Querying Documents

You can use the Query and Criteria classes to express your queries. They have method names that mirror the native MongoDB operator names, such as lt, lte, is, and others. The Query and Criteria classes follow a fluent API style so that you can chain together multiple method criteria and queries while having easy-to-understand code. To improve readability, static imports let you avoid using the 'new' keyword for creating Query and Criteria instances. You can also use BasicQuery to create Query instances from plain JSON Strings, as shown in the following example:

Example 1. Creating a Query instance from a plain JSON String
BasicQuery query = new BasicQuery("{ age : { $lt : 50 }, accounts.balance : { $gt : 1000.00 }}");
List<Person> result = mongoTemplate.find(query, Person.class);

Querying Documents in a Collection

Earlier, we saw how to retrieve a single document by using the findOne and findById methods on MongoTemplate. These methods return a single domain object right way or using a reactive API a Mono emitting a single element. We can also query for a collection of documents to be returned as a list of domain objects. Assuming that we have a number of Person objects with name and age stored as documents in a collection and that each person has an embedded account document with a balance, we can now run a query using the following code:

Querying for documents using the MongoTemplate

  • Imperative

  • Reactive

import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query.query;

// ...

List<Person> result = template.query(Person.class)
  .matching(query(where("age").lt(50).and("accounts.balance").gt(1000.00d)))
  .all();
import static org.springframework.data.mongodb.core.query.Criteria.where;
import static org.springframework.data.mongodb.core.query.Query.query;

// ...

Flux<Person> result = template.query(Person.class)
  .matching(query(where("age").lt(50).and("accounts.balance").gt(1000.00d)))
  .all();

All find methods take a Query object as a parameter. This object defines the criteria and options used to perform the query. The criteria are specified by using a Criteria object that has a static factory method named where to instantiate a new Criteria object. We recommend using static imports for org.springframework.data.mongodb.core.query.Criteria.where and Query.query to make the query more readable.

The query should return a List or Flux of Person objects that meet the specified criteria. The rest of this section lists the methods of the Criteria and Query classes that correspond to the operators provided in MongoDB. Most methods return the Criteria object, to provide a fluent style for the API.

Methods of the Criteria Class

The Criteria class provides the following methods, all of which correspond to operators in MongoDB:

  • Criteria all (Object o) Creates a criterion using the $all operator

  • Criteria and (String key) Adds a chained Criteria with the specified key to the current Criteria and returns the newly created one

  • Criteria andOperator (Criteria…​ criteria) Creates an and query using the $and operator for all of the provided criteria (requires MongoDB 2.0 or later)

  • Criteria andOperator (Collection<Criteria> criteria) Creates an and query using the $and operator for all of the provided criteria (requires MongoDB 2.0 or later)

  • Criteria elemMatch (Criteria c) Creates a criterion using the $elemMatch operator

  • Criteria exists (boolean b) Creates a criterion using the $exists operator

  • Criteria gt (Object o) Creates a criterion using the $gt operator

  • Criteria gte (Object o) Creates a criterion using the $gte operator

  • Criteria in (Object…​ o) Creates a criterion using the $in operator for a varargs argument.

  • Criteria in (Collection<?> collection) Creates a criterion using the $in operator using a collection

  • Criteria is (Object o) Creates a criterion using field matching ({ key:value }). If the specified value is a document, the order of the fields and exact equality in the document matters.

  • Criteria lt (Object o) Creates a criterion using the $lt operator

  • Criteria lte (Object o) Creates a criterion using the $lte operator

  • Criteria mod (Number value, Number remainder) Creates a criterion using the $mod operator

  • Criteria ne (Object o) Creates a criterion using the $ne operator

  • Criteria nin (Object…​ o) Creates a criterion using the $nin operator

  • Criteria norOperator (Criteria…​ criteria) Creates an nor query using the $nor operator for all of the provided criteria

  • Criteria norOperator (Collection<Criteria> criteria) Creates an nor query using the $nor operator for all of the provided criteria

  • Criteria not () Creates a criterion using the $not meta operator which affects the clause directly following

  • Criteria orOperator (Criteria…​ criteria) Creates an or query using the $or operator for all of the provided criteria

  • Criteria orOperator (Collection<Criteria> criteria) Creates an or query using the $or operator for all of the provided criteria

  • Criteria regex (String re) Creates a criterion using a $regex

  • Criteria sampleRate (double sampleRate) Creates a criterion using the $sampleRate operator

  • Criteria size (int s) Creates a criterion using the $size operator

  • Criteria type (int t) Creates a criterion using the $type operator

  • Criteria matchingDocumentStructure (MongoJsonSchema schema) Creates a criterion using the $jsonSchema operator for JSON schema criteria. $jsonSchema can only be applied on the top level of a query and not property specific. Use the properties attribute of the schema to match against nested fields.

  • Criteria bits() is the gateway to MongoDB bitwise query operators like $bitsAllClear.

The Criteria class also provides the following methods for geospatial queries.

  • Criteria within (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.

  • Criteria within (Box box) Creates a geospatial criterion using a $geoWithin $box operation.

  • Criteria withinSphere (Circle circle) Creates a geospatial criterion using $geoWithin $center operators.

  • Criteria near (Point point) Creates a geospatial criterion using a $near operation

  • Criteria nearSphere (Point point) Creates a geospatial criterion using $nearSphere$center operations. This is only available for MongoDB 1.7 and higher.

  • Criteria minDistance (double minDistance) Creates a geospatial criterion using the $minDistance operation, for use with $near.

  • Criteria maxDistance (double maxDistance) Creates a geospatial criterion using the $maxDistance operation, for use with $near.

The Query class has some additional methods that allow to select certain fields as well as to limit and sort the result.

Methods of the Query class

  • Query addCriteria (Criteria criteria) used to add additional criteria to the query

  • Field fields () used to define fields to be included in the query results

  • Query limit (int limit) used to limit the size of the returned results to the provided limit (used for paging)

  • Query skip (int skip) used to skip the provided number of documents in the results (used for paging)

  • Query with (Sort sort) used to provide sort definition for the results

  • Query with (ScrollPosition position) used to provide a scroll position (Offset- or Keyset-based pagination) to start or resume a Scroll

The template API allows direct usage of result projections that enable you to map queries against a given domain type while projecting the operation result onto another one as outlined below.

class

template.query(SWCharacter.class)
    .as(Jedi.class)

For more information on result projections please refer to the Projections section of the documentation.

Selecting fields

MongoDB supports projecting fields returned by a query. A projection can include and exclude fields (the _id field is always included unless explicitly excluded) based on their name.

Example 2. Selecting result fields
public class Person {

    @Id String id;
    String firstname;

    @Field("last_name")
    String lastname;

    Address address;
}

query.fields().include("lastname");              (1)

query.fields().exclude("id").include("lastname") (2)

query.fields().include("address")                (3)

query.fields().include("address.city")           (4)
1 Result will contain both _id and last_name via { "last_name" : 1 }.
2 Result will only contain the last_name via { "_id" : 0, "last_name" : 1 }.
3 Result will contain the _id and entire address object via { "address" : 1 }.
4 Result will contain the _id and and address object that only contains the city field via { "address.city" : 1 }.

Starting with MongoDB 4.4 you can use aggregation expressions for field projections as shown below:

Example 3. Computing result fields using expressions
query.fields()
  .project(MongoExpression.create("'$toUpper' : '$last_name'"))         (1)
  .as("last_name");                                                     (2)

query.fields()
  .project(StringOperators.valueOf("lastname").toUpper())               (3)
  .as("last_name");

query.fields()
  .project(AggregationSpELExpression.expressionOf("toUpper(lastname)")) (4)
  .as("last_name");
1 Use a native expression. The used field name must refer to field names within the database document.
2 Assign the field name to which the expression result is projected. The resulting field name is not mapped against the domain model.
3 Use an AggregationExpression. Other than native MongoExpression, field names are mapped to the ones used in the domain model.
4 Use SpEL along with an AggregationExpression to invoke expression functions. Field names are mapped to the ones used in the domain model.

@Query(fields="…") allows usage of expression field projections at Repository level as described in MongoDB JSON-based Query Methods and Field Restriction.

Additional Query Options

MongoDB offers various ways of applying meta information, like a comment or a batch size, to a query.Using the Query API directly there are several methods for those options.

Hints

Index hints can be applied in two ways, using the index name or its field definition.

template.query(Person.class)
    .matching(query("...").withHint("index-to-use"));

template.query(Person.class)
    .matching(query("...").withHint("{ firstname : 1 }"));

Cursor Batch Size

The cursor batch size defines the number of documents to return in each response batch.

Query query = query(where("firstname").is("luke"))
    .cursorBatchSize(100)

Collations

Using collations with collection operations is a matter of specifying a Collation instance in your query or operation options, as the following two examples show:

Collation collation = Collation.of("de");

Query query = new Query(Criteria.where("firstName").is("Amél"))
    .collation(collation);

List<Person> results = template.find(query, Person.class);

Read Preference

The ReadPreference to use can be set directly on the Query object to be run as outlined below.

template.find(Person.class)
    .matching(query(where(...)).withReadPreference(ReadPreference.secondary()))
    .all();
The preference set on the Query instance will supersede the default ReadPreference of MongoTemplate.

Comments

Queries can be equipped with comments which makes them easier to look up in server logs.

template.find(Person.class)
    .matching(query(where(...)).comment("Use the force luke!"))
    .all();

Query Distinct Values

MongoDB provides an operation to obtain distinct values for a single field by using a query from the resulting documents. Resulting values are not required to have the same data type, nor is the feature limited to simple types. For retrieval, the actual result type does matter for the sake of conversion and typing. The following example shows how to query for distinct values:

Example 4. Retrieving distinct values
template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .all();                     (3)
1 Query the Person collection.
2 Select distinct values of the lastname field. The field name is mapped according to the domain types property declaration, taking potential @Field annotations into account.
3 Retrieve all distinct values as a List of Object (due to no explicit result type being specified).

Retrieving distinct values into a Collection of Object is the most flexible way, as it tries to determine the property value of the domain type and convert results to the desired type or mapping Document structures.

Sometimes, when all values of the desired field are fixed to a certain type, it is more convenient to directly obtain a correctly typed Collection, as shown in the following example:

Example 5. Retrieving strongly typed distinct values
template.query(Person.class)  (1)
  .distinct("lastname")       (2)
  .as(String.class)           (3)
  .all();                     (4)
1 Query the collection of Person.
2 Select distinct values of the lastname field. The fieldname is mapped according to the domain types property declaration, taking potential @Field annotations into account.
3 Retrieved values are converted into the desired target type — in this case, String. It is also possible to map the values to a more complex type if the stored field contains a document.
4 Retrieve all distinct values as a List of String. If the type cannot be converted into the desired target type, this method throws a DataAccessException.

+= GeoSpatial Queries

MongoDB supports GeoSpatial queries through the use of operators such as $near, $within, geoWithin, and $nearSphere. Methods specific to geospatial queries are available on the Criteria class. There are also a few shape classes (Box, Circle, and Point) that are used in conjunction with geospatial related Criteria methods.

Using GeoSpatial queries requires attention when used within MongoDB transactions, see Special behavior inside transactions.

To understand how to perform GeoSpatial queries, consider the following Venue class (taken from the integration tests and relying on the rich MappingMongoConverter):

Venue.java 
@Document(collection="newyork")
public class Venue {

  @Id
  private String id;
  private String name;
  private double[] location;

  @PersistenceConstructor
  Venue(String name, double[] location) {
    super();
    this.name = name;
    this.location = location;
  }

  public Venue(String name, double x, double y) {
    super();
    this.name = name;
    this.location = new double[] { x, y };
  }

  public String getName() {
    return name;
  }

  public double[] getLocation() {
    return location;
  }

  @Override
  public String toString() {
    return "Venue [id=" + id + ", name=" + name + ", location="
        + Arrays.toString(location) + "]";
  }
}

To find locations within a Circle, you can use the following query:

Circle circle = new Circle(-73.99171, 40.738868, 0.01);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(circle)), Venue.class);

To find venues within a Circle using spherical coordinates, you can use the following query:

Circle circle = new Circle(-73.99171, 40.738868, 0.003712240453784);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").withinSphere(circle)), Venue.class);

To find venues within a Box, you can use the following query:

//lower-left then upper-right
Box box = new Box(new Point(-73.99756, 40.73083), new Point(-73.988135, 40.741404));
List<Venue> venues =
    template.find(new Query(Criteria.where("location").within(box)), Venue.class);

To find venues near a Point, you can use the following queries:

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).maxDistance(0.01)), Venue.class);
Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(Criteria.where("location").near(point).minDistance(0.01).maxDistance(100)), Venue.class);

To find venues near a Point using spherical coordinates, you can use the following query:

Point point = new Point(-73.99171, 40.738868);
List<Venue> venues =
    template.find(new Query(
        Criteria.where("location").nearSphere(point).maxDistance(0.003712240453784)),
        Venue.class);

Geo-near Queries

Changed in 2.2!
MongoDB 4.2 removed support for the geoNear command which had been previously used to run the NearQuery.

Spring Data MongoDB 2.2 MongoOperations#geoNear uses the $geoNear aggregation instead of the geoNear command to run a NearQuery.

The calculated distance (the dis when using a geoNear command) previously returned within a wrapper type now is embedded into the resulting document. If the given domain type already contains a property with that name, the calculated distance is named calculated-distance with a potentially random postfix.

Target types may contain a property named after the returned distance to (additionally) read it back directly into the domain type as shown below.

GeoResults<VenueWithDistanceField> = template.query(Venue.class) (1)
    .as(VenueWithDistanceField.class)                            (2)
    .near(NearQuery.near(new GeoJsonPoint(-73.99, 40.73), KILOMETERS))
    .all();
1 Domain type used to identify the target collection and potential query mapping.
2 Target type containing a dis field of type Number.

MongoDB supports querying the database for geo locations and calculating the distance from a given origin at the same time. With geo-near queries, you can express queries such as "find all restaurants in the surrounding 10 miles". To let you do so, MongoOperations provides geoNear(…) methods that take a NearQuery as an argument (as well as the already familiar entity type and collection), as shown in the following example:

Point location = new Point(-73.99171, 40.738868);
NearQuery query = NearQuery.near(location).maxDistance(new Distance(10, Metrics.MILES));

GeoResults<Restaurant> = operations.geoNear(query, Restaurant.class);

We use the NearQuery builder API to set up a query to return all Restaurant instances surrounding the given Point out to 10 miles. The Metrics enum used here actually implements an interface so that other metrics could be plugged into a distance as well. A Metric is backed by a multiplier to transform the distance value of the given metric into native distances. The sample shown here would consider the 10 to be miles. Using one of the built-in metrics (miles and kilometers) automatically triggers the spherical flag to be set on the query. If you want to avoid that, pass plain double values into maxDistance(…). For more information, see the Javadoc of NearQuery and Distance.

The geo-near operations return a GeoResults wrapper object that encapsulates GeoResult instances. Wrapping GeoResults allows accessing the average distance of all results. A single GeoResult object carries the entity found plus its distance from the origin.

GeoJSON Support

MongoDB supports GeoJSON and simple (legacy) coordinate pairs for geospatial data. Those formats can both be used for storing as well as querying data. See the MongoDB manual on GeoJSON support to learn about requirements and restrictions.

GeoJSON Types in Domain Classes

Usage of GeoJSON types in domain classes is straightforward. The org.springframework.data.mongodb.core.geo package contains types such as GeoJsonPoint, GeoJsonPolygon, and others. These types are extend the existing org.springframework.data.geo types. The following example uses a GeoJsonPoint:

public class Store {

	String id;

	/**
	 * { "type" : "Point", "coordinates" : [ x, y ] }
	 */
	GeoJsonPoint location;
}

If the coordinates of a GeoJSON object represent latitude and longitude pairs, the longitude goes first followed by latitude.
GeoJsonPoint therefore treats getX() as longitude and getY() as latitude.

GeoJSON Types in Repository Query Methods

Using GeoJSON types as repository query parameters forces usage of the $geometry operator when creating the query, as the following example shows:

public interface StoreRepository extends CrudRepository<Store, String> {

	List<Store> findByLocationWithin(Polygon polygon);  (1)

}

/*
 * {
 *   "location": {
 *     "$geoWithin": {
 *       "$geometry": {
 *         "type": "Polygon",
 *         "coordinates": [
 *           [
 *             [-73.992514,40.758934],
 *             [-73.961138,40.760348],
 *             [-73.991658,40.730006],
 *             [-73.992514,40.758934]
 *           ]
 *         ]
 *       }
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (2)
  new GeoJsonPolygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006),
    new Point(-73.992514, 40.758934)));                 (3)

/*
 * {
 *   "location" : {
 *     "$geoWithin" : {
 *        "$polygon" : [ [-73.992514,40.758934] , [-73.961138,40.760348] , [-73.991658,40.730006] ]
 *     }
 *   }
 * }
 */
repo.findByLocationWithin(                              (4)
  new Polygon(
    new Point(-73.992514, 40.758934),
    new Point(-73.961138, 40.760348),
    new Point(-73.991658, 40.730006)));
1 Repository method definition using the commons type allows calling it with both the GeoJSON and the legacy format.
2 Use GeoJSON type to make use of $geometry operator.
3 Note that GeoJSON polygons need to define a closed ring.
4 Use the legacy format $polygon operator.

Metrics and Distance calculation

Then MongoDB $geoNear operator allows usage of a GeoJSON Point or legacy coordinate pairs.

NearQuery.near(new Point(-73.99171, 40.738868))
{
  "$geoNear": {
    //...
    "near": [-73.99171, 40.738868]
  }
}
NearQuery.near(new GeoJsonPoint(-73.99171, 40.738868))
{
  "$geoNear": {
    //...
    "near": { "type": "Point", "coordinates": [-73.99171, 40.738868] }
  }
}

Though syntactically different the server is fine accepting both no matter what format the target Document within the collection is using.

There is a huge difference in the distance calculation. Using the legacy format operates upon Radians on an Earth like sphere, whereas the GeoJSON format uses Meters.

To avoid a serious headache make sure to set the Metric to the desired unit of measure which ensures the distance to be calculated correctly.

In other words:

Assume you’ve got 5 Documents like the ones below:

{
    "_id" : ObjectId("5c10f3735d38908db52796a5"),
    "name" : "Penn Station",
    "location" : { "type" : "Point", "coordinates" : [  -73.99408, 40.75057 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
}
{
    "_id" : ObjectId("5c10f3735d38908db52796ab"),
    "name" : "Momofuku Milk Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.985839, 40.731698 ] }
}

Fetching all Documents within a 400 Meter radius from [-73.99171, 40.738868] would look like this using GeoJSON:

Example 6. GeoNear with GeoJSON
{
    "$geoNear": {
        "maxDistance": 400, (1)
        "num": 10,
        "near": { type: "Point", coordinates: [-73.99171, 40.738868] },
        "spherical":true, (2)
        "key": "location",
        "distanceField": "distance"
    }
}

Returning the following 3 Documents:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 69.3582262492474 (3)
}
1 Maximum distance from center point in Meters.
2 GeoJSON always operates upon a sphere.
3 Distance from center point in Meters.

Now, when using legacy coordinate pairs one operates upon Radians as discussed before. So we use Metrics#KILOMETERS when constructing the `$geoNear command. The Metric makes sure the distance multiplier is set correctly.

Example 7. GeoNear with Legacy Coordinate Pairs
{
    "$geoNear": {
        "maxDistance": 0.0000627142377, (1)
        "distanceMultiplier": 6378.137, (2)
        "num": 10,
        "near": [-73.99171, 40.738868],
        "spherical":true, (3)
        "key": "location",
        "distanceField": "distance"
    }
}

Returning the 3 Documents just like the GeoJSON variant:

{
    "_id" : ObjectId("5c10f3735d38908db52796a6"),
    "name" : "10gen Office",
    "location" : { "type" : "Point", "coordinates" : [ -73.99171, 40.738868 ] }
    "distance" : 0.0 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796a9"),
    "name" : "City Bakery ",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}
{
    "_id" : ObjectId("5c10f3735d38908db52796aa"),
    "name" : "Splash Bar",
    "location" : { "type" : "Point", "coordinates" : [ -73.992491, 40.738673 ] }
    "distance" : 0.0693586286032982 (4)
}
1 Maximum distance from center point in Radians.
2 The distance multiplier so we get Kilometers as resulting distance.
3 Make sure we operate on a 2d_sphere index.
4 Distance from center point in Kilometers - take it times 1000 to match Meters of the GeoJSON variant.

Full-text Search

Since version 2.6 of MongoDB, you can run full-text queries by using the $text operator. Methods and operations specific to full-text queries are available in TextQuery and TextCriteria. When doing full text search, see the MongoDB reference for its behavior and limitations.

Before you can actually use full-text search, you must set up the search index correctly. See Text Index for more detail on how to create index structures. The following example shows how to set up a full-text search:

db.foo.createIndex(
{
  title : "text",
  content : "text"
},
{
  weights : {
              title : 3
            }
}
)

A query searching for coffee cake can be defined and run as follows:

Example 8. Full Text Query
Query query = TextQuery
  .queryText(new TextCriteria().matchingAny("coffee", "cake"));

List<Document> page = template.find(query, Document.class);

To sort results by relevance according to the weights use TextQuery.sortByScore.

Example 9. Full Text Query - Sort by Score
Query query = TextQuery
  .queryText(new TextCriteria().matchingAny("coffee", "cake"))
  .sortByScore() (1)
  .includeScore(); (2)

List<Document> page = template.find(query, Document.class);
1 Use the score property for sorting results by relevance which triggers .sort({'score': {'$meta': 'textScore'}}).
2 Use TextQuery.includeScore() to include the calculated relevance in the resulting Document.

You can exclude search terms by prefixing the term with - or by using notMatching, as shown in the following example (note that the two lines have the same effect and are thus redundant):

// search for 'coffee' and not 'cake'
TextQuery.queryText(new TextCriteria().matching("coffee").matching("-cake"));
TextQuery.queryText(new TextCriteria().matching("coffee").notMatching("cake"));

TextCriteria.matching takes the provided term as is. Therefore, you can define phrases by putting them between double quotation marks (for example, \"coffee cake\") or using by TextCriteria.phrase. The following example shows both ways of defining a phrase:

// search for phrase 'coffee cake'
TextQuery.queryText(new TextCriteria().matching("\"coffee cake\""));
TextQuery.queryText(new TextCriteria().phrase("coffee cake"));

You can set flags for $caseSensitive and $diacriticSensitive by using the corresponding methods on TextCriteria. Note that these two optional flags have been introduced in MongoDB 3.2 and are not included in the query unless explicitly set.

Query by Example

Query by Example can be used on the Template API level run example queries.

The following snipped shows how to query by example:

Typed Example Query
Person probe = new Person();
probe.lastname = "stark";

Example example = Example.of(probe);

Query query = new Query(new Criteria().alike(example));
List<Person> result = template.find(query, Person.class);

By default Example is strictly typed. This means that the mapped query has an included type match, restricting it to probe assignable types. For example, when sticking with the default type key (_class), the query has restrictions such as (_class : { $in : [ com.acme.Person] }).

By using the UntypedExampleMatcher, it is possible to bypass the default behavior and skip the type restriction. So, as long as field names match, nearly any domain type can be used as the probe for creating the reference, as the following example shows:

Example 10. Untyped Example Query
class JustAnArbitraryClassWithMatchingFieldName {
  @Field("lastname") String value;
}

JustAnArbitraryClassWithMatchingFieldNames probe = new JustAnArbitraryClassWithMatchingFieldNames();
probe.value = "stark";

Example example = Example.of(probe, UntypedExampleMatcher.matching());

Query query = new Query(new Criteria().alike(example));
List<Person> result = template.find(query, Person.class);

When including null values in the ExampleSpec, Spring Data Mongo uses embedded document matching instead of dot notation property matching. Doing so forces exact document matching for all property values and the property order in the embedded document.

UntypedExampleMatcher is likely the right choice for you if you are storing different entities within a single collection or opted out of writing type hints.

Also, keep in mind that using @TypeAlias requires eager initialization of the MappingContext. To do so, configure initialEntitySet to to ensure proper alias resolution for read operations.

Spring Data MongoDB provides support for different matching options:

StringMatcher options
Matching Logical result

DEFAULT (case-sensitive)

{"firstname" : firstname}

DEFAULT (case-insensitive)

{"firstname" : { $regex: firstname, $options: 'i'}}

EXACT (case-sensitive)

{"firstname" : { $regex: /^firstname$/}}

EXACT (case-insensitive)

{"firstname" : { $regex: /^firstname$/, $options: 'i'}}

STARTING (case-sensitive)

{"firstname" : { $regex: /^firstname/}}

STARTING (case-insensitive)

{"firstname" : { $regex: /^firstname/, $options: 'i'}}

ENDING (case-sensitive)

{"firstname" : { $regex: /firstname$/}}

ENDING (case-insensitive)

{"firstname" : { $regex: /firstname$/, $options: 'i'}}

CONTAINING (case-sensitive)

{"firstname" : { $regex: /.*firstname.*/}}

CONTAINING (case-insensitive)

{"firstname" : { $regex: /.*firstname.*/, $options: 'i'}}

REGEX (case-sensitive)

{"firstname" : { $regex: /firstname/}}

REGEX (case-insensitive)

{"firstname" : { $regex: /firstname/, $options: 'i'}}

Query a collection for matching JSON Schema

You can use a schema to query any collection for documents that match a given structure defined by a JSON schema, as the following example shows:

Example 11. Query for Documents matching a $jsonSchema
MongoJsonSchema schema = MongoJsonSchema.builder().required("firstname", "lastname").build();

template.find(query(matchingDocumentStructure(schema)), Person.class);

Please refer to the JSON Schema section to learn more about the schema support in Spring Data MongoDB.