MongoDB: What, why, when
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MongoDB is a document database that provides a more flexible schema than relational databases. It allows embedding related data and easier updates than relational databases with object-relational mapping. MongoDB scales horizontally through sharding and provides high availability through replica sets. It supports different consistency models including eventual and strong consistency through write concerns and read preferences.
![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" }
]
}](https://image.slidesharecdn.com/mongodb2-140610051950-phpapp01/85/MongoDB-What-why-when-15-320.jpg)
![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, … }
]
}](https://image.slidesharecdn.com/mongodb2-140610051950-phpapp01/85/MongoDB-What-why-when-25-320.jpg)
![{!
_id : "mySet",!
members : [!
{_id : 0, host : "A", tags : {"dc": "ny"}},!
{_id : 1, host : "B", tags : {"dc": "ny"}},!
{_id : 2, host : "C", tags : {"dc": "sf"}},!
{_id : 3, host : "D", tags : {"dc": "sf"}},!
{_id : 4, host : "E", tags : {"dc": "cloud"}}],!
settings : {!
getLastErrorModes : {!
allDCs : {"dc" : 3},!
someDCs : {"dc" : 2}} }!
}!
> db.blogs.insert({...})!
> db.runCommand({getLastError : 1, w : "someDCs"})
Tagging Example](https://image.slidesharecdn.com/mongodb2-140610051950-phpapp01/85/MongoDB-What-why-when-41-320.jpg)
MongoDB: What, why, when
- 1. MongoDB: What,why,when. SolutionsArchitect,MongoDB Inc. Massimo Brignoli #mongodb
- 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” from Big Data Executive Summary – 50+ top executives from Government and F500 firms 64% - Ingest diverse, new data in real-time 15% - More than 100TB of data 20% - Less than 100TB (average of all? <20TB)
- 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. Would you use these technologies for your business today?
- 9. Including the Relational Database
- 10. For which computers the relational model has been designed for?
- 12. And Storage!
- 13. RDBMS Makes Development Hard Relational Database Object Relational Mapping Application Code XML Config DB Schema
- 14. And Even Harder To Iterate New Table New Table New Column Name Pet Phone Email New Column 3 months later…
- 15. 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" } ] }
- 16. MongoDB The leading NoSQL database Document Database Open- Source General Purpose
- 17. 7,000,000+ MongoDB Downloads 150,000+ Online Education Registrants 25,000+ MongoDB User Group Members 25,000+ MongoDB DaysAttendees 20,000+ MongoDB Management Service (MMS) Users Global Community
- 18. 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
- 19. Enterprise Big Data Stack EDWHadoop Management&Monitoring Security&Auditing RDBMS CRM, ERP, Collaboration, Mobile, BI OS & Virtualization, Compute, Storage, Network RDBMS Applications Infrastructure Data Management Online Data Offline Data
- 22. Key → Value • One-dimensional storage • Single value is a blob • Query on key only • No schema • Value cannot be updated,only replaced Key Blob
- 23. Relational/Wide Column • Two-dimensional storage (tuples) • Each field contains a single value • Query on anyfield • Very structured schema (table) • In-place updates • Normalization process requires many tables, joins, indexes,and poor data locality Primary Key
- 24. Document • N-dimensional storage • Each field can contain 0,1, many,or embedded values • Query on anyfield & level • Flexible schema • Inline updates * • Embedding related data has optimal data locality, requires fewer indexes,has better performance _id
- 25. 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, … } ] }
- 26. 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 – Manytypes of applications are a good fit • Reduces the need for joins,disk seeks – Programming is more simple – Performance can be delivered at scale
- 27. Developers are more productive
- 28. Developers are more productive
- 29. Automatic Sharding • Three types of sharding: hash-based, range-based, tag- aware! • Increase or decrease capacity as you go! • Automatic balancing
- 30. Query Routing • Multiple query optimization models! • Each sharding option appropriate for different apps!
- 31. HighAvailability–Ensure application availabilityduring 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
- 32. Replica Sets • Replica Set – two or more copies! • “Self-healing” shard! • Addresses many concerns:! - High Availability! - Disaster Recovery! - Maintenance
- 35. Write Concern • Network acknowledgement • Wait for error • Wait for journal sync • Wait for replication
- 36. Unacknowledged
- 37. MongoDB Acknowledged (wait for error)
- 38. Wait for Journal Sync
- 40. Tagging • Control where data is written to,and read from • Each member can have one or more tags – tags: {dc:"ny"} – tags: {dc:"ny", subnet:"192.168", rack:"row3rk7"} • Replica set defines rules for write concerns • Rules can change without changing app code
- 41. {! _id : "mySet",! members : [! {_id : 0, host : "A", tags : {"dc": "ny"}},! {_id : 1, host : "B", tags : {"dc": "ny"}},! {_id : 2, host : "C", tags : {"dc": "sf"}},! {_id : 3, host : "D", tags : {"dc": "sf"}},! {_id : 4, host : "E", tags : {"dc": "cloud"}}],! settings : {! getLastErrorModes : {! allDCs : {"dc" : 3},! someDCs : {"dc" : 2}} }! }! > db.blogs.insert({...})! > db.runCommand({getLastError : 1, w : "someDCs"}) Tagging Example
- 42. Wait for Replication (Tagging)
- 43. Read Preference Modes • 5 modes – primary(only)-Default – primaryPreferred – secondary – secondaryPreferred – Nearest ! When more than one node is possible,closest node is used for reads (all modes but primary)
- 44. 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 –ASecondary – B Secondary – BSecondary – CSecondary – C
- 45. 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
- 46. Active/Active Data Center • Tolerates server, rack, data center failures, network partitions Data Center - West Primary –A Primary – B Primary – C Secondary –A Secondary – BSecondary – 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
- 47. Global Data Distribution Real-time Real-time Real-time Real-time Real-time Real-time Real-time Primary Secondary Secondary Secondary Secondary Secondary Secondary Secondary
- 49. Common Use Cases
- 50. 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
- 51. 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
- 52. 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
- 53. 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
- 54. Questions?