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DOCSP-49410-subset-pattern (#12001)
* DOCSP-49410-subset-pattern * fixes * test * feedback * edit code examples
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‎content/manual/upcoming/source/data-modeling/design-patterns/group-data.txt

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Bucket Pattern </data-modeling/design-patterns/group-data/bucket-pattern>
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Outlier Pattern </data-modeling/design-patterns/group-data/outlier-pattern>
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Attribute Pattern </data-modeling/design-patterns/group-data/attribute-pattern>
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Subset Pattern </data-modeling/design-patterns/group-data/subset-pattern>

‎content/manual/upcoming/source/data-modeling/design-patterns/group-data/subset-pattern.txt

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.. _group-data-subset-pattern:
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==================================
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Group Data with the Subset Pattern
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==================================
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.. meta::
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:description: Improve query access and minimize working by using the subset pattern to group highly accessed data into one collection and less frequently accessed data into another collection.
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.. contents:: On this page
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:local:
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:backlinks: none
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:depth: 1
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:class: singlecol
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MongoDB keeps frequently accessed data, referred to as the
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:term:`working set`, in RAM. When the working set of data
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and indexes grows beyond the physical RAM allotted, performance
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is reduced as disk accesses starts to occur and data is no longer retrieved from RAM.
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To solve this problem, you can shard your collection. However,
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sharding can create additional costs and complexities that your
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application may not be ready for. Rather than sharding your collection,
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you can reduce the size of your working set by using the subset pattern.
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The subset pattern is a data modeling technique used to handle
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scenarios where you have a large array of items within a document,
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but need to access frequently a small subset of those items.
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In this case, the document size can often cause the working set to exceed
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the computer's RAM capacities. The subset pattern helps optimize performance by reducing
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the amount of data that needs to be read from the database for common queries.
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About this Task
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---------------
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Consider an e-commerce site that has a list of reviews for a product, stored in a
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collection called ``products``. The e-commerce site inserts
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documents with the following schema into the ``products`` collection:
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.. code-block:: javascript
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db.collection('products').insertOne( [
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{
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_id: ObjectId("507f1f77bcf86cd99338452"),
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name: "Super Widget",
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description: "This is the most useful item in your toolbox."
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price: { value: NumberDecimal("119.99"), currency: "USD" },
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reviews: [
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{
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review_id: 786,
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review_author: "Kristina",
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review_text: "This is indeed an amazing widgt.",
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published_date: ISODate("2019-02-18")
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},
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{
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review_id: 785,
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review_author: "Trina",
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review_text: "Very nice product, slow shipping.",
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published_date: ISODate("2019-02-17")
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},
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[...],
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{
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review_id: 1,
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review_author: "Hans",
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review_text: "Meh, it's ok.",
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published_date: ISODate("2017-12-06")
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}
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]
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}
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] )
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When accessing a product’s data, you likely only need the most recent reviews.
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The following procedure demonstrates how to apply the subset pattern to the above schema.
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Steps
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-----
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.. procedure::
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:style: normal
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.. step:: Identify the subset of frequently accessed data.
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In an array field containing information about a document, determine the subset of
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information you need to access the most. For example, in the ``products``
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collection, you might only need to access the ten most recent reviews.
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.. step:: Separate the subset into different collections.
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Instead of storing all the reviews with the product, split your collection
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into two collections: one for your most accessed data, and one for your least
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accessed data. This allows for quick access to the most relevant data without
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having to load the entire array.
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The first collection, the ``products`` collection, contains the
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most frequently used data, such as current reviews:
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.. code-block:: javascript
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db.collection('products').insertOne( [
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{
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_id: ObjectId("507f1f77bcf86cd99338452"),
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name: "Super Widget",
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description: "This is the most useful item in your toolbox."
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price: { value: NumberDecimal("119.99"), currency: "USD" },
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reviews: [
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{
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review_id: 786,
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review_author: "Kristina",
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review_text: "This is indeed an amazing widget.",
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published_date: ISODate("2019-02-18")
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},
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[...],
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{
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review_id: 776,
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review_author: "Pablo",
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review_text: "Amazing!",
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published_date: ISODate("2019-02-15")
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}
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]
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}
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] )
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The ``products`` collection only contains the ten most recent reviews.
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This reduces the working set by only loading in a portion, or a subset, of the overall data.
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The second collection, the ``reviews`` collection, contains less frequently used data, such as old reviews:
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.. code-block:: javascript
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db.collection('review').insertOne( [
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{
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review_id: 786,
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review_author: "Kristina",
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review_text: "This is indeed an amazing widget.",
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product_id: ObjectId("507f1f77bcf86cd99338452"),
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published_date: ISODate("2019-02-18")
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},
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{
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review_id: 785,
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review_author: "Trina",
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review_text: "Very nice product, slow shipping.",
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product_id: ObjectId("507f1f77bcf86cd99338452"),
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published_date: ISODate("2019-02-17")
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},
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[...],
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{
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review_id: 1,
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review_author: "Hans",
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review_text: "Meh, it's ok.",
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product_id: ObjectId("507f1f77bcf86cd99338452"),
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published_date: ISODate("2017-12-06")
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}
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] )
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You can access the ``reviews`` collection whenever you need to see additional
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reviews. When considering where to split your data, store the most used fields
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of your documents in your main collection and the less frequently used data in a new collection.
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Results
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-------
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By using smaller documents with more frequently accessed data, you reduce the overall size
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of the working set. This allows for shorter disk access times for the most frequently used
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information that your application needs.
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.. note::
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The subset pattern requires you to manage two collections, rather than one, as well as query
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multiple databases when you need to gather comprehensive information on a document, rather than
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the subset.

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