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EclipseLink Solutions Guide for EclipseLink
Release 2.4
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Implementing the Solution

These tasks provide general instructions for ensuring that a EclipseLink application scales in an application server cluster. Complete the tasks prior to deploying an application.

This section contains the following tasks:

Task 1: Configure Cache Consistency

This task includes different configuration options that mitigate the possibility that an application might use stale data when deployed to an application server cluster. The cache coordination option is specifically designed for applications that are clustered; however, evaluate all the options and use them together (if applicable) to create a solution that results in the best application performance. Properly configuring a cache can, in some cases, eliminate the need to use cache coordination. For additional details on these options, see:

http://wiki.eclipse.org/Introduction_to_Cache_%28ELUG%29#Handling_Stale_Data

The following are the configuration options:


NoteNote:

Oracle provides a EclipseLink and Coherence integration that allows EclipseLink to use Coherence as the L2 cache. For details on EclipseLink Grid, see


Disabling Entity Caching

Disable the shared cache for highly volatile entities or for all entities as required. To disable the shared cache for all objects, use the <shared-cache-mode> element in the persistence.xml file. For example:

<shared-cache-mode>NONE</shared-cache-mode>

The default configuration is DISABLE_SELECTIVE and allows caching to be disabled per entity. To selectively enable or disable the shared cache, use the shared attribute of the @Cache annotation when defining an entity. For example:

@Entity
@Cache(shared=false)
public class Employee {
}

Refreshing the Cache

Refreshing a cache reloads the cache from the database to ensure that an application is using current data. There are different ways to refresh a cache.

The @Cache annotation provides the alwaysRefresh and refreshOnlyIfNewer attributes which force all queries that go to the database to refresh the cache. The cache is only actually refreshed if the optimistic lock value in the database is newer than in the cache.

@Entity
@Cache(
   alwaysRefresh=true, 
   refreshOnlyIfNewer=true)
public class Employee {
}

The javax.persistence.Cache interface includes methods that remove stale objects if the cache is out of date:

  • The evictAll method invalidates all of the objects in the cache.

    em.getEntityManagerFactory().getCache().evictAll();
    
  • The evict method invalidates specific classes.

    em.getEntityManagerFactory().getCache().evict(MyClass);
    

The preceding methods are passive and refresh objects only the next time the cache is accessed. To actively refresh an object, use the EntityManager.refresh method. The method refreshes a single object at a time.

Another possibility is to use the setHint method to set a query hint that triggers the query to refresh the cache. For example:

Query query = em.createQuery("Select e from Employee e");
query.setHint("javax.persistence.cache.storeMode", "REFRESH");

Lastly, native API methods are also available. For details, see the ClassDescriptor documentation in Java API Reference for EclipseLink.

Setting Entity Caching Expiration

Cache expiration makes a cached object instance invalid after a specified amount of time. Any attempt to use the object causes the most up-to-date version of the object to be reloaded from the data source. Expiration can help ensure that an application is always using the most recent data. There are different ways to set expiration.

The @Cache annotation provides the expiry and expiryTimeOfDay attributes, which remove cache instances after a specific amount of time. The expiry attribute is entered in milliseconds. The default value if no value is specified is -1, which indicates that expiry is disabled. The expiryTimeOfDay attribute is an instance of the org.eclipse.persistence.annotations.TimeOfDay interface. The following example sets the object to expire after 5 minutes:

@Entity
@Cache(expiry=300000)
public class Employee {
}

Setting Optimistic Locking

Optimistic locking prevents one user from writing over another user's work. Locking is important when multiple servers or multiple applications access the same data and is relevant in both single-server and multiple-server environments. In a multiple-server environment, locking is still required if an application uses cache refreshing or cache coordination. There are different ways to set optimistic locking.

The standard JPA @Version annotation is used for single valued value and timestamp based locking. However, for advanced locking features use the @OptimisticLocking annotation. The @OptimisticLocking annotation specifies the type of optimistic locking to use when updating or deleting entities. Optimistic locking is supported on an @Entity or @MappedSuperclass annotation. The following policies are available and are set within the type attribute:

  • ALL_COLUMNS: This policy compares every field in the table in the WHERE clause when performing an update or delete operation.

  • CHANGED_COLUMNS: This policy compares only the changed fields in the WHERE clause when performing an update operation. A delete operation compares only the primary key.

  • SELECTED_COLUMNS: This policy compares selected fields in the WHERE clause when performing an update or delete operation. The fields that are specified must be mapped and not be primary keys.

  • VERSION_COLUMN: (Default) This policy allows a single version number to be used for optimistic locking. The version field must be mapped and not be the primary key. To automatically force a version field update on a parent object when its privately owned child object's version field changes, use the cascaded method set to true. The method is set to false by default.

Using Cache Coordination

Cache coordination synchronizes changes among distributed sessions. Cache coordination is most useful in application server clusters where maintaining consistent data for all applications is challenging. Moreover, cache consistency becomes increasingly more difficult as the number of servers within an environment increases.

Cache coordination works by broadcasting notifications of transactional object changes among sessions (EntityManagerFactory or persistence unit) in the cluster. Cache coordination is most useful for applications that are primarily read-based and when changes are performed by the same application operating with multiple, distributed sessions.

Cache coordination significantly minimizes stale data, but does not completely eliminate the possibility that stale data might occur because of latency. In addition, cache coordination reduces the number of optimistic lock exceptions encountered in distributed architectures, and reduces the number of failed or repeated transactions in an application. However, cache coordination in no way eliminates the need for an effective locking policy. To ensure the most current data, use cache coordination with optimistic or pessimistic locking; optimistic locking is preferred.

Cache coordination is supported over the Remote Method Invocation (RMI) and Java Message Service (JMS) protocols and is configured either declaratively by using persistence properties in a persistence.xml file or by using the cache coordination API. System properties that match the persistence properties are available as well.

For additional details on cache coordination see:

Java Persistence API (JPA) Extensions Reference for Oracle TopLink

Setting Cache Synchronization

Cache synchronization determines how notifications of object changes are broadcast among session members. The following synchronization options are available:

  • SEND_OBJECT_CHANGES: (Default) This option broadcasts a list of changed objects including data about the changes. This data is merged into the receiving cache.

  • INVALIDATE_CHANGED_OBJECTS: This option broadcasts a list of the identities of the objects that have changed. The receiving cache invalidates the objects rather than changing any of the data. This option is the lightest in terms of data sent and processing done in other cluster members.

  • SEND_NEW_OBJECTS_WITH_CHANGES: This option is the same as the SEND_OBJECT_CHANGES option except it also includes any newly created objects from the transaction.

  • NONE: This option does no cache coordination.

The @Cache annotation coordinationType attribute is used to specify synchronization. For example:

@Entity
@Cache(CacheCoordinationType.SEND_NEW_OBJECTS_CHANGES)
public class Employee {
}

The ClassDescriptor.setCacheSynchronizationType native API method can also be used to specify synchronization. For details, see the ClassDescriptor documentation in Java API Reference for EclipseLink.

Configuring JMS Cache Coordination Using Persistence Properties

The following example demonstrates how to configure cache coordination in the persistence.xml file and uses JMS for broadcast notification. For JMS, provide a JMS topic JNDI name and topic connection factory JNDI name. The JMS topic should not be JTA-enabled and should not have persistent messages.

<property name="eclipselink.cache.coordination.protocol" value="jms" />
<property name="eclipselink.cache.coordination.jms.topic" 
   value="jms/EmployeeTopic" />
<property name="eclipselink.cache.coordination.jms.factory"
   value="jms/EmployeeTopicConnectionFactory" />

Applications that run in a cluster generally do not require a URL because the topic provides enough to locate and use the resource. For applications that run outside the cluster, a URL is required. The following example is a URL for a WebLogic Server cluster:

<property name="eclipselink.cache.coordination.jms.host"
   value="t3://myserver:7001/" />

A user name and password for accessing the servers can also be set if required. For example:

<property name="eclipselink.cache.coordination.jndi.user" value="user" />
<property name="eclipselink.cache.coordination.jndi.password" value="password" />

Configuring RMI Cache Coordination Using Persistence Properties

The following example demonstrates how to configure cache coordination in the persistence.xml file and uses RMI for broadcast notification:

<property name="eclipselink.cache.coordination.protocol" value="rmi" />

Applications that run in a cluster generally do not require a URL because JNDI is replicated and servers can look up each other. If an application runs outside of a cluster, or if JNDI is not replicated, then each server must provide its URL. This could be done through the persistence.xml file; however, different persistence.xml files (thus JAR or EAR) for each server is required, which is usually not desirable. A second option is to set the URL programmatically using the cache coordination API. For more details, see "Configuring Cache Coordination Using the Cache Coordination API". The final option is to set the URL as a system property on each application server. The following example sets the URL for a WebLogic Server cluster using a system property:

-Declipselink.cache.coordination.jms.host=t3://myserver:7001/

A user name and password for accessing the servers can also be set if required; for example:

<property name="eclipselink.cache.coordination.jndi.user" value="user" /><property name="eclipselink.cache.coordination.jndi.password" value="password" />

RMI cache coordination can use either asynchronous or synchronous broadcasting notification; asynchronous is the default. Synchronous broadcasting ensures that all of the servers are updated before a request returns. The following example configures synchronous broadcasting.

<property name="eclipselink.cache.coordination.propagate-asynchronously"
   value="false" />

If multiple applications on the same server or network use cache coordination, then a separate channel can be used for each application. For example:

<property name="eclipselink.cache.coordination.channel" value="EmployeeChannel" />

Last, if required, change the default RMI multicast socket address that allows servers to find each other. The following example explicitly configures the multicast settings:

<property name="eclipselink.cache.coordination.rmi.announcement-delay"
   value="1000" />
<property name="eclipselink.cache.coordination.rmi.multicast-group"
   value="239.192.0.0" />
<property name="eclipselink.cache.coordination.rmi.multicast-group.port"
   value="3121" />
<property name="eclipselink.cache.coordination.packet-time-to-live" value="2" />

Configuring Cache Coordination Using the Cache Coordination API

The CommandManager interface allows you to programmatically configure cache coordination for a session. The interface is accessed using the getCommandManager method from the DatabaseSession interface.

Task 2: Ensure EclipseLink Is Enabled

Ensure that the EclipseLink JAR files are included on the classpath of each application server in the cluster to which the EclipseLink application is deployed and configure EclipseLink as the persistence provider. For detailed instructions about setting up EclipseLink with WebLogic Server and GlassFish Server, see Chapter 3, "Using EclipseLink with WebLogic Server," and Chapter 4, "Using EclipseLink with GlassFish Server," respectively.

Task 3: Ensure All Application Servers Are Part of the Cluster

Configure an application server cluster that includes each application server that hosts the EclipseLink application:


NoteNote:

TopLink relies on JMS and RMI and does not use the application server's cluster communication.


Using Data Partitioning to Scale Data

Data partitioning allows an application to scale its data across more than one database machine. Data partitioning is supported at the entity level to allow a different set of entity instances for the same class to be stored in a different physical database or different node within a database cluster. Both regular databases and clustered databases are supported. Data can be partitioned both horizontally and vertically.

Partitioning can be enabled on an entity, a relationship, a query, or a persistence unit. To configure data partitioning, use the @Partitioned annotation and one or more partitioning policy annotations. Table 10-1 describes the partitioning policies

Table 10-1 Partitioning Policies

Annotation Description

@HashPartitioning

Partitions access to a database cluster by the hash of a field value from the object, such as the object's ID, location, or tenant. The hash indexes into the list of connection pools/nodes. All write or read request for objects with that hash value are sent to the same server. If a query does not include the hash field as a parameter, it can be sent to all servers and unioned, or it can be left to the session's default behavior.

@PinnedPartitioning

Pins requests to a single connection pool/node. This allows for vertical partitioning.

@RangePartitioning

Partitions access to a database cluster by a field value from the object, such as the object's ID, location, or tenant. Each server is assigned a range of values. All write or read requests for objects with that value are sent to the same server. If a query does not include the field as a parameter, then it can either be sent to all servers and unioned, or left to the session's default behavior.

@ReplicationPartitioning

Sends requests to a set of connection pools/nodes. This policy is for replicating data across a cluster of database machines. Only modification queries are replicated.

@RoundRobinPartitioning

Sends requests in a round-robin fashion to the set of connection pools/nodes. This policy is used for load balancing read queries across a cluster of database machines. It requires that the full database be replicated on each machine, so it does not support partitioning. The data should either be read-only, or writes should be replicated.

@UnionPartitioning

Sends queries to all connection pools and unions the results. This is for queries or relationships that span partitions when partitioning is used, such as on a ManyToMany cross partition relationship.

@ValuePartitioning

Partitions access to a database cluster by a field value from the object, such as the object's location or tenant. Each value is assigned a specific server. All write or read requests for objects with that value are sent to the same server. If a query does not include the field as a parameter, then it can be sent to all servers and unioned, or it can be left to the session's default behavior.

@Partitioning

Partitions access to a database cluster by a custom partitioning policy. A class that extends the PartitioningPolicy class must be provided.


Partitioning policies are globally-named objects in a persistence unit and are reusable across multiple descriptors or queries. This improves the usability of the configuration, specifically with JPA annotations and XML.

The persistence unit properties support adding named connection pools in addition to the existing configuration for read/write/sequence. Connection pools are defined in the persistence.xml file for each participating database. Partition policies select the appropriate connection based on their particular algorithm.

If a transaction modifies data from multiple partitions, JTA should be used to ensure 2-phase commit of the data. An exclusive connection can also be configured in an EntityManager implementation to ensure only a single node is used for a single transaction.

The following example partitions the Employee data by location. The two primary sites, Ottawa and Toronto, are each stored on a separate database. All other locations are stored on the default database. Project is range partitioned by its ID. Each range of ID values are stored on a different database.

@Entity
@IdClass(EmployeePK.class)
@UnionPartitioning(
        name="UnionPartitioningAllNodes",
        replicateWrites=true)
@ValuePartitioning(
        name="ValuePartitioningByLOCATION",
        partitionColumn=@Column(name="LOCATION"),
        unionUnpartitionableQueries=true,
        defaultConnectionPool="default",
        partitions={
            @ValuePartition(connectionPool="node2", value="Ottawa"),
            @ValuePartition(connectionPool="node3", value="Toronto")
        })
@Partitioned("ValuePartitioningByLOCATION")
public class Employee {
    @Id
    @Column(name = "EMP_ID")
    private Integer id;
     @Id
    private String location;
    ...
 
    @ManyToMany(cascade = { PERSIST, MERGE })
    @Partitioned("UnionPartitioningAllNodes")
    private Collection<Project> projects;
    ...
}

The employee/project relationship is an example of a cross partition relationship. To allow the employees and projects to be stored on different databases a union policy is used and the join table is replicated to each database.

@Entity
@RangePartitioning(
        name="RangePartitioningByPROJ_ID",
        partitionColumn=@Column(name="PROJ_ID"),
        partitionValueType=Integer.class,
        unionUnpartitionableQueries=true,
        partitions={
            @RangePartition(connectionPool="default", startValue="0",
               endValue="1000"),
            @RangePartition(connectionPool="node2", startValue="1000",
               endValue="2000"),
            @RangePartition(connectionPool="node3", startValue="2000")
        })
@Partitioned("RangePartitioningByPROJ_ID")
public class Project {
    @Id
    @Column(name="PROJ_ID")
    private Integer id;
    ...
}

Clustered Databases and Oracle RAC

Some databases support clustering the database across multiple servers. Oracle Real Application Clusters (RAC) allows for a single database to span multiple different server nodes. Oracle RAC also supports table and node partitioning of data. A database cluster allows for any of the data to be accessed from any node in the cluster. However, it is generally more efficient to partition the data access to specific nodes, to reduce cross node communication. Partitioning can be used in conjunction with a clustered database to reduce cross node communication, and improve scalability. For details on using EclipseLink with Oracle RAC, see Using EclipseLink with Oracle RAC.

Adhere to the following requirements when using data partitioning with a database cluster:

  • Partition policy should not enable replication, as database cluster makes data available to all nodes.

  • Partition policy should not use unions, as database cluster returns the complete query result from any node.

  • A DataSource and connection pool should be defined for each node in the cluster.

  • The application's data access and data partitioning should be designed to have each transaction only require access to a single node.

  • Usage of an exclusive connection for an EntityManager is recommended to avoid having multiple nodes in a single transaction and avoid 2-phase commit.

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