This document is an introduction to a series of webinars on building applications with MongoDB, covering topics such as data modeling, query models, scalability, and deployment. It highlights key features of MongoDB, including its document-oriented structure, rich querying capabilities, and high availability through replica sets. Upcoming sessions will focus on data schema design and application architecture.

1. Construire une application avec MongoDB
Introduction à MongoDB
Alain Hélaïli
@AlainHelaili
#MongoDBBasics
Tugdual Grall
@tgrall

2. Introduction
• A propos de la série de Webinaires
• Modèle de Donnée
• Modèle de Requête
• Montée en charge (Scalability)
• Disponibilité (Availability)
• Déploiement
• Performances
• Prochaine Session
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3. A propos des Webinaires
• Série divisée en 2 sections
– Développement d‟Applications (4 parties)
•
•
•
•
Conception/Modèle de données
Interactions avec la base: requêtes et mises à jour
Indexation
Reporting
– Opérations/Administration (3 parties)
• Déploiement – Montée en charge et haute disponibilité
• Monitoring et performance
• Sauvegarde et Restauration
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4. Application : Vue d’ensemble
• Système de Gestion de Contenus
– Utilise :
•
•
•
•
•
•
Opérateurs de requêtes et mise à jour
Framework d‟agrégation
Requêtes Géo-spatiales
Rapports pré-agrégés
Documents polymorphiques
Et plus…
• Une approche que vous pouvez utiliser dans vos
applications
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5. Q&A
• Virtual Genius Bar
– Utilisez le Chat pour poser vos questions
– Réponses au fil de l‟eau et à la fin
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6. MongoDB

7. Base de donnée opérationnelle
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8. Modèle de donnée Document
Document - Collections
Relationnel - Tables
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{ 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
• 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
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10. Simplify development
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11. Simplify development
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12. Rich database interaction
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13. Query Model

14. Shell and Drivers
Drivers
Drivers for 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 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?)
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{ 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
}
})
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{ 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
}
})
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{ 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 }
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{ 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. Scalability

20. Automatic Sharding
• Three types of sharding: hash-based, range-based, tagaware
• Increase or decrease capacity as you go
• Automatic balancing
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21. Query Routing
• Multiple query optimization models
• Each sharding option appropriate for different apps
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22. Availability

23. Availability Considerations
• 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
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24. Replica Sets
• Replica Set – two or more copies
• “Self-healing” shard
• Addresses many concerns:
- High Availability
- Disaster Recovery
- Maintenance
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25. Replica Set Benefits
Business Needs
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
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Replica Set Benefits
Tunable Consistency

26. Performance

27. Performance
Better Data
Locality
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In-Memory
Caching
In-Place
Updates

28. Conclusion
• Modèle Documentaire
– Simplifie le développement
– Simplifie la montée en charge horizontale (scale out)
– Améliore les performances
• MongoDB
– Base de donnée généraliste
– Haute disponibilité et tolérance aux pannes incluses
– Support de la montée en charge horizontale
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29. La Semaine Prochaine – 12 Mars
• Alain Hélaïli & Tugdual Grall
– Schéma de données pour l‟application CMS
• Collections
• Options de conception
– Architecture de l‟application
• Technologies utilisées
• Interface REST
• Nous avons choisi Python pour cette application
– Code Exemple
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