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MongoDB : What, why, when.

MongoDB : What, why, when. Massimo Brignoli . # mongodb. Solutions Architect, MongoDB Inc. Who Am I?. Solutions Architect/Evangelist in MongoDB Inc. 24 years of experience in databases and software development Former MySQL employee Previous life: web, web, web. Innovation.

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MongoDB : What, why, when.

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  1. MongoDB: What, why, when. Massimo Brignoli #mongodb Solutions Architect, MongoDB Inc.

  2. Who Am I? • Solutions Architect/Evangelist in MongoDB Inc. • 24 years of experience in databases and software development • Former MySQL employee • Previous life: web, web, web

  3. Innovation

  4. Understanding Big Data – It’s Not Very “Big” 64% - Ingest diverse, new data in real-time 15% - More than 100TB of data 20% - Less than 100TB (average of all? <20TB) from Big Data Executive Summary – 50+ top executives from Government and F500 firms

  5. “I have not failed. I've just found 10,000 ways that won't work.” ― Thomas A. Edison

  6. Back in 1970…Cars Were Great!

  7. Lots of Great Innovations Since 1970

  8. Wouldyou use thesetechnologies for your business today?

  9. Including the Relational Database

  10. For whichcomputers the relational model hasbeendesigned for?

  11. So Were Computers!

  12. And Storage!

  13. RDBMS Makes Development Hard Code XML Config DB Schema Application Object Relational Mapping Relational Database

  14. And Even Harder To Iterate New Table New Column New Table Name Pet Phone Email New Column 3 months later…

  15. From Complexity to Simplicity RDBMS MongoDB { _id : ObjectId("4c4ba5e5e8aabf3"), employee_name: "Dunham, Justin", department : "Marketing", title : "Product Manager, Web", report_up: "Neray, Graham", pay_band: “C", benefits : [ { type :  "Health", plan : "PPO Plus" }, { type :   "Dental", plan : "Standard" } ] }

  16. MongoDB The leading NoSQL database Document Database General Purpose Open-Source

  17. Global Community 7,000,000+ MongoDB Downloads 150,000+ Online Education Registrants 25,000+ MongoDB User Group Members 25,000+ MongoDB Days Attendees 20,000+ MongoDB Management Service (MMS) Users

  18. MongoDB Vision To provide the best database for how we build and run apps today Build • New and complex data • Flexible • New languages • Faster development Run • Big Data scalability • Real-time • Commodity hardware • Cloud

  19. Enterprise Big Data Stack Applications CRM, ERP, Collaboration, Mobile, BI Data Management Management & Monitoring Security & Auditing Hadoop EDW Online Data Offline Data RDBMS RDBMS Infrastructure OS & Virtualization, Compute, Storage, Network

  20. MongoDB Overview Agile Scalable

  21. Operational Database Landscape

  22. Document Data Model Relational MongoDB { first_name: ‘Paul’, surname: ‘Miller’, city: ‘London’, location: [45.123,47.232], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, {model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] }

  23. Document Model Benefits • Agility and flexibility • Data models can evolve easily • Companies can adapt to changes quickly • Intuitive, natural data representation • Developers are more productive • Many types of applications are a good fit • Reduces the need for joins, disk seeks • Programming is more simple • Performance can be delivered at scale

  24. Developers are more productive

  25. Developers are more productive

  26. Automatic Sharding • Three types of sharding: hash-based, range-based, tag-aware • Increase or decrease capacity as you go • Automatic balancing

  27. Query Routing • Multiple query optimization models • Each sharding option appropriate for different apps

  28. Availability Considerations High Availability – Ensure application availability during many types of failures Disaster Recovery – Address the RTO and RPO goals for business continuity Maintenance – Perform upgrades and other maintenance operations with no application downtime

  29. Replica Sets • Replica Set – two or more copies • “Self-healing” shard • Addresses many concerns: • High Availability • Disaster Recovery • Maintenance

  30. Single Data Center • Automated failover • Tolerates server failures • Tolerates rack failures • Number of replicas defines failure tolerance Primary – B Primary – C Primary – A Secondary – B Secondary – A Secondary – A Secondary – B Secondary – C Secondary – C

  31. Active/Standby Data Center Primary – B Primary – C Primary – A Secondary – B Secondary – A Secondary – C Secondary – B Secondary – C Secondary – A Data Center - West Data Center - East • Tolerates server and rack failure • Standby data center

  32. Active/Active Data Center Primary – B Secondary – B Secondary – C Primary – C Primary – A Secondary – A Secondary – C Secondary – A Secondary – B Secondary – A Secondary – C Secondary – B Arbiter – B Arbiter – C Arbiter – A Data Center - West Data Center - Central Data Center - East • Tolerates server, rack, data center failures, network partitions

  33. Global Data Distribution Real-time Real-time Secondary Secondary Secondary Real-time Real-time Primary Secondary Real-time Secondary Real-time Secondary Real-time Secondary

  34. Read Global/Write Local Primary:LON Secondary:NYC Secondary:SYD Primary:NYC Secondary:LON Secondary:SYD Primary:SYD Secondary:LON Secondary:NYC

  35. Common Use Cases

  36. Machine Generated Data • More machine forms, sensors & data • Variably structured High Volume Data Feeds • Securities Data • High frequency trading • Daily closing price • Social Media / • General Public • Multiple data sources • Each changes their format consistently • Student Scores, ISP logs

  37. Ad Targeting • Large volume of users • Very strict latency requirements • Sentiment Analysis Operational Intelligence Real time dashboards • Expose data to millions of customers • Reports on large volumes of data • Reports that update in real time • Social Media Monitoring • Join the conversation • Catered Games • Customized Surveys

  38. Product Catalogue • Diverse product portfolio • Complex querying and filtering • Multi-faceted product attributes Metadata Data analysis • Data mining • Call records • Insurance Claims • Biometric • Retina Scans • Fingerprints

  39. News Site • Comments and user generated content • Personalization of content and layout Content Management Multi-device rendering • Generate layout on the fly • No need to cache static pages • Sharing • Store large objects • Simpler modeling of metadata

  40. Questions?

  41. Thanks! Massimo Brignoli #MongoDB @massimobrignoli Solutions Architect, MongoDB Inc. massimo@mongodb.com

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