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In the following section, you will learn about dynamic discovery of devices and detecting newly available devices. This is a very important feature that the DPWS adds to the Web Services standard.

They believe that they now have all of the records in the data warehouse that were modified since they did the initial pull They may actually have all of the records, but just as likely they may not This technique does work on some other databases ones that employ a locking system whereby reads are blocked by writes and vice versa But in a system where you have non-blocking reads, the logic is flawed To see the flaw in this example, all we need to do is assume that at 9:00 am there was at least one open, uncommitted transaction At 8:59:30 am, it had updated a row in the table we were to copy At 9:00 am, when we started pulling the data, reading the data in this table, we would not see the modifications to that row; we would see the last committed version of it.

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A business object s AddBusinessRules() method would associate a property to this rule like this: Protected Overrides Sub AddBusinessRules() ValidationRules.AddRule( _ AddressOf CommonRules.StringMaxLength, _ New CommonRules.MaxLengthRuleArgs("Name", 50)) End Sub Remember that in 3 the ValidationRules.AddRule() method included an overload that accepted a rule method delegate along with a RuleArgs object. In this case, the RuleArgs object is an instance of MaxLengthRuleArgs, initialized with the property name and the maximum length allowed for the property. The CommonRules class includes other similar rule method implementations that you may choose to use as is, or as the basis for creating your own library of reusable rules for an application.

If it was locked when we got to it in our query, we would read around the lock If it was committed by the time we got to it, we would still read around it since read consistency permits us to read only data that was committed in the database when our statement began We would not read that new version of the row during the 9:00 am initial pull, but nor would we read the modified row during the 10:00 am refresh The reason The 10:00 am refresh would only pull records modified since 9:00 am that morning but this record was modified at 8:59:30 am We would never pull this changed record In many other databases where reads are blocked by writes and a committed but inconsistent read is implemented, this refresh process would work perfectly.

Figure 1-9 illustrates common locations for validation and manipulation business logic in a typical application. Most applications have the same logic in at least a couple of these locations.

Before we explore the discovery mechanisms, you need to understand addressing and know the difference between an endpoint and transport address. An endpoint address (also referred to as a logical address) is a unique transport-neutral mechanism to define service and message addressing, as shown in the following example: urn:uuid:c5201073-fa27-c8c9-9634-0000001dd159

If at 9:00 am when we did the initial pull of data we hit that row and it was locked, we would have blocked and waited for it, and read the committed version If it were not locked, we would just read whatever was there, committed So, does this mean the preceding logic just cannot be used No, it means that we need to get the right now time a little differently We need to query V$TRANSACTION and find out which is the earliest of the current time and the time recorded in the START_TIME column of this view We will need to pull all records changed since the start time of the oldest transaction (or the current SYSDATE value if there are no active transactions): select nvl( min(to_date(start_time,'mm/dd/rr hh24:mi:ss')),sysdate) from v$transaction; In this example, that would be 8:59:30 am when the transaction that modified the row started.

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