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} catch (Exception e) { log("Can't close context. "+e); } } private static void log(String msg) { System.out.println(new java.util.Date()+" "+THIS+" "+msg); } }

period, and one or more additional dimensions, representing what is to be sampled They are restated here, with references to dimensions in bold type: Daily snapshot of balance for each account Daily snapshot of in-stock product levels by product and location Monthly snapshot of balance by account In each of these grain statements, the dimensional references describe the circumstances in which a row will be recorded in the fact table These references describe unique natural key values When these dimensions undergo type 2 slow changes, any given natural key may have more than one row in the dimension table The snapshot, however, must record a row for only one row of each natural key To record more than one would introduce the very real possibility of double-counting Consider the month-end snapshot of account balances from Figure 11-4.

When executed, the following output is from the sending client:

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Suppose a particular account, perhaps your own, has undergone a type 2 change as a result of a change of address This means your account will be represented by two rows in the account dimension: one with your old address and the other with your new address When the month-end snapshot is recorded, the fact table should contain only one row for your account, using whichever version was current at the time Were the snapshot to record more than one row for your account, your balance would probably be double-counted in many queries While you might like what this does to your net worth, it is of course inaccurate The inventory example, which involves two dimensions in addition to the snapshot period in its grain statement, will record one row for each product/store combination.

Sun Sun Sun Sun Sun Sun Sun Sun Sun Sun Sun Sun Sun Sun Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct Oct 01 01 01 01 01 01 01 01 01 01 01 01 01 01 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 17:28:48 EDT EDT EDT EDT EDT EDT EDT EDT EDT EDT EDT EDT EDT EDT 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 2006 PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend PTPSend Started. News item #1 {bid=101, symbol=ucny, ask=102} News item #3 (Interesting) {bid=103, symbol=ucny, ask=104} News item #5 {bid=105, symbol=ucny, ask=106} (Interesting) News item #7 {bid=107, symbol=ucny, ask=108} News item #9 (Interesting) {bid=109, symbol=ucny, ask=110} ***CLOSE*** (Interesting) ***CLOSE*** Finished.

Products and stores may undergo type 2 changes, but each snapshot row will only refer to what was current at the end of the snapshot period TIP A periodic snapshot records only one row for each combination of natural keys in the dimensions that define its grain Understanding this requirement is essential for all members of the warehouse team Although the impact on the ETL process is most obvious, it is important that all team members be made aware of this requirement and that documentation of the star reflects this state of affairs If an account had multiple versions that were current during different parts of the period, report developers should not assume that all are present in the snapshot Such an assumption could lead to serious consequences..

When executed, here is the output from the receiving client when it is executed with an argument Interesting=true, which becomes the message filter, and when it is executed with no argument, which means it must read all items in the queue:

You can execute this example from Query Analyzer, as shown in Figure 9-4.

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