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

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

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

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

Back in 1970…Cars Were Great!

Lots of Great Innovations Since 1970

Would you use these technologies for your business today?

Including the Relational Database

For which computers the relational model has been designed for?

So Were Computers!

And Storage!

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

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

RDBMS From Complexity to Simplicity 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" } ] }

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

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 Global Community

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

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

Agile MongoDB Overview Scalable

Operational Database Landscape

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

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

Developers are more productive

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

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

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 Availability Considerations

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

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

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

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

Global Data Distribution Real-time Primary Secondary

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

Common Use Cases

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

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

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

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

Questions?

Massimo Brignoli #MongoDB Solutions Architect, MongoDB Inc.