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Listing 3.29 contains the EquityBean EJB source, showing the implementation of the home method. For this recipe, the EquityBean EJB is a BMP bean. Notice that the home method is prefixed with ejbHome. This indicates to the container that this is a home method and can be executed directly from the home object. The remaining source would remain unchanged and is not shown.

@Stateful public class PlaceOrderBean implements PlaceOrder { @Resource(name="jms/QueueConnectionFactory") private ConnectionFactory connectionFactory; @Resource(name="jms/OrderBillingQueue") private Destination billingQueue; ... @Remove public Long confirmOrder() { Order order = new Order(); order.setBidderId(bidderId); order.setItems(items);

The monitor of line 1 in Listing 9-28 checks the number of incoming orders. Every five orders, the monitor emits a notification event, which is received by the listener of line 13. The monitor of line 20 checks the status attribute of the Stats bean and emits a notification when the attribute s state changes. These notifications are received by the listener of line 34. When run, the application gives the output that is shown in Listing 9-29. Listing 9-29. The Output of the JMX Example JMX notification Order #1 Order #2 Order #3 Order #4 Order #5 JMX notification Order #6 JMX notification Order #7 JMX notification Order #8 Order #9 Order #10 JMX notification JMX notification - Process OK

order.setShippingInfo(shippingInfo); order.setBillingInfo(billingInfo); saveOrder(order); billOrder(order); return order.getOrderId(); } ... private billOrder(Order order) { try { Connection connection = connectionFactory.createConnection(); Session session = Contains JMS connection.createSession(false, setup code Session.AUTO_ACKNOWLEDGE); MessageProducer producer = session.createProducer(billingQueue); ObjectMessage message = session.createObjectMessage(); Creates and sends the message.setObject(order); message producer.send(message); producer.close(); session.close(); Releases JMS resources connection.close(); } catch(Exception e){ e.printStackTrace(); } }

public class EquityBean implements EntityBean { //other bean methods not shown public int ejbHomeGetCountOfSymbols() { System.out.println("Executing Home Method: getCountOfSymbols"); PreparedStatement ps = null; Connection con=null; int count=0; try { String query="select count(*) from EQUITIES"; con = getConnection(); ps = con.prepareStatement(query); ps.executeQuery(); ResultSet rs = ps.getResultSet(); if( rs.next() ) { count = rs.getInt(1); } } catch (SQLException sqe) { throw new EJBException (sqe); } finally { try{ if(ps != null) ps.close(); if(con != null) con.close(); } catch(SQLException e){} } return count; } }

Not surprisingly, the code to send the message in listing 2.5 is heavily dependent on the JMS API. In fact, that s all that the code in the billOrder method consists of. If you re familiar with JDBC, the flavor of the code in the method might seem familiar. The end result of the code is that the newly created Order object is sent as a message to a JMS destination named jms/OrderBillingQueue. We won t deal with the intricacies of JMS immediately, but we ll save a detailed discussion of this essential messaging API for chapter 4. It is important to note a few things right now, though. The first thing is that two JMS resources, including the message destination, are injected using the @Resource annotation b instead of being looked up.

In the relational world, it is extremely important that the same conceptual data not be replicated throughout the database. The importance of avoiding redundancy stems from two facts. First, most databases hold a huge amount of data. For example, it is easy to think that storing department name and location in a table with employee information is no big deal. The problem is that if a thousand employees work in the same department, the department information would be duplicated across a thousand employee table rows! If a department location changes, you would have to accurately update each of the records for the thousand employees who work for the department. Second, this redundancy can easily lead to inconsistency. Both of these problems can be solved by storing a foreign key to the department table (say department ID) in the employee table instead. Relational theory has formalized the process of checking the database design for redundancy. This process is called database normalization. IBM researchers initially proposed three different levels of normalization: first, second, and third normal form, each consisting of a well-defined, incrementally strict set of rules to check for database fitness. Later, more levels were introduced: BCNF (Boyce-Codd Normal Form), fourth, and fifth normal form. Relational theory recognizes the fact that normalization can lead to trading off speed for space efficiency. Most DBAs go through the process of selective denormalization when faced with tricky performance issues.