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Introduction to Schema Design
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Introduction
Concepts clés
Flexible schema
Application driven-schema
Rich document
Pre join / Embed document
No mongo join
No constraints
No transactions
Atomicity
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One to one
relationship
users
{
_id: "joe",
name: "Joe Bookreader"
}
addresses
{
user_id: "joe",
street: "123 Fake Street",
city: "Faketon",
state: "MA",
zip: "12345"
}
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One to one
relationship
users
{
_id: "joe",
name: "Joe Bookreader",
address: {
street: "123 Fake Street",
city: "Faketon",
state: "MA"
}
}
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Frequency of access
Document Growth
Atomicity
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One to many relationship
cities
{
name: "Paris",
region: "IDF",
people: [
...
]
}
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Too many people...
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One to many relationship
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Duplication of data = inconsistency
people
{
name: "Joe",
city: {
name: "Paris",
region: "IDF"
}
}
people
{
name: "Jane",
city: {
name: "Paris",
region: "IDF"
}
}
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One to many relationship
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Make sure the city exist (no foreign key)
people
{
name: "Joe",
city: "Paris"
}
cities
{
_id: "Paris",
region: "IDF"
}
Multiple queries
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One to few
relationship
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No data inconsistency
people
{
name: "Joe",
addresses: [
{...},
{...},
{...}
]
}
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One to many / One to few
relationship
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What is the cardinality of the relationship ?
One to many => referencing
One to few => embedding
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Use multikey indexes for performance
students
{
_id: 0,
name: 'joe',
teachers: [2, 11, 41]
}
teachers
{
_id: 11,
name: 'John',
students: [...]
}
Use reference
Many to many
relationship
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Find the descendants and the ancestors of a node
Tree structure
Array of ancestors
ancestors: [ "Books", "Programming", "Databases" ]
ancestors: [ "Books", "Programming" ]
ancestors: [ "Books" ]
ancestors: [ ]
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JSON schema validation since MongoDB 3.6
Schema validation
db.createCollection("students", { validator: { $jsonSchema: { bsonType: "object", required: [ "name", "year", "major", "gpa" ], properties: { name: { bsonType: "string", description: "must be a string and is required" }, ... } } } })
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MongoDB 4.0 will support Multi-Document ACID Transactions
The futur of MongoDB
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Aggregation
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Types d'aggregations
Single Purpose Aggregation Operations
Aggregation Pipeline
Map-Reduce
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Single Purpose Aggregation Operation
count
distinct
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Exemple
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Aggregation Pipeline
Un Framework pour effectuer des analyses ou statistiques et générer des rapports pré-agrégés
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Exemple
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Map-Reduce
Map Reduce opération sous stéroïde
Limitation à 16MB d'output en BSON
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Map-Reduce
Redondance et Disponibilité des données
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Replicat Sets
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Réplication
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Réplication
Un noeud Primary
Transparent pour le développeur
Toutes les opérations se font par défaut sur le Primary
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Disponibilité
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Disponibilité
Si un noeud Primaire ne réponds pas pendant 10s un autre noeud prendra sa place
Election - scrutin majoritaire - pour élire un nouveau noeud primaire
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Disponibilité - Election
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Disponibilité - Election
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Exemple
Distribution des données sur plusieurs machines
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Sharding
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Pourquoi ?
Vertical Scaling
Horizontal Scaling
- Trop de données (1terra byte)
- Trop d'opération (1million req/s)
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Sharded Cluster
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Shard Keys
- Une collection
- Un index simple ou composé
- Champ obligatoire / immutable
- Ne peux pas être changé
- Performance et scalability
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Chunk
+ Sharded Cluster Balancer
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Sharding Strategy
- Ranged Sharding
- Hashed Sharding
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Ranged sharding
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Ranged sharding
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Hashed sharding
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Tips
Mongodb calcule automatiquement le hash dans les requêtes
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Ranged vs Hashed
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Ranged vs Hashed
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Exemple
sh.shardCollection("db.articles", {name: "hashed"})
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Sharding Conclusion
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Avantages
- Storage Capacity
- Reads / Writes (Load balancing)
- High Availability
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Inconvénients
- Filtrer les requêtes via la shared key
updateOne(), removeOne(), deleteOne()
- Tous les index doivent être préfixés par la shared key
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Thank you !
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