AbstractionandPerformanceinDatabaseSystems

ChristophKochEPFLDATALab

Contents•  Expressivenessvs.efficientevaluationofdeclarative

languages–  HowGeorgshapedmeandthistalk

•  Domain-specificlanguagesarehotacrosscomputerscience–  DSLsvsdeclarativelanguages

•  EpidemiologyofDatabasePeopleMissingBoatsDisorder(DMBD)–  InDBsystems:TheScalabilityBlunder:NoSQL–  InDBsystems:HowDSLsmakeDBperformanceworkmainstream…andfolklore.

–  InDBtheory:WherearethePODSpeopleintheDSLrevolution?•  Opportunities:Non-TuringcompleteDSLs&FMT•  WhatIdo

MycollaborationwithGeorg•  20+jointpapersonexpressiveness,

complexity,andefficientevaluationofdeclarative/querylanguages.

•  ThingsIlearnedfromGeorg:–  Howtodoresearch,really–  HowtowriteaPODSpaperJ–  Usingdeclarativelanguagescreatively–  Expressivenessvs.complexityisnota

zero-sumgame!–  Onecan’tjustwritepapersandhavea

careerhere,butadvancehumanknowledge!

–  Muchmore

Theyears0to13AG•  Ididmoreworkondeclarativelanguages

–  E.g.forprobabilisticdatabasesandvideogames•  Imovedmoreintosystems

•  HowcouldIcombinedeclarativelanguages,expressiveness/efficiencywithsystems?– Domain-specificlanguages– Databasesandcompilation/codegenerationforperformance.

•  ThisiswhatIcurrentlymostlydo.

DeclarativelanguagesandDSLs•  Domain-specificlanguages(DSLs)

–  Engineeredlanguages–  UsuallyTuring-complete–  EmbeddedDSL:classicalPL(e.g.Java)+library(domain-specificvocabulary)

•  SQLisaDSL(domain=databasequerying)–  ButmostnewDSLsarenotverydeclarative.

•  InTuring-completeDSLs:(compiler)optimizationstendtobelocalandsometimesbrittle.

DSLsarehot!•  Motivation:notdeclarativitybutperformance

–  CompensateforthefailureofDennardscalingandMoore’slaw.

– Wedon’tknowhowtobuildrobustoptimizingcompilerswithdeep/globaloptimizations.

–  Consequence:Domain-specificcompilation–opportunitiesforautomaticsoftwarespecialization.

•  PeoplealloverCSareflockingtoDSLs–  Computerarchitecture.ASPLOS;Chisel,…– HPC&Graphics:OpenGL,Halide,…–  Systems,databases:LegoBase,S-Store…

DSLsandcodegeneration•  Softwarespecializationbycompilation.

•  Staging/partialevaluation(e.g.specializeDBMScodeforagivenschema).

•  DSLcompilerframeworksallowtoeasilyadddomain-specificcodeoptimizations.•  Usageindomainmakesthemrobust.•  Squid:github.com/epfldata/squid[Parreaux,Shaikhha,K.,GPCE2017,

Scala2017,POPL2018]

•  Increasingly,DSLsenablecodegenerationthatmatchesoroutperformshumansystemsprogrammingexperts!•  Observedinmultipledomains,e.g.lineartransforms[Spiral],OLAP

[LegoBase],OLTP[S-Store]•  “AbstractionwithoutRegret”[Rompf&Odersky,CACM;K.,

CIDR2013]

S-StoreTPC-Cbenchmarkresults

8

OLTPX

Dashti,John,K.,2014

DSLsandtheroleofdatabaseresearch

•  Relationaldatabasescreatedmanyfirsts.•  SQLisstillthemostsuccessfulDSL•  RDBMSshowshowtobuildanentiresystem,theentirestack,for

executingSQLefficiently.•  Algebras,planlanguages,cost-basedoptimization,logicalvs.physical

datarepresentation;managingthememoryhierarchy,memhierarchy-awareoperatorimplementation.

–  ThebasicpipelineandarchitectureisthefoundationofallmodernDSL-basedsystems.

–  Somecreditisgiven(e.g.GraphLab),butthedatabasecontribsareincreasinglytakenasahistoricalfootnoteacrossCS.•  Also,arewestillinnovatinginanysignificantway?

DSLsandtheroleofdatabaseresearch

Databaseperformancetechniquesarebecomingmainstream… andtheroleofdatabasesfadesaway.Intwoways:

– ThecontributionsoftheDBcommunityarebecomingahistoricalfootnote.

•  Databaseideasstopbeingconsidereddatabaseideas.– Databasesfunctionalityisintegratedintootherkindsofsystems,andclassicalDBMSwillbeusedinfewerscenarios.

Example1:row/columnarrepresentations

•  Muchhyped(M.Stonebraker).VariousDBMSbuilt– Vertica,SAPHana,…

•  But:It’sCSfolklorenow.•  Ubiquituousinprogrammingtools

–  List:n+1objects–  Pair:3objects– MakesahugeperformancedifferenceinOOruntimesystems,e.g.JVM–boxing/unboxingoverheads!!!

•  HeavilyusedinHPC,graphics,ML,…

Example2:GRACEHashjoin

•  Classicaldatabasecoursematerial.Seemsuniquelyaboutdatabases(?)

•  Main-memDBcase:hashjoinbecomesthetrivalimplementation.

•  GRACEhash-join=mainmemhashjoin+stagingforthememhierarchy.

•  Memhierarchyconsiderationshavebynowbeenbetteranalyzed/addressedbythecompilers,computerarchitectureandHPCcommunities.–  general/automaticalgotransformationtechniquesexist(looptiling&superoptimization;seeAhoetal.DragonBook2ndEd.Chapter11)

Acaseofmissingtheboat

•  IsthereanythingaboutDBPerformancethatwon’tbeabsorbedintotheCSsystems/performancemainstream?

•  Conjecture:No.•  ExperienceintheDBLabproject(github.com/epfldata/

dblab)[Shaikhha,…,K.,VDLB2014,SIGMOD2016,TODS2018,JFP2018].–  Wearebuildingalibraryofcompileroptimizationsfordata-intensivesystems,byabstractingfromadatabasesystem(LegoBase).

–  Aftercleaningup,noneseemreallyspecifictodatabases.

•  Thisisaproblemforthefutureofdatabaseresearch.

DatabasePeopleMissingBoatsDisorder(DMBD)–apandemic?

•  Causes:– LackofcaretorecognizemajorCStrends(early)– Lackofefforttoabstract&generalizeresults– Cateringtoomuchtoreviewersinacalcified&brokensystemofconferences.

•  Symptoms:Rectalpain,depression•  Treatment:???

AnothercaseofMtBinDBsystems:NoSQL

•  Therealwayswasdistributedandparalleldatabasesresearch.–  Bannedfromfirst-ratepublicationvenues–  Fewsystemsbuilt–not“sexy”enough.

•  ThenGoogleandFacebookwantedscalabledatabases,andwecouldn’tofferthem.

•  Consequencestoday:–  Amassivelossofprestigeforourcommunity–  Awidely-heldbeliefthatonehastolookforSOSPrather

thanSIGMODforgoodDBresearch.–  GenuinecontributionsoftheDBcommunitydonotget

acknowledgedandcited,butreinvented.

AthirdMtBcase:DBTheory

•  Estimated#ofPODSpaperstalkingofDSLs,ever:0

•  Pub.venuesforfoundationalDSLwork:POPL,SIGGRAPH,ASPLOS,…– Citationin-degreeintoDBtheoryliterature:~0

Opportunities

•  ManyresultsfromDBTheory,finitemodeltheoryonnon-TuringcompletelanguagescarryovertomodernDSLs.

•  Peopleinotherdomainsdonotknowtheseresultsandfindthemexciting,whenappliedtotheirDSL.

•  E.g.collectionprogramminglanguageslikeSparkareessentiallyjustnestedrelationalalgebra…

•  MyexperienceataDSLsummerschool.

QuizFrom:K,“ExploitingDomain-SpecificKnowledge:[…]Part1:LessonsonDSLslearned

bytheDBcommunity”,DSLDesign&ImplementationSummerSchool,2016.ConsiderthefollowingDSL:•  purelyfunctionalScala,with“if”astheonlycontrolstructure•  TypesbuiltfromInt,List,andtuples•  Listops:singletonconstr,emptylist,map(x=>…),flatten,listconcat++•  Tupleconstruction(…)andprojection_i•  (deep)equalitytest=;theidentityfunctionLetuscallthislanguage(Scala/List)MonadCalculus(MC)tohavealabel.Example:scala> val R = List(1)++List(2); val S = List(1)++List(3)R: List[Int] = List(1, 2)S: List[Int] = List(1, 3)

scala> R.map(r => S.map(s => 
 if (r==s) List((r,s)) else List()).flatten).flattenres2: List[(Int, Int)] = List((1,1))

for(r

Quiz:WhatcanyoudoinMC?

R.map(r => S.map(s => 
 if (r==s) List((r,s)) else List()).flatten).flatten

•  Joins? ---yes•  Arbitrary“conjunctivequeries” ---yes•  ArbitrarySQLselect-from-wherequeries ---no,conditions( x==a) == List()•  Aggregations:selectcount(*)from… ---no•  Testingonorder/looksideways,sortingalistofintegers? ---no•  Reachabilityinagraphgivenbytheedgerelation? ---no

Quiz:WhatcanyoudoinMC?

R.map(r => S.map(s => 
 if (r==s) List((r,s)) else List()).flatten).flatten

•  Doeseveryprogramterminate? ---yes•  Howbigisthelargestvaluethancanbeproduced? ---polynomialininput•  Howquicklydoeseveryprogterminate? ---PTIME•  Allqueriesofrelationalalgebra ---yes!!!•  Onlyqueriesexpressibleinrelationalalgebra ---yes%repr!!!!!!!!!!•  Caneveryprogrambeparallelized? ---yes,fantastically

well!(AC0)--givenpolynomiallymuchhardware,everyprogramrunsinCONSTANTtime!!!!--ifyouhaveonlyconstantlymuchhardware=>BrentSchedulingPrinciple

Quiz:ExtendingMCR.map(r => S.map(s => 
 if (r==s) List((r,s)) else List()).flatten).flatten

•  Testingonorder/looksideways,sortingalistofintegers? ---no•  List.mappreservesorderbutcan’t“query”it.•  ButwhatifIwantaDSLthatcandothis?CouldaddList.foldLeft,andnothingelse.•  Doeseveryprogramstillterminate?---yes•  DoeseveryprogramstillruninPTIME?---no,nonelementary!

TheFO[X]DSLZoo

Databasetheoryworkthatweneedmoreof

1.  Resultsoncomplexityandefficiencythatsystemspeoplecanunderstandtoberelevanttothem,andwhichcarryovertonewlanguages,e.g.– Georg’sworkonhypertreedecompositions– Resultcardinalitybounds–AGMbound– Worst-caseoptimaljoins– …

2.  ResultsthatbridgethegapbetweenPODSandPOPL/SIGGRAPH/ASPLOSwork.

Summary

•  TrynottomisstheDSLboat.

•  Ifthisadviceisusefultoyou,youultimatelyhaveGeorgtothankforitJ