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Schema Design Anti-Patterns

Bloated Documents

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  • About this Task
  • Example
  • Join Collections with $lookup
  • Learn More

Storing data fields that are related to each other but not accessed together can create bloated documents that lead to excessive RAM and bandwidth usage. The working set, consisting of frequently accessed data and indexes, is stored in the RAM allotment. When the working set fits in RAM, MongoDB can query from memory instead of from disk, which improves performance. However, if documents are too large, the working set might not fit into RAM, causing performance to degrade as MongoDB has to access data from disk.

To prevent bloated documents, restructure your schema with smaller documents and use document references to separate fields that aren't returned together. This approach reduces the working set size and improves performance.

About this Task

Consider the following schema that contains book information used on a bookstore website's main page. The main page only displays the book title, author, and front cover image. You must click on the book to see additional details.

{
   title: "Tale of Two Cities",
   author: "Charles Dickens",
   genre: "Historical Fiction",
   cover_image: "<url>",
   year: 1859,
   pages: 448,
   price: 15.99,
   description: "A historical novel set during the French Revolution.
}

In the current schema, to display the information for the website's main page, all of the book information must be queried. To reduce document size and streamline queries, you can split the large document into two smaller collections.

Example

In the following example, the book information is split into two collections: mainBookInfo and additionalBookDetails.

  • The mainBookInfo collection contains the information displayed on the website's main page.

  • The additionalBookDetails collection contains extra details revealed after a user clicks on the book.

The mainBookInfo collection:

db.mainBookInfo.insertOne(
   {
      _id: 1234,
      title: "Tale of Two Cities",
      author: "Charles Dickens",
      genre: "Historical Fiction",
      cover_image: "<url>"
   }
)

The additionalBookDetails collection:

db.additionalBookDetails.insertOne(
   {
      title: "Tale of Two Cities",
      bookId: 1234,
      year: 1859,
      pages: 448,
      price: 15.99,
      description: "A historical novel set during the French Revolution."
   }
)

The two collections are linked by the _id field in the mainBookInfo collection and the bookId field in the additionalBookDetails collection. On the home page, only the mainBookInfo collection is used to provide the necessary information. When a user selects a book to learn more about, the website queries the additionalBookDetails collection using the _id field to match with the bookId field.

By splitting the information into two collections, you ensure that your documents do not grow too large and exceed RAM allotment.

Join Collections with $lookup

To join the data from the mainBookInfo collection and the additionalBookDetails collection, the application needs to perform a $lookup operation.

The following aggregation operation joins the mainBookInfo and additionalBookDetails collection from the previous example.

db.mainBookInfo.aggregate( [
   {
      $lookup: {
         from: "additionalBookDetails",
         localField: "_id",
         foreignField: "bookId",
         as: "details"
      }
   },
   {
      $replaceRoot: {
         newRoot: { $mergeObjects: [ { $arrayElemAt: [ "$details", 0 ] }, "$$ROOT" ] }
      }
   },
   {
      $project: { details: 0 }
   }
 ] )

The operation returns the following:

[
   {
     _id: ObjectId('666b1235eda086b5e22dbcf1'),
     title: 'Tale of Two Cities',
     author: 'Charles Dickens',
     genre: 'Historical Fiction',
     cover_image: '<url>',
     bookId: 1234,
     year: 1859,
     pages: 448,
     price: 15.99,
     description: 'A historical novel set during the French Revolution.'
   }
]

In this example, the $lookup operation joins the mainBookInfo collection with the additionalBookDetails collection using the _id and bookId fields. The $mergeObjects and $replaceRoot operations merge the joined documents from the mainBookInfo and additionalBookDetails collections.

Learn More

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