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How to Run Atlas Search Queries Against Objects in Arrays

On this page

  • Create a Sample Collection and Load the Data
  • Create an Atlas Search Index
  • Run Atlas Search Queries Against Embedded Document Fields

This tutorial describes how to index and run queries against fields in documents that are inside an array. This tutorial takes you through the following steps:

  1. Create a sample collection named schools with embedded documents in your Atlas cluster.

  2. Set up an Atlas Search index with embeddedDocuments fields configured at the teachers and teachers.classes paths.

  3. Run Atlas Search queries that search the embedded documents in the the schools collection using the compound operator with the embeddedDocument and text operators.

Before you begin, ensure that your Atlas cluster meets the requirements described in the Prerequisites. For this tutorial, you don't need to upload the sample data because you will create a new collection and load the documents that you need to run the queries in this tutorial.

Each document in the sample collection named schools contains the name and mascot of the school, school teacher's first and last name, the classes that each teacher teaches including the subject name and grade level.

You must begin by creating a collection named schools in an existing or new database on your Atlas cluster. After creating the collection, you must upload the sample data into your collection. The steps in this section walk you through creating a new database and collection, and loading the sample data into your collection.

1
  1. Log in to your cluster.

  2. Click your cluster name.

  3. Click Collections.

2
  1. Click Create Database to create a new database.

  2. Enter the database name and collection name.

    • In the Database Name field, specify local_school_district.

    • For the Collection Name field, specify schools.

3
  1. Select the schools collection if it's not selected.

  2. Click Insert Document for each of the sample documents to add to the collection.

  3. Click the JSON view ({}) to replace the default document.

  4. Copy and paste the following sample documents, one at a time, and click Insert to add the documents, one at a time, to the collection.

    {
    "_id": 0,
    "name": "Springfield High",
    "mascot": "Pumas",
    "teachers": [{
    "first": "Jane",
    "last": "Smith",
    "classes": [{
    "subject": "art of science",
    "grade": "12th"
    },
    {
    "subject": "applied science and practical science",
    "grade": "9th"
    },
    {
    "subject": "remedial math",
    "grade": "12th"
    },
    {
    "subject": "science",
    "grade": "10th"
    }]
    },
    {
    "first": "Bob",
    "last": "Green",
    "classes": [{
    "subject": "science of art",
    "grade": "11th"
    },
    {
    "subject": "art art art",
    "grade": "10th"
    }]
    }]
    }
    {
    "_id": 1,
    "name": "Evergreen High",
    "mascot": "Jaguars",
    "teachers": [{
    "first": "Jane",
    "last": "Earwhacker",
    "classes": [{
    "subject": "art",
    "grade": "9th"
    },
    {
    "subject": "science",
    "grade": "12th"
    }]
    },
    {
    "first": "John",
    "last": "Smith",
    "classes": [{
    "subject": "math",
    "grade": "12th"
    },
    {
    "subject": "art",
    "grade": "10th"
    }]
    }]
    }
    {
    "_id": 2,
    "name": "Lincoln High",
    "mascot": "Sharks",
    "teachers": [{
    "first": "Jane",
    "last": "Smith",
    "classes": [{
    "subject": "science",
    "grade": "9th"
    },
    {
    "subject": "math",
    "grade": "12th"
    }]
    },
    {
    "first": "John",
    "last": "Redman",
    "classes": [{
    "subject": "art",
    "grade": "12th"
    }]
    }]
    }

In this section, you will create an Atlas Search index for the fields in the embedded documents in the local_school_district.schools collection.

1
  1. If it isn't already displayed, select the organization that contains your desired project from the Organizations menu in the navigation bar.

  2. If it isn't already displayed, select your desired project from the Projects menu in the navigation bar.

  3. Click your cluster's name.

  4. Click the Search tab.

2
3
  • For a guided experience, select Visual Editor.

  • To edit the raw index definition, select JSON Editor.

4
  1. In the Index Name field, enter default.

    Note

    If you name your index default, you don't need to specify an index parameter when using the $search pipeline stage. Otherwise, you must specify the index name using the index parameter.

  2. In the Database and Collection section, find the local_school_district database, and select the schools collection.

5

The following index definition specifies that documents in the arrays at the teachers and teachers.classes paths must be indexed as embeddedDocuments, and the fields inside the documents must be dynamically indexed.

6

Atlas displays a modal window to let you know your index is building.

7

The index should take about one minute to build. While it is building, the Status column reads Build in Progress. When it is finished building, the Status column reads Active.

You can run queries against the embedded document fields. This tutorial uses embeddedDocument and text operators inside the compound operator in the queries.

In this section, you will connect to your Atlas cluster and run the sample queries using the operators against the fields in the schools collection.


Use the Select your language drop-down menu on this page to set the language of the examples in this section.


←  How to Run Atlas Search Compound Queries with Weighted FieldsHow to Run Atlas Search Queries with a Date Range Filter →
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