The Right (and Wrong) Use Cases for MongoDB

1. The Right (And Wrong) Use Cases for MongoDB Damon LaCaille damon.lacaille@mongodb.com

2. We’ll be covering... • About MongoDB • Benefits of using MongoDB • Specific Use Cases – Good: Single View, Mobile, Analytics – Not So Good... • Q&A

3. About MongoDB

4. Benefits of MongoDB • Performance • Availability and Scalability • Data Model • Query Model

5. Benefits of MongoDB Performance Scalability and Performance Depth of Functionality

6. Benefits of MongoDB Performance Scalability and Performance Key/Value Stores Cache Only Depth of Functionality

7. Benefits of MongoDB Performance Scalability and Performance Key/Value Stores Depth of Functionality RDBMS Cache Only Highly Transactional

8. Benefits of MongoDB Performance Scalability and Performance Key/Value Stores MongoDB Depth of Functionality RDBMS Mobile Content Mgmt. Analytics Cache Only Highly Transactional Internet of Things Single View Personalization Catalog

9. Benefits of MongoDB Availability and Scalability Application mongod mongod mongod

10. Benefits of MongoDB Availability and Scalability Application mongod mongod mongod mongod mongod mongod mongos [A-L] [M-Z]

11. Benefits of MongoDB Availability and Scalability Application mongod mongod mongod mongod mongod mongod mongos | mongos | mongos mongod mongod mongod mongod mongod mongod mongod mongod mongod [A-E] [P-T] [F-J] [K-O] [U-Z]

12. Benefits of MongoDB RDBMS -Hard to Make Changes New Table New Table New Column Name Age Phone Email

13. Benefits of MongoDB RDBMS vs. Document Data Model Mental model Relational model

14. Benefits of MongoDB RDBMS vs. Document Data Model Mental model Document model

15. Benefits of MongoDB { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Natural Data Model

16. Benefits of MongoDB { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Natural Data Model Field Names

17. Benefits of MongoDB { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Natural Data Model Field Names Typed field values

18. Benefits of MongoDB { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Natural Data Model Field Names Typed field values Fields can contain arrays

19. Benefits of MongoDB { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Natural Data Model Field Names Typed field values Fields can contain arrays } Fields can contain an array of sub-documents

20. Benefits of MongoDB Rich Query Model { : ‘Paul’, surname: ‘Miller’, cell: ‘+447557505611’ location: [45.123,47.232], profession: [banking, finance, trader], cars: [ { model: ‘Bentley’, year: 1973, value: 100000, … }, { model: ‘Rolls Royce’, year: 1965, value: 330000, … } ] } Rich Queries • Find Paul’s car • Find everybody in London with a car built between 1970 and 1980 Geo • Find all car owners within 10m of Manhattan Text Search • Find all cars whose VIN starts with ZA1 Aggregation • Calculate the average value of Paul’s car collection Map Reduce • What is the ownership pattern of colors by geography over time? (Is silver trending up in the US?)

21. Benefits of MongoDB Relational Database Challenges

22. Benefits of MongoDB Relational Database Challenges • Data Types • Unstructured data • Semi-structured data • Polymorphic data

23. Benefits of MongoDB Relational Database Challenges • Data Types • Unstructured data • Semi-structured data • Polymorphic data • Agile Development • Iterative • Short development cycles • New workloads

24. Benefits of MongoDB Relational Database Challenges • Data Types • Unstructured data • Semi-structured data • Polymorphic data • Agile Development • Iterative • Short development cycles • New workloads • New Architectures • Horizontal scaling • Commodity servers • Cloud computing

25. Benefits of MongoDB Relational Database Challenges • Data Types • Unstructured data • Semi-structured data • Polymorphic data • Agile Development • Iterative • Short development cycles • New workloads • Volume of Data • Petabytes of data • Trillions of records • Millions of queries per second • New Architectures • Horizontal scaling • Commodity servers • Cloud computing

26. Good Use Cases

27. Use Cases • Applications never before possible • Volume, Variety, Velocity • Developer Productivity

28. Use Case - Single View

29. Use Case - Single View • 70+ systems • 50 million policies • 118 million customers • 190 million documents

30. Use Case - Single View • 70+ systems • 50 million policies • 118 million customers • 190 million documents

31. Use Case - Single View • 70+ systems • 50 million policies • 118 million customers • 190 million documents • Rolling out internationally • A version for its sales force • Real time prediction of customer attrition rates.

32. Use Case -Mobile

35. Use Case -Mobile • 2 million requests per day • 40 million users w/ alerts

36. Use Case -Mobile • 2 million requests per day • 40 million users w/ alerts • Mobile backend as a service for Facebook • 270,000+ apps • Unknown workload types • API requests growing 500% annually

37. Use Case -Analytics

38. Use Case -Analytics

39. Use Case -Analytics • Pairs MongoDB-powered analytics with visual maps • Provides real-time and historical information • Finds trends and relationships

40. Use Case -Analytics • Pairs MongoDB-powered analytics with visual maps • Provides real-time and historical information • Finds trends and relationships What’s next? The SmartData Platform • Predict best ways to cut traffic congestion • Analyze energy usage trends to improve efficiency • Map crime stats and city data relationships so crime can actually be reduced

41. Use Case -Analytics Real Time Analytics • Light-weight analytics very quickly • Sub-second response times • High availability requirements

42. Use Case -Analytics Real Time Analytics Batch Analytics • Complex, long-running analyses • Slower response times (minutes/days) • Lower availability requirements • Light-weight analytics very quickly • Sub-second response times • High availability requirements

43. Use Case -Analytics Informs Populates

44. “Bad” Use Cases

45. Use Case - Cache Only

46. Wrong Use Case • Developer productivity and satisfaction is not a concern for you • Limited vertical scalability is a comfort blanket • “It’s always been done this way” • You can see the future and know all possibilities for your schema