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![Document Data Model
Document - Collections
Relational - Tables
8
{ 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/webinar-gettingstartedwithmongodb-backtobasics-140129131943-phpapp01/75/Webinar-Getting-Started-with-MongoDB-Back-to-Basics-8-2048.jpg)
![MongoDB is full featured
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?)
15
{ 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/webinar-gettingstartedwithmongodb-backtobasics-140129131943-phpapp01/75/Webinar-Getting-Started-with-MongoDB-Back-to-Basics-15-2048.jpg)
![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
}
})
16
{ 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/webinar-gettingstartedwithmongodb-backtobasics-140129131943-phpapp01/75/Webinar-Getting-Started-with-MongoDB-Back-to-Basics-16-2048.jpg)
![Geo Spatial Example
Geospatial
• Find all of the car owners within 5km of
Trafalgar Sq.
db.cars.find( {
location:
{ $near :
{ $geometry :
{
type: 'Point' ,
coordinates :
[-0.128, 51.507]
}
},
$maxDistance :5000
}
})
17
{ first_name: „Paul‟,
surname: „Miller‟,
city: „London‟,
location: {
type: “Point”,
coordinates :
[-0.128, 51.507]
},
cars: [
{ model: „Bentley‟,
year: 1973,
value: 100000, … },
{ model: „Rolls Royce‟,
year: 1965,
value: 330000, … }
}
}](https://image.slidesharecdn.com/webinar-gettingstartedwithmongodb-backtobasics-140129131943-phpapp01/75/Webinar-Getting-Started-with-MongoDB-Back-to-Basics-17-2048.jpg)
![Aggregation Framework Example
Aggregation
• Calculate the average value of Paul’s
car collection
db.cars.aggregate( [
{$match : {"first_name" : "Paul"}},
{$project : {"first_name":1,"cars":1}},
{$unwind : "$cars"},
{ $group : {_id:"$first_name",
average : {
$avg : "$cars.value"}}}
])
{ "_id" : "Paul", "average" : 215000 }
18
{ 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/webinar-gettingstartedwithmongodb-backtobasics-140129131943-phpapp01/75/Webinar-Getting-Started-with-MongoDB-Back-to-Basics-18-2048.jpg)
Uploaded byMongoDB
Webinar: Getting Started with MongoDB - Back to Basics
The document outlines a webinar series on application development using MongoDB, covering topics such as data modeling, query modeling, scalability, and performance. It highlights the advantages of the document data model over traditional relational databases, including flexibility, ease of development, and performance. Additionally, the series will provide practical examples and sessions for questions with MongoDB experts.
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Webinar: Getting Started with MongoDB - Back to Basics
- 1. Application Development Series Backto Basics – Introduction Daniel Roberts @dmroberts #MongoDBBasics
- 2. Introduction • About theWebinar Series • Data Model • Query Model • Scalability • Availability • Deployment Architectures • Performance • Next Session 2
- 3. Series Outline &Approach • Split into 2 sections – Application Development (4 parts) • • • • Schema Design Interacting with the database query and update operators Indexing Reporting – Operations (3 parts) • Deployment – scale out and high availability • Monitoring and performance tuning • Backup and recovery 3
- 4. Application Overview • ContentManagement System – Will utilise : • • • • • • Query & update operators Aggregation Framework Geospatial queries Pre Aggregated reports for fast analytics Polymorphic documents And more… • Take away framework • An approach that you can reuse in your own applications 4
- 5. Q&A • Virtual GeniusBar – Use the chat to post questions – EMEA Solution Architecture team are on hand – Make use of them during the sessions!!! 5
- 6.
- 7.
- 8. Document Data Model Document- Collections Relational - Tables 8 { 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, … } } }
- 9. Document Model • Agilityand 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 9
- 10.
- 11.
- 12.
- 13.
- 14. Shell and Drivers Drivers Driversfor most popular programming languages and frameworks Java Ruby JavaScript Python Shell Command-line shell for interacting directly with database 14 Perl Haskell > db.collection.insert({company:“10gen”, product:“MongoDB”}) > > db.collection.findOne() { “_id” : ObjectId(“5106c1c2fc629bfe52792e86”), “company” : “10gen” “product” : “MongoDB” }
- 15. MongoDB is fullfeatured 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?) 15 { 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. 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 } }) 16 { 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. Geo Spatial Example Geospatial •Find all of the car owners within 5km of Trafalgar Sq. db.cars.find( { location: { $near : { $geometry : { type: 'Point' , coordinates : [-0.128, 51.507] } }, $maxDistance :5000 } }) 17 { first_name: „Paul‟, surname: „Miller‟, city: „London‟, location: { type: “Point”, coordinates : [-0.128, 51.507] }, cars: [ { model: „Bentley‟, year: 1973, value: 100000, … }, { model: „Rolls Royce‟, year: 1965, value: 330000, … } } }
- 18. Aggregation Framework Example Aggregation •Calculate the average value of Paul’s car collection db.cars.aggregate( [ {$match : {"first_name" : "Paul"}}, {$project : {"first_name":1,"cars":1}}, {$unwind : "$cars"}, { $group : {_id:"$first_name", average : { $avg : "$cars.value"}}} ]) { "_id" : "Paul", "average" : 215000 } 18 { 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, … } } }
- 19.
- 20. Automatic Sharding • Threetypes of sharding: hash-based, range-based, tagaware • Increase or decrease capacity as you go • Automatic balancing 20
- 21. Query Routing • Multiplequery optimization models • Each sharding option appropriate for different apps 21
- 22.
- 23. Availability Considerations • HighAvailability – 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 23
- 24. Replica Sets • ReplicaSet – two or more copies • “Self-healing” shard • Addresses many concerns: - High Availability - Disaster Recovery - Maintenance 24
- 25. Replica Set Benefits BusinessNeeds 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 25 Replica Set Benefits Tunable Consistency
- 26.
- 27.
- 28. Summary • Document Model –Simplify development – Simplify scale out – Improve performance • MongoDB – Rich general purpose database – Built in High Availability and Failover – Built in scale out 28
- 29. Next Week –6th February • Matt Bates – Schema design for the CMS application • Collections • Design decisions – Application architecture • Example technologies • RESTful interface • We‟ve chosen python for the examples – Code Examples 29