Kumar Anant - Graph Database Modeling with neo4j

I
CopyrightedMaterial
GraphDatabaseModelingwithneo4j
Copyright2020-21byAjitSingh,AllRightsReserved.

Nopartofthispublicationmaybereproduced,storedinaretrievalsystem or transmitted,inanyform orbyanymeanselectronic,mechanical,photocopying, recordingorotherwisewithoutpriorwrittenpermissionfromtheauthor,exceptfor theinclusionofbriefquotationsinareview.

Forinformationaboutthistitleortoorderotherbooksand/orelectronicmedia, contactthepublisher:

AjitSingh&AnantKumar
e:ajit_singh24@yahoo.com
e:anant@jdwcpatna.com
w:https://www.ajitvoice.wordpress.com

Preface

Thisbookisdesignedtowalkyouthroughthegraphdatamodeling. Youwillbeintroducedtothe basicprocessofdesigningagraphdatamodelthatcananswerawiderangeofbusiness questionsacrossavarietyofdomains.

Graphdatamodelingistheprocessinwhichauserdescribesanarbitrarydomainasa connectedgraphofnodesandrelationshipswithpropertiesandlabels.Agraphdatamodelis designedtoanswerquestionsintheform ofCypherqueriesandsolvebusinessandtechnical problemsbyorganizingadatastructureforthegraphdatabase.

Thisbookissimplytheintroductiontodatamodelingusingasimple,straightforwardscenario. Thereareplentyofopportunitiesthroughouttheupcomingguidestopracticemodelingdomains andanalyzingchangestothemodelthatmightneedtobemade.

Dataislikewater.Itsprobablyuselessifyoudontputitinahelpfulcontainer.Theshape,size andfunctionalityofthatcontainerdependsonyourintendeduse,butingeneral,acontaineris necessary.

Thesamegoeswithdata.Whenitcomestocreatinganewapplicationordatasolution,you needtoprovideastructureforthatdata.Thatstructuringprocessisknownasdatamodeling.

Oftenreservedsolelyforseniordatabaseadministrators(DBAs)orprincipaldevelopers,data modelingissometimespresentedasanesotericartunknownabletomeremortals.Youmay worshiptheexpertdatamodelerfromafar.

Whilesomedatamodelingscenariosreallyarebestleftuptotheexperts,itdoesnthavetobe difficultbydefault.Infact,datamodelingisasmuchabusinessconcernasatechnologicalone. Soifyoudontknowasinglelineofcode,youreinluck.

Anyonecandobasicdatamodeling,andwiththeadventofgraphdatabasetechnology, matchingyourdatatoacoherentmodeliseasierthanever.Datamodelingisanabstraction process.Youstartwithyourbusinessanduserneeds(i.e.,whatyouwantyourapplicationtodo). Then,inthemodelingprocessyoumapthoseneedsintoastructureforstoringandorganizing yourdata.

Everydatamodelisunique,dependingontheusecaseandthetypesofquestionsthatusers needtoanswerwiththedata.Becauseofthis,thereisnoone-size-fits-allapproachtodata modeling.Usingbestpracticesandcarefulmodelingwillprovidethemostvaluableresultin producinganaccuratedatamodelthatbenefitsyourprocessesandusecase.

Thegraphdatabasesarenecessaryforaveryconcretedatasets:hugeamounts ofdataofhigh complexity,whereentitiesareveryrelatedtooneanother.Thatisbecause,they efficientlyquerythroughtherelationshipsamongentities,incontrasttorelational databases.
Graphdatabasessupportalgorithmstoperform concretequeriesthatareoutof reachtorelationaldatabases,fortheirtabularstructureandstaticschema.Also,the biggerthevolumeofdata,theslowerthequerieswouldbeinSQL,becausethey would requireto lookup joined tableswith agreatnumberoftuples.Graph databasesallow totraversethroughthegraphandreachahighlevelofdepth, withouthavingtoreadallthedatastored.

Neo4jisanopensourceNoSQLgraphdatabase.Itisafullytransactionaldatabase (ACID)thatstoresdatastructuredasgraphsconsistingofnodes,connectedby relationships.Inspiredbythestructureoftherealworld,itallowsforhighquery performanceoncomplexdata,whileremainingintuitiveandsimpleforthedeveloper.

Neo4jis,byfar,theleadingtechnologyofgraphdatabases.Itanalyzeandtraverseof alldatainrealtimeandgivestheresultsveryfast.Ithasgreatuserinterfaceand support.Butthegreatestfeatureofitis;evendatasizegrow exponentially, performanceofNeo4jdoesnotaffectedbyit.

Usingthisbook,you'llgetto learnthetheoryofgraphdatabaseandhowtouse Neo4jtobuilduprecommendations,relationships,andcalculatetheshortestroute betweentwolocations.Withexampledatamodels,bestpractices,use-cases,andan applicationputtingeverythingtogether,thisbookwillgiveyoueverythingyouneedto reallygetstartedwithNeo4j.Startingwithabriefintroductiontographtheory,this bookwillshow youtheadvantagesofusinggraphdatabasesalongwithdata modelingtechniquesforgraphdatabases.You'llgainpracticalhands-onexperience withcommonlyusedandlesserknownfeaturesforupdatinggraphstorewith Neo4j'sCypherquerylanguage.Thisbookincludesalotofbackgroundinformation, helpsyougraspthefundamentalconceptsbehindthisradicalnewwayofdealing with connected data,and willgiveyou lotsofexamplesofuse casesand environmentswhereagraphdatabasewouldbeagreatinterest.

Neo4jisbeingusedbysocialmediaandecommerceindustrygiants.Youcantake advantageofNeo4j'spowerfulfeaturesandbenefits-addBeginningNeo4jtoyour librarytoday.

Contents

1.GraphDataModel
Graphdatabases
2.GraphSchemas
Selectingvertexlabels
Examplesoflabelselection
Drawingagraphschema
Summary
3.ConvertingERmodelstographschemas
ERmodelsanddiagrams
Example
ProceduretoconvertanERmodeltoagraphschema
Rule#1:Entitytypesbecomevertextypes
Rule#2:Binaryrelationshiptypesbecomeedgetypes
Rule#3:Naryrelationshiptypesbecomevertextypes
Conversionexample
Verticesarevertices,andedgesareedges
Summary
4.NormalizingGraphSchemas
Normalizationofrelationaldatabases
Transformationrulesthatproduceequivalentschemas
RuleA:Renamingpropertiesandlabels
RuleB:Reversingedgedirections
RuleC:Propertydisplacement RuleD:Specializationandgeneralization
RuleE:Edgepromotion
RuleF:Propertypromotion
RuleG:Propertyexpansion
Summary
5.Onemetarulefornormalization
Schemasandconstraints
Graphuniverses,transformationsandequivalence
Derivedtypes
Metarule:Addingandremovingderivedtypes
Provingthemetarule
Provingthe7rules:Renaming,Reversing,PropertyDisplacement,
Beyondtransformationrules
Summary
6.Validatinggraphschemas
7.Pixy:Firstorderlogicongraphdatabases
Background
OnSQL
Onfirstorderlogic
OnGremlin
Pixy:FirstorderlogicwithGremlin
ERmodelsinPixy
Queryrequirementsdon'tusuallymatterwhilemodeling
7.IntroductiontoDatabase
StateoftheartofDatabases
TypesofDBMS
NoSQLDBMS ComparisonofDBMS
Currenttrends
9.GraphDatabases
GraphTheoryandItsApplications
ConceptsofGraphDatabases
Queryperformance
10.Neo4j
IntroductionofNeo4j
AdvantagesofNeo4j
PropertiesofNeo4j
PerformanceInNeo4j
HowToIncreasePerformanceOfNeo4j?
CypherQueryLanguage
Structure
OperationsInCypher
LoadingDataWithCypher
UseCasesofNeo4j
11.Gettingstartedwithneo4j
InstallationorSetup
Installation&StartingaNeo4jserver
StartNeo4jfromconsole(headless,withoutwebserver)
StartNeo4jwebserver
StartNeo4jwebserver
Deleteoneofthedatabases
CypherQueryLanguage
RDBMSVsGraphDatabase
Cypher-Implementation
Creation Createanode
Createarelationship
QueryTemplates
CreateanEdge
Deletion
Deleteallnodes
Deleteallnodesofaspecificlabel
Match(capturegroup)andlinkmatchednodes
UpdateaNode
DeleteAllOrphanNodes
Python&Noe4j
12.Neo4jApplication
UseCaseSelected
Data
ImplementingData
Exportdata
QueryExamples(Neo4j-SQL)
ShortestPath
Betweennesscentrality:
Closenesscentrality:
PageRank:
CommunityDetection:
PossiblequeriesonSQL Bibliography

PartI