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Difference between revisions of "EclipseLink/UserGuide/JPA/Advanced JPA Development/Data Partitioning"

(Data Affinity and Oracle RAC Support)
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The persistence unit properties support adding named connection pools in addition to the existing configuration for read/write/sequence.  
 
The persistence unit properties support adding named connection pools in addition to the existing configuration for read/write/sequence.  
  
==Data Affinity and Oracle RAC Support==
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==Clustered Databases and Oracle RAC==
  
Some cluster-enabled databases define their own DataSource implementation that is cluster-aware. Some support data affinity and integration with a data affinity service such as EclipseLink provides.  
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Some databases support clustering the database across multiple machines.  Oracle RAC allows for a single database to span multiple different server nodes.
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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 it is more efficient to partition the data access to specific nodes, to reduce cross node communication.
  
Oracle RAC is supported.
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EclipseLink partitioning can be used in conjunction with a clustered database to reduce cross node communication, and improve scalability.
  
A DataSource is required for every node.
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To use partitioning with a database cluster to following is required:
 
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* Partition policy should not enable replication, as database cluster makes data available to all nodes.
A generic <tt>DataPartitioningCallback</tt> interface is defined in EclipseLink (<tt>platform.database.partitioning</tt>) to support integration with an external DataSource data affinity. The callback is given a chance to register itself with the DataSource on connect. The PartitioningPolicys set the partition ID into the callback instead of acquiring a connection from a connection pool.
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* Partition policy should not use unions, as database cluster returns the complete query result from any node.
 
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* A DataSource and EclipseLink connection pool should be defined for each node in the cluster.
Replication is not required.  
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* The application's data access and data partitioning should be designed to have each transaction only require access to a single node.
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* Usage of an exclusive connection for an EntityManager is recommended to avoid having multiple nodes in a single transaction and avoid 2-phase commit.
  
 
== Data Partitioning Examples ==
 
== Data Partitioning Examples ==

Revision as of 10:26, 9 March 2011

EclipseLink JPA


Data Partitioning

With data partitioning, you can subdivide a database table, index or index-organized table into smaller units. That makes it possible to manage and access those objects at a finer level of granularity, thereby improving manageability, performance, and availability. For example, data partitioning facilitates load-balancing and replicating data across multiple different databases or across a database cluster.

Partitioning can be enabled on an an entity, a relationship, a query, a session unit, or a persistence unit.

Partitioning Policies

To configure data partitioning, use the @Partitioned annotation and one or more partitioning policy annotations. The annotations for defining the different kinds of policies are:

  • @HashPartitioning - Partitions access to a database cluster by the hash of a field value from the object, such as the object's location, or tenant. The hash indexes into the list of connection pools. All write or read request for objects with that hash value are sent to the server. If a query does not include the field as a parameter, it can be sent to all servers and unioned, or it can be left to the session's default behavior.
  • @Partitioning - Partitions the data for a class across multiple difference databases or across a database cluster such as Oracle RAC. Partitioning can provide improved scalability by allowing multiple database machines to service requests. This annotation configures a custom partitioning policy.
  • @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 server. Each server is assigned a range of values. All write or read request for object's with that value are sent to the server. If a query does not include the field as a parameter, then it can either be sent to all server's and unioned, or left to the sesion's default behavior. [In Javadoc, sters (RAC). Partitioning can provide improved scalability by allowing multiple database machines to service requests. (If multiple partitions are used to process a single transaction, JTA should be used for proper XA transaction support.)
  • @ReplicationPartitioning - Sends requests to a set of connection pools. 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. It is 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 on the database.
  • @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 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.

All policies provide an exclusive connection option. This assigns an accessor to the client session on the first query execution and uses that connection for the duration of the session. This ensures that the entire transaction stays on the same node.

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.

Clustered Databases and Oracle RAC

Some databases support clustering the database across multiple machines. Oracle 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 it is more efficient to partition the data access to specific nodes, to reduce cross node communication.

EclipseLink partitioning can be used in conjunction with a clustered database to reduce cross node communication, and improve scalability.

To use partitioning with a database cluster to following is required:

  • 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 EclipseLink 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.

Data Partitioning Examples

@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;
    ...
}
@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;
    ...
}


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