We've already spent quite some time talking about collections. In this section we will highlight a couple more issues about how collections behave at runtime. Hibernate defines three basic kinds of collections: collections of values one to many associations many to many associations 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: indexed collections sets bags All indexed collections (maps, lists, arrays) have a primary key consisting of the <key> and <index> columns. In this case collection updates are usually extremely efficient - the primary key may be efficiently indexed and a particular row may be efficiently located when Hibernate tries to update or delete it. Sets have a primary key consisting of <key> and element columns. This may be less efficient for some types of collection element, particularly composite elements or large text or binary fields; the database may not be able to index a complex primary key as efficently. On the other hand, for one to many or many to many associations, particularly in the case of synthetic identifiers, it is likely to be just as efficient. (Side-note: if you want SchemaExport to actually create the primary key of a <set> for you, you must declare all columns as not-null="true".) <idbag> mappings define a surrogate key, so they are always very efficient to update. In fact, they are the best case. Bags are the worst case. Since a bag permits duplicate element values and has no index column, no primary key may 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 might be very inefficient. Note that for a one-to-many association, the "primary key" may not be the physical primary key of the database table - but even in this case, the above classification is still useful. (It still reflects how Hibernate "locates" individual rows of the collection.) From the discussion above, it should be clear that indexed collections and (usually) 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 doesn't ever 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, we would conclude that lists, maps and idbags are the most performant (non-inverse) collection types, with sets not far behind. Sets are expected 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, we usually see that 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. Just before you ditch bags forever, there is a particular case 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 Collection.add() or Collection.addAll() must always return true for a bag or List (unlike a Set). This can make the following common code much faster. Occasionally, deleting collection elements one by one can be extremely inefficient. Hibernate isn't completely stupid, so it 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 and we are done! Suppose we add 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 delete eighteen rows one by one and then insert three rows remove the whole collection (in one SQL DELETE) and insert all five current elements (one by one) Hibernate isn't smart enough to know that the second option is probably quicker in this case. (And it would probably be undesirable for Hibernate to be that smart; such behaviour might confuse database triggers, etc.) Fortunately, you can force this behaviour (ie. the second strategy) at any time by discarding (ie. dereferencing) the original collection and returning a newly instantiated collection with all the current elements. This can be very useful and powerful from time to time. Of course, one-shot-delete does not apply to collections mapped inverse="true". We have already shown how you can use lazy initialization for persistent collections in the chapter about collection mappings. A similar effect is achievable for ordinary object references, using CGLIB proxies. We have also mentioned how Hibernate caches persistent objects at the level of a Session. More aggressive caching strategies may be configured upon a class-by-class basis. In the next section, we show you how to use these features, which may be used to achieve much higher performance, where necessary. Hibernate implements lazy initializing proxies for persistent objects using runtime bytecode enhancement (via the excellent CGLIB library). The mapping file may declare an interface to use as the proxy interface for that class. By default, Hibernate uses a subclass of the class itself. (The proxied class must implement a default constructor with at least package visibility.) There are some gotchas to be aware of when extending this approach to polymorphic classes, eg. ...... ..... ]]> Firstly, instances of Cat will never be castable to DomesticCat, even if the underlying instance is an instance of DomesticCat. Secondly, it is possible to break proxy ==. 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: Third, you may not use a CGLIB proxy for a final class or a class with any final methods. Finally, if your persistent object acquires any resources upon instantiation (eg. 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 inheritence model. If you wish 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. eg. ...... ..... ]]> where Cat implements the interface ICat and DomesticCat implements the interface IDomesticCat. Then proxies for instances of Cat and DomesticCat may be returned by load() or iterate(). (Note that find() does not usually return proxies.) 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 equals(), if the persistent class does not override equals() hashCode(), if the persistent class does not override hashCode() The identifier getter method Hibernate will detect persistent classes that override equals() or hashCode(). Exceptions that occur while initializing a proxy are wrapped in a LazyInitializationException. Sometimes we need to ensure that a proxy or collection is initialized before closing the Session. Of course, we can alway force initialization by calling cat.getSex() or cat.getKittens().size(), for example. But that is confusing to readers of the code and is not convenient for generic code. The static methods Hibernate.initialize() and Hibernate.isInitialized() provide the application with a convenient way of working with lazyily 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. Hibernate can make efficient use of batch fetching, that is, Hibernate can load several uninitialized proxies if one proxy is accessed. Batch fetching is an optimization for the lazy loading strategy. There are two ways you can tune batch fetching: on the class and the collection level. Batch fetching for classes/entities is easier to understand. Imagine you have the following situation at runtime: You have 25 Cat instances loaded in a Session, 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: ...]]> Hibernate will now execute only three queries, the pattern is 10, 10, 5. You can see that batch fetching is a blind guess, as far as performance optimization goes, it depends on the number of unitilized proxies in a particular Session. You may 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 Sesssion, 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: ... ]]> With a batch-size of 3, Hibernate will load 3, 3, 3, 1 collections in 4 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, ie. the typical bill-of-materials pattern. Hibernate3 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, as in practice, optimizing row reads is much more important than optimization of column reads. However, only loading some properties of a class might be useful in extreme cases, when legacy tables have hundreds of columns and the data model can not be improved. To enable lazy property loading, set the lazy attribute on your particular property mappings: ]]> Lazy property loading requires buildtime bytecode instrumentation! If your persistent classes are not enhanced, Hibernate will silently ignore lazy property settings and fall back to immediate fetching. For bytecode instrumentation, use the following Ant task: ]]> A different (better?) way to avoid unnecessary column reads, at least for read-only transactons is to use the projection features of HQL. This avoids the need for buildtime bytecode processing. TODO: Document issues with lazy property loading Any kind of lazy fetching is extremely vulnerable to N+1 selects problems. So usually, we choose lazy fetching only as a "default" strategy, and override it for a particular transaction, using the HQL LEFT JOIN FETCH clause. This tells Hibernate to fetch the association in the first select, using an outer join. In the Criteria API, you would use setFetchMode(FetchMode.EAGER). You can always force outer join association fetching in the mapping file, by setting outer-join="true". We don't recommend this setting, especially not for collections, since it is incredibly rare to find an entity which is always used when an associated entity is used, at least in a sufficiently large system. A completely different way to avoid problems with N+1 selects is to use the second-level cache. A Hibernate Session is a transaction-level cache of persistent data. It is possible to configure a cluster or JVM-level (SessionFactory-level) cache on a class-by-class and collection-by-collection basis. You may even plug in a clustered cache. Be careful. Caches are never aware of changes made to the persistent store by another application (though they may be configured to regularly expire cached data). By default, Hibernate uses EHCache for JVM-level caching. (JCS support is now deprecated and will be removed in a future version of Hibernate.) You may choose a different implementation by specifying the name of a class that implements org.hibernate.cache.CacheProvider using the property hibernate.cache.provider_class. Cache Provider class Type Cluster Safe Query Cache Supported Hashtable (not intended for production use) org.hibernate.cache.HashtableCacheProvider memory yes EHCache org.hibernate.cache.EhCacheProvider memory, disk yes OSCache org.hibernate.cache.OSCacheProvider memory, disk yes SwarmCache org.hibernate.cache.SwarmCacheProvider clustered (ip multicast) yes (clustered invalidation) JBoss TreeCache org.hibernate.cache.TreeCacheProvider clustered (ip multicast), transactional yes (replication) yes (clock sync req.) The <cache> element of a class or collection mapping has the following form: ]]> usage specifies the caching strategy: transactional, read-write, nonstrict-read-write or read-only Alternatively (preferrably?), you may specify <class-cache> and <collection-cache> elements in hibernate.cfg.xml. The usage attribute specifies a cache concurrency strategy. If your application needs to read but never modify instances of a persistent class, a read-only cache may be used. This is the simplest and best performing strategy. Its even perfectly 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, 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 wish to use this strategy in a cluster, you should ensure that the underlying cache implementation supports locking. The built-in cache providers do not. .... .... ]]> If the application only occasionally needs to update data (ie. 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 may only be used in a JTA environment and you must specify hibernate.transaction.manager_lookup_class. None of the cache providers support all of the cache concurrency strategies. The following table shows which providers are compatible with which concurrency strategies. Cache read-only nonstrict-read-write read-write transactional Hashtable (not intended for production use) yes yes yes EHCache yes yes yes OSCache yes yes yes SwarmCache yes yes JBoss TreeCache yes yes Whenever you pass an object to save(), update() or saveOrUpdate() and whenever you retrieve an object using load(), get(), list(), iterate() or scroll(), that object is added to the internal cache of the Session. When flush() is subsequently called, the state of that object will be synchronized with the database. If you do not want this synchronization to occur or if you are processing a huge number of objects and need to manage memory efficiently, the evict() method may be used to remove the object and its collections from the cache. Hibernate will evict associated entities automatically if the association is mapped with cascade="all" or cascade="all-delete-orphan". The Session also provides a contains() method to determine if an instance belongs to the session cache. To completely evict all objects from the session cache, call Session.clear() For the second-level cache, there are methods defined on SessionFactory for evicting the cached state of an instance, entire class, collection instance or entire collection role. Query result sets may also be cached. This is only useful for queries that are run frequently with the same parameters. To use the query cache you must first enable it by setting the property hibernate.cache.use_query_cache=true. This causes the creation of two cache regions - one holding cached query result sets (org.hibernate.cache.QueryCache), the other holding timestamps of most recent updates to queried tables (org.hibernate.cache.UpdateTimestampsCache). Note that the query cache does not cache the state of any entities in the result set; it caches only identifier values and results of value type. So the query cache is usually used in conjunction with the second-level cache. Most queries do not benefit from caching, so by default queries are not cached. To enable caching, call 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 may specify a named cache region for a particular query by calling Query.setCacheRegion(). If the query should force a refresh of its query cache region, you may call Query.setForceCacheRefresh() to true. This is particularly useful in cases where underlying data may have been updated via a seperate process (i.e., not modified through Hibernate) and allows the application to selectively refresh the query cache regions based on its knowledge of those events. This is an alternative to eviction of a query cache region. If you need fine-grained refresh control for many queries, use this function instead of a new region for each query.