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Determining and Setting the Status of the Statistics
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Execute the DBMS_STATS.GET_PREFS procedure to determine the publishing status of statistics in your database: SQL> select dbms_stats.get_prefs('PUBLISH') publish from dual; PUBLISH -------TRUE SQL>
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CHAPTER 19 IM PR OVING DA TA BAS E PERFORM ANC E: S QL QUE RY OPTIMIZA TION
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The value TRUE indicates that the database automatically publishes all statistics after it collects them. This is the default behavior of the database. If the query had returned the value FALSE, it means that the database will keep new statistics pending until you decide to formally publish them. You can also execute the GET_PREFS function to find out the publishing mode for a single table: SQL> SELECT dbms_stats.get_prefs('PUBLISH','stats','test_table') FROM dual; You can change the publishing settings for objects at the database or at the object (table) level by executing the SET_TABLE_PREFS function. For example, to keep the database from automatically publishing the statistics it collects for the table EMPLOYEES, execute this function: SQL> exec dbms_stats.set_table_prefs ('HR','EMPLOYEES', 'PUBLISH','FALSE'); The database stores pending statistics in the DBA_TAB_PENDING_STATS view and it stores the published statistics in the DBA_TAB_STATS view.
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You can test any pending statistics in your database to see how they affect performance. If they help performance, you can publish them for use by the optimizer; otherwise, just drop the statistics. You publish the pending statistics, that is, make them available to the optimizer for testing purposes, by setting the initialization parameter OPTIMIZER_USE_PENDING_STATISTICS. By default, this parameter is set to FALSE, which means the optimizer will bypass the pending statistics, as shown here: SQL> show parameter optimizer_use_pending_statistics NAME -------------------------------optimizer_use_pending_statistics SQL> You can make the optimizer take the pending statistics into account by setting the OPTIMIZER_ USE_PENDING_STATISTICS parameter to TRUE, as shown here: SQL> ALTER SESSION SET optimizer_use_pending_statistics=TRUE ; The optimizer will use the pending statistics once you run the previous statement. Once your tests confirm that the new statistics are OK, you can make the pending statistics public by executing the PUBLISH_PENDING_STATS procedure: SQL> EXEC dbms_stats.publish_pending_stats (NULL,NULL); If you want to publish statistics for a single table, you can do so as well: SQL> EXEC dbms_stats.publish_pending_stats('HR','EMPLOYEES'); If you conclude, on the other hand, that the pending statistics aren t helpful, delete them by executing the DELETE_PENDING_STATS procedure: SQL> EXEC dbms_stats.delete_pending_stats ('HR','EMPLOYEES'); You can also test the pending statistics you collect in one database in another database, by using the EXPORT_PENDING_STATS procedure: SQL> EXEC dbms_stats.export_pending_stats ('HR', 'EMPLOYEES'); TYPE ------boolean VALUE -----FALSE
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CHAPTER 19 IMPROV IN G DA TA BAS E PERFORMAN CE: SQL QUERY OPTIMIZA TION
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The statistics that the database collects are sometimes unrepresentative of the true data. Oracle provides the capability for collecting extended statistics under some circumstances to mitigate the problems in statistics collection. Extended statistics include the collection of multicolumn statistics for groups of columns and expression statistics that collect statistics on function-based columns. I explain both types of extended optimizer statistics in the following sections.
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When Oracle collects statistics on a table, it estimates the selectivity of each column separately, even when two or more columns may be closely related. Oracle assumes that the selectivity estimates of the individual columns are independent of each other and simply multiplies the independent predicates selectivity to figure out selectivity of the group of predicates. This approach leads to an underestimation of the true selectivity of the group of columns. You can collect statistics for a group of columns to avoid this underestimation. I use a simple example to show why collecting statistics for column groups instead of individual columns is a good idea when the columns are related. In the SH.CUSTOMERS table, the CUST_ STATE_PROVINCE and the COUNTRY_ID columns are correlated, with the former column determining the value of the latter column. Here s a query that shows the relationship between the two columns: SQL> SELECT count(*) FROM sh.customers WHERE cust_state_province = 'CA'; COUNT(*) ---------3341 SQL> The previous query uses only a single column, CUST_STATE_PROVINCE, to get a count of the number of customers from the province named CA. The following query also involves the COUNTRY_ID column, but returns the same count, 3341. SQL> SELECT count(*) FROM customers WHERE cust_state_province = 'CA' AND country_id=52790; COUNT(*) ---------3341 SQL> Obviously, the same query with a different value for the COUNTRY_ID column will return a different count (most likely zero, since CA stands for California and it s unlikely that a city of the same name is present in other countries). You can collect statistics on a set of related columns such as CUST_STATE_PROVINCE and COUNTRY_ID by estimating the combined selectivity of the two columns. The database can collect statistics for column groups based on the database workload, but you create column groups by using the DBMS_STATS.CREATE_EXTENDED_STATS function, as I explain next.
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