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Provides equal distribution of work across servers Used when one result set is much smaller than the other, so sending the smaller result set to the larger set is more efficient Used for parallel sort operations so further sorting is not required Used for some types of joins so that only one side of the join needs redistribution Typically used as the final redistribution before data is returned, when the order of access does not matter

If this were part of a real application, it might make sense to make this an AJAX-enabled web site and wrap all this in an UpdatePanel to get much snappier performance. For this example, that is neither necessary nor particularly noticeable.

When Einstein developed his general theory of relativity, some of the paradoxes inherent in special relativity were resolved (These paradoxes are avoided here because discussing them would only confuse you) In particular, light rays from distant stars were observed as they passed close to the Sun to see whether or not the Sun s gravitational field, which is quite strong near its surface, would bend the light rays This bending would be observed as a change in the apparent position of a distant star in the sky as the Sun passes close to it

RANGE KEY ROUND ROBIN TABLE 2-2

Incredibly extreme acceleration!

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The results of the join are sent to a set of parallel servers for sorting, and finally to a set of parallel servers for the aggregation process Although this discussion has mentioned several different sets of parallel servers, any SQL statement can only use two sets of parallel servers at a time These two sets of servers can act together, as producers and consumers In this example, the set of parallel servers that was going to perform the sort act as consumers of the results from the parallel servers that were doing the join operation

Drag a Label control onto the page and set its ID to lblMessage. Clear its Text property. Drag a DropDownList control onto the page. Set its ID to ddl and its Visible property to False, so that it will initially be invisible. The Design view will look something like Figure 7-11.

Laser Incredibly extreme force! Figure 16-10 If the acceleration is great enough, the spatial curvature becomes extreme

The reason why the previous pages went into some depth on the mechanics of parallel processing is to allow you to understand one of the great goals of parallel execution: partition-wise parallel joins Parallel processing is whizzing around in the Oracle database, with loads of parallel processes working together, completing tasks in a fraction of the time of a single serial process Allowing parallelization for subtasks is a great way to amplify the overall gains from parallelism, but this method can also add some overhead In the previous description, you may have noticed one particular type of overhead that could be significant the redistribution of data in order to perform a join There is a way you can completely avoid this overhead through the use of parallelism in coordination with partitioning Figure 2-12 illustrates the difference between a standard parallelized join operation, which requires redistribution of data, and a partition-wise parallel join In order to accomplish a partition-wise parallel join, both tables have to be equipartitioned using the same partitioning method, and both tables have to have an equal number of partitions In the join illustrated on the right in Figure 2-12, the two tables that are to be joined are both partitioned on the key value used in the join The underlying partitions ensure that the results of a table scan from a partition on one table will only ever be joined with the results of the table scan on the matching partition in the other table By eliminating the need to redistribute data, a partition-wise parallel join can deliver better response time than queries that require standard joins The superior performance of the partition-wise parallel join can influence

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