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Improving entity performance
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remodeled our Item entity to carve out two entities: Item and ItemDetails. We then established a one-to-one relationship between these entities and set the load type for relationship to be LAZY as follows:
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@Entity @Table(name = "ITEMS") public class Item implements Serializable { @Id private Long itemId; private String title; ... @OneToOne(fetch = FetchType.LAZY) @PrimaryKeyJoinColumn(name = "ITEM_ID", referencedColumnName = "ITEM_DETAIL_ID") private ItemDetails itemDetails; } @Entity @Table(name = "ITEM_DETAILS") public class ItemDetails implements Serializable { @Id private Long itemDetailsId; private String originalPurchaseDate; @Lob private byte[] itemPicture; ... }
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This change gives us the performance we are looking for, and helps us overcome the reality that not all implementations of the EJB 3 specification are done the same way. This way, we remain neutral with respect to application server vendors, and therefore our application is more portable across persistence provider implementations. Choosing the right inheritance strategy As you learned in chapter 8, EJB 3 supports three type of inheritance mapping strategies. Each has its own advantages and disadvantages, but the single-table strategy will probably give you the best performance. This is because all entities are stored in a single table and JOINs between tables are avoided. As discussed in chapter 8, we can create a single table named USERS for User and all its subclasses such as Bidder, Seller, Admin, etc., and use a discriminator column to track the subtypes. Consider the following:
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@Table(name = "USERS") @Inheritance(strategy = InheritanceType.SINGLE_TABLE)
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Sets inheritance type strategy
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Taming wild EJBs: performance and scalability
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@DiscriminatorColumn(name = "USER_TYPE", discriminatorType = DiscriminatorType.STRING, length = 1) Configures public class User ...
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discriminator column
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@Entity @DiscriminatorValue(value = "S") public class Seller extends User ... @Entity @DiscriminatorValue(value ="B") public class Bidder extends User
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Specifies Seller discriminator value Specifies Bidder discriminator value
Notice c that discriminatorType has a data type of DiscriminatorType.STRING and a length of 1. These are set in conjunction with the inheritance strategy b of SINGLE_TABLE. This means that you can assign the actual value as a Java String type, but the value will be whatever your application decides to use for the various subtypes supported. In this case "S" is used to represent a Seller d and "B" represents a Bidder e. But then you already figured that out, didn t you
13.2.2 Tuning the JDBC layer
You remember from our discussion earlier in this book that JPA internally uses JDBC to store and retrieve entities. When you deploy your applications using the EJB 3 JPA in an application server environment, you are taking advantage of the JDBC connection pooling configuration of the application server. Tuning the application server may improve performance of your applications. Properly sizing the connection pool Every application server supports pooling connections, and you should not forget to size the connection pool appropriately. In a high-transaction system, if there are more concurrent users than the available number of connections, users have to wait until connections are released in order for their requested functions to be performed. This may degrade performance of your applications. You have to properly size the pool s startup and runtime characteristics (such as both the minimum and maximum number of connections in the pool, and the timeout before an unused connection is automatically returned to the pool) based on your application requirements. Review your vendor s documentation for available options. Caching SQL statements The persistence provider executes SQL on your behalf. In a transaction-centric system like ActionBazaar, it is likely that the same SQL statement will be executed by the persistence provider many times. Applications servers such as BEA WebLogic
Improving entity performance
and Oracle Application Server provide the ability to cache SQL statements and reuse them. This lowers the overhead of cursor creation as well as the parsing of SQL statements both of which can be very time consuming. Typically this is configured in the data-source configuration of the application server as follows:
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