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Model One-to-One Relationships with Embedded Documents

Model One-to-One Relationships with Embedded Documents¶

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Overview
Embedded Document Pattern
Subset Pattern

Overview¶

This page describes a data model that uses embedded documents to describe a one-to-one relationship between connected data. Embedding connected data in a single document can reduce the number of read operations required to obtain data. In general, you should structure your schema so your application receives all of its required information in a single read operation.

Embedded Document Pattern¶

Consider the following example that maps patron and address relationships. The example illustrates the advantage of embedding over referencing if you need to view one data entity in context of the other. In this one-to-one relationship between patron and address data, the address belongs to the patron.

In the normalized data model, the address document contains a reference to the patron document.

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// patron document
{
   _id: "joe",
   name: "Joe Bookreader"
}

// address document
{
   patron_id: "joe", // reference to patron document
   street: "123 Fake Street",
   city: "Faketon",
   state: "MA",
   zip: "12345"
}

If the address data is frequently retrieved with the name information, then with referencing, your application needs to issue multiple queries to resolve the reference. The better data model would be to embed the address data in the patron data, as in the following document:

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{
   _id: "joe",
   name: "Joe Bookreader",
   address: {
              street: "123 Fake Street",
              city: "Faketon",
              state: "MA",
              zip: "12345"
            }
}

With the embedded data model, your application can retrieve the complete patron information with one query.

Subset Pattern¶

A potential problem with the embedded document pattern is that it can lead to large documents that contain fields that the application does not need. This unnecessary data can cause extra load on your server and slow down read operations. Instead, you can use the subset pattern to retrieve the subset of data which is accessed the most frequently in a single database call.

Consider an application that shows information on movies. The database contains a movie collection with the following schema:

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{
  "_id": 1,
  "title": "The Arrival of a Train",
  "year": 1896,
  "runtime": 1,
  "released": ISODate("01-25-1896"),
  "poster": "http://ia.media-imdb.com/images/M/MV5BMjEyNDk5MDYzOV5BMl5BanBnXkFtZTgwNjIxMTEwMzE@._V1_SX300.jpg",
  "plot": "A group of people are standing in a straight line along the platform of a railway station, waiting for a train, which is seen coming at some distance. When the train stops at the platform, ...",
  "fullplot": "A group of people are standing in a straight line along the platform of a railway station, waiting for a train, which is seen coming at some distance. When the train stops at the platform, the line dissolves. The doors of the railway-cars open, and people on the platform help passengers to get off.",
  "lastupdated": ISODate("2015-08-15T10:06:53"),
  "type": "movie",
  "directors": [ "Auguste Lumière", "Louis Lumière" ],
  "imdb": {
    "rating": 7.3,
    "votes": 5043,
    "id": 12
  },
  "countries": [ "France" ],
  "genres": [ "Documentary", "Short" ],
  "tomatoes": {
    "viewer": {
      "rating": 3.7,
      "numReviews": 59
    },
    "lastUpdated": ISODate("2020-01-09T00:02:53")
  }
}

Currently, the movie collection contains several fields that the application does not need to show a simple overview of a movie, such as fullplot and rating information. Instead of storing all of the movie data in a single collection, you can split the collection into two collections:

The movie collection contains basic information on a movie. This is the data that the application loads by default:

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// movie collection

{
  "_id": 1,
  "title": "The Arrival of a Train",
  "year": 1896,
  "runtime": 1,
  "released": ISODate("1896-01-25"),
  "type": "movie",
  "directors": [ "Auguste Lumière", "Louis Lumière" ],
  "countries": [ "France" ],
  "genres": [ "Documentary", "Short" ],
}

The movie_details collection contains additional, less frequently-accessed data for each movie:

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// movie_details collection

{
  "_id": 156,
  "movie_id": 1, // reference to the movie collection
  "poster": "http://ia.media-imdb.com/images/M/MV5BMjEyNDk5MDYzOV5BMl5BanBnXkFtZTgwNjIxMTEwMzE@._V1_SX300.jpg",
  "plot": "A group of people are standing in a straight line along the platform of a railway station, waiting for a train, which is seen coming at some distance. When the train stops at the platform, ...",
  "fullplot": "A group of people are standing in a straight line along the platform of a railway station, waiting for a train, which is seen coming at some distance. When the train stops at the platform, the line dissolves. The doors of the railway-cars open, and people on the platform help passengers to get off.",
  "lastupdated": ISODate("2015-08-15T10:06:53"),
  "imdb": {
    "rating": 7.3,
    "votes": 5043,
    "id": 12
  },
  "tomatoes": {
    "viewer": {
      "rating": 3.7,
      "numReviews": 59
    },
    "lastUpdated": ISODate("2020-01-29T00:02:53")
  }
}

This method improves read performance because it requires the application to read less data to fulfill its most common request. The application can make an additional database call to fetch the less-frequently accessed data if needed.

Tip

When considering where to split your data, the most frequently-accessed portion of the data should go in the collection that the application loads first.

Trade-Offs of the Subset Pattern¶

Using smaller documents containing more frequently-accessed data reduces the overall size of the working set. These smaller documents result in improved read performance and make more memory available for the application.

However, it is important to understand your application and the way it loads data. If you split your data into multiple collections improperly, your application will often need to make multiple trips to the database and rely on JOIN operations to retrieve all of the data that it needs.

In addition, splitting your data into many small collections may increase required database maintenance, as it may become difficult to track what data is stored in which collection.

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