An Introduction to Mongo DB
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This document introduces MongoDB, a leading NoSQL document database. It discusses challenges with relational databases for unstructured and large volumes of data. MongoDB uses a flexible document data model and dynamic schemas. It provides rich querying and indexing capabilities. MongoDB allows for horizontal scaling through automatic sharding and ensures high availability through replica sets. It offers a simplified development experience and improves performance through better data locality. MongoDB is suited for various use cases including big data, mobile apps, content management, and more.
![4
Document Data Model
Relational - Tables
{ first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location: {!
!type: “Point”,
!coordinates :!
! ![-0.128, 51.507]!
!},!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!
Document - Collections](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-4-320.jpg)
![5
Dynamic Schema Documents
name: “jeff”,
eyes: “blue”,
height: 72,
boss: “ben”}
{name: “brendan”,
aliases: [“el diablo”]}
name: “ben”,
hat: ”yes”}
{name: “matt”,
pizza: “DiGiorno”,
height: 72,
boss: 555.555.1212}
{name: “will”,
eyes: “blue”,
birthplace: “NY”,
aliases: [“bill”, “la
ciacco”],
gender: ”???”,
boss: ”ben”}](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-5-320.jpg)
![13
Features In Practice
Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
Text Search
• Find all the cars described as having
leather seats
Aggregation
• Calculate the average value of Paul’s
car collection
Map Reduce
• What is the ownership pattern of colors
by geography over time? (is purple
trending up in China?)
{ first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location: {!
!type: “Point”,
!coordinates :!
! ![-0.128, 51.507]!
!},!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-13-320.jpg)
![14
Query Example
Rich Queries
• Find Paul’s cars
• Find everybody in London with a car
built between 1970 and 1980
db.cars.find({
first_name: ‘Paul’!
})
db.cars.find({
!city: ‘London’,
”cars.year" : {
$gte : 1970,
$lte : 1980
}
})
!
{ first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location: {!
!type: “Point”,
!coordinates :!
! ![-0.128, 51.507]!
!},!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-14-320.jpg)
![15
Geo Spatial Example
db.cars.find( {
location:
{ $near :
{ $geometry :
{ type: 'Point' ,
coordinates :
[-0.128,51.507]
}
},
$maxDistance :5000
}
} )
!
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
{ first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location: {!
!type: “Point”,
!coordinates :!
! ![-0.128, 51.507]!
!},!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-15-320.jpg)
![16
Aggregation Framework Example
db.cars.aggregate( [
{$match : {"first_name" : "Paul"}},
{$project : {"first_name":1,"cars":1}},
{$unwind : "$cars"},
{ $group : {_id:"$first_name",
average : {
$avg : "$cars.value"}}}
])
{ "_id" : "Paul", "average" : 215000 }
!
Aggregation
• Calculate the average value of Paul’s
car collection
{ first_name: ‘Paul’,!
surname: ‘Miller’,!
city: ‘London’,!
location: {!
!type: “Point”,
!coordinates :!
! ![-0.128, 51.507]!
!},!
cars: [ !
{ model: ‘Bentley’,!
year: 1973,!
value: 100000, … },!
{ model: ‘Rolls Royce’,!
year: 1965,!
value: 330000, … }!
}!
}!](https://image.slidesharecdn.com/anintroductiontomongodb-140429053028-phpapp01/85/An-Introduction-to-Mongo-DB-16-320.jpg)
An Introduction to Mongo DB
- 1. Introduction to MongoDB Matthew Bates Solution Architect MongoDB EMEA
- 2. 2 MongoDB The leading NoSQL database Document Database Open- Source General Purpose
- 3. 3 Relational Database Challenges Data Types • Unstructured data • Semi-structured data • Polymorphic data Volume of Data • Petabytes of data • Trillions of records • Millions of queries per second Agile Development • Iterative • Short development cycles • New workloads New Architectures • Horizontal scaling • Commodity servers • Cloud computing
- 4. 4 Document Data Model Relational - Tables { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {! !type: “Point”, !coordinates :! ! ![-0.128, 51.507]! !},! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }! Document - Collections
- 5. 5 Dynamic Schema Documents name: “jeff”, eyes: “blue”, height: 72, boss: “ben”} {name: “brendan”, aliases: [“el diablo”]} name: “ben”, hat: ”yes”} {name: “matt”, pizza: “DiGiorno”, height: 72, boss: 555.555.1212} {name: “will”, eyes: “blue”, birthplace: “NY”, aliases: [“bill”, “la ciacco”], gender: ”???”, boss: ”ben”}
- 6. 6 Document Model • Agility and flexibility – dynamic schema – Data models can evolve easily – Companies can adapt to changes quickly • Intuitive, natural data representation – Remove impedance mismatch – 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
- 10. 10 MongoDB: An Operational Database
- 11. 11 Shell Command-line shell for interacting directly with database Shell and Drivers Drivers Drivers for most popular programming languages and frameworks > db.collection.insert({company:“10gen”, product:“MongoDB”}) > > db.collection.findOne() { “_id” : ObjectId(“5106c1c2fc629bfe52792e86”), “company” : “10gen” “product” : “MongoDB” } Java Python Perl Ruby Haskell JavaScript
- 12. 12 • Ad-hoc queries • Real-time aggregation • Rich query capabilities • Index support - secondary, compound, text, geo and more • Strong (traditional) consistency • Geospatial features • Support for most programming languages • Flexible schema MongoDB is Full Featured
- 13. 13 Features In Practice Queries • Find Paul’s cars • Find everybody in London with a car built between 1970 and 1980 Geospatial • Find all of the car owners within 5km of Trafalgar Sq. Text Search • Find all the cars described as having leather seats Aggregation • Calculate the average value of Paul’s car collection Map Reduce • What is the ownership pattern of colors by geography over time? (is purple trending up in China?) { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {! !type: “Point”, !coordinates :! ! ![-0.128, 51.507]! !},! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
- 14. 14 Query Example Rich Queries • Find Paul’s cars • Find everybody in London with a car built between 1970 and 1980 db.cars.find({ first_name: ‘Paul’! }) db.cars.find({ !city: ‘London’, ”cars.year" : { $gte : 1970, $lte : 1980 } }) ! { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {! !type: “Point”, !coordinates :! ! ![-0.128, 51.507]! !},! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
- 15. 15 Geo Spatial Example db.cars.find( { location: { $near : { $geometry : { type: 'Point' , coordinates : [-0.128,51.507] } }, $maxDistance :5000 } } ) ! Geospatial • Find all of the car owners within 5km of Trafalgar Sq. { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {! !type: “Point”, !coordinates :! ! ![-0.128, 51.507]! !},! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
- 16. 16 Aggregation Framework Example db.cars.aggregate( [ {$match : {"first_name" : "Paul"}}, {$project : {"first_name":1,"cars":1}}, {$unwind : "$cars"}, { $group : {_id:"$first_name", average : { $avg : "$cars.value"}}} ]) { "_id" : "Paul", "average" : 215000 } ! Aggregation • Calculate the average value of Paul’s car collection { first_name: ‘Paul’,! surname: ‘Miller’,! city: ‘London’,! location: {! !type: “Point”, !coordinates :! ! ![-0.128, 51.507]! !},! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! }! }!
- 17. Scalability
- 18. 18 Automatic Sharding • Three types of sharding: hash-based, range-based, tag- aware • Increase or decrease capacity as you go • Automatic balancing
- 19. 19 Query Routing • Multiple query optimization models • Each sharding option appropriate for different apps
- 20. Availability
- 21. 21 Replica Sets • Replica Set – two or more copies • “Self-healing” shard • Addresses many concerns: - High Availability - Disaster Recovery - Maintenance
- 22. 22 Replica Set Benefits Business Needs Replica Set Benefits High Availability Automated failover Disaster Recovery Hot backups offsite Maintenance Rolling upgrades Low Latency Locate data near users Workload Isolation Read from non-primary replicas Data Privacy Restrict data to physical location Data Consistency Tunable Consistency
- 23. Performance
- 25. 25 Disk Seeks and Data Locality Seek = 5+ ms Read = really really fast User Comment Article
- 26. 26 Disk Seeks and Data Locality Article User CommentCommentCommentCommentComment
- 27. Use Cases
- 28. 28 MongoDB Use Cases Big Data Product & Asset Catalogs Security & Fraud Internet of Things Database-as-a- Service Mobile Apps Customer Data Management Data Hub Social & Collaboration Content Management Intelligence Agencies Top Investment and Retail Banks Top US Retailer Top Global Shipping Company Top Industrial Equipment Manufacturer Top Media Company Top Investment and Retail Banks
- 29. 29 • Document Model – Simplify development – Simplify scale out – Improve performance • MongoDB – leading NoSQL database – Rich general purpose database – Fully featured – Built-in HA (High Availability) and automated failover – Built-in horizontal scale-out Summary
- 33. 33 For More Information Resource Location MongoDB Downloads mongodb.com/download Free Online Training education.mongodb.com Webinars and Events mongodb.com/events White Papers mongodb.com/white-papers Case Studies mongodb.com/customers Presentations mongodb.com/presentations Documentation docs.mongodb.org Additional Info info@mongodb.com Resource Location