Introduction Therelationaldatabasehasbeenthefoundationofenterprisedatamanagementforoverthirtyyears.Buttheway,webuildandrunapplicationstoday,coupledwithunrelentinggrowthinnewdatasourcesandgrowinguserloadsarepushingrelationaldatabasesbeyondtheirlimits.So,weareseeingalotofapplicationsbeginningtomigratetoNoSQL,likeMongoDBcompletelyormovetoahybridmodelutilizingbothRDBMSandMongoDBforpersistingdifferentpartsofthesamesystem.ThiswhitepaperdiscusseshowMongoDB,theleadingnosqldatabaseoffersanewandbetterapproachforpersistingdatathanrelationaldatabases.Table of Contents

MongoDBadvantagesRelationalDatabasechallengesMongoDBSolution

AgilityPerformanceScalabilityHighAvailability

DetailedExplanationFromrigidtablestoflexibleanddynamicBSONdocumentsApplicationintegrationAtomicityinMongoDBMigrationtoolsfromrelationaltoMongoDBMongoDBMMS

Conclusion

MongoDB advantages DynamicSchemadesignenablesrapiddevelopmentwithoutalonginitialtableandforeignkeydesignsencountered,whenusingarelationaldatastore.Horizontalscalingoncommodityhardware:TheproductionsitecanmanageseveralTBinasingletable(collection),w/obeinglimitedbyaddingnewfieldsorbeinglimitedbygrowth.Shardingenableslinearandscaleoutgrowthw/orunningoutofbudget.RapidreplicaSetenablesmeetingregulationwitheasytosetupmultidatacenterDRPandHAsolution.Highwritethroughput:Withalargenumberinserts,MongoDBarchitectureisgreatforasystemthatmustsupporthighinsertload.YetyoucanguaranteetransactionswithfindAndModify(whichisslower)andtwophasecommit(applicationwise).MongoDBsupportsfulltextsearchindexesandrequiresnoadditionalinfrastructuretobedeployed.Developerorientedqueries,enabledeveloperswriteaelegantqueries.

Avarietyofindexesareavailable.Especiallyusefulisbuiltinlocationindex,whichcanbeutilizedtobuildgeospatialapplications.Map/Reduce:Ifyouhappentoneedthis,thereisbuiltinsupport.

Relational Database challenges Relationaldatabaseshavethefollowingbroadcategoriesofchallenges:

1. Datatypes:Theyarenotdesignedtosupportunstructured,semistructuredorpolymorphicdata.

2. VolumeofData:Whenitcomestosupportingpetabytesofdata,trillionsofrecordsand/ormillionsofqueriespersecond,RDBMSsstrugglebecauseofinherentdesignlimitation.

3. AgileDevelopment:RDBMSisillsuitedforiterativedevelopment,becauseyouwouldneedtofreezedatamodelbeforeanydevelopmentcanstart.Ifitsoundslikewaterfallmodel,itsureis.Also,shortdevelopmentcyclesandnewworkloadscanposechallengesinanyproject/productusingRDBMS.

4. NewArchitecture:AsRDBMSweredesigned30yearsago,theydonthavehorizontalscalingbuiltintothemyet.Theystruggletoscaleusingcommodityserversandcloudcomputing.

MongoDB Solution Agility MuchofthedataweusetodayhascomplexstructuresthatcanbemodeledandrepresentedmoreefficientlyusingJSON(JavaScriptObjectNotation)documents,ratherthantables.MongoDBstoresJSONdocumentsinabinaryrepresentationcalledBSON(BinaryJSON).BSONencodingextendsthepopularJSONrepresentationtoincludeadditionaldatatypessuchasint,longandfloatingpoint.

Bycontrast,tryingtomaptheobjectrepresentationofthedatatothetabularrepresentationofanRDBMSslowsdowndevelopment.AddingObjectRelationalMappers(ORMs)cancreateadditionalcomplexitybyreducingtheflexibilitytoevolveschemasandtooptimizequeriestomeetnewapplicationrequirements.

Performance MongoDBachievesbetterperformancebyusinginmemorycaching,betterdatalocalityandinplaceupdates.

Scalability MongoDBusesshardingtosupportdeploymentswithverylargedatasetsandhighthroughputoperations,whichisamethodforstoringdataacrossmultiplemachines.

High Availability Replicasetsprovidehighavailabilityusingautomaticfailover.Failoverallowsasecondarymemberstobecomeprimaryifprimaryisunavailable.Failover,inmostsituationsdoesnotrequiremanualintervention.

Detailed Explanation Wewillexpandsomeofthepointscoveredabovenext.

From rigid tables to flexible and dynamic BSON documents MuchofthedataweusetodayhascomplexstructuresthatcanbemodeledandrepresentedmoreefficientlyusingJSON(JavaScriptObjectNotation)documents,ratherthantables.MongoDBstoresJSONdocumentsinabinaryrepresentationcalledBSON(BinaryJSON).

BSONencodingextendsthepopularJSONrepresentationtoincludeadditionaldatatypessuchasint,longandfloatingpoint.Withsubdocumentsandarrays,JSONdocumentsalsoalignwiththestructureofobjectsattheapplicationlevel.Thismakesiteasyfordeveloperstomapthedatausedintheapplicationtoitsassociateddocumentinthedatabase.Bycontrast,tryingtomaptheobjectrepresentationofthedatatothetabularrepresentationofanRDBMSslowsdowndevelopment.AddingObjectRelationalMappers(ORMs)cancreateadditionalcomplexitybyreducingtheflexibilitytoevolveschemasandtooptimizequeriestomeetnewapplicationrequirements.Theprojectteamshouldstarttheschemadesignprocessbyconsideringtheapplicationsrequirements.Itshouldmodelthedatainawaythattakesadvantageofthedocumentmodelsflexibility.Inschemamigrations,itmaybeeasytomirrortherelationaldatabasesflatschematothedocumentmodel.However,thisapproachnegatestheadvantagesenabledbythedocumentmodelsrich,embeddeddatastructures.Forexample,datathatbelongstoaparentchildrelationshipintwoRDBMStableswouldcommonlybecollapsed(embedded)intoasingledocumentinMongoDB.Byusinganelegantdocumentdatabaseschemadesignandmakinguseofatomicityatdocumentlevel,JOIN'sareredundant.MostofJOINsleadtoalotofperformanceissuesinrelationaldatabases.Somemoreadvantagesofthismodelare:

Anaggregateddocumentcanbeaccessedwithasinglecalltothedatabase,ratherthanhavingtoJOINmultipletablestorespondtoaquery.TheMongoDBdocumentisphysicallystoredasasingleobject,requiringonlyasinglereadfrommemoryordisk.Ontheotherhand,RDBMSJOINsrequiremultiplereadsfrommultiplephysicallocations.

Asdocumentsareselfcontained,distributingthedatabaseacrossmultiplenodes(aprocesscalledsharding)becomessimplerandmakesitpossibletoachievemassivehorizontalscalabilityoncommodityhardware.TheDBAnolongerneedstoworryabouttheperformancepenaltyofexecutingcrossnodeJOINs(shouldtheyevenbepossibleintheexistingRDBMS)tocollectdatafromdifferenttables.

Application integration EaseofuseanddeveloperproductivityaresomeofMongoDBscoredesigngoals.OnefundamentaldifferencebetweenaSQLbasedRDBMSandMongoDBisthattheMongoDBinterfaceisimplementedasmethods(orfunctions)withintheAPIofaspecificprogramminglanguage,asopposedtoacompletelyseparatelanguagelikeSQL.This,coupledwiththeaffinitybetweenMongoDBsBSONdocumentmodelandthedatastructuresusedinobjectorientedprogramming,makesapplicationintegrationsimple.MongoDBhasidiomaticdriversforthemostpopularlanguages,includingoveradozendevelopedandsupportedbyMongoDB(e.g.,Java,Python,.NET,PERL)andmorethan30communitysupporteddrivers.

Atomicity in MongoDB Relationaldatabasestypicallyhavewelldevelopedfeaturesfordataintegrity,includingACIDtransactionsandconstraintenforcement.Rightly,usersdonotwanttosacrificedataintegrityastheymovetonewtypesofdatabases.WithMongoDB,userscanmaintainmanycapabilitiesof

relationaldatabases,eventhoughthetechnicalimplementationofthosecapabilitiesmaybedifferentwehavealreadyseenthiswithJOINs.MongoDBwriteoperationsareatomicatthedocumentlevelincludingtheabilitytoupdateembeddedarraysandsubdocumentsatomically.Byembeddingrelatedfieldswithinasingledocument,usersgetthesameintegrityguaranteesasatraditionalRDBMS,whichhastosynchronizecostlyACIDoperationsandmaintainreferentialintegrityacrossseparatetables.DocumentlevelatomicityinMongoDBensurescompleteisolationasadocumentisupdatedanyerrorscausetheoperationtorollbackandclientsreceiveaconsistentviewofthedocument.Despitethepowerofsingledocumentatomicoperations,theremaybecasesthatrequiremultidocumenttransactions.Therearemultipleapproachestothisincludingusingthefindandmodifycommandthatallowsadocumenttobeupdatedatomicallyandreturnedinthesameroundtrip.findandmodifyisapowerfulprimitiveontopofwhichuserscanbuildothermorecomplextransactionprotocols.Forexample,usersfrequentlybuildatomicsoftstatelocks,jobqueues,countersandstatemachinesthatcanhelpcoordinatemorecomplexbehaviors.Anotheralternativeentailsimplementingatwophasecommittoprovidetransactionlikesemantics.

Migration tools from relational to MongoDB Manyuserscreatetheirownscripts,whichtransformsourcedataintoahierarchicalJSONstructurethatcanbeimportedintoMongoDBusingthemongoimporttool.ExtractTransformLoad(ETL)toolsarealsocommonlyusedwhenmigratingdatafromrelationaldatabasestoMongoDB.AnumberofETLvendorsincludingInformatica,PentahoandTalendhavedevelopedMongoDBconnectorsthatenableaworkflowinwhichdataisextractedfromthesourcedatabase,transformedintothetargetMongoDBschema,stagedthenloadedintodocumentcollections.ManymigrationsinvolverunningtheexistingRDBMSinparallelwiththenewMongoDBdatabase,incrementallytransferringproductiondata:

1. AsrecordsareretrievedfromtheRDBMS,theapplicationwritesthembackouttoMongoDBintherequireddocumentschema.

2. Consistencycheckers,forexampleusingMD5checksums,canbeusedtovalidatethemigrateddata.

3. AllnewlycreatedorupdateddataiswrittentoMongoDBonly.

MongoDB MMS MongoDBManagementandMonitoringservice(MMSinshort)availableoncloudandonpremisecanbeusedtodoperformancemonitoringanddobackupandrestores.

Conclusion MongoDBistheleadingnosqldatabaseandisbeingincreasinglyusedinhybridapplicationsalongwithRDBMSslikeMySQLaswellasinsomecases,asstandalonedatabase.