Tutorial: Create and Query an Atlas Search Index¶

Navigate to the Atlas Cluster Overview page.¶

In the Atlas UI, navigate to the Clusters page for your project.

Click the cluster name to view cluster details.¶

Click the Search tab.¶

Optional: If you do not have the sample dataset on your cluster, click Load Sample Dataset.¶

Click Load Sample Dataset again in the Load Sample Dataset confirmation window. The sample dataset takes a few minutes to load. When it finishes, proceed to the next step.

Click Create Search Index.¶

Select a Configuration Method and click Next.¶

• For a guided experience, select Visual Editor.
• To edit the raw index definition, select JSON Editor.

Enter the Index Name, and set the Database and Collection.¶

1. In the Index Name field, enter default.

   The index name defaults to default. You can leave the default name in place or choose one of your own.

   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.

   Index names must be unique within their namespace.

2. In the Database and Collection section, find the sample_mflix database, and select the movies collection.

   Important

   The Database and Collection picker is available for M10 and higher clusters only. For free and shared tier clusters, you must enter a Database and Collection in the corresponding fields.

Specify an index definition.¶

The movies collection is large, so in order to save space we will only index the title, genres, and plot fields.

The above index definition specifies the standard analyzer as the default analyzer for all three indexed fields. It also specifies the keyword analyzer as an alternate analyzer for the title field, with the name keywordAnalyzer. The keyword analyzer indexes the entire field as a single term, so it only returns results if the search term and the specified field match exactly. The index definition also specifies standard analyzer as the analyzer by default for queries on the genres field, which is an array of strings. For indexing arrays, Atlas Search only requires the data type of the array elements. You don't have to specify that the data is contained in an array in the index definition.

For more information about static and dynamic field mappings, see index definitions. For more information about multi analyzer designations, see Path Construction.

Click Create Search Index.¶

Close the You're All Set! Modal Window.¶

A modal window appears to let you know your index is building. Click the Close button.

Wait for the index to finish building.¶

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.

Connect to your cluster in the mongo shell.¶

Open the mongo shell in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect to a Cluster.

Use the sample_mflix database.¶

Run the following command at the mongo shell prompt:

use sample_mflix

Run your first Atlas Search query.¶

The following query searches for the word baseball in the plot field. It includes a $limit stage to limit the output to 5 results and a $project stage to exclude all fields except title and plot.

db.movies.aggregate([
  {
    $search: {
      "text": {
        "query": "baseball",
        "path": "plot"
      }
    }
  },
  {
    $limit: 5
  },
  {
    $project: {
      "_id": 0,
      "title": 1,
      "plot": 1
    }
  }
])

The above query returns the following results:

{
  "plot" : "A trio of guys try and make up for missed opportunities
  in childhood by forming a three-player baseball team to compete
  against standard children baseball squads.",
  "title" : "The Benchwarmers"
}
{
  "plot" : "A young boy is bequeathed the ownership of a
  professional baseball team.",
  "title" : "Little Big League"
}
{
  "plot" : "A trained chimpanzee plays third base for a
  minor-league baseball team.",
  "title" : "Ed"
}
{
  "plot" : "The story of the life and career of the famed baseball
  player, Lou Gehrig.",
  "title" : "The Pride of the Yankees"
}
{
  "plot" : "Babe Ruth becomes a baseball legend but is unheroic to those who know him.",
  "title" : "The Babe"
}

For more information about the $search pipeline stage, see its reference page. For complete aggregation pipeline documentation, see the MongoDB Server Manual.

Run a more complex query.¶

$search has several operators for constructing different types of queries. The following query uses the compound operator to combine several operators into a single query. It has the following search criteria:

• The plot field must contain either Hawaii or Alaska.
• The plot field must contain a four-digit number, such as a year.
• The genres field must not contain either Comedy or Romance.
• The title field must not contain Beach or Snow.

db.movies.aggregate([
  {
    $search: {
      "compound": {
        "must": [ {
          "text": {
             "query": ["Hawaii", "Alaska"],
             "path": "plot"
          },
        },
        {
          "regex": {
             "query": "([0-9]{4})",
             "path": "plot",
             "allowAnalyzedField": true
          }
        } ],
        "mustNot": [ {
          "text": {
            "query": ["Comedy", "Romance"],
            "path": "genres"
          }
        },
        {
          "text": {
            "query": ["Beach", "Snow"],
            "path": "title"
          }
        } ]
      }
    }
  },
  {
    $project: {
      "title": 1,
      "plot": 1,
      "genres": 1,
      "_id": 0
    }
  }
])

The above query returns the following results:

{
  "plot" : "A modern aircraft carrier is thrown back in time to
  1941 near Hawaii, just hours before the Japanese attack on Pearl
  Harbor.",
  "genres" : [ "Action", "Sci-Fi" ],
  "title" : "The Final Countdown"
}
{
  "plot" : "Follows John McCain's 2008 presidential campaign, from
  his selection of Alaska Governor Sarah Palin as his running mate
  to their ultimate defeat in the general election.",
  "genres" : [ "Biography", "Drama", "History" ],
  "title" : "Game Change"
}
{
  "plot" : "A devastating and heartrending take on grizzly bear
  activists Timothy Treadwell and Amie Huguenard, who were killed
  in October of 2003 while living among grizzlies in Alaska.",
  "genres" : [ "Documentary", "Biography" ],
  "title" : "Grizzly Man"
}
{
  "plot" : "Truman Korovin is a lonely, sharp-witted cab driver in
  Fairbanks, Alaska, 1980. The usual routine of picking up fares
  and spending his nights at his favorite bar, the Boatel, is
  disrupted ...",
  "genres" : [ "Drama" ],
  "title" : "Chronic Town"
}

Run a query using a multi analyzer.¶

In the index definition you created in step 9, you specified that the title field should be able to use either the standard analyzer or the keyword analyzer for queries. The following query uses the alternate analyzer, named keywordAnalyzer, to search for exact matches on the string The Count of Monte Cristo.

db.movies.aggregate([
  {
    $search: {
      "text": {
        "query": "The Count of Monte Cristo",
        "path": { "value": "title", "multi": "keywordAnalyzer" }
      }
    }
  },
  {
    $project: {
      "title": 1,
      "year": 1,
      "_id": 0
    }
  }
])

The above query returns the following results:

{ "title" : "The Count of Monte Cristo", "year" : 1934 }
{ "title" : "The Count of Monte Cristo", "year" : 1954 }
{ "title" : "The Count of Monte Cristo", "year" : 1998 }

By contrast, the same query using the standard analyzer would find all the movies with the word Count or Monte or Cristo in the title.