mongodbmongodb technical overview v1

1、MongoDBaaS proposalAgendaTopic1.Introductions/Agenda2.3.2 Release Update3.Data Model4.Architecture5.Ops Manager6.MongoDB Support ModelFast Growing Community & EcosystemOver 10,000,000downloads300,000Students for MongoDB University35,000attendees to MongoDB events annuallyOver 1,000PartnersOver 2,000

2、 Paying CustomersFirst Serious Challenger to RDBMS1.OracleRelational DBMS1,442-5%2.MySQLRelational DBMS1,2942%Microsoft SQLServer3.Relational DBMS1,131-10%5.PostgreSQLRelational DBMS27340%6.DB2Relational DBMS20111%7.Microsoft AccessRelational DBMS146-26%8.CassandraWide Column10787%9.SQLiteRelational

3、 DBMS10519%Source: DB-engines database popularity rankings; November 20154.MongoDBDocument Store277172%RANKDBMSMODELSCOREGROWTH (20 MO)I/jobtrendsNovember 23, 2015#2 MOST DESIRED SKILL SET OF ANY JOB IN IT!MongoDB Point of ViewThe World Has ChangedDataVolume Velocity VarietyTimeIterative Ag

4、ileShort CyclesRiskAlways On Scale GlobalCostOpen-Source Cloud CommodityRelationalExpressive Query LanguageFlexibilityStrong Consistency ScalabilitySecondary IndexesPerformanceNoSQLExpressive Query LanguageFlexibilityStrong Consistency ScalabilitySecondary IndexesPerformanceRelationaNlexus Architect

5、ureNoSQLExpressive Query LanguageFlexibilityStrong Consiste ncy Scalability Secondary IndexesPerformanceRelational + NoSQLMongoDB 3.0: 7x-10x Performance,50%-80% Less StorageHow: WiredTiger Storage EngineSame data model, same querylanguage, same opsWrite performance gains driven by document-level co

6、ncurrency controlStorage savings driven by native compression100% backwards compatible Non-disruptive upgradeMongoDB 2.6MongoDB 3.0PerformanceWhats New inMongoDB 3.2OverviewStorage Engine Architecture in 3.2Content RepoIoT Sensor BackendCustomer AnalyticsAd ServiceArchiveSupported in MongoDB 3.2Secu

7、rityManagement3rd partyEncryptedIn-memory (beta)MMAPWTMongoDB Document Data ModelMongoDB Query Language (MQL) + Native DriversWiredTiger is the New DefaultWiredTiger widely deployed with 3.0 is now thedefault storage engine for MongoDB.Best general purpose storage engine7-10x better write throughput

8、Up to 80% compressionEncrypted Storage EngineEncrypted storage engine for end-to-endencryption of sensitive data in regulated industriesReduces the management and performance overheadof external encryption mechanismsAES-256 Encryption, FIPS 140-2 option availableKey management: Local key management

9、via keyfile orintegration with 3rd party key management appliance via KMIPOffered as an option for WiredTiger storage engineIn-Memory Storage EngineHandle ultra-high throughput with lowlatency and high availabilityDelivers the extreme throughput and predictable latency required by the most demanding

10、 apps in Adtech, finance, and more.Achieve data durability with replica set members running disk-backed storage engineAvailable for beta testing and is expected for GA inearly 2016Data Governance with DocumentValidationImplement data governance withoutsacrificing agility that comes from dynamic sche

11、maEnforce data quality across multiple teams andapplicationsUse familiar MongoDB expressions to control document structureValidation is optional and can be as simple as asingle field, all the way to every field, including existence, data types, and regular expressionsEnhancements for your mission-cr

12、iticalappsMore improvements in 3.2 that optimize the database for your mission-criticalapplicationsMeet stringent SLAs with fast-failover algorithmUnder 2 seconds to detect and recover fromreplica set primary failureSimplified management of sharded clustersallow you to easily scale to many data cent

13、ersConfig servers are now deployed as replicasets; up to 50 membersMongoDB Connector for BIVisualize and explore multi-dimensional documents using SQL-based BI tools. Theconnector does the following:Provides the BI tool with the schema of the MongoDB collection to be visualizedTranslates SQL stateme

14、nts issued by the BI tool into equivalent MongoDB queries that are sent to MongoDB for processingConverts the results into the tabular format expected by the BI tool, which can then visualizethe data based on user requirementsAnalytics & BI IntegrationMongoDB-Hadoop ConnectorDistributed AnalyticsApp

15、licationsMongoDBConnector for Hadoop Low latency Rich fast querying Flexible indexing Aggregations in database Longer jobs Batch analytics Highly parallel processing Unknown data relationshipsKnown data relationshipsGreat for looking at all data or large subsets Great for any subset of dataDynamic L

16、ookupCombine data from multiple collections with left outer joins for richer analytics & moreflexibility in data modelingBlend data from multiple sources for analysisHigher performance analytics with less application- side code and less effort from your developersExecuted via the new $lookup operato

17、r, a stage inthe MongoDB Aggregation Framework pipelineImproved In-Database Analytics &SearchNew Aggregation operators extend options for performing analytics and ensure that answers are delivered quickly and simplywith lower developer complexityArray operators: $slice, $arrayElemAt,$concatArrays, $

18、filter, $min, $max, $avg, $sum, and moreNew mathematical operators: $stdDevSamp,$stdDevPop, $sqrt, $abs, $trunc, $ceil, $floor,$log, $pow, $exp, and moreCase sensitive text search and support for additional languages such as Arabic, Farsi,Chinese, and moreMongoDB CompassFor fast schema discovery and

19、 visualconstruction of ad-hoc queriesVisualize schemaFrequency of fieldsFrequency of typesDetermine validator rulesView Documents Graphically build queriesAuthenticated accessIntegrations with APM PlatformsEasily incorporate MongoDB performance metrics into your existing APM dashboardsfor global ove

20、rsight of your entire IT stackMongoDB drivers enhanced with new API thatexposed query performance metrics to APM toolsIn addition, Ops and Cloud Manager can complement this functionality with rich databasemonitoring.Ops Manager Enhancements3.2 includes Ops Manager enhancements toimprove the producti

21、vity of your ops teams andfurther simplify installation and managementMongoDB backup on standard network-mountable filesystems; integrates with your existing storage infrastructureAutomated database restores; Build clusters from backup in a few clicksFaster time to first database snapshot Support fo

22、r maintenance windowsCentralized UI for installation and config of all application andbackup componentsMongoDB Use Cases & PaaS at ScaleRich data model and rich functionality- General PurposeBig DataProduct & Asset CatalogsSecurity & FraudInternet of ThingsDatabase-as-a- ServiceIntelligence Agencies

23、Mobile AppsCustomer Data ManagementData HubSocial & CollaborationContent ManagementDemonstrating its commitment to best-in-class technology,Goldman Sachs developed an enterprise-scale private cloud to host applications and has selected MongoDB as one of their first database offerings based on its ag

24、ile, scalable and resilient architecture.At Citi MongoDB-as-a-Service (MDBaaS) now enablesdevelopers to provision a three-node MongoDB cluster in 17 minutes (with elastic cloud and horizontal scale capabilities), which, at best, previously would take several days. MongoDB also helped cut application

25、 development time from point of concept to production in less than four months.“Because of MongoDBs architectural affinity to be built as aservice, we were able to improve DevOps capability and put more responsibility and power into the hands of developers and architects,” - Mike Simone, CitiData Pl

26、atform EngineeringCapital Markets Common UsesFunctional AreasUse Cases to ConsiderRisk Analysis & ReportingFirm-wide Aggregate Risk PlatformIntraday Market & Counterparty Risk Analysis Risk Exception Workflow OptimizationLimit Management ServiceRegulatory ComplianceCross-silo Reporting: Volker, Dodd

27、-Frank, EMIR, MiFID II, etc. Online Long-term Audit TrailAggregate Know Your Customer (KYC) RepositoryBuy-Side PortalResponsive Portfolio ReportingTrade ManagementCross-product (Firm-wide) Trademart Flexible OTC Derivatives Trade CaptureFront Office Structuring & TradingComplex Product DevelopmentSt

28、rategy BacktestingStrategy Performance AnalysisReference Data ManagementReference Data Distribution HubMarket Data ManagementTick Data CaptureInvestment AdvisoryCross-channel Informed Cross-sell Enriched Investment ResearchRetail Banking - Common UsesFunctional AreasUse Cases to ConsiderCustomer Eng

29、agementSingle View of a Customer Customer Experience Management Responsive Digital BankingGamification of Consumer Applications Agile Next-generation Digital PlatformMarketingMulti-channel Customer Activity Capture Real-time Cross-channel Next Best Offer Location-based OffersRisk Analysis & Reportin

30、gFirm-wide Liquidity Risk Analysis Transaction Reporting and AnalysisRegulatory ComplianceFlexible Cross-silo Reporting: Basel III, Dodd-Frank, etc. Online Long-term Audit TrailAggregate Know Your Customer (KYC) RepositoryReference Data ManagementGlobal Reference Data Distribution HubPaymentsCorpora

31、te Transaction ReportingFraud DetectionAggregate Activity Repository Cybersecurity Threat AnalysisMongoDB Data ModelDocument Data ModelMongoDBRelationalfirst_name: Paul, surname: Miller, city: London, location:45.123,47.232,cars: model: Bentley,year: 1973,value: 100000, , model: Rolls Royce, year: 1

32、965,value: 330000, Documents are Rich Data Structuresfirst_name: Paul,Typed field valuessurname: Miller,cell: +447557505611city: London,location: 45.123,47.232,Profession: banking, finance, cars: FieldsFields can contain arraystrader,model: Bentley,year: 1973,value: 100000, , model: Rolls Royce, yea

33、r: 1965,value: 330000, Fields can contain an array of sub- documentsDrivers & EcosystemSupport for the most popular languages and frameworksJavaRubyPythonPerlMEAN StackMorphiaQuery ModelDo More With Your DataMongoDBfirst_name: Paul, surname: Miller, city: London, location:45.123,47.232,cars: model:

34、Bentley,year: 1973,value: 100000, , model: Rolls Royce, year: 1965,value: 330000, Rich QueriesFind Pauls carsFind everybody in London with a car built between 1970 and 1980GeospatialFind all of the car owners within 5km of Trafalgar Sq.Text SearchFind all the cars described as having leather seatsAg

35、gregationCalculate the average value of Pauls car collectionMap ReduceWhat is the ownership pattern of colors by geography over time? (is purple trending up in China?)Scalability & AvailabilityArchitectureAutomatic ShardingThree types: hash-based, range-based, location-aware Increase or decrease cap

36、acity as you goAutomatic balancingQuery RoutingMultiple query optimization modelsEach sharding option appropriatefor different appsReplica SetsReplica Set 2 to 50 copiesSelf-healing shardData Center AwareAddresses availability considerations:High AvailabilityDisaster RecoveryMaintenanceWorkload Isol

37、ation: operational & analyticsMongoDB Enterprise DeploymentManagement with Ops ManagerMongoDB Ops ManagerThe Best Way to Manage MongoDB In Your Data CenterUp to 95% Reduction in Operational OverheadSingle-click provisioning, scaling &upgrades, admin tasksMonitoring, with charts, dashboardsand alerts on 100+ metricsBackup and restore, with point-in-timerecovery, support for sharded clustersMongoDB Support ModelMongoDB Enterprise SupportUnlimited Professional Web & Phone based Support 24/7, 365Access to MongoDB Enterprise Capabilities & Features Including Ops ManagerUnlim