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more data must be physically read from physical devices, the more physical devices have to work, the longer it takes. It s not a database problem; it s a physics problem. Now if you recall from previous chapters, I ve spent some time showing you the consequences of physics and a database. You can use several tactics to divide and conquer in order to improve the speed and efficiency of retrieving data for reporting: Use pseudo-constants and PL/SQL IF statements to replace code tables in a FROM clause of a SELECT statement. This reduces the use of physical devices. Retrieve data from the smallest table that will produce the smallest set of results, and then do programmatic nested loops with cursors to retrieve the rest of the data. This reduces the use of physical devices. Create thin (narrow) row extracts of data, and then join them together in a SELECT statement. This reduces the use of memory and physical devices. For example, if the source table has an average row size of 297 bytes, you may extract data from it into a temporary table that has an average row size of 13 bytes. The temporary table is what I call a narrow row table. Post-sort results. Sort the rows for a report after the report data has already been placed in a staging table(s). This reduces the use of memory and CPU cycles. However, you can use these tactics only if you write stored procedures to produce the result sets for your reports, placing the results in staging tables, which are then later queried by the presentation layer. What I m advocating here is heresy to some, and bliss for others. I know many of you may want to send me an email explaining why this is such a bad idea. Do it. I would love to argue with you about it. Here s what I do know: In applications with well over one million rows in each content table, I m able to produce short reports in less than 3 seconds, most in less than 1 second. That s compared to over 3 minutes trying to accomplish the same task with a large, complicated SQL SELECT statement. In the same large enterprise applications, I ve been able to produce large reports in less than 3 minutes. That s compared to 20 minutes to 28 hours for some of the largest reports. Yes, even reports that took 28 hours to produce using a very large and complicated SQL SELECT statement took less than 3 minutes when these divide and conquer principles were applied. And no, there were no accidental Cartesian products in the SELECT statement. When this architecture is adopted, the business intelligence on how to produce the report permanently resides in the database where it is easiest and most efficient to process data. This means that any presentation layer can be used to produce the result set for a given report by executing its corresponding stored procedure in the database, and then simply formatting and displaying the data in whatever fashion is desired. Presentation layers come and go. Does the business knowledge that produces your reports have to go with it No. Figure 10-8 demonstrates the reporting architecture I m advocating. In this architecture, producing a report consists of these three high-level steps:
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1. The presentation layer calls a function get_report() in the database, passing it any required parameters for producing a report. 2. get_report() produces the result set for the report, placing the results in a staging table specific to the report. Then get_report() returns an ID for the results. 3. The presentation layer executes a SELECT statement against the report s staging table, where the report ID is the one returned from get_report(), and orders the results by the report s sequence.
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