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 5.14. 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 5.15. 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 5.16. 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. For people familiar with JBoss Seam, this is
similar to the component factory pattern, but the annotation is
different!
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 5.17. 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 5.18. 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)