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Webinar: Utilisations courantes de MongoDB

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MongoDB ne fonctionne pas comme les autres bases de données. Son modèle de données orienté documents, son partitionnement en gammes et sa cohérence forte sont bien adaptés à certains problèmes et …

MongoDB ne fonctionne pas comme les autres bases de données. Son modèle de données orienté documents, son partitionnement en gammes et sa cohérence forte sont bien adaptés à certains problèmes et moins adaptés à d'autres. Dans ce séminaire Web, nous étudierons des exemples réels d'utilisation de MongoDB mettant à profit ces fonctionnalités uniques. Nous évoquerons le cas de clients spécifiques qui utilisent MongoDB et nous verrons la façon dont ils ont implémenté leur solution. Nous vous montrerons également comment construire une solution du même type pour votre entreprise.

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  • In the beginning, there was RDBMS, and if you needed to store data, that was what you used. But RDBMS is performance critical, and BI workloads tended to suck up system resources. So we carved off the data warehouse as a place to store a copy of the operational data for use in analytical queries. This offloaded work from the RDBMS and bought us cycles to scale higher. Today, we’re seeing another split. There’s a new set of workloads that are saturating RDBMS, and these are being carved off into yet another tier of our data architecture: the NoSQL store.
  • These are some of the qualities of workloads that necessitate a move to NoSQL. Each of these qualities is difficult to achieve in an RDBMS, but is well addressed by NoSQL data stores.
  • These are some of the qualities of workloads that necessitate a move to NoSQL. Each of these qualities is difficult to achieve in an RDBMS, but is well addressed by NoSQL data stores.
  • Transcript

    • 1. When to use MongoDB
    • 2. Part 1 of a seriesReal-TimeAnalytics withMongodbApril 12thContentManagement withMongoDBMay 17th@forjared
    • 3. TodayLast 10 yearsEmerging NoSQL SpaceRDBMSDataWarehouseNoSQLRDBMSDataWarehouseThe beginningRDBMS
    • 4. Qualities of NoSQLWorkloadsFlexible data models• Lists, Nested Objects• Sparse schemas• Semi-structured data• Agile DevelopmentHigh Throughput• Lots of reads• Lots of writesLarge Data Sizes• Aggregate data size• Number of objectsLow Latency• Both reads and writes• Millisecond latencyCloud Computing• Run anywhere• No assumptions abouthardware• No / Few KnobsCommodityHardware• Ethernet• Local disks
    • 5. MongoDB was designed forthisFlexible data models• Lists, Nested Objects• Sparse schemas• Semi-structured data• Agile DevelopmentHigh Throughput• Lots of reads• Lots of writesLarge Data Sizes• Aggregate data size• Number of objectsLow Latency• Both reads and writes• Millisecond latencyCloud Computing• Run anywhere• No assumptions abouthardware• No / Few KnobsCommodityHardware• Ethernet• Local disks• JSON basedobject model• Dynamicschemas• Replica Sets toscale reads• Sharding toscale writes• 1000’s of shardsin a single DB• Partitioning ofdata• In-memorycache• Scale-outworking set• Scale-out toovercomehardwarelimitations• Designed for“typical” OS andlocal file system
    • 6. Example customersUser Data Management High Volume Data FeedsContent Management Operational Intelligence Product Data Management
    • 7. USE CASES THATLEVERAGE NOSQL
    • 8. High Volume Data Feeds• More machines, more sensors, moredata• Variably structuredMachineGeneratedData• High frequency tradingStock MarketData• Multiple sources of data• Each changes their format constantlySocial MediaFirehose
    • 9. High Volume Data FeedDataSourcesAsynchronous writesFlexible documentmodel can adapt tochanges in sensorformatWrite to memory withperiodic disk flushDataSourcesDataSourcesDataSourcesScale writes overmultiple shards
    • 10. Operational Intelligence• Large volume of state about users• Very strict latency requirementsAd Targeting• Expose report data to millions of customers• Report on large volumes of data• Reports that update in real timeCustomerFacingDashboards• Need to join the conversation _now_Social MediaMonitoring
    • 11. Operational IntelligenceDashboardsAPILow latency readsParallelize queriesacross replicas andshardsIn databaseaggregationFlexible schemaadapts to changinginput dataCan use same clusterto collect, store, andreport on data
    • 12. Behavioral Profiles123See AdSee Ad4ClickConvert{ cookie_id: ‚1234512413243‛,advertiser:{apple: {actions: [{ impression: ‘ad1’, time: 123 },{ impression: ‘ad2’, time: 232 },{ click: ‘ad2’, time: 235 },{ add_to_cart: ‘laptop’,sku: ‘asdf23f’,time: 254 },{ purchase: ‘laptop’, time: 354 }]}}}Rich profilescollecting multiplecomplex actionsScale out to supporthigh throughput ofactivities trackedIndexing andquerying to supportmatching, frequencycappingDynamic schemasmake it easy to trackvendor specificattributes
    • 13. Product Data• Diverse product portfolio• Complex querying and filteringE-CommerceProductCatalog• Scale for short bursts of high volume traffic• Scalable, but consistent view of inventoryFlash Sales
    • 14. Product Data{ sku: ‚00e8da9b‛,type: ‚MP3‛,details: {artist: ‚John Coltrane‛,title: ‚A love supreme‛,length: 123}}{ sku: ‚00a9f3a‛,type: ‚Book‛,details: {author: ‚David Eggers‛,title: ‚You shall know our velocity‛,isbn: ‚0-9703355-5-5‛}}Flexible data modelfor similar, butdifferent objectsIndexing and richquery API for easysearching and sortingdb.products.find({ ‚details.author”: ‚David Eggers‛ }).sort({ ‚title‛ : -1 });
    • 15. Content Management• Comments and user generatedcontent• Personalization of content, layoutNews Site• Generate layout on the fly for eachdevice that connects• No need to cache static pagesMulti-Devicerendering• Store large objects• Simple modeling of metadataSharing
    • 16. Content Management{ camera: ‚Nikon d4‛,location: [ -122.418333, 37.775 ]}{ camera: ‚Canon 5d mkII‛,people: [ ‚Jim‛, ‚Carol‛ ],taken_on: ISODate("2012-03-07T18:32:35.002Z")}{ origin: ‚facebook.com/photos/xwdf23fsdf‛,license: ‚Creative Commons CC0‛,size: {dimensions: [ 124, 52 ],units: ‚pixels‛}}Flexible data modelfor similar, butdifferent objectsHorizontal scalabilityfor large data setsGeo spatial indexingfor location basedsearchesGridFS for largeobject storage
    • 17. User Data Management• User state and sessionmanagementVideo Games• Scale out to large graphs• Easy to search and processSocial Graphs• Authentication, Authorizationand AccountingIdentityManagement
    • 18. User Game StateFlexible documentssupports new gamefeatures withoutschema migrationSharding enableswhole data set to bein memory, ensuringlow latencyJSON data modelmaps well toHTML5/JS & Flashbased clientsEasy to store entireplayer state in asingle document.
    • 19. Social GraphSocial GraphsDocuments enabledisk locality of allprofile data for a userSharding partitionsuser profiles acrossavailable serversNative support forArrays makes it easyto store connectionsinside user profile
    • 20. IS MY USE CASE A GOODFIT FOR MONGODB?
    • 21. Good fits for MongoDBApplication Characteristic Why MongoDB might be a good fitLarge number of objects tostoreSharding lets you split objects across multipleserversHigh write or read throughput Sharding + Replication lets you scale read andwrite traffic across multiple serversLow Latency Access Memory Mapped storage engine cachesdocuments in RAM, enabling in-memoryperformance. Data locality of documents cansignificantly improve latency over join basedapproachesVariable data in objects Dynamic schema and JSON data model enableflexible data storage without sparse tables orcomplex joinsCloud based deployment Sharding and replication let you work aroundhardware limitations in clouds.
    • 22. Thanks!Real-Time AnalyticsApril 12thMongoDB and AWS CloudFormationApril 25thNew Aggregation FrameworkMay 10thContent ManagementMay 17th

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