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• ?12c database ????Adaptive Execution Plans ????????

  - by Liu Maclean(???)

  12c R1 ????SQL??????- Adaptive Execution Plans ????????,???????optimizer ??????(runtime)???????????????, ????????????????????? SQL???????? ????????????, ?????????????????????????????????????????????????????????????adaptive plan ????????????????????????????????????,?????subplan???????????????????? ??????, ???????? ???????????????,?????????, ?????? ???????????????”???”????, ???????????????????buffer ???????  ????????????,?????,??????????????????? ???optimizer ?????????????????????????,?????????????????????????????????????????plan???? ??12C?????????????, ???????????????????,?????? ???????????? ????????????2???: Dynamic Plans????: ???????????????????????;??????,???optimizer??????????subplans??????????????, ???????????????????,?????????????? Reoptimization????: ?Dynamic Plans????,Reoptimization??????????????????????Reoptimization??,?????????????????????????,??reoptimization????? OPTIMIZER_ADAPTIVE_REPORTING_ONLY ???? report-only????????????????TRUE,?????????report-only????,???????????????,??????????????? Dynamic Plans ??????????????,????????????????????????, ?????????????,???????????,????????????????????????????????????????? ?????????????final plan??????????????default plan, ??final plan?default plan???????,????????????? subplan ???????????????,???????????????????????? ??????,???????statistics collector ?buffer???????????statistics collector?????????????????,???????????????????????????? ?????????????????????????????????????????,??????????,?????????????? ???????????,???????buffer???? ???????????????,?????????????????????????????,??????buffer,??????final plan? ????????,???????????????????????,????????????????? ?V$SQL??????IS_RESOLVED_DYNAMIC_PLAN??????????final plan???default plan? ??????dynamic plan ???????SQL PLAN directives?????? declare cursor PLAN_DIRECTIVE_IDS is select directive_id from DBA_SQL_PLAN_DIRECTIVES; begin for z in PLAN_DIRECTIVE_IDS loop DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE(z.directive_id); end loop; end; / explain plan for select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; select * from table(dbms_xplan.display()); Plan hash value: 1255158658 www.askmaclean.com ------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time | ------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 4 | 128 | 7 (0)| 00:00:01 | | 1 | NESTED LOOPS | | | | | | | 2 | NESTED LOOPS | | 4 | 128 | 7 (0)| 00:00:01 | |* 3 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 (0)| 00:00:01 | |* 4 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK | 1 | | 0 (0)| 00:00:01 | | 5 | TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION | 1 | 20 | 1 (0)| 00:00:01 | ------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1) 4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") alter session set events '10053 trace name context forever,level 1'; OR alter session set events 'trace[SQL_Plan_Directive] disk highest'; select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id; ---------------------------------------------------------------+-----------------------------------+ | Id | Operation | Name | Rows | Bytes | Cost | Time | ---------------------------------------------------------------+-----------------------------------+ | 0 | SELECT STATEMENT | | | | 7 | | | 1 | HASH JOIN | | 4 | 128 | 7 | 00:00:01 | | 2 | NESTED LOOPS | | | | | | | 3 | NESTED LOOPS | | 4 | 128 | 7 | 00:00:01 | | 4 | STATISTICS COLLECTOR | | | | | | | 5 | TABLE ACCESS FULL | ORDER_ITEMS | 4 | 48 | 3 | 00:00:01 | | 6 | INDEX UNIQUE SCAN | PRODUCT_INFORMATION_PK| 1 | | 0 | | | 7 | TABLE ACCESS BY INDEX ROWID | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | | 8 | TABLE ACCESS FULL | PRODUCT_INFORMATION | 1 | 20 | 1 | 00:00:01 | ---------------------------------------------------------------+-----------------------------------+ Predicate Information: ---------------------- 1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") 5 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1)) 6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID") ===================================== SPD: BEGIN context at statement level ===================================== Stmt: ******* UNPARSED QUERY IS ******* SELECT /*+ OPT_ESTIMATE (@"SEL$1" JOIN ("P"@"SEL$1" "O"@"SEL$1") ROWS=13.000000 ) OPT_ESTIMATE (@"SEL$1" TABLE "O"@"SEL$1" ROWS=13.000000 ) */ "P"."PRODUCT_NAME" "PRODUCT_NAME" FROM "OE"."ORDER_ITEMS" "O","OE"."PRODUCT_INFORMATION" "P" WHERE "O"."UNIT_PRICE"=15 AND "O"."QUANTITY">1 AND "P"."PRODUCT_ID"="O"."PRODUCT_ID" Objects referenced in the statement PRODUCT_INFORMATION[P] 92194, type = 1 ORDER_ITEMS[O] 92197, type = 1 Objects in the hash table Hash table Object 92197, type = 1, ownerid = 6573730143572393221: No Dynamic Sampling Directives for the object Hash table Object 92194, type = 1, ownerid = 17822962561575639002: No Dynamic Sampling Directives for the object Return code in qosdInitDirCtx: ENBLD =================================== SPD: END context at statement level =================================== ======================================= SPD: BEGIN context at query block level ======================================= Query Block SEL$1 (#0) Return code in qosdSetupDirCtx4QB: NOCTX ===================================== SPD: END context at query block level ===================================== SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Inserted felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: qosdCreateFindingSingTab retCode = CREATED, fid = 2896834833840853267 SPD: qosdCreateDirCmp retCode = CREATED, fid = 2896834833840853267 SPD: Return code in qosdDSDirSetup: NOCTX, estType = TABLE SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SKIP_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = JOIN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_SCAN SPD: Return code in qosdDSDirSetup: NOCTX, estType = INDEX_FILTER SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92197, objtyp = 1, vecsize = 6, colvec = [4, 5, ], fid = 2896834833840853267 SPD: Modified felem, fid=2896834833840853267, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = YES, keep = YES SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 5618517328604016300 SPD: Modified felem, fid=5618517328604016300, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 1, objcnt = 1, obItr = 0, objid = 92194, objtyp = 1, vecsize = 2, colvec = [1, ], fid = 1142802697078608149 SPD: Modified felem, fid=1142802697078608149, ftype = 1, freason = 1, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO SPD: Generating finding id: type = 1, reason = 2, objcnt = 2, obItr = 0, objid = 92194, objtyp = 1, vecsize = 0, obItr = 1, objid = 92197, objtyp = 1, vecsize = 0, fid = 1437680122701058051 SPD: Modified felem, fid=1437680122701058051, ftype = 1, freason = 2, dtype = 0, dstate = 0, dflag = 0, ver = NO, keep = NO select * from table(dbms_xplan.display_cursor(format=>'report')) ; ????report????adaptive plan Adaptive plan: ------------- This cursor has an adaptive plan, but adaptive plans are enabled for reporting mode only.  The plan that would be executed if adaptive plans were enabled is displayed below. ------------------------------------------------------------------------------------------ | Id  | Operation          | Name                | Rows  | Bytes | Cost (%CPU)| Time     | ------------------------------------------------------------------------------------------ |   0 | SELECT STATEMENT   |                     |       |       |     7 (100)|          | |*  1 |  HASH JOIN         |                     |     4 |   128 |     7   (0)| 00:00:01 | |*  2 |   TABLE ACCESS FULL| ORDER_ITEMS         |     4 |    48 |     3   (0)| 00:00:01 | |   3 |   TABLE ACCESS FULL| PRODUCT_INFORMATION |     1 |    20 |     1   (0)| 00:00:01 | ------------------------------------------------------------------------------------------ SQL> select SQL_ID,IS_RESOLVED_DYNAMIC_PLAN,sql_text from v$SQL WHERE SQL_TEXT like '%MALCEAN%' and sql_text not like '%like%'; SQL_ID IS -------------------------- -- SQL_TEXT -------------------------------------------------------------------------------- 6ydj1bn1bng17 Y select /*MALCEAN*/ product_name from oe.order_items o, oe.product_information p where o.unit_price=15 and quantity>1 and p.product_id=o.product_id ???? explain plan for ????default plan, ??????optimizer???final plan,??V$SQL.IS_RESOLVED_DYNAMIC_PLAN???Y,????????????? DBA_SQL_PLAN_DIRECTIVES?????????????SQL PLAN DIRECTIVES, ???12c? ???MMON?????DML ???column usage??????????,????SMON??? MMON????SGA??PLAN DIRECTIVES??? ?????DBMS_SPD.flush_sql_plan_directive???? select directive_id,type,reason from DBA_SQL_PLAN_DIRECTIVES / DIRECTIVE_ID TYPE REASON ----------------------------------- -------------------------------- ----------------------------- 10321283028317893030 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 4757086536465754886 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE 16085268038103121260 DYNAMIC_SAMPLING JOIN CARDINALITY MISESTIMATE SQL> set pages 9999 SQL> set lines 300 SQL> col state format a5 SQL> col subobject_name format a11 SQL> col col_name format a11 SQL> col object_name format a13 SQL> select d.directive_id, o.object_type, o.object_name, o.subobject_name col_name, d.type, d.state, d.reason 2 from dba_sql_plan_directives d, dba_sql_plan_dir_objects o 3 where d.DIRECTIVE_ID=o.DIRECTIVE_ID 4 and o.object_name in ('ORDER_ITEMS') 5 order by d.directive_id; DIRECTIVE_ID OBJECT_TYPE OBJECT_NAME COL_NAME TYPE STATE REASON ------------ ------------ ------------- ----------- -------------------------------- ----- ------------------------------------- --- 1.8156E+19 COLUMN ORDER_ITEMS UNIT_PRICE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 TABLE ORDER_ITEMS DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1.8156E+19 COLUMN ORDER_ITEMS QUANTITY DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE DBA_SQL_PLAN_DIRECTIVES????? _BASE_OPT_DIRECTIVE ? _BASE_OPT_FINDING SELECT d.dir_own#, d.dir_id, d.f_id, decode(type, 1, 'DYNAMIC_SAMPLING', 'UNKNOWN'), decode(state, 1, 'NEW', 2, 'MISSING_STATS', 3, 'HAS_STATS', 4, 'CANDIDATE', 5, 'PERMANENT', 6, 'DISABLED', 'UNKNOWN'), decode(bitand(flags, 1), 1, 'YES', 'NO'), cast(d.created as timestamp), cast(d.last_modified as timestamp), -- Please see QOSD_DAYS_TO_UPDATE and QOSD_PLUS_SECONDS for more details -- about 6.5 cast(d.last_used as timestamp) - NUMTODSINTERVAL(6.5, 'day') FROM sys.opt_directive$ d ??dbms_spd??? SQL PLAN DIRECTIVES, SQL PLAN DIRECTIVES???retention ???53?: Package: DBMS_SPD This package provides subprograms for managing Sql Plan Directives(SPD). SPD are objects generated automatically by Oracle server. For example, if server detects that the single table cardinality estimated by optimizer is off from the actual number of rows returned when accessing the table, it will automatically create a directive to do dynamic sampling for the table. When any Sql statement referencing the table is compiled, optimizer will perform dynamic sampling for the table to get more accurate estimate. Notes: DBMSL_SPD is a invoker-rights package. The invoker requires ADMINISTER SQL MANAGEMENT OBJECT privilege for executing most of the subprograms of this package. Also the subprograms commit the current transaction (if any), perform the operation and commit it again. DBA view dba_sql_plan_directives shows all the directives created in the system and the view dba_sql_plan_dir_objects displays the objects that are included in the directives. -- Default value for SPD_RETENTION_WEEKS SPD_RETENTION_WEEKS_DEFAULT CONSTANT varchar2(4) := '53'; | STATE : NEW : Newly created directive. | : MISSING_STATS : The directive objects do not | have relevant stats. | : HAS_STATS : The objects have stats. | : PERMANENT : A permanent directive. Server | evaluated effectiveness and these | directives are useful. | | AUTO_DROP : YES : Directive will be dropped | automatically if not | used for SPD_RETENTION_WEEKS. | This is the default behavior. | NO : Directive will not be dropped | automatically. Procedure: flush_sql_plan_directive This procedure allows manually flushing the Sql Plan directives that are automatically recorded in SGA memory while executing sql statements. The information recorded in SGA are periodically flushed by oracle background processes. This procedure just provides a way to flush the information manually. ????”_optimizer_dynamic_plans”(enable dynamic plans)????????,???TRUE??DYNAMIC PLAN? ???FALSE???????????? ????,Dynamic Plan????????????Nested Loop?Hash Join???case ,????????Nested loop???????????HASH JOIN,?HASH JOIN????????????????? ????????subplan?????,???? pass?? ?join method???,?????STATISTICS COLLECTOR???cardinality?,???????HASH JOIN?????Nested Loop,????????????subplan?????access path; ???????Sales??????????????????,????HASH JOIN,??SUBPLAN??customers?????????;?????Nested Loop,???????cust_id?????Range Scan+Access by Rowid? Cardinality feedback Cardinality feedback????????11.2????,????????re-optimization???;  ???????????,Cardinality feedback?????????????????????????? ???????????????????,?????????????????,??????????Cardinality feedback????????????? ????????????????????????? ??????????????Cardinality feedback ??: ????????,???????????,??????????,????????????????selectivity ??? ????????????: ??????,?????????????????????????????????,??????????????????? ????????????????????????????????????????,?????????????????????????? ?????????,???????????????,?????????? ??????????Cardinality ????,??????join Cardinality ????????? Cardinality feedback???????cursor?,?Cursor???aged out????? SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ---------------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | OMem | 1Mem | Used-Mem | ---------------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | 20 | | | | |* 1 | HASH JOIN | | 1 | 4 | 13 |00:00:00.01 | 24 | 20 | 2061K| 2061K| 429K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 4 | 13 |00:00:00.01 | 7 | 6 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.01 | 17 | 14 | | | | ---------------------------------------------------------------------------------------------------------------------------------------- SELECT /*+ gather_plan_statistics */ product_name FROM order_items o, product_information p WHERE o.unit_price = 15 AND quantity > 1 AND p.product_id = o.product_id Plan hash value: 1553478007 ------------------------------------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | OMem | 1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 13 |00:00:00.01 | 24 | | | | |* 1 | HASH JOIN | | 1 | 13 | 13 |00:00:00.01 | 24 | 2061K| 2061K| 413K (0)| |* 2 | TABLE ACCESS FULL| ORDER_ITEMS | 1 | 13 | 13 |00:00:00.01 | 7 | | | | | 3 | TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 288 | 288 |00:00:00.01 | 17 | | | | ------------------------------------------------------------------------------------------------------------------------------- Note ----- - statistics feedback used for this statement SQL> select count(*) from v$SQL where SQL_ID='cz0hg2zkvd10y'; COUNT(*) ---------- 2 SQL>select sql_ID,USE_FEEDBACK_STATS FROM V$SQL_SHARED_CURSOR where USE_FEEDBACK_STATS ='Y'; SQL_ID U ------------- - cz0hg2zkvd10y Y ????????Cardinality feedback????,???????????????????????????,????????????order_items???????? ????2??????plan hash value??(??????????),?????2????child cursor??????gather_plan_statistics???actual : A-ROWS  estimate :E-ROWS????????? Automatic Re-optimization ???dynamic plan, Re-optimization???????????????  ?  ??????????????? ????????????????????????????????  ???????????,??????????????, ???????????????????? ???????????  Re-optimization??, ????????????????????? Re-optimization????dynamic plan??????????  dynamic plan????????????????????, ???????????????????? ????,??????????join order ??????????????,?????????????join order????? ??????,????????Re-optimization, ??Re-optimization ??????????????????? ?Oracle database 12c?,join statistics?????????????????????,??????????????????????Re-optimization???????????adaptive cursor sharing????? ????????????????,???????????? ????? ???????statistics collectors ????????????????????Re-optimization??????2?????????????,???????????????? ??????????????Re-optimization?????,?????????????????????? ???v$SQL??????IS_REOPTIMIZABLE?????????????????????Re-optimization,??????????Re-optimization???,?????Re-optimization ,???????reporting????? IS_REOPTIMIZABLE VARCHAR2(1) This columns shows whether the next execution matching this child cursor will trigger a reoptimization. The values are:   Y: If the next execution will trigger a reoptimization R: If the child cursor contains reoptimization information, but will not trigger reoptimization because the cursor was compiled in reporting mode N: If the child cursor has no reoptimization information ??1: select plan_table_output from table (dbms_xplan.display_cursor('gwf99gfnm0t7g',NULL,'ALLSTATS LAST')); SQL_ID  gwf99gfnm0t7g, child number 0 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 1906736282 ------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation             | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | ------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT      |                     |      1 |        |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   1 |  NESTED LOOPS         |                     |      1 |      1 |    269 |00:00:00.02 |    1336 |     18 |       |       |          | |   2 |   MERGE JOIN CARTESIAN|                     |      1 |      4 |   9135 |00:00:00.02 |      34 |     15 |       |       |          | |*  3 |    TABLE ACCESS FULL  | PRODUCT_INFORMATION |      1 |      1 |     87 |00:00:00.01 |      33 |     14 |       |       |          | |   4 |    BUFFER SORT        |                     |     87 |    105 |   9135 |00:00:00.01 |       1 |      1 |  4096 |  4096 | 4096  (0)| |   5 |     INDEX FULL SCAN   | ORDER_PK            |      1 |    105 |    105 |00:00:00.01 |       1 |      1 |       |       |          | |*  6 |   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |   9135 |      1 |    269 |00:00:00.01 |    1302 |      3 |       |       |          | ------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID") SQL_ID  gwf99gfnm0t7g, child number 1 ------------------------------------- SELECT /*+ SFTEST gather_plan_statistics */ o.order_id, v.product_name FROM  orders o,   ( SELECT order_id, product_name FROM order_items o, product_information p     WHERE  p.product_id = o.product_id AND list_price < 50 AND min_price < 40  ) v WHERE o.order_id = v.order_id Plan hash value: 35479787 -------------------------------------------------------------------------------------------------------------------------------------------- | Id  | Operation              | Name                | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem | -------------------------------------------------------------------------------------------------------------------------------------------- |   0 | SELECT STATEMENT       |                     |      1 |        |    269 |00:00:00.01 |      63 |      3 |       |       |          | |   1 |  NESTED LOOPS          |                     |      1 |    269 |    269 |00:00:00.01 |      63 |      3 |       |       |          | |*  2 |   HASH JOIN            |                     |      1 |    313 |    269 |00:00:00.01 |      42 |      3 |  1321K|  1321K| 1234K (0)| |*  3 |    TABLE ACCESS FULL   | PRODUCT_INFORMATION |      1 |     87 |     87 |00:00:00.01 |      16 |      0 |       |       |          | |   4 |    INDEX FAST FULL SCAN| ORDER_ITEMS_UK      |      1 |    665 |    665 |00:00:00.01 |      26 |      3 |       |       |          | |*  5 |   INDEX UNIQUE SCAN    | ORDER_PK            |    269 |      1 |    269 |00:00:00.01 |      21 |      0 |       |       |          | -------------------------------------------------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): ---------------------------------------------------    2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")    3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))    5 - access("O"."ORDER_ID"="ORDER_ID") Note -----    - statistics feedback used for this statement    SQL> select IS_REOPTIMIZABLE,child_number FROM V$SQL  A where A.SQL_ID='gwf99gfnm0t7g'; IS CHILD_NUMBER -- ------------ Y             0 N             1    1* select child_number,other_xml From v$SQL_PLAN  where SQL_ID='gwf99gfnm0t7g' and other_xml is not nul SQL> / CHILD_NUMBER OTHER_XML ------------ --------------------------------------------------------------------------------            1 <other_xml><info type="cardinality_feedback">yes</info><info type="db_version">1              2.1.0.1</info><info type="parse_schema"><![CDATA["OE"]]></info><info type="plan_              hash">35479787</info><info type="plan_hash_2">3382491761</info><outline_data><hi              nt><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]></hint><hint><![CDATA[OPTIMIZER_FEATUR              ES_ENABLE('12.1.0.1')]]></hint><hint><![CDATA[DB_VERSION('12.1.0.1')]]></hint><h              int><![CDATA[ALL_ROWS]]></hint><hint><![CDATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></              hint><hint><![CDATA[MERGE(@"SEL$2")]]></hint><hint><![CDATA[OUTLINE(@"SEL$1")]]>              </hint><hint><![CDATA[OUTLINE(@"SEL$2")]]></hint><hint><![CDATA[FULL(@"SEL$F5BB7              4E1" "P"@"SEL$2")]]></hint><hint><![CDATA[INDEX_FFS(@"SEL$F5BB74E1" "O"@"SEL$2"              ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PRODUCT_ID"))]]></hint><hint><![CDATA[I              NDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA[              LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$2" "O"@"SEL$1")]]></hint><hint><![C              DATA[USE_HASH(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint><hint><![CDATA[USE_NL(@"SEL$              F5BB74E1" "O"@"SEL$1")]]></hint></outline_data></other_xml>            0 <other_xml><info type="db_version">12.1.0.1</info><info type="parse_schema"><![C              DATA["OE"]]></info><info type="plan_hash">1906736282</info><info type="plan_hash              _2">2579473118</info><outline_data><hint><![CDATA[IGNORE_OPTIM_EMBEDDED_HINTS]]>              </hint><hint><![CDATA[OPTIMIZER_FEATURES_ENABLE('12.1.0.1')]]></hint><hint><![CD              ATA[DB_VERSION('12.1.0.1')]]></hint><hint><![CDATA[ALL_ROWS]]></hint><hint><![CD              ATA[OUTLINE_LEAF(@"SEL$F5BB74E1")]]></hint><hint><![CDATA[MERGE(@"SEL$2")]]></hi              nt><hint><![CDATA[OUTLINE(@"SEL$1")]]></hint><hint><![CDATA[OUTLINE(@"SEL$2")]]>              </hint><hint><![CDATA[FULL(@"SEL$F5BB74E1" "P"@"SEL$2")]]></hint><hint><![CDATA[              INDEX(@"SEL$F5BB74E1" "O"@"SEL$1" ("ORDERS"."ORDER_ID"))]]></hint><hint><![CDATA              [INDEX(@"SEL$F5BB74E1" "O"@"SEL$2" ("ORDER_ITEMS"."ORDER_ID" "ORDER_ITEMS"."PROD              UCT_ID"))]]></hint><hint><![CDATA[LEADING(@"SEL$F5BB74E1" "P"@"SEL$2" "O"@"SEL$1              " "O"@"SEL$2")]]></hint><hint><![CDATA[USE_MERGE_CARTESIAN(@"SEL$F5BB74E1" "O"@"              SEL$1")]]></hint><hint><![CDATA[USE_NL(@"SEL$F5BB74E1" "O"@"SEL$2")]]></hint></o              utline_data></other_xml> ??2: SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 0 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 -------------------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | Reads | -------------------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | 14 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 8 | 29 |00:00:00.01 | 17 | 14 | -------------------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OWNER OBJECT_NAME COL_NAME OBJECT TYPE STATE REASON ----------------------- ----- ------------- ----------- ------ ---------------- ----- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING NEW SINGLE TABLE CARDINALITY MISESTIMATE SELECT /*+gather_plan_statistics*/ * FROM customers WHERE cust_state_province='CA' AND country_id='US'; ELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID b74nw722wjvy3, child number 1 ------------------------------------- select /*+gather_plan_statistics*/ * from customers where CUST_STATE_PROVINCE='CA' and country_id='US' Plan hash value: 1683234692 ----------------------------------------------------------------------------------------- | Id | Operation | Name | Starts | E-Rows | A-Rows | A-Time | Buffers | ----------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 29 |00:00:00.01 | 17 | |* 1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 29 | 29 |00:00:00.01 | 17 | ----------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US')) Note ----- - cardinality feedback used for this statement SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE FROM V$SQL WHERE SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%'; SQL_ID CHILD_NUMBER SQL_TEXT I ------------- ------------ ----------- - b74nw722wjvy3 0 select /*+g Y ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' b74nw722wjvy3 1 select /*+g N ather_plan_ statistics* / * from cu stomers whe re CUST_STA TE_PROVINCE ='CA' and c ountry_id=' US' SELECT /*+gather_plan_statistics*/ CUST_EMAIL FROM CUSTOMERS WHERE CUST_STATE_PROVINCE='MA' AND COUNTRY_ID='US'; SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST')); PLAN_TABLE_OUTPUT ------------------------------------- SQL_ID 3tk6hj3nkcs2u, child number 0 ------------------------------------- Select /*+gather_plan_statistics*/ cust_email From customers Where cust_state_province='MA' And country_id='US' Plan hash value: 1683234692 ------------------------------------------------------------------------------- |Id | Operation | Name | Starts|E-Rows|A-Rows| A-Time |Buffers| ------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | | 2 |00:00:00.01| 16 | |*1 | TABLE ACCESS FULL| CUSTOMERS | 1 | 2| 2 |00:00:00.01| 16 | ----------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US')) Note ----- - dynamic sampling used for this statement (level=2) - 1 Sql Plan Directive used for this statement EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE; SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME, o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON FROM DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o WHERE d.DIRECTIVE_ID=o.DIRECTIVE_ID AND o.OWNER IN ('SH') ORDER BY 1,2,3,4,5; DIR_ID OW OBJECT_NA COL_NAME OBJECT TYPE STATE REASON ------------------- -- --------- ---------- ------- --------------- ------------- ------------------------ 1484026771529551585 SH CUSTOMERS COUNTRY_ID COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE 1484026771529551585 SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY PROVINCE MISESTIMATE 1484026771529551585 SH CUSTOMERS TABLE DYNAMIC_SAMPLING MISSING_STATS SINGLE TABLE CARDINALITY MISESTIMATE

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• Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

  - by Mark Hornick

  The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3),         count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(cv.count = cv)   }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(year=x$year[1], cv.count = cv)   }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res)    year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   data.frame(year=x$year[1], mean.count = mean.count)   }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res)    year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) {   total.count <- sum(x$count)   data.frame(year=x$year[1], count=x$count, total.count = total.count)   }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res)    year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),       cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) {   mu <- mean(x$count)   sigma <- sd(x$count)   cv <- sigma/mu   data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv)   }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res)    year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize,            homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) {   data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr))   }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res)    year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),                  y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() +   geom_point(data = df, aes(x = gp, y = y)) +   geom_point(data = ds, aes(x = gp, y = mean),              colour = 'red', size = 3) +   geom_errorbar(data = ds, aes(x = gp, y = mean,                                ymin = mean - sd, ymax = mean + sd),              colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) {   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) {   df <- df[,1:2]   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4)   }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

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• Launching php script through comman line - keeping terminal window open after execution

  - by somethis

  Oh, my girlfriend really likes it when I launch php scripts! There's something special about them, she says ... Thus, I coded this script to run throught the CLI (Command Line Interface) - so it's running locally, not on a web server. It launches just fine through right click open run in terminal but closes right after execution. **Is there a way to keep the terminal window open? Of course I can launch it through a terminal window - which would stay open - but I'm looking for a one click action. With bash scripts I use $SHELL but that didn't work (see code below). So far, the only thing I came up with is sleep(10); which gives me 10 seconds for my girl to check the output. I'd rather close the terminal window manually, though. #!/usr/bin/php -q <?php echo "Hello World \n"; # wait before closing terminal window sleep(10); # the following line doesn't work $SHELL; ?> (PHP 5.4.6-1ubuntu1.2 (cli) (built: Mar 11 2013 14:57:54) Copyright (c) 1997-2012 The PHP Group Zend Engine v2.4.0, Copyright (c) 1998-2012 Zend Technologies )

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• Execution plan warnings–All that glitters is not gold

  - by Dave Ballantyne

  In a previous post, I showed you the new execution plan warnings related to implicit and explicit warnings.  Pretty much as soon as i hit ’post’,  I noticed something rather odd happening. This statement : select top(10) SalesOrderHeader.SalesOrderID, SalesOrderNumberfrom Sales.SalesOrderHeaderjoin Sales.SalesOrderDetail on SalesOrderHeader.SalesOrderID = SalesOrderDetail.SalesOrderID   Throws the “Type conversion may affect cardinality estimation” warning.     Ive done no such conversion in my statement why would that be ?  Well, SalesOrderNumber is a computed column , “(isnull(N'SO'+CONVERT([nvarchar](23),[SalesOrderID],0),N'*** ERROR ***'))”,  so thats where the conversion is.   Wait!!! Am i saying that every type conversion will throw the warning ?  Thankfully, no.  It only appears for columns that are used in predicates ,even if the predicate / join condition is fine ,  and the column is indexed ( and/or , presumably has statistics).    Hopefully , this wont lead to to many wild goose chases, but is definitely something to bear in mind.  If you want to see this fixed then upvote my connect item here.

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• Work Execution in EAM

  - by Annemarie Provisero

  ADVISOR WEBCAST: Work Execution in EAM PRODUCT FAMILY: Manufacturing Enterprise Asset Management July 5, 2011 at 8 am PT, 9 am MT, 11 am ET The purpose of this webcast is to discuss EAM Work Order Management. This one-hour session is ideal for Functional Users, System Administrators, Database Administrators, and Customers with a basic knowledge of EAM and who raise or manage work orders and related processes. During this webcast, Zar will cover the various types of work orders and look at all the related activities associated with work orders including: setup, operations, tasks, work order transactions, relationship and planning. TOPICS WILL INCLUDE: Work Order Types (Routine, Planned Maintenance, Rebuild, Easy) Work Order statuses and other important setups Operations and Tasks Relationships Work Order Transactions Work Order Planning A short, live demonstration (only if applicable) and question and answer period will be included. Oracle Advisor Webcasts are dedicated to building your awareness around our products and services. This session does not replace offerings from Oracle Global Support Services. Click here to register for this session ------------------------------------------------------------------------------------------------------------- The above webcast is a service of the E-Business Suite Communities in My Oracle Support. For more information on other webcasts, please reference the Oracle Advisor Webcast Schedule.Click here to visit the E-Business Communities in My Oracle Support Note that all links require access to My Oracle Support.

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• SSIS Catalog: How to use environment in every type of package execution

  - by Kevin Shyr

  Here is a good blog on how to create a SSIS Catalog and setting up environments.  http://sqlblog.com/blogs/jamie_thomson/archive/2010/11/13/ssis-server-catalogs-environments-environment-variables-in-ssis-in-denali.aspx Here I will summarize 3 ways I know so far to execute a package while using variables set up in SSIS Catalog environment. First way, we have SSIS project having reference to environment, and having one of the project parameter using a value set up in the environment called "Development".  With this set up, you are limited to calling the packages by right-clicking on the packages in the SSIS catalog list and select Execute, but you are free to choose absolute or relative path of the environment. The following screenshot shows the 2 available paths to your SSIS environments.  Personally, I use absolute path because of Option 3, just to keep everything simple for myself. The second option is to call through SQL Job.  This does require you to configure your project to already reference an environment and use its variable.  When a job step is set up, the configuration part will require you to select that reference again.  This is more useful when you want to automate the same package that needs to be run in different environments. The third option is the most important to me as I have a SSIS framework that calls hundreds of packages.  The main part of the stored procedure is in this post (http://geekswithblogs.net/LifeLongTechie/archive/2012/11/14/time-to-stop-using-ldquoexecute-package-taskrdquondash-a-way-to.aspx).  But the top part had to be modified to include the logic to use environment reference. CREATE PROCEDURE [AUDIT].[LaunchPackageExecutionInSSISCatalog] @PackageName NVARCHAR(255) , @ProjectFolder NVARCHAR(255) , @ProjectName NVARCHAR(255) , @AuditKey INT , @DisableNotification BIT , @PackageExecutionLogID INT , @EnvironmentName NVARCHAR(128) = NULL , @Use32BitRunTime BIT = FALSE AS BEGIN TRY DECLARE @execution_id BIGINT = 0; -- Create a package execution IF @EnvironmentName IS NULL BEGIN   EXEC [SSISDB].[catalog].[create_execution]     @package_name=@PackageName,     @execution_id=@execution_id OUTPUT,     @folder_name=@ProjectFolder,     @project_name=@ProjectName,     @use32bitruntime=@Use32BitRunTime; END ELSE BEGIN   DECLARE @EnvironmentID AS INT   SELECT @EnvironmentID = [reference_id]    FROM SSISDB.[internal].[environment_references] WITH(NOLOCK)    WHERE [environment_name] = @EnvironmentName     AND [environment_folder_name] = @ProjectFolder      EXEC [SSISDB].[catalog].[create_execution]     @package_name=@PackageName,     @execution_id=@execution_id OUTPUT,     @folder_name=@ProjectFolder,     @project_name=@ProjectName,     @reference_id=@EnvironmentID,     @use32bitruntime=@Use32BitRunTime; END

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• Delay command execution over sockets

  - by David

  I've been trying to fix the game loop in a real time (tick delay) MUD. I realized using Thread.Sleep would seem clunky when the user spammed commands through their choice of client (Zmud, etc) e.g. east;south;southwest would wait three move ticks and then output everything from the past couple rooms. The game loop basically calls a Flush and Fill method for each socket during each tick (50ms) private void DoLoop() { Stopwatch stopWatch = new Stopwatch(); stopWatch.Start(); while (running) { // for each socket, flush and fill ConnectionMonitor.Update(); stopWatch.Stop(); WaitIfNeeded(stopWatch.ElapsedMilliseconds); stopWatch.Reset(); } } The Fill method fires the command events, but as mentioned before, they currently block using Thread.Sleep. I tried adding a "ready" flag to the state object that attempts to execute the command along with a queue of spammed commands, but it ends up executing one command and queuing up the rest i.e. each subsequent command executes something that got queued up that should've been executed before. I must be missing something about the timer. private readonly Queue<SpammedCommand> queuedCommands = new Queue<SpammedCommand>(); private bool ready = true; private void TryExecuteCommand(string input) { var commandContext = CommandContext.Create(input); var player = Server.Current.Database.Get<Player>(Session.Player.Key); var commandInfo = Server.Current.CommandLookup .FindCommand(commandContext.CommandName, player.IsAdmin); if (commandInfo != null) { if (!ready) { // queue command queuedCommands.Enqueue(new SpammedCommand() { Context = commandContext, Info = commandInfo }); return; } if (queuedCommands.Count > 0) { // queue the incoming command queuedCommands.Enqueue(new SpammedCommand() { Context = commandContext, Info = commandInfo, }); // dequeue and execute var command = queuedCommands.Dequeue(); command.Info.Command.Execute(Session, command.Context); setTimeout(command.Info.TickLength); return; } commandInfo.Command.Execute(Session, commandContext); setTimeout(commandInfo.TickLength); } else { Session.WriteLine("Command not recognized"); } } Finally, setTimeout was supposed to set the execution delay (TickLength) for that command, and makeReady just sets the ready flag on the state object to true. private void setTimeout(TickDelay tickDelay) { ready = false; var t = new System.Timers.Timer() { Interval = (long) tickDelay, AutoReset = false, }; t.Elapsed += makeReady; t.Start(); // fire this in tickDelay ms } // MAKE READYYYYY!!!! private void makeReady(object sender, System.Timers.ElapsedEventArgs e) { ready = true; } Am I missing something about the System.Timers.Timer created in setTimeout? How can I execute (and output) spammed commands per TickLength without using Thread.Sleep?

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• Using the StopWatch class to calculate the execution time of a block of code

  - by vik20000in

    Many of the times while doing the performance tuning of some, class, webpage, component, control etc. we first measure the current time taken in the execution of that code. This helps in understanding the location in code which is actually causing the performance issue and also help in measuring the amount of improvement by making the changes. This measurement is very important as it helps us understand the problem in code, Helps us to write better code next time (as we have already learnt what kind of improvement can be made with different code) . Normally developers create 2 objects of the DateTime class. The exact time is collected before and after the code where the performance needs to be measured.  Next the difference between the two objects is used to know about the time spent in the code that is measured. Below is an example of the sample code.             DateTime dt1, dt2;             dt1 = DateTime.Now;             for (int i = 0; i < 1000000; i++)             {                 string str = "string";             }             dt2 = DateTime.Now;             TimeSpan ts = dt2.Subtract(dt1);             Console.WriteLine("Time Spent : " + ts.TotalMilliseconds.ToString());   The above code works great. But the dot net framework also provides for another way to capture the time spent on the code without doing much effort (creating 2 datetime object, timespan object etc..). We can use the inbuilt StopWatch class to get the exact time spent. Below is an example of the same work with the help of the StopWatch class.             Stopwatch sw = Stopwatch.StartNew();             for (int i = 0; i < 1000000; i++)             {                 string str = "string";             }             sw.Stop();             Console.WriteLine("Time Spent : " +sw.Elapsed.TotalMilliseconds.ToString());   [Note the StopWatch class resides in the System.Diagnostics namespace] If you use the StopWatch class the time taken for measuring the performance is much better, with very little effort. Vikram

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• Asynchronous Vs. Synchronous Execution, What does it really mean?

  - by tush1r

  Asynchronous Vs. Synchronous Execution, What does it really mean?

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• Execution plan warnings–The final chapter

  - by Dave Ballantyne

  In my previous posts (here and here), I showed examples of some of the execution plan warnings that have been added to SQL Server 2012.  There is one other warning that is of interest to me : “Unmatched Indexes”. Firstly, how do I know this is the final one ?  The plan is an XML document, right ? So that means that it can have an accompanying XSD.  As an XSD is a schema definition, we can poke around inside it to find interesting things that *could* be in the final XML file. The showplan schema is stored in the folder Microsoft SQL Server\110\Tools\Binn\schemas\sqlserver\2004\07\showplan and by comparing schemas over releases you can get a really good idea of any new functionality that has been added. Here is the section of the Sql Server 2012 showplan schema that has been interesting me so far : <xsd:complexType name="AffectingConvertWarningType"> <xsd:annotation> <xsd:documentation>Warning information for plan-affecting type conversion</xsd:documentation> </xsd:annotation> <xsd:sequence> <!-- Additional information may go here when available --> </xsd:sequence> <xsd:attribute name="ConvertIssue" use="required"> <xsd:simpleType> <xsd:restriction base="xsd:string"> <xsd:enumeration value="Cardinality Estimate" /> <xsd:enumeration value="Seek Plan" /> <!-- to be extended here --> </xsd:restriction> </xsd:simpleType> </xsd:attribute> <xsd:attribute name="Expression" type ="xsd:string" use="required" /></xsd:complexType><xsd:complexType name="WarningsType"> <xsd:annotation> <xsd:documentation>List of all possible iterator or query specific warnings (e.g. hash spilling, no join predicate)</xsd:documentation> </xsd:annotation> <xsd:choice minOccurs="1" maxOccurs="unbounded"> <xsd:element name="ColumnsWithNoStatistics" type="shp:ColumnReferenceListType" minOccurs="0" maxOccurs="1" /> <xsd:element name="SpillToTempDb" type="shp:SpillToTempDbType" minOccurs="0" maxOccurs="unbounded" /> <xsd:element name="Wait" type="shp:WaitWarningType" minOccurs="0" maxOccurs="unbounded" /> <xsd:element name="PlanAffectingConvert" type="shp:AffectingConvertWarningType" minOccurs="0" maxOccurs="unbounded" /> </xsd:choice> <xsd:attribute name="NoJoinPredicate" type="xsd:boolean" use="optional" /> <xsd:attribute name="SpatialGuess" type="xsd:boolean" use="optional" /> <xsd:attribute name="UnmatchedIndexes" type="xsd:boolean" use="optional" /> <xsd:attribute name="FullUpdateForOnlineIndexBuild" type="xsd:boolean" use="optional" /></xsd:complexType> I especially like the “to be extended here” comment,  high hopes that we will see more of these in the future.   So “Unmatched Indexes” was a warning that I couldn’t get and many thanks must go to Fabiano Amorim (b|t) for showing me the way.   Filtered indexes were introduced in Sql Server 2008 and are really useful if you only need to index only a portion of the data within a table.  However,  if your SQL code uses a variable as a predicate on the filtered data that matches the filtered condition, then the filtered index cannot be used as, naturally,  the value in the variable may ( and probably will ) change and therefore will need to read data outside the index.  As an aside,  you could use option(recompile) here , in which case the optimizer will build a plan specific to the variable values and use the filtered index,  but that can bring about other problems.   To demonstrate this warning, we need to generate some test data :   DROP TABLE #TestTab1GOCREATE TABLE #TestTab1 (Col1 Int not null, Col2 Char(7500) not null, Quantity Int not null)GOINSERT INTO #TestTab1 VALUES (1,1,1),(1,2,5),(1,2,10),(1,3,20), (2,1,101),(2,2,105),(2,2,110),(2,3,120)GO and then add a filtered index CREATE INDEX ixFilter ON #TestTab1 (Col1)WHERE Quantity = 122 Now if we execute SELECT COUNT(*) FROM #TestTab1 WHERE Quantity = 122 We will see the filtered index being scanned But if we parameterize the query DECLARE @i INT = 122SELECT COUNT(*) FROM #TestTab1 WHERE Quantity = @i The plan is very different a table scan, as the value of the variable used in the predicate can change at run time, and also we see the familiar warning triangle. If we now look at the properties pane, we will see two pieces of information “Warnings” and “UnmatchedIndexes”. So, handily, we are being told which filtered index is not being used due to parameterization.

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• Spooling in SQL execution plans

  - by Rob Farley

  Sewing has never been my thing. I barely even know the terminology, and when discussing this with American friends, I even found out that half the words that Americans use are different to the words that English and Australian people use. That said – let’s talk about spools! In particular, the Spool operators that you find in some SQL execution plans. This post is for T-SQL Tuesday, hosted this month by me! I’ve chosen to write about spools because they seem to get a bad rap (even in my song I used the line “There’s spooling from a CTE, they’ve got recursion needlessly”). I figured it was worth covering some of what spools are about, and hopefully explain why they are remarkably necessary, and generally very useful. If you have a look at the Books Online page about Plan Operators, at http://msdn.microsoft.com/en-us/library/ms191158.aspx, and do a search for the word ‘spool’, you’ll notice it says there are 46 matches. 46! Yeah, that’s what I thought too... Spooling is mentioned in several operators: Eager Spool, Lazy Spool, Index Spool (sometimes called a Nonclustered Index Spool), Row Count Spool, Spool, Table Spool, and Window Spool (oh, and Cache, which is a special kind of spool for a single row, but as it isn’t used in SQL 2012, I won’t describe it any further here). Spool, Table Spool, Index Spool, Window Spool and Row Count Spool are all physical operators, whereas Eager Spool and Lazy Spool are logical operators, describing the way that the other spools work. For example, you might see a Table Spool which is either Eager or Lazy. A Window Spool can actually act as both, as I’ll mention in a moment. In sewing, cotton is put onto a spool to make it more useful. You might buy it in bulk on a cone, but if you’re going to be using a sewing machine, then you quite probably want to have it on a spool or bobbin, which allows it to be used in a more effective way. This is the picture that I want you to think about in relation to your data. I’m sure you use spools every time you use your sewing machine. I know I do. I can’t think of a time when I’ve got out my sewing machine to do some sewing and haven’t used a spool. However, I often run SQL queries that don’t use spools. You see, the data that is consumed by my query is typically in a useful state without a spool. It’s like I can just sew with my cotton despite it not being on a spool! Many of my favourite features in T-SQL do like to use spools though. This looks like a very similar query to before, but includes an OVER clause to return a column telling me the number of rows in my data set. I’ll describe what’s going on in a few paragraphs’ time. So what does a Spool operator actually do? The spool operator consumes a set of data, and stores it in a temporary structure, in the tempdb database. This structure is typically either a Table (ie, a heap), or an Index (ie, a b-tree). If no data is actually needed from it, then it could also be a Row Count spool, which only stores the number of rows that the spool operator consumes. A Window Spool is another option if the data being consumed is tightly linked to windows of data, such as when the ROWS/RANGE clause of the OVER clause is being used. You could maybe think about the type of spool being like whether the cotton is going onto a small bobbin to fit in the base of the sewing machine, or whether it’s a larger spool for the top. A Table or Index Spool is either Eager or Lazy in nature. Eager and Lazy are Logical operators, which talk more about the behaviour, rather than the physical operation. If I’m sewing, I can either be all enthusiastic and get all my cotton onto the spool before I start, or I can do it as I need it. “Lazy” might not the be the best word to describe a person – in the SQL world it describes the idea of either fetching all the rows to build up the whole spool when the operator is called (Eager), or populating the spool only as it’s needed (Lazy). Window Spools are both physical and logical. They’re eager on a per-window basis, but lazy between windows. And when is it needed? The way I see it, spools are needed for two reasons. 1 – When data is going to be needed AGAIN. 2 – When data needs to be kept away from the original source. If you’re someone that writes long stored procedures, you are probably quite aware of the second scenario. I see plenty of stored procedures being written this way – where the query writer populates a temporary table, so that they can make updates to it without risking the original table. SQL does this too. Imagine I’m updating my contact list, and some of my changes move data to later in the book. If I’m not careful, I might update the same row a second time (or even enter an infinite loop, updating it over and over). A spool can make sure that I don’t, by using a copy of the data. This problem is known as the Halloween Effect (not because it’s spooky, but because it was discovered in late October one year). As I’m sure you can imagine, the kind of spool you’d need to protect against the Halloween Effect would be eager, because if you’re only handling one row at a time, then you’re not providing the protection... An eager spool will block the flow of data, waiting until it has fetched all the data before serving it up to the operator that called it. In the query below I’m forcing the Query Optimizer to use an index which would be upset if the Name column values got changed, and we see that before any data is fetched, a spool is created to load the data into. This doesn’t stop the index being maintained, but it does mean that the index is protected from the changes that are being done. There are plenty of times, though, when you need data repeatedly. Consider the query I put above. A simple join, but then counting the number of rows that came through. The way that this has executed (be it ideal or not), is to ask that a Table Spool be populated. That’s the Table Spool operator on the top row. That spool can produce the same set of rows repeatedly. This is the behaviour that we see in the bottom half of the plan. In the bottom half of the plan, we see that the a join is being done between the rows that are being sourced from the spool – one being aggregated and one not – producing the columns that we need for the query. Table v Index When considering whether to use a Table Spool or an Index Spool, the question that the Query Optimizer needs to answer is whether there is sufficient benefit to storing the data in a b-tree. The idea of having data in indexes is great, but of course there is a cost to maintaining them. Here we’re creating a temporary structure for data, and there is a cost associated with populating each row into its correct position according to a b-tree, as opposed to simply adding it to the end of the list of rows in a heap. Using a b-tree could even result in page-splits as the b-tree is populated, so there had better be a reason to use that kind of structure. That all depends on how the data is going to be used in other parts of the plan. If you’ve ever thought that you could use a temporary index for a particular query, well this is it – and the Query Optimizer can do that if it thinks it’s worthwhile. It’s worth noting that just because a Spool is populated using an Index Spool, it can still be fetched using a Table Spool. The details about whether or not a Spool used as a source shows as a Table Spool or an Index Spool is more about whether a Seek predicate is used, rather than on the underlying structure. Recursive CTE I’ve already shown you an example of spooling when the OVER clause is used. You might see them being used whenever you have data that is needed multiple times, and CTEs are quite common here. With the definition of a set of data described in a CTE, if the query writer is leveraging this by referring to the CTE multiple times, and there’s no simplification to be leveraged, a spool could theoretically be used to avoid reapplying the CTE’s logic. Annoyingly, this doesn’t happen. Consider this query, which really looks like it’s using the same data twice. I’m creating a set of data (which is completely deterministic, by the way), and then joining it back to itself. There seems to be no reason why it shouldn’t use a spool for the set described by the CTE, but it doesn’t. On the other hand, if we don’t pull as many columns back, we might see a very different plan. You see, CTEs, like all sub-queries, are simplified out to figure out the best way of executing the whole query. My example is somewhat contrived, and although there are plenty of cases when it’s nice to give the Query Optimizer hints about how to execute queries, it usually doesn’t do a bad job, even without spooling (and you can always use a temporary table). When recursion is used, though, spooling should be expected. Consider what we’re asking for in a recursive CTE. We’re telling the system to construct a set of data using an initial query, and then use set as a source for another query, piping this back into the same set and back around. It’s very much a spool. The analogy of cotton is long gone here, as the idea of having a continual loop of cotton feeding onto a spool and off again doesn’t quite fit, but that’s what we have here. Data is being fed onto the spool, and getting pulled out a second time when the spool is used as a source. (This query is running on AdventureWorks, which has a ManagerID column in HumanResources.Employee, not AdventureWorks2012) The Index Spool operator is sucking rows into it – lazily. It has to be lazy, because at the start, there’s only one row to be had. However, as rows get populated onto the spool, the Table Spool operator on the right can return rows when asked, ending up with more rows (potentially) getting back onto the spool, ready for the next round. (The Assert operator is merely checking to see if we’ve reached the MAXRECURSION point – it vanishes if you use OPTION (MAXRECURSION 0), which you can try yourself if you like). Spools are useful. Don’t lose sight of that. Every time you use temporary tables or table variables in a stored procedure, you’re essentially doing the same – don’t get upset at the Query Optimizer for doing so, even if you think the spool looks like an expensive part of the query. I hope you’re enjoying this T-SQL Tuesday. Why not head over to my post that is hosting it this month to read about some other plan operators? At some point I’ll write a summary post – once I have you should find a comment below pointing at it. @rob_farley

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• Plugin execution not covered by lifecycle configuration: org.codehaus.mojo:aspectj-maven-plugin:1.0

  - by alexm

  I switched from q4e Helios to Indigo m2e plugin and my Maven 2 project no longer works. I had a ROO-generated Spring MVC project. This is what I get: Plugin execution not covered by lifecycle configuration: org.codehaus.mojo:aspectj-maven-plugin:1.0:test-compile (execution: default, phase: process-test-sources) Plugin execution not covered by lifecycle configuration: org.codehaus.mojo:aspectj-maven-plugin:1.0:compile (execution: default, phase: process-sources) Any insight is greatly appreciated. Thank you.

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• mysql query execution time - can i get this in milliseconds?

  - by Max Williams

  I'm comparing a few different approaches to getting some data in mysql, directly at the console, using the SQL_NO_CACHE option to make sure mysql keeps running the full query every time. Mysql gives me the execution time back in seconds, to two decimal places. I'd really like to get the result back in milliseconds (ideally to one or two decimal places), to get a better idea of improvements (or lack of). Is there an option i can set in mysql to achieve this? thanks, max

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• Thread vs async execution. What's different?

  - by Eonil

  I believed any kind of asynchronous execution makes a thread in invisible area. But if so, Async codes does not offer any performance gain than threaded codes. But I can't understand why so many developers are making many features async form. Could you explain about difference and cost of them?

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• Where is my app.config for SSIS?

  Sometimes when working with SSIS you need to add or change settings in the .NET application configuration file, which can be a bit confusing when you are building a SSIS package not an application. First of all lets review a couple of examples where you may need to do this. You are using referencing an assembly in a Script Task that uses Enterprise Library (aka EntLib), so you need to add the relevant configuration sections and settings, perhaps for the logging application block. You are using using Enterprise Library in a custom task or component, and again you need to add the relevant configuration sections and settings. You are using a web service with Microsoft Web Services Enhancements (WSE) 3.0 and hosting the proxy in SSIS, in an assembly used by your package, and need to add the configuration sections and settings. You need to change behaviours of the .NET framework which can be influenced by a configuration file, such as the System.Net.Mail default SMTP settings. Perhaps you wish to configure System.Net and the httpWebRequest header for parsing unsafe header (useUnsafeHeaderParsing), which will change the way the HTTP Connection manager behaves. You are consuming a WCF service and wish to specify the endpoint in configuration. There are no doubt plenty more examples but each of these requires us to identify the correct configuration file and and make the relevant changes. There are actually several configuration files, each used by a different execution host depending on how you are working with the SSIS package. The folders we need to look in will actually vary depending on the version of SQL Server as well as the processor architecture, but most are all what we can call the Binn folder. The SQL Server 2005 Binn folder is at C:\Program Files\Microsoft SQL Server\90\DTS\Binn\, compared to C:\Program Files\Microsoft SQL Server\100\DTS\Binn\ for SQL Server 2008. If you are on a 64-bit machine then you will see C:\Program Files (x86)\Microsoft SQL Server\90\DTS\Binn\ for the 32-bit executables and C:\Program Files\Microsoft SQL Server\90\DTS\Binn\ for 64-bit, so be sure to check all relevant locations. Of course SQL Server 2008 may have a C:\Program Files (x86)\Microsoft SQL Server\100\DTS\Binn\ on a 64-bit machine too. To recap, the version of SQL Server determines if you look in the 90 or 100 sub-folder under SQL Server in Program Files (C:\Program Files\Microsoft SQL Server\nn\) . If you are running a 64-bit operating system then you will have two instances program files, C:\Program Files (x86)\ for 32-bit and  C:\Program Files\ for 64-bit. You may wish to check both depending on what you are doing, but this is covered more under each section below. There are a total of five specific configuration files that you may need to change, each one is detailed below: DTExec.exe.config DTExec.exe is the standalone command line tool used for executing SSIS packages, and therefore it is an execution host with an app.config file. e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\DTExec.exe.config The file can be found in both the 32-bit and 64-bit Binn folders. DtsDebugHost.exe.config DtsDebugHost.exe is the execution host used by Business Intelligence Development Studio (BIDS) / Visual Studio when executing a package from the designer in debug mode, which is the default behaviour. e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\DtsDebugHost.exe.config The file can be found in both the 32-bit and 64-bit Binn folders. This may surprise some people as Visual Studio is only 32-bit, but thankfully the debugger supports both. This can be set in the project properties, see the Run64BitRuntime property (true or false) in the Debugging pane of the Project Properties. dtshost.exe.config dtshost.exe is the execution host used by what I think of as the built-in features of SQL Server such as SQL Server Agent e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\dtshost.exe.config This file can be found in both the 32-bit and 64-bit Binn folders devenv.exe.config Something slightly different is devenv.exe which is Visual Studio. This configuration file may also need changing if you need a feature at design-time such as in a Task Editor or Connection Manager editor. Visual Studio 2005 for SQL Server 2005  - C:\Program Files\Microsoft Visual Studio 8\Common7\IDE\devenv.exe.config Visual Studio 2008 for SQL Server 2008  - C:\Program Files\Microsoft Visual Studio 9.0\Common7\IDE\devenv.exe.config Visual Studio is only available for 32-bit so on a 64-bit machine you will have to look in C:\Program Files (x86)\ only. DTExecUI.exe.config The DTExec UI tool can also have a configuration file and these cab be found under the Tools folders for SQL Sever as shown below. C:\Program Files\Microsoft SQL Server\90\Tools\Binn\VSShell\Common7\IDE\DTExecUI.exe C:\Program Files\Microsoft SQL Server\100\Tools\Binn\VSShell\Common7\IDE\DTExecUI.exe A configuration file may not exist, but if you can find the matching executable you know you are in the right place so can go ahead and add a new file yourself. In summary we have covered the assembly configuration files for all of the standard methods of building and running a SSIS package, but obviously if you are working programmatically you will need to make the relevant modifications to your program’s app.config as well.

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• Where is my app.config for SSIS?

  Sometimes when working with SSIS you need to add or change settings in the .NET application configuration file, which can be a bit confusing when you are building a SSIS package not an application. First of all lets review a couple of examples where you may need to do this. You are using referencing an assembly in a Script Task that uses Enterprise Library (aka EntLib), so you need to add the relevant configuration sections and settings, perhaps for the logging application block. You are using using Enterprise Library in a custom task or component, and again you need to add the relevant configuration sections and settings. You are using a web service with Microsoft Web Services Enhancements (WSE) 3.0 and hosting the proxy in SSIS, in an assembly used by your package, and need to add the configuration sections and settings. You need to change behaviours of the .NET framework which can be influenced by a configuration file, such as the System.Net.Mail default SMTP settings. Perhaps you wish to configure System.Net and the httpWebRequest header for parsing unsafe header (useUnsafeHeaderParsing), which will change the way the HTTP Connection manager behaves. You are consuming a WCF service and wish to specify the endpoint in configuration. There are no doubt plenty more examples but each of these requires us to identify the correct configuration file and and make the relevant changes. There are actually several configuration files, each used by a different execution host depending on how you are working with the SSIS package. The folders we need to look in will actually vary depending on the version of SQL Server as well as the processor architecture, but most are all what we can call the Binn folder. The SQL Server 2005 Binn folder is at C:\Program Files\Microsoft SQL Server\90\DTS\Binn\, compared to C:\Program Files\Microsoft SQL Server\100\DTS\Binn\ for SQL Server 2008. If you are on a 64-bit machine then you will see C:\Program Files (x86)\Microsoft SQL Server\90\DTS\Binn\ for the 32-bit executables and C:\Program Files\Microsoft SQL Server\90\DTS\Binn\ for 64-bit, so be sure to check all relevant locations. Of course SQL Server 2008 may have a C:\Program Files (x86)\Microsoft SQL Server\100\DTS\Binn\ on a 64-bit machine too. To recap, the version of SQL Server determines if you look in the 90 or 100 sub-folder under SQL Server in Program Files (C:\Program Files\Microsoft SQL Server\nn\) . If you are running a 64-bit operating system then you will have two instances program files, C:\Program Files (x86)\ for 32-bit and  C:\Program Files\ for 64-bit. You may wish to check both depending on what you are doing, but this is covered more under each section below. There are a total of five specific configuration files that you may need to change, each one is detailed below: DTExec.exe.config DTExec.exe is the standalone command line tool used for executing SSIS packages, and therefore it is an execution host with an app.config file. e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\DTExec.exe.config The file can be found in both the 32-bit and 64-bit Binn folders. DtsDebugHost.exe.config DtsDebugHost.exe is the execution host used by Business Intelligence Development Studio (BIDS) / Visual Studio when executing a package from the designer in debug mode, which is the default behaviour. e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\DtsDebugHost.exe.config The file can be found in both the 32-bit and 64-bit Binn folders. This may surprise some people as Visual Studio is only 32-bit, but thankfully the debugger supports both. This can be set in the project properties, see the Run64BitRuntime property (true or false) in the Debugging pane of the Project Properties. dtshost.exe.config dtshost.exe is the execution host used by what I think of as the built-in features of SQL Server such as SQL Server Agent e.g. C:\Program Files\Microsoft SQL Server\90\DTS\Binn\dtshost.exe.config This file can be found in both the 32-bit and 64-bit Binn folders devenv.exe.config Something slightly different is devenv.exe which is Visual Studio. This configuration file may also need changing if you need a feature at design-time such as in a Task Editor or Connection Manager editor. Visual Studio 2005 for SQL Server 2005  - C:\Program Files\Microsoft Visual Studio 8\Common7\IDE\devenv.exe.config Visual Studio 2008 for SQL Server 2008  - C:\Program Files\Microsoft Visual Studio 9.0\Common7\IDE\devenv.exe.config Visual Studio is only available for 32-bit so on a 64-bit machine you will have to look in C:\Program Files (x86)\ only. DTExecUI.exe.config The DTExec UI tool can also have a configuration file and these cab be found under the Tools folders for SQL Sever as shown below. C:\Program Files\Microsoft SQL Server\90\Tools\Binn\VSShell\Common7\IDE\DTExecUI.exe C:\Program Files\Microsoft SQL Server\100\Tools\Binn\VSShell\Common7\IDE\DTExecUI.exe A configuration file may not exist, but if you can find the matching executable you know you are in the right place so can go ahead and add a new file yourself. In summary we have covered the assembly configuration files for all of the standard methods of building and running a SSIS package, but obviously if you are working programmatically you will need to make the relevant modifications to your program’s app.config as well.

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• ParallelWork: Feature rich multithreaded fluent task execution library for WPF

  ParallelWork is an open source free helper class that lets you run multiple work in parallel threads, get success, failure and progress update on the WPF UI thread, wait for work to complete, abort all work (in case of shutdown), queue work to run after certain time, chain parallel work one after an

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• Mitigate the Security Risks of PHP System Command Execution

  Learn how the security-conscious ways to integrate your PHP-driven web sites with the underlying ope ...

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• Mitigate the Security Risks of PHP System Command Execution

  Learn how the security-conscious ways to integrate your PHP-driven web sites with the underlying ope ...

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• Typo3 allows remote command execution via PHP

  <b>The H Open:</b> "In versions 4.3.0, 4.3.1 and 4.3.2 of Typo3 (as well as previous versions of the 4.4 development branch), attackers can inject PHP code from an external server and execute it within the Typo3 context."

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• Vectorization of matlab code for faster execution

  - by user3237134

  My code works in the following manner: 1.First, it obtains several images from the training set 2.After loading these images, we find the normalized faces,mean face and perform several calculation. 3.Next, we ask for the name of an image we want to recognize 4.We then project the input image into the eigenspace, and based on the difference from the eigenfaces we make a decision. 5.Depending on eigen weight vector for each input image we make clusters using kmeans command. Source code i tried: clear all close all clc % number of images on your training set. M=1200; %Chosen std and mean. %It can be any number that it is close to the std and mean of most of the images. um=60; ustd=32; %read and show images(bmp); S=[]; %img matrix for i=1:M str=strcat(int2str(i),'.jpg'); %concatenates two strings that form the name of the image eval('img=imread(str);'); [irow icol d]=size(img); % get the number of rows (N1) and columns (N2) temp=reshape(permute(img,[2,1,3]),[irow*icol,d]); %creates a (N1*N2)x1 matrix S=[S temp]; %X is a N1*N2xM matrix after finishing the sequence %this is our S end %Here we change the mean and std of all images. We normalize all images. %This is done to reduce the error due to lighting conditions. for i=1:size(S,2) temp=double(S(:,i)); m=mean(temp); st=std(temp); S(:,i)=(temp-m)*ustd/st+um; end %show normalized images for i=1:M str=strcat(int2str(i),'.jpg'); img=reshape(S(:,i),icol,irow); img=img'; end %mean image; m=mean(S,2); %obtains the mean of each row instead of each column tmimg=uint8(m); %converts to unsigned 8-bit integer. Values range from 0 to 255 img=reshape(tmimg,icol,irow); %takes the N1*N2x1 vector and creates a N2xN1 matrix img=img'; %creates a N1xN2 matrix by transposing the image. % Change image for manipulation dbx=[]; % A matrix for i=1:M temp=double(S(:,i)); dbx=[dbx temp]; end %Covariance matrix C=A'A, L=AA' A=dbx'; L=A*A'; % vv are the eigenvector for L % dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx'; [vv dd]=eig(L); % Sort and eliminate those whose eigenvalue is zero v=[]; d=[]; for i=1:size(vv,2) if(dd(i,i)>1e-4) v=[v vv(:,i)]; d=[d dd(i,i)]; end end %sort, will return an ascending sequence [B index]=sort(d); ind=zeros(size(index)); dtemp=zeros(size(index)); vtemp=zeros(size(v)); len=length(index); for i=1:len dtemp(i)=B(len+1-i); ind(i)=len+1-index(i); vtemp(:,ind(i))=v(:,i); end d=dtemp; v=vtemp; %Normalization of eigenvectors for i=1:size(v,2) %access each column kk=v(:,i); temp=sqrt(sum(kk.^2)); v(:,i)=v(:,i)./temp; end %Eigenvectors of C matrix u=[]; for i=1:size(v,2) temp=sqrt(d(i)); u=[u (dbx*v(:,i))./temp]; end %Normalization of eigenvectors for i=1:size(u,2) kk=u(:,i); temp=sqrt(sum(kk.^2)); u(:,i)=u(:,i)./temp; end % show eigenfaces; for i=1:size(u,2) img=reshape(u(:,i),icol,irow); img=img'; img=histeq(img,255); end % Find the weight of each face in the training set. omega = []; for h=1:size(dbx,2) WW=[]; for i=1:size(u,2) t = u(:,i)'; WeightOfImage = dot(t,dbx(:,h)'); WW = [WW; WeightOfImage]; end omega = [omega WW]; end % Acquire new image % Note: the input image must have a bmp or jpg extension. % It should have the same size as the ones in your training set. % It should be placed on your desktop ed_min=[]; srcFiles = dir('G:\newdatabase\*.jpg'); % the folder in which ur images exists for b = 1 : length(srcFiles) filename = strcat('G:\newdatabase\',srcFiles(b).name); Imgdata = imread(filename); InputImage=Imgdata; InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]); temp=InImage; me=mean(temp); st=std(temp); temp=(temp-me)*ustd/st+um; NormImage = temp; Difference = temp-m; p = []; aa=size(u,2); for i = 1:aa pare = dot(NormImage,u(:,i)); p = [p; pare]; end InImWeight = []; for i=1:size(u,2) t = u(:,i)'; WeightOfInputImage = dot(t,Difference'); InImWeight = [InImWeight; WeightOfInputImage]; end noe=numel(InImWeight); % Find Euclidean distance e=[]; for i=1:size(omega,2) q = omega(:,i); DiffWeight = InImWeight-q; mag = norm(DiffWeight); e = [e mag]; end ed_min=[ed_min MinimumValue]; theta=6.0e+03; %disp(e) z(b,:)=InImWeight; end IDX = kmeans(z,5); clustercount=accumarray(IDX, ones(size(IDX))); disp(clustercount); Running time for 50 images:Elapsed time is 103.947573 seconds. QUESTIONS: 1.It is working fine for M=50(i.e Training set contains 50 images) but not for M=1200(i.e Training set contains 1200 images).It is not showing any error.There is no output.I waited for 10 min still there is no output. I think it is going infinite loop.What is the problem?Where i was wrong?

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• Should integer divide by zero halt execution?

  - by Pyrolistical

  I know that modern languages handle integer divide by zero as an error just like the hardware does, but what if we could design a whole new language? Ignoring existing hardware, what should a programming language does when an integer divide by zero occurs? Should it return a NaN of type integer? Or should it mirror IEEE 754 float and return +/- Infinity? Or is the existing design choice correct, and an error should be thrown? Is there a language that handles integer divide by zero nicely? EDIT When I said ignore existing hardware, I mean don't assume integer is represented as 32 bits, it can be represented in anyway you can to imagine.

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• Business Insight, IT Execution: 9 Project Management Tips

  - by Sylvie MacKenzie, PMP

  Excerpt from Profit Magazine - by David Rosenbaum When Marcos Baccetto was first asked to be the business-side project lead on Eaton Corporation’s Vehicle Group South America (VGSA) Oracle project, the operations services manager responsible for running manufacturing was, he confesses, “a little afraid” because of his lack of IT experience. Today, Baccetto calls the project “a fantastic experience,” and he is a true believer in the benefits of a close relationship between IT implementers and their line-of-business peers. Through his partnership with Jesiele Lima, then VGSA IT manager, Baccetto and Eaton’s South American operations team came to understand several important principles of business and IT. Here he shares nine tips managers should consider when working on an enterprise technology project. 1. Make it a business project, not an IT project. All levels of functional management must have ownership, responsibility, and accountability for the success of the implementation. 2. Share responsibility. Business owners should sign off on tests and data conversion. 3. Clean your data. Dedicating a team to improve core data quality prior to project launch can be a significant time-saver. 4. Select resources properly. Have functional people who can translate business needs to IT and can influence organizational change. 5. Manage scope. Follow project management methodologies and disciplines. 6. Adopt common processes, global solutions. Avoid customized, local solutions. The big-picture business goals can get lost in the details. 7. Implement processes prior to the go-live date. Change management can be key. Keep the workforce informed and train users in advance. 8. Define metrics milestones. Assume there will be a crisis during deployment. Having baseline metrics to compare against will help implementers keep their cool—and the project moving forward. 9. The sponsor’s commitment is critical. It is needed to support the truly difficult decisions.

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• htaccess execution order and priority

  - by ChrisRamakers

  Can anyone explain to me in what order apache executes .htaccess files residing in different levels of the same path and how the rewrite rules therein are prioritized? For example, why doesn't the rewrite rule in the first .htaccess below work and is the one in /blog prioritized? .htaccess in / RewriteEngine on RewriteBase / RewriteRule ^blog offline.html [L] .htaccess in /blog RewriteEngine On RewriteBase /blog/ RewriteCond %{REQUEST_FILENAME} !-f RewriteCond %{REQUEST_FILENAME} !-d RewriteRule . /blog/index.php [L] Ps: i'm not simply looking for an answer but for a way to understand the apache/modrewrite internals ... why is more important to me than how to fix this :) Thanks!

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• jQuery in Action 2nd edition: Queuing functions for execution

  This article is taken from the book jQuery in Action, second edition. This segment shows how you can create your own queue to queue up any functions that you'd like to have executed in serial order. An understanding of jQuery animation is assumed. Presented By: NEC   Ads by Pheedo

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