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Part IV:

ASM automatically stripes files across multiple physical disks, which increases the access performance by spreading I/O over multiple disk heads This placement avoids excessive hot spots in disk files, and also contributes to better I/O performance ASM normally creates stripes based on the size of the allocation unit (AU) assigned to a disk group, but for some files, such as redo logs, ASM stripes are smaller to reduce I/O latency on these crucial files The I/O performance produced by ASM is roughly equivalent to the performance of a raw partition, without having the more extensive management overhead of raw storage In some cases, such as a stretch cluster, where nodes of a cluster are widely separated, ASM can give better performance by reading from a more local mirrored copy of data, rather than trying to access the primary copy at a more distant location This capability is configured by means of an initialization parameter Oracle Database 11g Release 2 introduced a new performance feature for ASM called Intelligent Data Placement A disk is circular, which means that the outer tracks of the disk are moving faster than the inner tracks, resulting in faster read times for data on the outer tracks You can specify that data for a file or an entire disk group is either on the outside or inside of the disk, providing better performance for the outer sections This Intelligent Data Placement works well with two disk groups, one for data and one for the Fast Recovery Area (FRA), which is used for backups The FRA can go on the inner tracks of the disk, ceding the better-performing sections to the data disk groups that are in active production use

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its relevant dimensions, and then joining the resultant information in what appears to be a full outer join through the dimensions This is known as a stitch query Joining in this fashion allows the stitch query to accommodate situations in which one set of facts exists for a particular dimensional entity while another set of facts does not For example, if you have a forecast for a new backpack called the Day Hiker 3, you may not have any sales yet for that product You might have some sales for an older backpack, say the Day Hiker 1, though you might not have forecast any sales for it because it is no longer being promoted The Day Hiker 2 backpack might have both sales and forecast information If you create a report for products, sales, and forecast, and if you have properly modeled the situation, the report will produce a stitch query That stitch query will first retrieve the sales data for the Day Hiker 1 and the Day Hiker 2 backpacks, as well as the forecast information for the Day Hiker 2 and Day Hiker 3 backpacks Stitching those two results sets together, Cognos 8 will display that you have only sales for the Day Hiker 1, sales and forecast for the Day Hiker 2, and only forecast for the Day Hiker 3 This is effectively a full outer join without having to model explicit outer joins in the relationships among these tables You will notice a coalesce statement if you look at the generated SQL in a report that uses a stitch query

In the Relationship Definition dialog s Relationship SQL tab, shown in Figure 15-28, Framework Manager will generate the SQL necessary to retrieve all of the query items from the two query subjects specified, joined by the link criteria in your relationship definition You can check the SQL to verify the join criteria You can also click the Test button to have Framework Manager execute the SQL to return a sampling of the rows in the result set You can then look at the results to see if the relationship criteria return the expected results

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