In Red Hat JBoss Data Grid, the identifier for all @Indexed objects is the key used to store the value. How the key is indexed can still be customized by using a combination of @Transformable, @ProvidedId, custom types and custom FieldBridge implementations.

The @DocumentId identifier does not apply to JBoss Data Grid values.

The Lucene-based Query API uses the following common annotations to map entities:

The @Indexed annotation declares a cached entry indexable. All entries not annotated with @Indexed are ignored.

@Indexed
public class Essay {
}

Optionally, specify the index attribute of the @Indexed annotation to change the default name of the index.

Each property or attribute of an entity can be indexed. Properties and attributes are not annotated by default, and therefore are ignored by the indexing process. The @Field annotation declares a property as indexed and allows the configuration of several aspects of the indexing process by setting one or more of the following attributes:

The analyze attribute is enabled by default. The Analyze.YES setting requires the property to be indexed via the Index.YES attribute.

The following attributes are used for sorting, and must not be analyzed.

The default for this attribute is Norms.YES. Disabling norms conserves memory, however no index time boosting information will be available.

The @NumericField annotation can be specified in the same scope as @Field.

The @NumericField annotation can be specified for Integer, Long, Float, and Double properties. At index time the value will be indexed using a Trie structure. When a property is indexed as numeric field, it enables efficient range query and sorting, orders of magnitude faster than doing the same query on standard @Field properties. The @NumericField annotation accept the following optional parameters:

@NumericField supports only Double, Long, Integer, and Float. It is not possible to take any advantage from a similar functionality in Lucene for the other numeric types, therefore remaining types must use the string encoding via the default or custom TwoWayFieldBridge.

Custom NumericFieldBridge can also be used. Custom configurations require approximation during type transformation. The following is an example defines a custom NumericFieldBridge.

public class BigDecimalNumericFieldBridge extends NumericFieldBridge {
    private static final BigDecimal storeFactor = BigDecimal.valueOf(100);

    @Override
    public void set(String name,
                    Object value,
                    Document document,
                    LuceneOptions luceneOptions) {
        if (value != null) {
            BigDecimal decimalValue = (BigDecimal) value;
            Long indexedValue = Long.valueOf(
                decimalValue
                .multiply(storeFactor)
                .longValue());
            luceneOptions.addNumericFieldToDocument(name, indexedValue, document);
        }
    }

    @Override
    public Object get(String name, Document document) {
        String fromLucene = document.get(name);
        BigDecimal storedBigDecimal = new BigDecimal(fromLucene);
        return storedBigDecimal.divide(storeFactor);
    }
}

Associated objects and embedded objects can be indexed as part of the root entity index. This allows searches of an entity based on properties of associated objects.

The aim of the following example is to return places where the associated city is Atlanta via the Lucene query address.city:Atlanta. The place fields are indexed in the Place index. The Place index documents also contain the following fields:

These fields are also able to be queried.

@Indexed
public class Place {

    @Field
    private String name;

    @IndexedEmbedded
    @ManyToOne(cascade = {CascadeType.PERSIST, CascadeType.REMOVE})
    private Address address;
}

public class Address {

    @Field
    private String street;

    @Field
    private String city;

    @ContainedIn
    @OneToMany(mappedBy = "address")
    private Set<Place> places;
}

When using the @IndexedEmbedded technique, data is denormalized in the Lucene index. As a result, the Lucene-based Query API must be updated with any changes in the Place and Address objects to keep the index up to date. Ensure the Place Lucene document is updated when its Address changes by marking the other side of the bidirectional relationship with @ContainedIn. @ContainedIn can be used for both associations pointing to entities and on embedded objects.

The @IndexedEmbedded annotation can be nested. Attributes can be annotated with @IndexedEmbedded. The attributes of the associated class are then added to the main entity index. In the following example, the index will contain the following fields:

@Indexed
public class Place {
    @Field
    private String name;

    @IndexedEmbedded
    @ManyToOne(cascade = {CascadeType.PERSIST, CascadeType.REMOVE})
    private Address address;
}

public class Address {
    @Field
    private String street;

    @Field
    private String city;

    @IndexedEmbedded(depth = 1, prefix = "ownedBy_")
    private Owner ownedBy;

    @ContainedIn
    @OneToMany(mappedBy = "address")
    private Set<Place> places;
}

public class Owner {
    @Field
    private String name;
}

The default prefix is propertyName, following the traditional object navigation convention. This can be overridden using the prefix attribute as it is shown on the ownedBy property.

The depth property is used when the object graph contains a cyclic dependency of classes. For example, if Owner points to Place. the Query Module stops including attributes after reaching the expected depth, or object graph boundaries. A self-referential class is an example of cyclic dependency. In the provided example, because depth is set to 1, any @IndexedEmbedded attribute in Owner is ignored.

Using @IndexedEmbedded for object associations allows queries to be expressed using Lucene’s query syntax. For example:

This operation is similar to the relational join operation, without data duplication. Out of the box, Lucene indexes have no notion of association; the join operation does not exist. It may be beneficial to maintain the normalized relational model while benefiting from the full text index speed and feature richness.

An associated object can be also be @Indexed. When @IndexedEmbedded points to an entity, the association must be directional and the other side must be annotated using @ContainedIn. If not, the Lucene-based Query API cannot update the root index when the associated entity is updated. In the provided example, a Place index document is updated when the associated Address instance updates.

It is possible to override the object type targeted using the targetElement parameter. This method can be used when the object type annotated by @IndexedEmbedded is not the object type targeted by the data grid and the Lucene-based Query API. This occurs when interfaces are used instead of their implementation.

@Indexed
public class Address {

    @Field
    private String street;

    @IndexedEmbedded(depth = 1, prefix = "ownedBy_", targetElement = Owner.class)
    private Person ownedBy;

    ...
}

public class Owner implements Person { ... }

Lucene uses boosting to attach more importance to specific fields or documents over others. Lucene differentiates between index and search-time boosting.

The @Boost annotation is used to define a static boost value for an indexed class or property. This annotation can be used within @Field, or can be specified directly on the method or class level.

In the following example:

@Indexed
@Boost(1.7f)
public class Essay {

    @Field(name = "Abstract", store=Store.YES, boost = @Boost(2f))
    @Boost(1.5f)
    public String getSummary() { return summary; }

    @Field(boost = @Boost(1.2f))
    public String getText() { return text; }

    @Field
    public String getISBN() { return isbn; }

}

The @Boost annotation defines a static boost factor that is independent of the state of the indexed entity at runtime. However, in some cases the boost factor may depend on the actual state of the entity. In this case, use the @DynamicBoost annotation together with an accompanying custom BoostStrategy.

@Boost and @DynamicBoost annotations can both be used in relation to an entity, and all defined boost factors are cumulative. The @DynamicBoost can be placed at either class or field level.

In the following example, a dynamic boost is defined on class level specifying VIPBoostStrategy as implementation of the BoostStrategy interface used at indexing time. Depending on the annotation placement, either the whole entity is passed to the defineBoost method or only the annotated field/property value. The passed object must be cast to the correct type.

public enum PersonType {
    NORMAL,
    VIP
}

@Indexed
@DynamicBoost(impl = VIPBoostStrategy.class)
public class Person {
    private PersonType type;
}

public class VIPBoostStrategy implements BoostStrategy {
    public float defineBoost(Object value) {
        Person person = (Person) value;
        if (person.getType().equals(PersonType.VIP)) {
            return 2.0f;
        }
        else {
            return 1.0f;
        }
    }
}

In the provided example all indexed values of a VIP would be twice the importance of the values of a non-VIP.

The default analyzer class is used to index tokenized fields, and is configurable through the default.analyzer property. The default value for this property is org.apache.lucene.analysis.standard.StandardAnalyzer.

The analyzer class can be defined per entity, property, and per @Field, which is useful when multiple fields are indexed from a single property.

In the following example, EntityAnalyzer is used to index all tokenized properties, such as name except, summary and body, which are indexed with PropertyAnalyzer and FieldAnalyzer respectively.

@Indexed
@Analyzer(impl = EntityAnalyzer.class)
public class MyEntity {

    @Field
    private String name;

    @Field
    @Analyzer(impl = PropertyAnalyzer.class)
    private String summary;

    @Field(analyzer = @Analyzer(impl = FieldAnalyzer.class))
    private String body;
}

The Query Module uses analyzer definitions to deal with the complexity of the Analyzer function. Analyzer definitions are reusable by multiple @Analyzer declarations and includes the following:

The Analyzer separates these components into multiple tasks, allowing individual components to be reused and components to be built with flexibility using the following procedure:

The Lucene-based Query API supports this infrastructure by utilizing the Solr analyzer framework.

Once defined, an analyzer definition can be reused by an @Analyzer annotation.

@Indexed
@AnalyzerDef(name = "customanalyzer")
public class Team {

    @Field
    private String name;

    @Field
    private String location;

    @Field
    @Analyzer(definition = "customanalyzer")
    private String description;
}

Analyzer instances declared by @AnalyzerDef are also available by their name in the SearchFactory, which is useful when building queries.

Analyzer analyzer = Search.getSearchManager(cache).getAnalyzer("customanalyzer")

When querying, fields must use the same analyzer that has been used to index the field. The same tokens are reused between the query and the indexing process.

When using Maven all required Apache Solr dependencies are now defined as dependencies of the artifact org.hibernate:hibernate-search-analyzers. Add the following dependency:

<dependency>
    <groupId>org.hibernate</groupId>
    <artifactId>hibernate-search-analyzers</artifactId>
    <version>${version.hibernate.search}</version>
<dependency>

In the following example, a CharFilter is defined by its factory. In this example, a mapping char filter is used, which will replace characters in the input based on the rules specified in the mapping file. Finally, a list of filters is defined by their factories. In this example, the StopFilter filter is built reading the dedicated words property file. The filter will ignore case.

Tokenizers, TokenFilters, and CharFilters can load resources such as configuration or metadata files using the StopFilterFactory.class or the synonym filter. The virtual machine default can be explicitly specified by adding a resource_charset parameter.

@AnalyzerDef(name = "customanalyzer",
    charFilters = {
        @CharFilterDef(factory = MappingCharFilterFactory.class, params = {
            @Parameter(name = "mapping",
                value =
                    "org/hibernate/search/test/analyzer/solr/mapping-chars.properties")
        })
    },
    tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class),
    filters = {
        @TokenFilterDef(factory = ISOLatin1AccentFilterFactory.class),
        @TokenFilterDef(factory = LowerCaseFilterFactory.class),
        @TokenFilterDef(factory = StopFilterFactory.class, params = {
            @Parameter(name="words",
                value= "org/hibernate/search/test/analyzer/solr/stoplist.properties"),
            @Parameter(name = "resource_charset", value = "UTF-16BE"),
            @Parameter(name = "ignoreCase", value = "true")
        })
    })
public class Team {
}

The Query Module uses the @AnalyzerDiscriminator annotation to enable the dynamic analyzer selection.

An analyzer can be selected based on the current state of an entity that is to be indexed. This is particularly useful in multilingual applications. For example, when using the BlogEntry class, the analyzer can depend on the language property of the entry. Depending on this property, the correct language-specific stemmer can then be chosen to index the text.

An implementation of the Discriminator interface must return the name of an existing Analyzer definition, or null if the default analyzer is not overridden.

The following example assumes that the language parameter is either 'de' or 'en', which is specified in the @AnalyzerDefs.

Retrieving an analyzer can be used when multiple analyzers have been used in a domain model, in order to benefit from stemming or phonetic approximation, etc. In this case, use the same analyzers to building a query. Alternatively, use the Lucene-based Query API, which selects the correct analyzer automatically. See Building a Lucene Query.

The scoped analyzer for a given entity can be retrieved using either the Lucene programmatic API or the Lucene query parser. A scoped analyzer applies the right analyzers depending on the field indexed. Multiple analyzers can be defined on a given entity, each working on an individual field. A scoped analyzer unifies these analyzers into a context-aware analyzer.

In the following example, the song title is indexed in two fields:

Using the analyzer provided by the search factory, the query uses the appropriate analyzer depending on the field targeted.

SearchManager manager = Search.getSearchManager(cache);

org.apache.lucene.queryparser.classic.QueryParser parser = new QueryParser(
        org.apache.lucene.util.Version.LUCENE_5_5_1,
        "title",
        manager.getAnalyzer(Song.class)
);

org.apache.lucene.search.Query luceneQuery =
        parser.parse("title:sky Or title_stemmed:diamond");

// wrap Lucene query in a org.infinispan.query.CacheQuery
CacheQuery cacheQuery = manager.getQuery(luceneQuery, Song.class);

List result = cacheQuery.list();
//return the list of matching objects

Apache Solr and Lucene ship with a number of default CharFilters, tokenizers, and filters. A complete list of CharFilter, tokenizer, and filter factories is available at http://wiki.apache.org/solr/AnalyzersTokenizersTokenFilters. The following tables provide some example CharFilters, tokenizers, and filters.

                    "á" => "a"
                    "ñ" => "n"
                    "ø" => "o"

When mapping entities, Lucene represents all index fields as strings. All entity properties annotated with @Field are converted to strings to be indexed. Built-in bridges automatically translates properties for the Lucene-based Query API. The bridges can be customized to gain control over the translation process.

The Lucene-based Query API includes a set of built-in bridges between a Java property type and its full text representation.