandbyaSnowflakeclient.

– AclientcanhavemanyVWs.– EachqueryishandledwithinoneVW.


Snowflakearchitecture


ProprietarySQLengine(withafewmoregoodies)ineachVirtualWarehouse

SnowflakeVirtualWarehouses

•  EveryVWhasaccesstothesamedata(fromS3)•  AworkerbelongstoexactlyoneVW•  VirtualWarehousescomein"T-shirtsizes"(XStoXXL)

– Hidescloudprovidernodes(eventheirnumber)fromSnowflakeclient

– AllowsindependentSnowflakepricingpolicy– Allows"smart"investmentofcloudservicescost:foraloadtask,bookaVWof(4nodesfora15h)vs.(32nodesfor2h).


SnowflakeVirtualWarehouses

•  S3muchsloweraccessthanthelocaldiskofanodeàaworker'slocaldiskactsascache–  LRUcachingofthedatalastneededonthisnode–  Cachegranularity:tablecolumns

•  ToavoidredundantcachingofthesamedatafragmentsacrossthenodesofasingleVW,theOptimizerassignsfiles(table)cachedatatonodesusinghashingonthethe(table,column)name–  Ifthisdataiscached,itisonlycachedonaspecificVWnode


QueryevaluationinSnowflake1.  Selectivedataaccess

–  Eachtableisstoredasassetofshards–  Insideeachshard,dataisstoredasasetof(compressed)columns

–  Headersbuiltforeachcolumnwithintheshard•  Minimumandmaximumvalues•  Noneedtoreadashardifthequerypredicateisincompatiblewiththeheaderinformation

2.Queryoptimizer–  Cost-andstatistic-based–  Headerscomputedevenonintermediaryresults–  Somedecisionstakenatruntime

3.Intermediaryqueryresultswritteninnodelocaldisks,then(ifneeded)toS3


ConcurrencycontrolinSnowflake

•  Handledgloballyusingfine-granularitydatastore•  Anupdatecreatesanewversionofatable(multiversionconcurrencycontrol):nofiner-granularityupdate

•  Eachversionhasatimestamp•  Possibletoexplicitlyquerytheversionatorafteracertaintimestamp

•  Eachversionstaysavailable90daysafterdeletion


GLOBALCOURSEWRAP-UP


BigDataArchitectures•  Moderndevelopmentofdistributedcomputingplatformsleadsto

unprecedentedexplosioninBigDataarchitecturesandsystems•  Dimensionsofanalysis:

–  Scaleofdistribution–  Datamodel,querylanguagetheysupport–  CAPcompromise(whichlevelofconsistency)–  Performanceinfluencedby:

•  Datastorage,indexing,queryevaluationalgorithms...•  Queryoptimization•  Synchronizationoperations

–  Successinfluencedbyperformance,easeofuse,killerapplications


References•  [CCM13]Webdataindexinginthecloud:efficiencyandcostreductions.JesusCamacho-Rodriguez,DarioColazzo,IoanaManolescu.EDBT,2013

•  [CHC+07]G.DeCandia,D.Hastorun,M.Jampanietal.Dynamo:Amazon'shighlyavailablekey-valuestore,ACMSOSP2007

•  [DGZ+16]TheSnowflakeElasticDataWarehouse.BenoîtDageville,ThierryCruanes,MarcinZukowskietal.,SIGMOD,2016

•  [SMK+01]Chord:Ascalablepeer-to-peerlookupserviceforinternetapplications,I.Stoica,R.Morris,F.Kaashoeketal.ACMSIGCOMMComputerCommunicationReview,2001
