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range

The range operator supports querying and scoring numeric and date values. This operator can be used to perform a search over:

  • Number fields of BSON int32, int64, and double data types.

  • Date fields of BSON date data type in ISODate format.

You can use the range operator to find results that are within a given numeric or date range.

range has the following syntax:

1{
2 "$search": {
3 "index": <index name>, // optional, defaults to "default"
4 "range": {
5 "path": "<field-to-search>",
6 "gt | gte": <value-to-search>,
7 "lt | lte": <value-to-search>,
8 "score": <score-options>
9 }
10 }
11}

range uses the following terms to construct a query:

Field
Type
Description
Necessity
gt or gte
BSON date or number

Find values greater than (>) or greater than or equal to (>=) the given value.

  • For number fields, the value can be an int32, int64, or double data type.

  • For date fields, the value must be an ISODate formatted date.

no
lt or lte
BSON date or number

Find values less than (<) or less than or equal to (<=) the given value.

  • For number fields, the value can be an int32, int64, or double data type.

  • For date fields, the value must be an ISODate formatted date.

no
path
string or array of strings
Indexed field or fields to search. See Path Construction.
yes
score
object

Modify the score assigned to matching search results. Options are:

  • boost: multiply the result score by the given number.

  • constant: replace the result score with the given number.

For information on using score in your query, see Customize and Normalize the Score in Results.

Note

When querying values in arrays, Atlas Search doesn't alter the score based on the number of values inside the array that matched the query. The score would be the same as a single match irrespective of the number of matches inside an array.

no

The following examples use the collection in the sample data. If you loaded the sample data on your cluster, you can create the indexes using the index definitions in the examples below and run the example queries on your cluster.

Tip

If you've already loaded the sample dataset, follow the Get Started with Atlas Search tutorial to create an index definition and run Atlas Search queries.

The following examples use indexes on numeric fields in the sample data and run range queries against the indexed fields.

The following example uses the range operator to query a date field in the sample_mflix.movies collection. For this example, you can use either static or dynamic mappings to index the date type field named released in the collection.

The following query searches for movies released between January 1, 2010 and January 1, 2015. It includes a $limit stage to limit the output to 5 results and a $project stage to exclude all fields except title and released.

1db.movies.aggregate([
2 {
3 "$search": {
4 "range": {
5 "path": "released",
6 "gt": ISODate("2010-01-01T00:00:00.000Z"),
7 "lt": ISODate("2015-01-01T00:00:00.000Z")
8 }
9 }
10 },
11 {
12 "$limit": 5
13 },
14 {
15 "$project": {
16 "_id": 0,
17 "title": 1,
18 "released": 1
19 }
20 }
21])

The above query returns the following search results:

1{ "title" : "The Fall of the House of Usher", "released" : ISODate("2011-09-20T00:00:00Z") }
2{ "title" : "The Blood of a Poet", "released" : ISODate("2010-05-20T00:00:00Z") }
3{ "title" : "Too Much Johnson", "released" : ISODate("2014-08-30T00:00:00Z") }
4{ "title" : "Stolen Desire", "released" : ISODate("2012-07-01T00:00:00Z") }
5{ "title" : "The Monkey King", "released" : ISODate("2012-01-12T00:00:00Z") }
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