Expert Oracle Exadata - Parent Directory

CHAPTER 2 OFFLOADING / SMART SCAN35tqjjq5vzg4b 0 Yes 3.00 59.79 select count(*) from1 row selected.SYS@SANDBOX1> alter session set "_cell_offload_virtual_columns"=true;Session altered.SYS@SANDBOX1> select count(*) from kso.temp_skew where col1_plus_pk=27998244;COUNT(*)----------21 row selected.SYS@SANDBOX1> @fsx4Enter value for sql_text: select count(*) from kso.temp_skew where col1_plus_pk=27998244Enter value for sql_id:Enter value for inst_id:SQL_ID CHILD OFFLOAD AVG_ETIME IO_SAVED_% SQL_TEXT------------- ------ ------- ------------- ---------- --------------------35tqjjq5vzg4b 0 Yes 3.00 59.79 select count(*) from35tqjjq5vzg4b 0 Yes 1.19 99.99 select count(*) from2 rows selected.This example shows that virtual column evaluation can be offloaded. It also shows that theoptimization can be controlled using the _CELL_OFFLOAD_VIRTUAL_COLUMNS parameter. Note that storageindexes are not built on virtual columns. As with Function Offloading, the real advantage of offloadingvirtual column calculations has more to do with reducing the volume of data returned to the databaseservers than with reducing CPU usage on the database tier.Data Mining Model ScoringSome of the data model scoring functions can be offloaded. Generally speaking this optimization isaimed at reducing the amount of data transferred to the database tier as opposed to pure CPUoffloading. As with other function Offloading, you can verify which data mining functions can beoffloaded by querying V$SQLFN_METADATA. The output looks like this:SYS@SANDBOX> select distinct name, version, offloadable2 from V$SQLFN_METADATA3 where name like 'PREDICT%'4 order by 1,2;NAME VERSION OFFLOADABLE------------------------------ ------------ -----------PREDICTIONV10R2 Oracle YESPREDICTION_BOUNDSV11R1 Oracle NOPREDICTION_COSTV10R2 Oracle YESPREDICTION_DETAILSV10R2 Oracle NO43