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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

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.

utl_file, and so on become viable (see 13). Of course, if the data source is within the database (e.g., in some other tables), or it is in some other database accessible to your database, you can and should use straight SQL-based solutions such as insert into <table_name> select col1, co2 from ....

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Listing 5-1. Contents of a Basic npt.conf Configuration File server xxx.xxx.xxx.xxx server 127.127.1.0 fudge 127.127.1.0 stratum 10 driftfile /var/lib/ntp/ntp.drift The xxx.xxx.xxx.xxx in Listing 5-1 represents the IP address of a device acting as an NTP server to this client. The 127.127.1.0 address represents the local clock (also referred to as undisciplined local clock), which is intended to act as a backup in case the connectivity to the xxx.xxx.xxx.xxx server is lost. The fudge command that normally follows the server command that s referencing the local clock can be used with several optional parameters. In the preceding example, the fudge command uses only the stratum option to identify a high stratum level. A high stratum number is assigned to the local clock

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 SHCUSTOMERS 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 shcustomers 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.

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