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decisions in the advice See section 326 for more details on capturing the exception object 324 The around advice The around advice surrounds the join point It has the ability to bypass the execution of the captured join point completely, or to execute the join point with the same or different arguments It may also execute the captured join points multiple times, each with different arguments Some typical uses of this advice are to perform additional execution before and after the advised join point, to bypass the original operation and perform some other logic in place of it, or to surround the operation with a try/catch block to perform an exception-handling policy If within the around advice you want to execute the operation that is at the join point, you must use a special keyword proceed() in the body of the advice.

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There s no built-in option to group by fiscal quarter. In the source data, you can add a column that calculates the fiscal quarter, then add that field to the pivot table.

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The use of statistics in cost-based optimization is not very complex Most database professionals interviewed seem to think the gathering and application of statistics is a complex and vital element of query optimization Although it is true that cost-based query optimization and even hybrid optimization schemes use statistics for cost and/or ranking, they are neither complex nor critical Take, for instance, the concept of evenly distributed values among attributes This concept alone is proof of the imprecise nature of the application of statistics Statistical calculations are largely categorical in nature and not designed to generate a precise value They merely assist in determining whether one query execution plan is generally more costly than another Frequency distribution of attribute values is a common method for predicting the size of query results.

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Unless you call proceed(), the captured join point will be bypassed When using proceed(), you can pass the context collected by the advice, if any, as the arguments to the captured operation or you can pass completely different arguments The important thing to remember is that you must pass the same number and types of arguments as collected by the advice Since proceed() causes the execution of the captured operation, it returns the same value returned by the captured operation For example, while in an advice to a method that returns a float value, invoking proceed() will return the same float value as the captured method We will study the details of returning a value from an around advice in section 327 In the following snippet, the around advice invokes proceed() with a try/catch block to handle exceptions.

By forming a distribution of possible (or actual5) values of an attribute, the database system can use the distribution to calculate a cost for a given query plan by predicting the number of tuples (or rows) that the plan must process Modern database systems, however, deal with frequency distributions of individual attributes only, because considering all possible combinations of attributes is very expensive This essentially corresponds to what is known as the attribute value independence assumption, and although rarely true, it is adopted by almost all relational database systems Gathering the distribution data requires either constant updating of the statistics or predictive analysis of the data Another tactic is the use of uniform distributions where the distribution of the attribute values is assumed to be equal for all distinct values.

This snippet also captures the context of the operation s target object and argument We discuss that part in section 326:.

You renamed an item, changing it from 3.00 to 3.01, and now you get an unusual error message when you try to group the items (see Figure 2-8).

For example, given 5,000 tuples and a possible 50 values for a given attribute, the uniform distribution assumes each value is represented 100 times This is rarely the case and is often incorrect However, given the absence of any statistics, it is still a reasonable approximation of reality in many cases The memory requirements and running time of dynamic programming grow exponentially with query size (ie, number of joins) in the worst case since all viable partial plans generated in each step must be stored to be used in the next one In fact, many modern systems place a.

void around(Account account, float amount) throws InsufficientBalanceException : call(* Account.debit(float) throws InsufficientBalanceException) && target(account) && args(amount) { try { proceed(account, amount); } catch (InsufficientBalanceException ex) { ... overdraft protection logic } }

In the previous advice, the advised join point is the call to the Account.debit() method that throws InsufficientBalanceException. We capture the Account

Figure 2-8. The error message that appears when you try to group items in the field with the renamed item

4. This practice is still in use today by most commercial database systems. 5. The accumulation of statistics in real time is called piggyback statistic generation.

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