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What happened here is that even if a table has an index, this doesn t mean that it s always good to use it under all circumstances The CBO always figures the best way to get a query s results, with or without using the index In this case, the query has to look at almost all the rows of the table, so using an index isn t the best way to go However, you haven t collected statistics for the table and the index, so Oracle has no way of knowing the distribution of the actual data in the courses table Lacking any statistics, it falls back to a rule-based approach Under a rule-based optimization, using an index occupies a lower rank and therefore indicates that this is the optimal approach here Let s see the results after analyzing the table..

The lost-update problem is caused by transactions trying to read data while it is being updated by other transactions. Say transaction A is reading a table s data while it is being updated by transaction B, and transaction B completes successfully and is committed. If transaction A has read the data before transaction B has fully completed, it might end up with intermediate data. The lost update anomaly occurs because two users have updated the same row, and since the second update overwrites the first, the first update is lost. Allowing transactions to read and update a table before the completion of another transaction causes the problem in this case.

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When a transaction finds that data it has read previously has been modified by some other transaction, you have a nonrepeatable-read (or fuzzy-read) problem. Suppose you access a table s data at a certain point in time, and then you try to access the same data a little later, only to find that the data values are different the second time around. This inconsistent data during the same transaction causes a nonrepeatable-read problem.

Normalizing the Data For Multiple Text Columns If you have two or more text columns, you should concatenate them before using the normalization technique. For example, if you have columns for Name and Region, as in the sample file MonthlyDataReg.xlsx, follow these steps: 1. Insert a blank column after Region, with the heading NameRegion. 2. In cell C2, enter the following formula, which combines the Name and the Region, with a dollar sign between them. Later, the dollar sign is used to separate the Name and Region into two columns. =A2 & "$" & B2 3. Copy the formula down to the last row of data. 4. Follow Steps 1 to 9 in the previous Normalizing the Data instructions, using columns C:O as the source range for the pivot table. 5. In the resulting list of data, rename the heading cells as NameRegion, Month, and Amount. 6. Select Column A (NameRegion), and move it to the right of the other columns. This prevents it from overwriting the other columns when you separate Name and Region in the next step. 7. With the NameRegion column selected, click the Data tab on the Ribbon. 8. In the Data Tools group, click Text to Columns (see Figure 1-6).

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As you can see, all the data problems are due to concurrent access you can safely assume that a transaction executed in isolation will always leave the database in a consistent state when the transaction completes. If the database permits concurrent access, then you need to consider the cumulative effect of all the transactions on database consistency. To do this, the database uses a schedule, which is a sequence of operations from one or more transactions. If all the transactions executed serially, one after another, the schedule would also be serial. If the database can produce a schedule that is equivalent in its effect to a serial schedule, even though it may be derived from a set of concurrent transactions, it is called a serializable schedule. The serializable schedule consists of a series of intermingled database operations drawn from several transactions, the final outcome of which is a consistent database. As you can surmise, deriving a schedule is not easy in practice. However, users don t have to concern themselves with the mechanics of serialization when they use their transactions. The Oracle database automatically derives serializable schedules through the use of isolation levels and the management of undo data. Let s look at these important concepts next.

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