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The second most important capability of Hibernate Search is the ability to execute Lucene queries and retrieve entities managed by a Hibernate session. The search provides the power of Lucene without leaving the Hibernate paradigm, giving another dimension to the Hibernate classic search mechanisms (HQL, Criteria query, native SQL query).
Preparing and executing a query consists of four simple steps:
Creating a FullTextSession
Creating a Lucene query either via the Hibernate Search query DSL (recommended) or by utilizing the Lucene query API
Wrapping the Lucene query using an
org.hibernate.Query
Executing the search by calling for example
list()
or
scroll()
To access the querying facilities, you have to use a
FullTextSession
. This Search specific session wraps a
regular org.hibernate.Session
in order to provide
query and indexing capabilities.
Example 5.1. Creating a FullTextSession
Session session = sessionFactory.openSession();
...
FullTextSession fullTextSession = Search.getFullTextSession(session);
Once you have a FullTextSession
you have two
options to build the full-text query: the Hibernate Search query DSL or the
native Lucene query.
If you use the Hibernate Search query DSL, it will look like this:
final QueryBuilder b = fullTextSession.getSearchFactory() .buildQueryBuilder().forEntity( Myth.class ).get(); org.apache.lucene.search.Query luceneQuery = b.keyword() .onField("history").boostedTo(3) .matching("storm") .createQuery();
org.hibernate.Query fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery );
List result = fullTextQuery.list(); //return a list of managed objects
You can alternatively write your Lucene query either using the Lucene query parser or Lucene programmatic API.
Example 5.2. Creating a Lucene query via the
QueryParser
SearchFactory searchFactory = fullTextSession.getSearchFactory(); org.apache.lucene.queryParser.QueryParser parser = new QueryParser("title", searchFactory.getAnalyzer(Myth.class) ); try { org.apache.lucene.search.Query luceneQuery = parser.parse( "history:storm^3" ); } catch (ParseException e) { //handle parsing failure }
org.hibernate.Query fullTextQuery = fullTextSession.createFullTextQuery(luceneQuery);
List result = fullTextQuery.list(); //return a list of managed objects
The Hibernate query built on top of the Lucene query is a regular
org.hibernate.Query
, which means you are in the same
paradigm as the other Hibernate query facilities (HQL, Native or
Criteria). The regular list()
,
uniqueResult()
, iterate()
and
scroll()
methods can be used.
In case you are using the Java Persistence APIs of Hibernate, the same extensions exist:
Example 5.3. Creating a Search query using the JPA API
EntityManager em = entityManagerFactory.createEntityManager();
FullTextEntityManager fullTextEntityManager =
org.hibernate.search.jpa.Search.getFullTextEntityManager(em);
...
final QueryBuilder b = fullTextEntityManager.getSearchFactory()
.buildQueryBuilder().forEntity( Myth.class ).get();
org.apache.lucene.search.Query luceneQuery =
b.keyword()
.onField("history").boostedTo(3)
.matching("storm")
.createQuery(); javax.persistence.Query fullTextQuery = fullTextEntityManager.createFullTextQuery( luceneQuery );
List result = fullTextQuery.getResultList(); //return a list of managed objects
The following examples we will use the Hibernate APIs but the same
example can be easily rewritten with the Java Persistence API by just
adjusting the way the FullTextQuery
is
retrieved.
Hibernate Search queries are built on top of Lucene queries which
gives you total freedom on the type of Lucene query you want to execute.
However, once built, Hibernate Search wraps further query processing using
org.hibernate.Query
as your primary query
manipulation API.
Using the Lucene API, you have several options. You can use the query parser (fine for simple queries) or the Lucene programmatic API (for more complex use cases). It is out of the scope of this documentation on how to exactly build a Lucene query. Please refer to the online Lucene documentation or get hold of a copy of Lucene In Action or Hibernate Search in Action.
Writing full-text queries with the Lucene programmatic API is quite complex. It's even more complex to understand the code once written. Besides the inherent API complexity, you have to remember to convert your parameters to their string equivalent as well as make sure to apply the correct analyzer to the right field (a ngram analyzer will for example use several ngrams as the tokens for a given word and should be searched as such).
The Hibernate Search query DSL makes use of a style of API called a fluent API. This API has a few key characteristics:
it has meaningful method names making a succession of operations reads almost like English
it limits the options offered to what makes sense in a given context (thanks to strong typing and IDE autocompletion).
It often uses the chaining method pattern
it's easy to use and even easier to read
Let's see how to use the API. You first need to create a query
builder that is attached to a given indexed entity type. This
QueryBuilder
will know what analyzer to use and
what field bridge to apply. You can create several
QueryBuilder
s (one for each entity type involved
in the root of your query). You get the
QueryBuilder
from the
SearchFactory
.
QueryBuilder mythQB = searchFactory.buildQueryBuilder().forEntity( Myth.class ).get();
You can also override the analyzer used for a given field or fields. This is rarely needed and should be avoided unless you know what you are doing.
QueryBuilder mythQB = searchFactory.buildQueryBuilder()
.forEntity( Myth.class )
.overridesForField("history","stem_analyzer_definition")
.get();
Using the query builder, you can then build queries. It is
important to realize that the end result of a
QueryBuilder
is a Lucene query. For this reason
you can easily mix and match queries generated via Lucene's query parser
or Query
objects you have assembled with the
Lucene programmatic API and use them with the Hibernate Search DSL. Just
in case the DSL is missing some features.
Let's start with the most basic use case - searching for a specific word:
Query luceneQuery = mythQB.keyword().onField("history").matching("storm").createQuery();
keyword()
means that you are trying to
find a specific word. onField()
specifies in
which Lucene field to look. matching()
tells
what to look for. And finally createQuery()
creates the Lucene query object. A lot is going on with this line of
code.
The value storm is passed through the
history
FieldBridge
: it
does not matter here but you will see that it's quite handy when
dealing with numbers or dates.
The field bridge value is then passed to the analyzer used
to index the field history
. This ensures that
the query uses the same term transformation than the indexing
(lower case, n-gram, stemming and so on). If the analyzing process
generates several terms for a given word, a boolean query is used
with the SHOULD
logic (roughly an
OR
logic).
Let's see how you can search a property that is not of type string.
@Entity
@Indexed
public class Myth {
@Field(index = Index.UN_TOKENIZED)
@DateBridge(resolution = Resolution.YEAR)
public Date getCreationDate() { return creationDate; }
public Date setCreationDate(Date creationDate) { this.creationDate = creationDate; }
private Date creationDate;
...
}
Date birthdate = ...;
Query luceneQuery = mythQb.keywork().onField("creationDate").matching(birthdate).createQuery();
In plain Lucene, you would have had to convert the
Date
object to its string representation (in
this case the year).
This conversion works for any object, not just
Date
, provided that the
FieldBridge
has an
objectToString
method (and all built-in
FieldBridge
implementations do).
We make the example a little more advanced now and have a look at how to search a field that uses ngram analyzers. ngram analyzers index succession of ngrams of your words which helps to recover from user typos. For example the 3-grams of the word hibernate are hib, ibe, ber, rna, nat, ate.
@AnalyzerDef(name = "ngram",
tokenizer = @TokenizerDef(factory = StandardTokenizerFactory.class ),
filters = {
@TokenFilterDef(factory = StandardFilterFactory.class),
@TokenFilterDef(factory = LowerCaseFilterFactory.class),
@TokenFilterDef(factory = StopFilterFactory.class),
@TokenFilterDef(factory = NGramFilterFactory.class,
params = {
@Parameter(name = "minGramSize", value = "3"),
@Parameter(name = "maxGramSize", value = "3") } )
}
)
@Entity
@Indexed
public class Myth {
@Field(analyzer=@Analyzer(definition="ngram")
@DateBridge(resolution = Resolution.YEAR)
public String getName() { return name; }
public String setName(Date name) { this.name = name; }
private String name;
...
}
Date birthdate = ...;
Query luceneQuery = mythQb.keyword().onField("name").matching("Sisiphus").createQuery();
The matching word "Sisiphus" will be lower-cased and then split
into 3-grams: sis, isi, sip, phu, hus. Each of these n-gram will be
part of the query. We will then be able to find the Sysiphus myth
(with a y
). All that is transparently done for
you.
If for some reason you do not want a specific field to use the
field bridge or the analyzer you can call the
ignoreAnalyzer()
or
ignoreFieldBridge()
functions
To search for multiple possible words in the same field, simply add them all in the matching clause.
//search document with storm or lightning in their history
Query luceneQuery =
mythQB.keyword().onField("history").matching("storm lightning").createQuery();
To search the same word on multiple fields, use the
onFields
method.
Query luceneQuery = mythQB
.keyword()
.onFields("history","description","name")
.matching("storm")
.createQuery();
Sometimes, one field should be treated differently from another
field even if searching the same term, you can use the
andField()
method for that.
Query luceneQuery = mythQB.keyword()
.onField("history")
.andField("name")
.boostedTo(5)
.andField("description")
.matching("storm")
.createQuery();
In the previous example, only field name is boosted to 5.
To execute a fuzzy query (based on the Levenshtein distance
algorithm), start like a keyword
query and add the
fuzzy flag.
Query luceneQuery = mythQB
.keyword()
.fuzzy()
.withThreshold( .8f )
.withPrefixLength( 1 )
.onField("history")
.matching("starm")
.createQuery();
threshold
is the limit above which two terms
are considering matching. It's a decimal between 0 and 1 and defaults
to 0.5. prefixLength
is the length of the prefix
ignored by the "fuzzyness": while it defaults to 0, a non zero value
is recommended for indexes containing a huge amount of distinct
terms.
You can also execute wildcard queries (queries where some of
parts of the word are unknown). ?
represents a
single character and *
represents any character
sequence. Note that for performance purposes, it is recommended that
the query does not start with either ?
or
*
.
Query luceneQuery = mythQB
.keyword()
.wildcard()
.onField("history")
.matching("sto*")
.createQuery();
Wildcard queries do not apply the analyzer on the matching
terms. Otherwise the risk of *
or
?
being mangled is too high.
So far we have been looking for words or sets of words, you can
also search exact or approximate sentences. Use
phrase()
to do so.
Query luceneQuery = mythQB
.phrase()
.onField("history")
.matching("Thou shalt not kill")
.createQuery();
You can search approximate sentences by adding a slop factor. The slop factor represents the number of other words permitted in the sentence: this works like a within or near operator
Query luceneQuery = mythQB
.phrase()
.withSlop(3)
.onField("history")
.matching("Thou kill")
.createQuery();
After looking at all these query examples for searching for to a given word, it is time to introduce range queries (on numbers, dates, strings etc). A range query searches for a value in between given boundaries (included or not) or for a value below or above a given boundary (included or not).
//look for 0 <= starred < 3
Query luceneQuery = mythQB
.range()
.onField("starred")
.from(0).to(3).excludeLimit()
.createQuery();
//look for myths strictly BC
Date beforeChrist = ...;
Query luceneQuery = mythQB
.range()
.onField("creationDate")
.below(beforeChrist).excludeLimit()
.createQuery();
Finally, you can aggregate (combine) queries to create more complex queries. The following aggregation operators are available:
SHOULD
: the query query should contain
the matching elements of the subquery
MUST
: the query must contain the matching
elements of the subquery
MUST NOT
: the query must not contain the
matching elements of the subquery
The subqueries can be any Lucene query including a boolean query itself. Let's look at a few examples:
//look for popular modern myths that are not urban Date twentiethCentury = ...; Query luceneQuery = mythQB .bool() .must( mythQB.keyword().onField("description").matching("urban").createQuery() ) .not() .must( mythQB.range().onField("starred").above(4).createQuery() ) .must( mythQB .range() .onField("creationDate") .above(twentiethCentury) .createQuery() ) .createQuery(); //look for popular myths that are preferably urban Query luceneQuery = mythQB .bool() .should( mythQB.keyword().onField("description").matching("urban").createQuery() ) .must( mythQB.range().onField("starred").above(4).createQuery() ) .createQuery(); //look for all myths except religious ones Query luceneQuery = mythQB .all() .except( monthQb .keyword() .onField( "description_stem" .matching( "religion" ) .createQuery() ) .createQuery();
We already have seen several query options in the previous example, but lets summarize again the options for query types and fields:
boostedTo
(on query type and on
field): boost the whole query or the specific field to a given
factor
withConstantScore
(on query): all
results matching the query have a constant score equals to the
boost
filteredBy(Filter)
(on query):
filter query results using the Filter
instance
ignoreAnalyzer
(on field): ignore
the analyzer when processing this field
ignoreFieldBridge
(on field):
ignore field bridge when processing this field
Let's check out an example using some of these options
Query luceneQuery = mythQB
.bool()
.should( mythQB.keyword().onField("description").matching("urban").createQuery() )
.should( mythQB
.keyword()
.onField("name")
.boostedTo(3)
.ignoreAnalyzer()
.matching("urban").createQuery() )
.must( mythQB
.range()
.boostedTo(5).withConstantScore()
.onField("starred").above(4).createQuery() )
.createQuery();
As you can see, the Hibernate Search query DSL is an easy to use and easy to read query API and by accepting and producing Lucene queries, you can easily incorporate query types not (yet) supported by the DSL. Please give us feedback!
So far we only covered the process of how to create your Lucene query (see Section 5.1, “Building queries”). However, this is only the first step in the chain of actions. Let's now see how to build the Hibernate Search query from the Lucene query.
Once the Lucene query is built, it needs to be wrapped into an Hibernate Query. If not specified otherwise, the query will be executed against all indexed entities, potentially returning all types of indexed classes.
Example 5.4. Wrapping a Lucene query into a Hibernate Query
FullTextSession fullTextSession = Search.getFullTextSession( session );
org.hibernate.Query fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery );
It is advised, from a performance point of view, to restrict the returned types:
Example 5.5. Filtering the search result by entity type
fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, Customer.class );
// or
fullTextQuery = fullTextSession.createFullTextQuery( luceneQuery, Item.class, Actor.class );
In Example 5.5, “Filtering the search result by entity type” the first
example returns only matching Customer
s, the
second returns matching Actor
s and
Item
s. The type restriction is fully
polymorphic which means that if there are two indexed subclasses
Salesman
and Customer
of
the baseclass Person
, it is possible to just
specify Person.class
in order to filter on
result types.
Out of performance reasons it is recommended to restrict the number of returned objects per query. In fact is a very common use case anyway that the user navigates from one page to an other. The way to define pagination is exactly the way you would define pagination in a plain HQL or Criteria query.
Example 5.6. Defining pagination for a search query
org.hibernate.Query fullTextQuery =
fullTextSession.createFullTextQuery( luceneQuery, Customer.class );
fullTextQuery.setFirstResult(15); //start from the 15th element
fullTextQuery.setMaxResults(10); //return 10 elements
It is still possible to get the total number of matching
elements regardless of the pagination via
fulltextQuery.
getResultSize()
Apache Lucene provides a very flexible and powerful way to sort results. While the default sorting (by relevance) is appropriate most of the time, it can be interesting to sort by one or several other properties. In order to do so set the Lucene Sort object to apply a Lucene sorting strategy.
Example 5.7. Specifying a Lucene Sort
in order to
sort the results
org.hibernate.search.FullTextQuery query = s.createFullTextQuery( query, Book.class );
org.apache.lucene.search.Sort sort = new Sort(new SortField("title"), SortField.STRING); query.setSort(sort);
List results = query.list();
Be aware that fields used for sorting must not be tokenized (see Section 4.1.1.2, “@Field”).
When you restrict the return types to one class, Hibernate Search loads the objects using a single query. It also respects the static fetching strategy defined in your domain model.
It is often useful, however, to refine the fetching strategy for a specific use case.
Example 5.8. Specifying FetchMode
on a
query
Criteria criteria =
s.createCriteria( Book.class ).setFetchMode( "authors", FetchMode.JOIN );
s.createFullTextQuery( luceneQuery ).setCriteriaQuery( criteria );
In this example, the query will return all Books matching the luceneQuery. The authors collection will be loaded from the same query using an SQL outer join.
When defining a criteria query, it is not necessary to restrict the returned entity types when creating the Hibernate Search query from the full text session: the type is guessed from the criteria query itself.
Only fetch mode can be adjusted, refrain from applying any other restriction.
One cannot use setCriteriaQuery
if
more than one entity type is expected to be returned.
For some use cases, returning the domain object (including its associations) is overkill. Only a small subset of the properties is necessary. Hibernate Search allows you to return a subset of properties:
Example 5.9. Using projection instead of returning the full domain object
org.hibernate.search.FullTextQuery query =
s.createFullTextQuery( luceneQuery, Book.class );
query.setProjection( "id", "summary", "body", "mainAuthor.name" );
List results = query.list();
Object[] firstResult = (Object[]) results.get(0);
Integer id = firstResult[0];
String summary = firstResult[1];
String body = firstResult[2];
String authorName = firstResult[3];
Hibernate Search extracts the properties from the Lucene index
and convert them back to their object representation, returning a list
of Object[]
. Projections avoid a potential
database round trip (useful if the query response time is critical).
However, it also has several constraints:
the properties projected must be stored in the index
(@Field(store=Store.YES)
), which increases the
index size
the properties projected must use a
FieldBridge
implementing
org.hibernate.search.bridge.TwoWayFieldBridge
or
org.hibernate.search.bridge.TwoWayStringBridge
,
the latter being the simpler version.
All Hibernate Search built-in types are two-way.
you can only project simple properties of the indexed entity or its embedded associations. This means you cannot project a whole embedded entity.
projection does not work on collections or maps which are
indexed via @IndexedEmbedded
Projection is also useful for another kind of use case. Lucene can provide metadata information about the results. By using some special projection constants, the projection mechanism can retrieve this metadata:
Example 5.10. Using projection in order to retrieve meta data
org.hibernate.search.FullTextQuery query =
s.createFullTextQuery( luceneQuery, Book.class );
query.setProjection(
FullTextQuery.SCORE,
FullTextQuery.THIS,
"mainAuthor.name" );
List results = query.list();
Object[] firstResult = (Object[]) results.get(0);
float score = firstResult[0];
Book book = firstResult[1];
String authorName = firstResult[2];
You can mix and match regular fields and projection constants. Here is the list of the available constants:
FullTextQuery.THIS
: returns the
initialized and managed entity (as a non projected query would
have done).
FullTextQuery.DOCUMENT
: returns the
Lucene Document related to the object projected.
FullTextQuery.OBJECT_CLASS
: returns
the class of the indexed entity.
FullTextQuery.SCORE
: returns the
document score in the query. Scores are handy to compare one
result against an other for a given query but are useless when
comparing the result of different queries.
FullTextQuery.ID
: the id property
value of the projected object.
FullTextQuery.DOCUMENT_ID
: the Lucene
document id. Careful, Lucene document id can change overtime
between two different IndexReader opening (this feature is
experimental).
FullTextQuery.EXPLANATION
: returns
the Lucene Explanation object for the matching object/document in
the given query. Do not use if you retrieve a lot of data. Running
explanation typically is as costly as running the whole Lucene
query per matching element. Make sure you use projection!
You can limit the time a query takes in Hibernate Search in two ways:
raise an exception when the limit is reached
limit to the number of results retrieved when the time limit is raised
You can decide to stop a query if when it takes more than a
predefined amount of time. Note that this is a best effort basis but
if Hibernate Search still has significant work to do and if we are
beyond the time limit, a
QueryTimeoutException
will be raised
(org.hibernate.QueryTimeoutException
or
javax.persistence.QueryTimeoutException
depending on your programmatic API).
To define the limit when using the native Hibernate APIs, use one of the following approaches
Example 5.11. Defining a timeout in query execution
Query luceneQuery = ...; FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class); //define the timeout in seconds query.setTimeout(5); //alternatively, define the timeout in any given time unit query.setTimeout(450, TimeUnit.MILLISECONDS); try { query.list(); } catch (org.hibernate.QueryTimeoutException e) { //do something, too slow }
Likewise getResultSize()
,
iterate()
and
scroll()
honor the timeout but only until
the end of the method call. That simply means that the methods of
Iterable
or the
ScrollableResults
ignore the timeout.
explain()
does not honor the
timeout: this method is used for debug purposes and in particular
to find out why a query is slow
When using JPA, simply use the standard way of limiting query execution time.
Example 5.12. Defining a timeout in query execution
Query luceneQuery = ...; FullTextQuery query = fullTextEM.createFullTextQuery(luceneQuery, User.class); //define the timeout in milliseconds query.setHint( "javax.persistence.query.timeout", 450 ); try { query.getResultList(); } catch (javax.persistence.QueryTimeoutException e) { //do something, too slow }
Remember, this is a best effort approach and does not guarantee to stop exactly on the specified timeout.
Alternatively, you can return the number of results which have already been fetched by the time the limit is reached. Note that only the Lucene part of the query is influenced by this limit. It is possible that, if you retrieve managed object, it takes longer to fetch these objects.
This approach is not compatible with the
setTimeout
approach.
To define this soft limit, use the following approach
Example 5.13. Defining a time limit in query execution
Query luceneQuery = ...; FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class); //define the timeout in seconds query.limitExecutionTimeTo(500, TimeUnit.MILLISECONDS); List results = query.list();
Likewise getResultSize()
,
iterate()
and
scroll()
honor the time limit but only until
the end of the method call. That simply means that the methods of
Iterable
or the
ScrollableResults
ignore the timeout.
You can determine if the results have been partially loaded by
invoking the hasPartialResults
method.
Example 5.14. Determines when a query returns partial results
Query luceneQuery = ...; FullTextQuery query = fullTextSession.createFullTextQuery(luceneQuery, User.class); //define the timeout in seconds query.limitExecutionTimeTo(500, TimeUnit.MILLISECONDS); List results = query.list(); if ( query.hasPartialResults() ) { displayWarningToUser(); }
If you use the JPA API,
limitExecutionTimeTo
and
hasPartialResults
are also available to
you.
This approach is considered experimental
Once the Hibernate Search query is built, executing it is in no way
different than executing a HQL or Criteria query. The same paradigm and
object semantic applies. All the common operations are available:
list()
, uniqueResult()
,
iterate()
,
scroll()
.
If you expect a reasonable number of results (for example using
pagination) and expect to work on all of them,
list()
or
uniqueResult()
are recommended.
list()
work best if the entity
batch-size
is set up properly. Note that Hibernate
Search has to process all Lucene Hits elements (within the pagination)
when using list()
,
uniqueResult()
and
iterate()
.
If you wish to minimize Lucene document loading,
scroll()
is more appropriate. Don't forget to
close the ScrollableResults
object when you're
done, since it keeps Lucene resources. If you expect to use
scroll,
but wish to load objects in batch, you
can use query.setFetchSize()
. When an object is
accessed, and if not already loaded, Hibernate Search will load the next
fetchSize
objects in one pass.
Pagination is a preferred over scrolling.
It is sometime useful to know the total number of matching documents:
for the Google-like feature "1-10 of about 888,000,000"
to implement a fast pagination navigation
to implement a multi step search engine (adding approximation if the restricted query return no or not enough results)
Of course it would be too costly to retrieve all the matching documents. Hibernate Search allows you to retrieve the total number of matching documents regardless of the pagination parameters. Even more interesting, you can retrieve the number of matching elements without triggering a single object load.
Example 5.15. Determining the result size of a query
org.hibernate.search.FullTextQuery query = s.createFullTextQuery( luceneQuery, Book.class ); //return the number of matching books without loading a single one assert 3245 == query.getResultSize(); org.hibernate.search.FullTextQuery query = s.createFullTextQuery( luceneQuery, Book.class ); query.setMaxResult(10); List results = query.list(); //return the total number of matching books regardless of pagination assert 3245 == query.getResultSize();
Like Google, the number of results is approximative if the index is not fully up-to-date with the database (asynchronous cluster for example).
As seen in Section 5.1.3.5, “Projection” projection results are
returns as Object
arrays. This data structure is
not always matching the application needs. In this cases It is possible
to apply a ResultTransformer
which post query
execution can build the needed data structure:
Example 5.16. Using ResultTransformer in conjunction with projections
org.hibernate.search.FullTextQuery query =
s.createFullTextQuery( luceneQuery, Book.class );
query.setProjection( "title", "mainAuthor.name" );
query.setResultTransformer(
new StaticAliasToBeanResultTransformer(
BookView.class,
"title",
"author" )
);
List<BookView> results = (List<BookView>) query.list();
for(BookView view : results) {
log.info( "Book: " + view.getTitle() + ", " + view.getAuthor() );
}
Examples of ResultTransformer
implementations can be found in the Hibernate Core codebase.
You will find yourself sometimes puzzled by a result showing up in
a query or a result not showing up in a query. Luke is a great tool to
understand those mysteries. However, Hibernate Search also gives you
access to the Lucene Explanation
object for a
given result (in a given query). This class is considered fairly
advanced to Lucene users but can provide a good understanding of the
scoring of an object. You have two ways to access the Explanation object
for a given result:
Use the fullTextQuery.explain(int)
method
Use projection
The first approach takes a document id as a parameter and return
the Explanation object. The document id can be retrieved using
projection and the FullTextQuery.DOCUMENT_ID
constant.
The Document id has nothing to do with the entity id. Do not mess up these two notions.
The second approach let's you project the
Explanation
object using the
FullTextQuery.EXPLANATION
constant.
Example 5.17. Retrieving the Lucene Explanation object using projection
FullTextQuery ftQuery = s.createFullTextQuery( luceneQuery, Dvd.class )
.setProjection(
FullTextQuery.DOCUMENT_ID,
FullTextQuery.EXPLANATION,
FullTextQuery.THIS );
@SuppressWarnings("unchecked") List<Object[]> results = ftQuery.list();
for (Object[] result : results) {
Explanation e = (Explanation) result[1];
display( e.toString() );
}
Be careful, building the explanation object is quite expensive, it is roughly as expensive as running the Lucene query again. Don't do it if you don't need the object
Apache Lucene has a powerful feature that allows to filter query results according to a custom filtering process. This is a very powerful way to apply additional data restrictions, especially since filters can be cached and reused. Some interesting use cases are:
security
temporal data (eg. view only last month's data)
population filter (eg. search limited to a given category)
and many more
Hibernate Search pushes the concept further by introducing the notion of parameterizable named filters which are transparently cached. For people familiar with the notion of Hibernate Core filters, the API is very similar:
Example 5.18. Enabling fulltext filters for a given query
fullTextQuery = s.createFullTextQuery( query, Driver.class ); fullTextQuery.enableFullTextFilter("bestDriver"); fullTextQuery.enableFullTextFilter("security").setParameter( "login", "andre" ); fullTextQuery.list(); //returns only best drivers where andre has credentials
In this example we enabled two filters on top of the query. You can enable (or disable) as many filters as you like.
Declaring filters is done through the
@FullTextFilterDef
annotation. This annotation can
be on any @Indexed
entity regardless of the query the
filter is later applied to. This implies that filter definitions are
global and their names must be unique. A
SearchException
is thrown in case two different
@FullTextFilterDef
annotations with the same name
are defined. Each named filter has to specify its actual filter
implementation.
Example 5.19. Defining and implementing a Filter
@Entity @Indexed @FullTextFilterDefs( { @FullTextFilterDef(name = "bestDriver", impl = BestDriversFilter.class), @FullTextFilterDef(name = "security", impl = SecurityFilterFactory.class) }) public class Driver { ... }
public class BestDriversFilter extends org.apache.lucene.search.Filter {
public DocIdSet getDocIdSet(IndexReader reader) throws IOException {
OpenBitSet bitSet = new OpenBitSet( reader.maxDoc() );
TermDocs termDocs = reader.termDocs( new Term( "score", "5" ) );
while ( termDocs.next() ) {
bitSet.set( termDocs.doc() );
}
return bitSet;
}
}
BestDriversFilter
is an example of a simple
Lucene filter which reduces the result set to drivers whose score is 5. In
this example the specified filter implements the
org.apache.lucene.search.Filter
directly and contains a
no-arg constructor.
If your Filter creation requires additional steps or if the filter you want to use does not have a no-arg constructor, you can use the factory pattern:
Example 5.20. Creating a filter using the factory pattern
@Entity
@Indexed
@FullTextFilterDef(name = "bestDriver", impl = BestDriversFilterFactory.class)
public class Driver { ... }
public class BestDriversFilterFactory {
@Factory
public Filter getFilter() {
//some additional steps to cache the filter results per IndexReader
Filter bestDriversFilter = new BestDriversFilter();
return new CachingWrapperFilter(bestDriversFilter);
}
}
Hibernate Search will look for a @Factory
annotated method and use it to build the filter instance. The factory must
have a no-arg constructor.
Named filters come in handy where parameters have to be passed to the filter. For example a security filter might want to know which security level you want to apply:
Example 5.21. Passing parameters to a defined filter
fullTextQuery = s.createFullTextQuery( query, Driver.class );
fullTextQuery.enableFullTextFilter("security").setParameter( "level", 5 );
Each parameter name should have an associated setter on either the filter or filter factory of the targeted named filter definition.
Example 5.22. Using parameters in the actual filter implementation
public class SecurityFilterFactory { private Integer level; /** * injected parameter */ public void setLevel(Integer level) { this.level = level; } @Key public FilterKey getKey() { StandardFilterKey key = new StandardFilterKey(); key.addParameter( level ); return key; } @Factory public Filter getFilter() { Query query = new TermQuery( new Term("level", level.toString() ) ); return new CachingWrapperFilter( new QueryWrapperFilter(query) ); } }
Note the method annotated @Key
returning a
FilterKey
object. The returned object has a special
contract: the key object must implement equals()
/ hashCode()
so that 2 keys are equal if and only
if the given Filter
types are the same and the set
of parameters are the same. In other words, 2 filter keys are equal if and
only if the filters from which the keys are generated can be interchanged.
The key object is used as a key in the cache mechanism.
@Key
methods are needed only if:
you enabled the filter caching system (enabled by default)
your filter has parameters
In most cases, using the StandardFilterKey
implementation will be good enough. It delegates the
equals()
/ hashCode()
implementation to each of the parameters equals and hashcode
methods.
As mentioned before the defined filters are per default cached and
the cache uses a combination of hard and soft references to allow disposal
of memory when needed. The hard reference cache keeps track of the most
recently used filters and transforms the ones least used to
SoftReferences
when needed. Once the limit of the
hard reference cache is reached additional filters are cached as
SoftReferences
. To adjust the size of the hard
reference cache, use
hibernate.search.filter.cache_strategy.size
(defaults
to 128). For advanced use of filter caching, you can implement your own
FilterCachingStrategy
. The classname is defined by
hibernate.search.filter.cache_strategy
.
This filter caching mechanism should not be confused with caching
the actual filter results. In Lucene it is common practice to wrap filters
using the IndexReader
around a
CachingWrapperFilter.
The wrapper will cache the
DocIdSet
returned from the
getDocIdSet(IndexReader reader)
method to avoid
expensive recomputation. It is important to mention that the computed
DocIdSet
is only cachable for the same
IndexReader
instance, because the reader
effectively represents the state of the index at the moment it was opened.
The document list cannot change within an opened
IndexReader
. A different/new
IndexReader
instance, however, works potentially on a
different set of Document
s (either from a different
index or simply because the index has changed), hence the cached
DocIdSet
has to be recomputed.
Hibernate Search also helps with this aspect of caching. Per default
the cache
flag of @FullTextFilterDef
is set to
FilterCacheModeType.INSTANCE_AND_DOCIDSETRESULTS
which
will automatically cache the filter instance as well as wrap the specified
filter around a Hibernate specific implementation of
CachingWrapperFilter
(org.hibernate.search.filter.CachingWrapperFilter
).
In contrast to Lucene's version of this class
SoftReference
s are used together with a hard
reference count (see discussion about filter cache). The hard reference
count can be adjusted using
hibernate.search.filter.cache_docidresults.size
(defaults to 5). The wrapping behaviour can be controlled using the
@FullTextFilterDef.cache
parameter. There are three
different values for this parameter:
Value | Definition |
---|---|
FilterCacheModeType.NONE | No filter instance and no result is cached by Hibernate Search. For every filter call, a new filter instance is created. This setting might be useful for rapidly changing data sets or heavily memory constrained environments. |
FilterCacheModeType.INSTANCE_ONLY | The filter instance is cached and reused across
concurrent Filter.getDocIdSet() calls.
DocIdSet results are not cached. This
setting is useful when a filter uses its own specific caching
mechanism or the filter results change dynamically due to
application specific events making
DocIdSet caching in both cases
unnecessary. |
FilterCacheModeType.INSTANCE_AND_DOCIDSETRESULTS | Both the filter instance and the
DocIdSet results are cached. This is the
default value. |
Last but not least - why should filters be cached? There are two areas where filter caching shines:
the system does not update the targeted entity index often (in other words, the IndexReader is reused a lot)
the Filter's DocIdSet is expensive to compute (compared to the time spent to execute the query)
It is possible, in a sharded environment to execute queries on a subset of the available shards. This can be done in two steps:
create a sharding strategy that does select a subset of
DirectoryProvider
s depending on sone filter
configuration
activate the proper filter at query time
Let's first look at an example of sharding strategy that query on a specific customer shard if the customer filter is activated.
public class CustomerShardingStrategy implements IndexShardingStrategy {
// stored DirectoryProviders in a array indexed by customerID
private DirectoryProvider<?>[] providers;
public void initialize(Properties properties, DirectoryProvider<?>[] providers) {
this.providers = providers;
}
public DirectoryProvider<?>[] getDirectoryProvidersForAllShards() {
return providers;
}
public DirectoryProvider<?> getDirectoryProviderForAddition(Class<?> entity, Serializable id, String idInString, Document document) {
Integer customerID = Integer.parseInt(document.getField("customerID").stringValue());
return providers[customerID];
}
public DirectoryProvider<?>[] getDirectoryProvidersForDeletion(Class<?> entity, Serializable id, String idInString) {
return getDirectoryProvidersForAllShards();
}
/**
* Optimization; don't search ALL shards and union the results; in this case, we
* can be certain that all the data for a particular customer Filter is in a single
* shard; simply return that shard by customerID.
*/
public DirectoryProvider<?>[] getDirectoryProvidersForQuery(FullTextFilterImplementor[] filters) {
FFullTextFilter filter = getCustomerFilter(filters, "customer");
if (filter == null) {
return getDirectoryProvidersForAllShards();
}
else {
return new DirectoryProvider[] { providers[Integer.parseInt(filter.getParameter("customerID").toString())] };
}
}
private FullTextFilter getFilter(FullTextFilterImplementor[] filters, String name) {
for (FullTextFilterImplementor filter: filters) {
if (filter.getName().equals(name)) return filter;
}
return null;
}
}
In this example, if the filter named customer
is present, we make sure to only use the shard dedicated to this
customer. Otherwise, we return all shards. A given Sharding strategy can
react to one or more filters and depends on their parameters.
The second step is simply to activate the filter at query time.
While the filter can be a regular filter (as defined in Section 5.3, “Filters”) which also filters Lucene results after the
query, you can make use of a special filter that will only be passed to
the sharding strategy and otherwise ignored for the rest of the query.
Simply use the ShardSensitiveOnlyFilter
class
when declaring your filter.
@Entity @Indexed @FullTextFilterDef(name="customer", impl=ShardSensitiveOnlyFilter.class) public class Customer { ... } FullTextQuery query = ftEm.createFullTextQuery(luceneQuery, Customer.class); query.enableFulltextFilter("customer").setParameter("CustomerID", 5); @SuppressWarnings("unchecked") List<Customer> results = query.getResultList();
Note that by using the
ShardSensitiveOnlyFilter
, you do not have to
implement any Lucene filter. Using filters and sharding strategy
reacting to these filters is recommended to speed up queries in a
sharded environment.
Query performance depends on several criteria:
the Lucene query itself: read the literature on this subject
the number of object loaded: use pagination (always ;-) ) or index projection (if needed)
the way Hibernate Search interacts with the Lucene readers: defines the appropriate Reader strategy.