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The same is true for any property that is of type byte[], Byte[], or java. sql.Blob. Note that for all cases, except properties that are of java.sql.Clob or java.sql.Blob type, the values are again loaded immediately by Hibernate, and not lazily on demand. Instrumenting bytecode with interception code is again an option to enable lazy loading of individual properties transparently. To create and set a java.sql.Blob or java.sql.Clob value, if you have these property types in your domain model, use the static Hibernate.createBlob() and Hibernate.createClob() methods and provide a byte array, an input stream, or a string. Finally, note that both Hibernate and JPA provide a serialization fallback for any property type that is Serializable. This mapping type converts the value of a property to a byte stream that is then stored in a VARBINARY (or equivalent) column. When the owner of the property is loaded, the property value is deserialized. Naturally, you should use this strategy with extreme caution (data lives longer than an application), and it may be useful only for temporary data (user preferences, login session data, and so on). JDK mapping types Table 5.4 lists Hibernate types for various other Java types of the JDK that may be represented as a VARCHAR in the database. You may have noticed that <property> isn t the only Hibernate mapping element that has a type attribute.

The last thing to consider is that as time passes, more history records will be added the database. You will need to create a process that moves records older than three years to the second database. Although you could do this using a custom application, it would be easier to simply create a SQL Server Integration Services (SSIS) package that can be scheduled to run once a day. The package would execute code that queried the database for records older than three years. If any are found, it can delete the records from the original database and insert them into the second database.

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All of the basic mapping types may appear almost anywhere in the Hibernate mapping document, on normal property, identifier property, and other mapping elements. The <id>, <property>, <version>, <discriminator>, <index> and <element> elements all define an attribute named type. You can see how useful the built-in mapping types are in this mapping for the BillingDetails class:

<class name="BillingDetails" table="BILLING_DETAILS"> <id name="id" type="long" column="BILLING_DETAILS_ID"> <generator class="native"/> </id> <discriminator type="character" column="BILLING_DETAILS_TYPE"/> <property name="number" type="string"/> .... </class>

Implementing a successful scale-out technique involves consideration of all potential solutions. It is possible that you might need to implement a combination of one or more techniques to accomplish your goal. This section presents techniques that can be used to target multiple servers and thus scale out your application.

The BillingDetails class is mapped as an entity. Its discriminator, identifier, and name properties are value typed, and we use the built-in Hibernate mapping types to specify the conversion strategy. It isn t often necessary to explicitly specify a built-in mapping type in the XML mapping document. For instance, if you have a property of Java type java.lang.String, Hibernate discovers this using reflection and selects string by default. We can easily simplify the previous mapping example:

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A Distributed Partition View (DPV) is a technique in which data partitioned horizontally across multiple servers is joined together to make it appear as if it comes from one table The data is partitioned by a key value from each table For example, the Product ID column could be used to partition the product tables in the AdventureWorks database The goal of a DPV is to transparently distribute the load of a database application across multiple servers This is different from the partition we created earlier, in which a table was assigned to multiple filegroups on the same server In the case of a DPV, the data is partitioned across two or more servers To create a DPV, you will first need to horizontally partition your table or tables.

<class name="BillingDetails" table="BILLING_DETAILS"> <id name="id" column="BILLING_DETAILS_ID"> <generator class="native"/> </id> <discriminator type="character" column="BILLING_DETAILS_TYPE"/> <property name="number"/> .... </class>

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