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Apply Design Patterns

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  • About this Task
  • Example
  • movie Collection
  • theater Collection
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Schema design patterns are ways to optimize your data model for your application's access patterns. They improve application performance and reduce schema complexity. Schema design patterns affect how your data is stored and what data is returned to your application.

For a list of schema design patterns and examples, see the Building with Patterns MongoDB blog series.

Before you implement schema design patterns, consider the problem that you are trying to solve. Each schema design pattern has different use cases and tradeoffs for data consistency, performance, and complexity. For example, some schema design patterns improve write performance, while others improve read performance.

Implementing a pattern without understanding your application and the data it needs can degrade application performance and cause unnecessary complications to your schema design.

Consider the following example patterns used by a movie theater franchise:

  • The schema contains a movie collection and a theater collection. The schema uses the subset pattern to duplicate a subset of information from the movie collection in the theater collection. The subset pattern reduces the size of documents returned to the application and improves read performance.

  • The movie collection contains a total_views field, which uses the computed pattern to calculate a running total of the number of times that customers view a movie across all of the theaters where the movie is shown.

db.movie.insertOne(
{
_id: 1,
title: "Titanic",
year: 1997,
director: "James Cameron",
runtime: 194,
distributor: "Paramount Pictures",
languages: [ "English" ],
total_views: 3500
}
)
db.theater.insertMany(
[
{
name: "Downtown Cinemas",
address: {
street: "2212 Taylor Street",
state: "NY"
},
movies: [
{
movie_id: 1,
title: "Titanic",
runtime: 194,
views: 1500
}
]
},
{
name: "Midtown Theater",
address: {
street: "1232 5th Street",
state: "NY"
},
movies: [
{
movie_id: 1,
title: "Titanic",
runtime: 194,
views: 2000
}
]
}
]
)
  • Model Specific Application Contexts

  • Handle Duplicate Data

  • Data Consistency

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