By default, Hibernate uses lazy select fetching for collections and lazy proxy fetching for single-valued associations. These defaults make sense for most associations in the majority of applications.

If you set hibernate.default_batch_fetch_size, Hibernate will use the batch fetch optimization for lazy fetching. This optimization can also be enabled at a more granular level.

Please be aware that access to a lazy association outside of the context of an open Hibernate session will result in an exception. For example:

s = sessions.openSession();
Transaction tx = s.beginTransaction();
            
User u = (User) s.createQuery("from User u where u.name=:userName")
    .setString("userName", userName).uniqueResult();
Map permissions = u.getPermissions();

tx.commit();
s.close();

Integer accessLevel = (Integer) permissions.get("accounts");  // Error!

Since the permissions collection was not initialized when the Session was closed, the collection will not be able to load its state. Hibernate does not support lazy initialization for detached objects. This can be fixed by moving the code that reads from the collection to just before the transaction is committed.

Alternatively, you can use a non-lazy collection or association, by specifying lazy="false" for the association mapping. However, it is intended that lazy initialization be used for almost all collections and associations. If you define too many non-lazy associations in your object model, Hibernate will fetch the entire database into memory in every transaction.

On the other hand, you can use join fetching, which is non-lazy by nature, instead of select fetching in a particular transaction. We will now explain how to customize the fetching strategy. In Hibernate, the mechanisms for choosing a fetch strategy are identical for single-valued associations and collections.

Select fetching (the default) is extremely vulnerable to N+1 selects problems, so we might want to enable join fetching in the mapping document:

<set name="permissions"
            fetch="join">
    <key column="userId"/>
    <one-to-many class="Permission"/>
</set

<many-to-one name="mother" class="Cat" fetch="join"/>

The fetch strategy defined in the mapping document affects:

Irrespective of the fetching strategy you use, the defined non-lazy graph is guaranteed to be loaded into memory. This might, however, result in several immediate selects being used to execute a particular HQL query.

Usually, the mapping document is not used to customize fetching. Instead, we keep the default behavior, and override it for a particular transaction, using left join fetch in HQL. This tells Hibernate to fetch the association eagerly in the first select, using an outer join. In the Criteria query API, you would use setFetchMode(FetchMode.JOIN).

If you want to change the fetching strategy used by get() or load(), you can use a Criteria query. For example:

User user = (User) session.createCriteria(User.class)
                .setFetchMode("permissions", FetchMode.JOIN)
                .add( Restrictions.idEq(userId) )
                .uniqueResult();

This is Hibernate's equivalent of what some ORM solutions call a "fetch plan".

A completely different approach to problems with N+1 selects is to use the second-level cache.

Lazy fetching for collections is implemented using Hibernate's own implementation of persistent collections. However, a different mechanism is needed for lazy behavior in single-ended associations. The target entity of the association must be proxied. Hibernate implements lazy initializing proxies for persistent objects using runtime bytecode enhancement which is accessed via the bytecode provider.

At startup, Hibernate generates proxies by default for all persistent classes and uses them to enable lazy fetching of many-to-one and one-to-one associations.

The mapping file may declare an interface to use as the proxy interface for that class, with the proxy attribute. By default, Hibernate uses a subclass of the class. The proxied class must implement a default constructor with at least package visibility. This constructor is recommended for all persistent classes.

There are potential problems to note when extending this approach to polymorphic classes.For example:

<class name="Cat" proxy="Cat">
    ......
    <subclass name="DomesticCat">
        .....
    </subclass>
</class>

Firstly, instances of Cat will never be castable to DomesticCat, even if the underlying instance is an instance of DomesticCat:

Cat cat = (Cat) session.load(Cat.class, id);  // instantiate a proxy (does not hit the db)
if ( cat.isDomesticCat() ) {                  // hit the db to initialize the proxy
    DomesticCat dc = (DomesticCat) cat;       // Error!
    ....
}

Secondly, it is possible to break proxy ==:

Cat cat = (Cat) session.load(Cat.class, id);            // instantiate a Cat proxy
DomesticCat dc = 
        (DomesticCat) session.load(DomesticCat.class, id);  // acquire new DomesticCat proxy!
System.out.println(cat==dc);                            // false

However, the situation is not quite as bad as it looks. Even though we now have two references to different proxy objects, the underlying instance will still be the same object:

cat.setWeight(11.0);  // hit the db to initialize the proxy
System.out.println( dc.getWeight() );  // 11.0

Third, you cannot use a bytecode provider generated proxy for a final class or a class with any final methods.

Finally, if your persistent object acquires any resources upon instantiation (e.g. in initializers or default constructor), then those resources will also be acquired by the proxy. The proxy class is an actual subclass of the persistent class.

These problems are all due to fundamental limitations in Java's single inheritance model. To avoid these problems your persistent classes must each implement an interface that declares its business methods. You should specify these interfaces in the mapping file where CatImpl implements the interface Cat and DomesticCatImpl implements the interface DomesticCat. For example:

<class name="CatImpl" proxy="Cat">
    ......
    <subclass name="DomesticCatImpl" proxy="DomesticCat">
        .....
    </subclass>
</class>

Then proxies for instances of Cat and DomesticCat can be returned by load() or iterate().

Cat cat = (Cat) session.load(CatImpl.class, catid);
Iterator iter = session.createQuery("from CatImpl as cat where cat.name='fritz'").iterate();
Cat fritz = (Cat) iter.next();

Relationships are also lazily initialized. This means you must declare any properties to be of type Cat, not CatImpl.

Certain operations do not require proxy initialization:

Hibernate will detect persistent classes that override equals() or hashCode().

By choosing lazy="no-proxy" instead of the default lazy="proxy", you can avoid problems associated with typecasting. However, buildtime bytecode instrumentation is required, and all operations will result in immediate proxy initialization.

A LazyInitializationException will be thrown by Hibernate if an uninitialized collection or proxy is accessed outside of the scope of the Session, i.e., when the entity owning the collection or having the reference to the proxy is in the detached state.

Sometimes a proxy or collection needs to be initialized before closing the Session. You can force initialization by calling cat.getSex() or cat.getKittens().size(), for example. However, this can be confusing to readers of the code and it is not convenient for generic code.

The static methods Hibernate.initialize() and Hibernate.isInitialized(), provide the application with a convenient way of working with lazily initialized collections or proxies. Hibernate.initialize(cat) will force the initialization of a proxy, cat, as long as its Session is still open. Hibernate.initialize( cat.getKittens() ) has a similar effect for the collection of kittens.

Another option is to keep the Session open until all required collections and proxies have been loaded. In some application architectures, particularly where the code that accesses data using Hibernate, and the code that uses it are in different application layers or different physical processes, it can be a problem to ensure that the Session is open when a collection is initialized. There are two basic ways to deal with this issue:

Sometimes you do not want to initialize a large collection, but still need some information about it, like its size, for example, or a subset of the data.

You can use a collection filter to get the size of a collection without initializing it:

( (Integer) s.createFilter( collection, "select count(*)" ).list().get(0) ).intValue()

The createFilter() method is also used to efficiently retrieve subsets of a collection without needing to initialize the whole collection:

s.createFilter( lazyCollection, "").setFirstResult(0).setMaxResults(10).list();

Using batch fetching, Hibernate can load several uninitialized proxies if one proxy is accessed. Batch fetching is an optimization of the lazy select fetching strategy. There are two ways you can configure batch fetching: on the class level and the collection level.

Batch fetching for classes/entities is easier to understand. Consider the following example: at runtime you have 25 Cat instances loaded in a Session, and each Cat has a reference to its owner, a Person. The Person class is mapped with a proxy, lazy="true". If you now iterate through all cats and call getOwner() on each, Hibernate will, by default, execute 25 SELECT statements to retrieve the proxied owners. You can tune this behavior by specifying a batch-size in the mapping of Person:

<class name="Person" batch-size="10">...</class>

With this batch-size specified, Hibernate will now execute queries on demand when need to access the uninitialized proxy, as above, but the difference is that instead of querying the exactly proxy entity that being accessed, it will query more Person's owner at once, so, when accessing other person's owner, it may already been initialized by this batch fetch with only a few ( much less than 25) queries will be executed.

This behavior is controlled by the batch-size and batch fetch style configuration. The batch fetch style configuration ( hibernate.batch_fetch_style ) is a new performance improvement since 4.2.0, there are 3 different strategies provided, which is legacy, padded and dynamic.

You can also enable batch fetching of collections. For example, if each Person has a lazy collection of Cats, and 10 persons are currently loaded in the Session, iterating through all persons will generate 10 SELECTs, one for every call to getCats(). If you enable batch fetching for the cats collection in the mapping of Person, Hibernate can pre-fetch collections:

<class name="Person">
    <set name="cats" batch-size="3">
        ...
    </set>
</class>

For example, with a batch-size of 3 and using legacy batch style, Hibernate will load 3, 3, 3, 1 collections in four SELECTs. Again, the value of the attribute depends on the expected number of uninitialized collections in a particular Session.

Batch fetching of collections is particularly useful if you have a nested tree of items, i.e. the typical bill-of-materials pattern. However, a nested set or a materialized path might be a better option for read-mostly trees.

If one lazy collection or single-valued proxy has to be fetched, Hibernate will load all of them, re-running the original query in a subselect. This works in the same way as batch-fetching but without the piecemeal loading.

Another way to affect the fetching strategy for loading associated objects is through something called a fetch profile, which is a named configuration associated with the org.hibernate.SessionFactory but enabled, by name, on the org.hibernate.Session. Once enabled on a org.hibernate.Session, the fetch profile will be in affect for that org.hibernate.Session until it is explicitly disabled.

So what does that mean? Well lets explain that by way of an example which show the different available approaches to configure a fetch profile:

Now normally when you get a reference to a particular customer, that customer's set of orders will be lazy meaning we will not yet have loaded those orders from the database. Normally this is a good thing. Now lets say that you have a certain use case where it is more efficient to load the customer and their orders together. One way certainly is to use "dynamic fetching" strategies via an HQL or criteria queries. But another option is to use a fetch profile to achieve that. The following code will load both the customer and their orders:

Hibernate supports the lazy fetching of individual properties. This optimization technique is also known as fetch groups. Please note that this is mostly a marketing feature; optimizing row reads is much more important than optimization of column reads. However, only loading some properties of a class could be useful in extreme cases. For example, when legacy tables have hundreds of columns and the data model cannot be improved.

To enable lazy property loading, set the lazy attribute on your particular property mappings:

<class name="Document">
       <id name="id">
        <generator class="native"/>
    </id>
    <property name="name" not-null="true" length="50"/>
    <property name="summary" not-null="true" length="200" lazy="true"/>
    <property name="text" not-null="true" length="2000" lazy="true"/>
</class>

Lazy property loading requires buildtime bytecode instrumentation. If your persistent classes are not enhanced, Hibernate will ignore lazy property settings and return to immediate fetching.

For bytecode instrumentation, use the following Ant task:

<target name="instrument" depends="compile">
    <taskdef name="instrument" classname="org.hibernate.tool.instrument.InstrumentTask">
        <classpath path="${jar.path}"/>
        <classpath path="${classes.dir}"/>
        <classpath refxml:id="lib.class.path"/>
    </taskdef>

    <instrument verbose="true">
        <fileset dir="${testclasses.dir}/org/hibernate/auction/model">
            <include name="*.class"/>
        </fileset>
    </instrument>
</target>

A different way of avoiding unnecessary column reads, at least for read-only transactions, is to use the projection features of HQL or Criteria queries. This avoids the need for buildtime bytecode processing and is certainly a preferred solution.

You can force the usual eager fetching of properties using fetch all properties in HQL.

As we have done in previous chapters we are looking at the two different possibiltites to configure caching. First configuration via annotations and then via Hibernate mapping files.

By default, entities are not part of the second level cache and we recommend you to stick to this setting. However, you can override this by setting the shared-cache-mode element in your persistence.xml file or by using the javax.persistence.sharedCache.mode property in your configuration. The following values are possible:

The cache concurrency strategy used by default can be set globaly via the hibernate.cache.default_cache_concurrency_strategy configuration property. The values for this property are:

Hibernate also let's you cache the content of a collection or the identifiers if the collection contains other entities. Use the @Cache annotation on the collection property.

Alternatively to <cache>, you can use <class-cache> and <collection-cache> elements in hibernate.cfg.xml.

Let's now have a closer look at the different usage strategies

If your application needs to read, but not modify, instances of a persistent class, a read-only cache can be used. This is the simplest and optimal performing strategy. It is even safe for use in a cluster.

If the application needs to update data, a read-write cache might be appropriate. This cache strategy should never be used if serializable transaction isolation level is required. If the cache is used in a JTA environment, you must specify the property hibernate.transaction.manager_lookup_class and naming a strategy for obtaining the JTA TransactionManager. In other environments, you should ensure that the transaction is completed when Session.close() or Session.disconnect() is called. If you want to use this strategy in a cluster, you should ensure that the underlying cache implementation supports locking. The built-in cache providers do not support locking.

If the application only occasionally needs to update data (i.e. if it is extremely unlikely that two transactions would try to update the same item simultaneously), and strict transaction isolation is not required, a nonstrict-read-write cache might be appropriate. If the cache is used in a JTA environment, you must specify hibernate.transaction.manager_lookup_class. In other environments, you should ensure that the transaction is completed when Session.close() or Session.disconnect() is called.

The transactional cache strategy provides support for fully transactional cache providers such as JBoss TreeCache. Such a cache can only be used in a JTA environment and you must specify hibernate.transaction.manager_lookup_class.

The following table shows which providers are compatible with which concurrency strategies.

Caching of query results introduces some overhead in terms of your applications normal transactional processing. For example, if you cache results of a query against Person Hibernate will need to keep track of when those results should be invalidated because changes have been committed against Person. That, coupled with the fact that most applications simply gain no benefit from caching query results, leads Hibernate to disable caching of query results by default. To use query caching, you will first need to enable the query cache:

hibernate.cache.use_query_cache true

This setting creates two new cache regions:

As mentioned above, most queries do not benefit from caching or their results. So by default, individual queries are not cached even after enabling query caching. To enable results caching for a particular query, call org.hibernate.Query.setCacheable(true). This call allows the query to look for existing cache results or add its results to the cache when it is executed.

If you require fine-grained control over query cache expiration policies, you can specify a named cache region for a particular query by calling Query.setCacheRegion().

List blogs = sess.createQuery("from Blog blog where blog.blogger = :blogger")
        .setEntity("blogger", blogger)
        .setMaxResults(15)
        .setCacheable(true)
        .setCacheRegion("frontpages")
        .list();

If you want to force the query cache to refresh one of its regions (disregard any cached results it finds there) you can use org.hibernate.Query.setCacheMode(CacheMode.REFRESH). In conjunction with the region you have defined for the given query, Hibernate will selectively force the results cached in that particular region to be refreshed. This is particularly useful in cases where underlying data may have been updated via a separate process and is a far more efficient alternative to bulk eviction of the region via org.hibernate.SessionFactory.evictQueries().

An entity can choose to implement this interface by itself, then it is the entity's responsibility to maintain the bi-association that essentially provides access to information about an instance's association to a Session/EntityManager. More info about org.hibernate.engine.spi.ManagedEntity please find from its javadoc.

Sometimes, you probably don't want to implement an intrusive interface, maybe due to portable concern, which is fine and Hibernate will take care of this internally with a wrapper class which implements that interface, and also an internal cache that maps this entity instance and the wrapper together.

Obviously, this is the easiest way to choose, since it doesn't require any change of the project source code, but it also cost more memory and CUP usage, comparing to the first one.

Besides the above two approaches, Hibernate also provides a third choice which is build time bytecode enhancement. Applications can use enhanced entity classes, annotated with either javax.persistence.Entity or composite javax.persistence.Embeddable.

Hibernate defines three basic kinds of collections:

This classification distinguishes the various table and foreign key relationships but does not tell us quite everything we need to know about the relational model. To fully understand the relational structure and performance characteristics, we must also consider the structure of the primary key that is used by Hibernate to update or delete collection rows. This suggests the following classification:

All indexed collections (maps, lists, and arrays) have a primary key consisting of the <key> and <index> columns. In this case, collection updates are extremely efficient. The primary key can be efficiently indexed and a particular row can be efficiently located when Hibernate tries to update or delete it.

Sets have a primary key consisting of <key> and element columns. This can be less efficient for some types of collection element, particularly composite elements or large text or binary fields, as the database may not be able to index a complex primary key as efficiently. However, for one-to-many or many-to-many associations, particularly in the case of synthetic identifiers, it is likely to be just as efficient. If you want SchemaExport to actually create the primary key of a <set>, you must declare all columns as not-null="true".

<idbag> mappings define a surrogate key, so they are efficient to update. In fact, they are the best case.

Bags are the worst case since they permit duplicate element values and, as they have no index column, no primary key can be defined. Hibernate has no way of distinguishing between duplicate rows. Hibernate resolves this problem by completely removing in a single DELETE and recreating the collection whenever it changes. This can be inefficient.

For a one-to-many association, the "primary key" may not be the physical primary key of the database table. Even in this case, the above classification is still useful. It reflects how Hibernate "locates" individual rows of the collection.

From the discussion above, it should be clear that indexed collections and sets allow the most efficient operation in terms of adding, removing and updating elements.

There is, arguably, one more advantage that indexed collections have over sets for many-to-many associations or collections of values. Because of the structure of a Set, Hibernate does not UPDATE a row when an element is "changed". Changes to a Set always work via INSERT and DELETE of individual rows. Once again, this consideration does not apply to one-to-many associations.

After observing that arrays cannot be lazy, you can conclude that lists, maps and idbags are the most performant (non-inverse) collection types, with sets not far behind. You can expect sets to be the most common kind of collection in Hibernate applications. This is because the "set" semantics are most natural in the relational model.

However, in well-designed Hibernate domain models, most collections are in fact one-to-many associations with inverse="true". For these associations, the update is handled by the many-to-one end of the association, and so considerations of collection update performance simply do not apply.

There is a particular case, however, in which bags, and also lists, are much more performant than sets. For a collection with inverse="true", the standard bidirectional one-to-many relationship idiom, for example, we can add elements to a bag or list without needing to initialize (fetch) the bag elements. This is because, unlike a set, Collection.add() or Collection.addAll() must always return true for a bag or List. This can make the following common code much faster:

Parent p = (Parent) sess.load(Parent.class, id);
Child c = new Child();
c.setParent(p);
p.getChildren().add(c);  //no need to fetch the collection!
sess.flush();

Deleting collection elements one by one can sometimes be extremely inefficient. Hibernate knows not to do that in the case of an newly-empty collection (if you called list.clear(), for example). In this case, Hibernate will issue a single DELETE.

Suppose you added a single element to a collection of size twenty and then remove two elements. Hibernate will issue one INSERT statement and two DELETE statements, unless the collection is a bag. This is certainly desirable.

However, suppose that we remove eighteen elements, leaving two and then add thee new elements. There are two possible ways to proceed

Hibernate cannot know that the second option is probably quicker. It would probably be undesirable for Hibernate to be that intuitive as such behavior might confuse database triggers, etc.

Fortunately, you can force this behavior (i.e. the second strategy) at any time by discarding (i.e. dereferencing) the original collection and returning a newly instantiated collection with all the current elements.

One-shot-delete does not apply to collections mapped inverse="true".

You can access SessionFactory metrics in two ways. Your first option is to call sessionFactory.getStatistics() and read or display the Statistics yourself.

Hibernate can also use JMX to publish metrics if you enable the StatisticsService MBean. You can enable a single MBean for all your SessionFactory or one per factory. See the following code for minimalistic configuration examples:

// MBean service registration for a specific SessionFactory
Hashtable tb = new Hashtable();
tb.put("type", "statistics");
tb.put("sessionFactory", "myFinancialApp");
ObjectName on = new ObjectName("hibernate", tb); // MBean object name

StatisticsService stats = new StatisticsService(); // MBean implementation
stats.setSessionFactory(sessionFactory); // Bind the stats to a SessionFactory
server.registerMBean(stats, on); // Register the Mbean on the server

// MBean service registration for all SessionFactory's
Hashtable tb = new Hashtable();
tb.put("type", "statistics");
tb.put("sessionFactory", "all");
ObjectName on = new ObjectName("hibernate", tb); // MBean object name

StatisticsService stats = new StatisticsService(); // MBean implementation
server.registerMBean(stats, on); // Register the MBean on the server

You can activate and deactivate the monitoring for a SessionFactory:

Statistics can be reset programmatically using the clear() method. A summary can be sent to a logger (info level) using the logSummary() method.

Hibernate provides a number of metrics, from basic information to more specialized information that is only relevant in certain scenarios. All available counters are described in the Statistics interface API, in three categories:

For example, you can check the cache hit, miss, and put ratio of entities, collections and queries, and the average time a query needs. Be aware that the number of milliseconds is subject to approximation in Java. Hibernate is tied to the JVM precision and on some platforms this might only be accurate to 10 seconds.

Simple getters are used to access the global metrics (i.e. not tied to a particular entity, collection, cache region, etc.). You can access the metrics of a particular entity, collection or cache region through its name, and through its HQL or SQL representation for queries. Please refer to the Statistics, EntityStatistics, CollectionStatistics, SecondLevelCacheStatistics, and QueryStatistics API Javadoc for more information. The following code is a simple example:

Statistics stats = HibernateUtil.sessionFactory.getStatistics();

double queryCacheHitCount  = stats.getQueryCacheHitCount();
double queryCacheMissCount = stats.getQueryCacheMissCount();
double queryCacheHitRatio =
  queryCacheHitCount / (queryCacheHitCount + queryCacheMissCount);

log.info("Query Hit ratio:" + queryCacheHitRatio);

EntityStatistics entityStats =
  stats.getEntityStatistics( Cat.class.getName() );
long changes =
        entityStats.getInsertCount()
        + entityStats.getUpdateCount()
        + entityStats.getDeleteCount();
log.info(Cat.class.getName() + " changed " + changes + "times"  );

You can work on all entities, collections, queries and region caches, by retrieving the list of names of entities, collections, queries and region caches using the following methods: getQueries(), getEntityNames(), getCollectionRoleNames(), and getSecondLevelCacheRegionNames().