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This log table holds information about how long it took to execute the process, row processing statistics, and the time when the process was started and stopped. The following is an example of the method start_logging() from package ON_DEMAND_PROCESS_LOGS: PROCEDURE start_logging( aiv_object_name in ON_DEMAND_PROCESS_LOG.object_name%TYPE, aiv_method_name in ON_DEMAND_PROCESS_LOG.method_name%TYPE) is pragma autonomous_transaction; begin n_start := to_number(to_char(SYSDATE, 'SSSSS')); n_on_demand_process_log_id := get_id();
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insert into ON_DEMAND_PROCESS_LOG ( on_demand_process_log_id, object_name, method_name ) values ( n_on_demand_process_log_id, upper(aiv_object_name), upper(aiv_method_name) ); commit; end start_logging; Variable n_start in method start_logging() is an instance (package body) variable used to capture the time when the method was called. Similarly, variable n_on_demand_process_ log_id is used to capture the new log entry s primary key so the same entry can be updated in method stop_logging(). In addition, this method employs the use of pragma autonomous_ transaction so a process that starts but fails will still leave a starting entry in the log. Now let s look at stop_logging().
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stop_logging()
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Method stop_logging() takes the row processing information and a result description passed to it, and uses that information along with the stored start time and primary key to update the starting log entry. As part of updating the log entry, the method calculates the elapsed time in seconds between the start and stop of logging. The following is an example of the method from package ON_DEMAND_PROCESS_LOGS: PROCEDURE stop_logging( ain_rows_selected in ON_DEMAND_PROCESS_LOG.rows_selected%TYPE, ain_rows_inserted in ON_DEMAND_PROCESS_LOG.rows_inserted%TYPE, ain_rows_updated in ON_DEMAND_PROCESS_LOG.rows_updated%TYPE, ain_rows_deleted in ON_DEMAND_PROCESS_LOG.rows_deleted%TYPE, aiv_result in ON_DEMAND_PROCESS_LOG.result%TYPE) is pragma autonomous_transaction; n_elapsed_time number;
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begin n_elapsed_time := to_number(to_char(SYSDATE, 'SSSSS')) - n_start;
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update ON_DEMAND_PROCESS_LOG set rows_selected rows_inserted rows_updated rows_deleted result elapsed_time update_user update_date where on_demand_process_log_id commit;
= = = = = = = = =
ain_rows_selected, ain_rows_inserted, ain_rows_updated, ain_rows_deleted, aiv_result, n_elapsed_time, USER, SYSDATE n_on_demand_process_log_id;
n_on_demand_process_log_id := NULL; -- Display the results if ain_rows_selected is not null then pl(to_char(ain_rows_selected)||' rows selected.'); end if; if ain_rows_inserted is not null then pl(to_char(ain_rows_inserted)||' rows inserted.'); end if; if ain_rows_updated is not null then pl(to_char(ain_rows_updated)||' rows updated.'); end if; if ain_rows_deleted is not null then pl(to_char(ain_rows_deleted)||' rows deleted.'); end if; if aiv_result is not null then pl(aiv_result); end if; pl('Elapsed time: '||to_char(n_elapsed_time)||' seconds.'); end stop_logging; In addition to recording the log statistics, stop_logging() also echoes those statistics to the screen if serveroutput is set to on in SQL*Plus. Like its sibling start_logging(), this method employs the use of pragma autonomous_transaction, so a process that fails can still leave a completed log entry. Having log information about when on-demand processes execute is an important part of managing their use. Access to statistics about each on-demand process is even more invaluable. Using these statistics, you can determine whether a process is fast and efficient, and make adjustments accordingly. If you find you re running an on-demand process every few minutes, or if you re interested in offloading a long-running process from the presentation layer, then the process is probably better done as a polling process. Let s look at that next.
CHAPTER 10 FAIRY TALES
Polling Data Processing
Unlike an on-demand process, a polling data process (PDP) just keeps running. It follows three high-level steps: 1. Check a queue for commands. 2. Process the next command. 3. Sleep for a predetermined period of time, then go back to step 1. I ll give you three common examples here of using divide and conquer to offload a portion of data processing to the database rather than the presentation or application layer. Doing so makes the presentation or application layer appear to be fast and efficient. And, since the best place to perform data processing is in the database, the offloaded portion of the larger process is fast and efficient. First, let s look at an example with the application layer. Let s say you have web or message services that, in turn, update your database whenever there is a transaction on an external system. Rather than tie up the services with the entire business process, you can have your services update a set of staging tables in your database, and then have a polling process on your database wake up every so often and finish processing the staged data. A second example is one where you have a long-running process in the presentation layer. Let s say you have a security system where an administrator can assign access to worker data based on business organization, location, and so on. Rather than tie up the administrator s computer while the mass assignments take place, you can have the presentation layer submit the processing request to your polling process s queue. Then, when your polling process wakes up from its sleep period, it will process your long-running request in the background. What s the result To the end user using the presentation layer, the application appears easy, fast, and efficient. A third example is along the same line of thinking. This time, however, you set up your end user s reporting subsystem to submit long-running reports to your polling process s queue, and then have your polling process email the end user with a link (URL) to a completed report when it finishes executing the report in the background. Once again, the end user s experience is one where submitting the report is easy, fast, and efficient. Using a polling process requires a different design than an on-demand process. Instead of logging each time a process executes (which you can still do), you need to create a queue for process and processing-related commands, and possibly a table to hold the current status of the polling process. I ll start out by showing you an example of the process method, and then follow up with discussions on each of the supporting methods.
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