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• Best design for a "Command Executer" class

  - by Justin984

  Sorry for the vague title, I couldn't think of a way to condense the question. I am building an application that will run as a background service and intermittently collect data about the system its running on. A second Android controller application will query the system over tcp/ip for statistics about the system. Currently, the background service has a tcp listener class that reads/writes bytes from a socket. When data is received, it raises an event to notify the service. The service takes the bytes, feeds them into a command parser to figure out what is being requested, and then passes the parsed command to a command executer class. When the service receives a "query statistics" command, it should return statistics over the tcp/ip connection. Currently, all of these classes are fully decoupled from each other. But in order for the command executer to return statistics, it will obviously need access to the socket somehow. For reasons I can't completely articulate, it feels wrong for the command executer to have a direct reference to the socket. I'm looking for strategies and/or design patterns I can use to return data over the socket while keeping the classes decoupled, if this is possible. Hopefully this makes sense, please let me know if I can include any info that would make the question easier to understand.

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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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• Nagios graphing solutions vs Munin/Cacti/Ganglia

  - by sumek

  I've got a nagios server setup for monitoring ~ 30 Windows servers. I want to add some trending charts. I've read that nagios graphing plugins are simple and many people use seperate, standalone charting/trending tools. What are the restrictions of the nagios graphing plugins vs standalone products like ganglia/munin/cacti? I'm interested in specific features and advantages that standalone packages offer and nagios graphing plugins don't.

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• Troubleshooting Application Timeouts in SQL Server

  - by Tara Kizer

  I recently received the following email from a blog reader: "We are having an OLTP database instance, using SQL Server 2005 with little to moderate traffic (10-20 requests/min). There are also bulk imports that occur at regular intervals in this DB and the import duration ranges between 10secs to 1 min, depending on the data size. Intermittently (2-3 times in a week), we face an issue, where queries get timed out (default of 30 secs set in application). On analyzing, we found two stored procedures, having queries with multiple table joins inside them of taking a long time (5-10 mins) in getting executed, when ideally the execution duration ranges between 5-10 secs. Execution plan of the same displayed Clustered Index Scan happening instead of Clustered Index Seek. All required Indexes are found to be present and Index fragmentation is also minimal as we Rebuild Indexes regularly alongwith Updating Statistics. With no other alternate options occuring to us, we restarted SQL server and thereafter the performance was back on track. But sometimes it was still giving timeout errors for some hits and so we also restarted IIS and that stopped the problem as of now." Rather than respond directly to the blog reader, I thought it would be more interesting to share my thoughts on this issue in a blog. There are a few things that I can think of that could cause abnormal timeouts: Blocking Bad plan in cache Outdated statistics Hardware bottleneck To determine if blocking is the issue, we can easily run sp_who/sp_who2 or a query directly on sysprocesses (select * from master..sysprocesses where blocking <> 0).  If blocking is present and consistent, then you'll need to determine whether or not to kill the parent blocking process.  Killing a process will cause the transaction to rollback, so you need to proceed with caution.  Killing the parent blocking process is only a temporary solution, so you'll need to do more thorough analysis to figure out why the blocking was present.  You should look into missing indexes and perhaps consider changing the database's isolation level to READ_COMMITTED_SNAPSHOT. The blog reader mentions that the execution plan shows a clustered index scan when a clustered index seek is normal for the stored procedure.  A clustered index scan might have been chosen either because that is what is in cache already or because of out of date statistics.  The blog reader mentions that bulk imports occur at regular intervals, so outdated statistics is definitely something that could cause this issue.  The blog reader may need to update statistics after imports are done if the imports are changing a lot of data (greater than 10%).  If the statistics are good, then the query optimizer might have chosen to scan rather than seek in a previous execution because the scan was determined to be less costly due to the value of an input parameter.  If this parameter value is rare, then its execution plan in cache is what we call a bad plan.  You want the best plan in cache for the most frequent parameter values.  If a bad plan is a recurring problem on your system, then you should consider rewriting the stored procedure.  You might want to break up the code into multiple stored procedures so that each can have a different execution plan in cache. To remove a bad plan from cache, you can recompile the stored procedure.  An alternative method is to run DBCC FREEPROCACHE which drops the procedure cache.  It is better to recompile stored procedures rather than dropping the procedure cache as dropping the procedure cache affects all plans in cache rather than just the ones that were bad, so there will be a temporary performance penalty until the plans are loaded into cache again. To determine if there is a hardware bottleneck occurring such as slow I/O or high CPU utilization, you will need to run Performance Monitor on the database server.  Hopefully you already have a baseline of the server so you know what is normal and what is not.  Be on the lookout for I/O requests taking longer than 12 milliseconds and CPU utilization over 90%.  The servers that I support typically are under 30% CPU utilization, but your baseline could be higher and be within a normal range. If restarting the SQL Server service fixes the problem, then the problem was most likely due to blocking or a bad plan in the procedure cache.  Rather than restarting the SQL Server service, which causes downtime, the blog reader should instead analyze the above mentioned things.  Proceed with caution when restarting the SQL Server service as all transactions that have not completed will be rolled back at startup.  This crash recovery process could take longer than normal if there was a long-running transaction running when the service was stopped.  Until the crash recovery process is completed on the database, it is unavailable to your applications. If restarting IIS fixes the problem, then the problem might not have been inside SQL Server.  Prior to taking this step, you should do analysis of the above mentioned things. If you can think of other reasons why the blog reader is facing this issue a few times a week, I'd love to hear your thoughts via a blog comment.

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• NET Math Libraries

  - by JoshReuben

  NET Mathematical Libraries   .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product  Price Update subscription Single Developer License $999  $399  Team License (3 developers) $1999  $799  Department License (8 developers) $3999  $1599  Site License (Unlimited developers in one physical location) $7999  $3199    NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product  Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554   DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods).   Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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• Getting started with Oracle Database In-Memory Part III - Querying The IM Column Store

  - by Maria Colgan

  In my previous blog posts, I described how to install, enable, and populate the In-Memory column store (IM column store). This weeks post focuses on how data is accessed within the IM column store. Let’s take a simple query “What is the most expensive air-mail order we have received to date?” SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE  lo_shipmode = 5; The LINEORDER table has been populated into the IM column store and since we have no alternative access paths (indexes or views) the execution plan for this query is a full table scan of the LINEORDER table. You will notice that the execution plan has a new set of keywords “IN MEMORY" in the access method description in the Operation column. These keywords indicate that the LINEORDER table has been marked for INMEMORY and we may use the IM column store in this query. What do I mean by “may use”? There are a small number of cases were we won’t use the IM column store even though the object has been marked INMEMORY. This is similar to how the keyword STORAGE is used on Exadata environments. You can confirm that the IM column store was actually used by examining the session level statistics, but more on that later. For now let's focus on how the data is accessed in the IM column store and why it’s faster to access the data in the new column format, for analytical queries, rather than the buffer cache. There are four main reasons why accessing the data in the IM column store is more efficient. 1. Access only the column data needed The IM column store only has to scan two columns – lo_shipmode and lo_ordtotalprice – to execute this query while the traditional row store or buffer cache has to scan all of the columns in each row of the LINEORDER table until it reaches both the lo_shipmode and the lo_ordtotalprice column. 2. Scan and filter data in it's compressed format When data is populated into the IM column it is automatically compressed using a new set of compression algorithms that allow WHERE clause predicates to be applied against the compressed formats. This means the volume of data scanned in the IM column store for our query will be far less than the same query in the buffer cache where it will scan the data in its uncompressed form, which could be 20X larger. 3. Prune out any unnecessary data within each column The fastest read you can execute is the read you don’t do. In the IM column store a further reduction in the amount of data accessed is possible due to the In-Memory Storage Indexes(IM storage indexes) that are automatically created and maintained on each of the columns in the IM column store. IM storage indexes allow data pruning to occur based on the filter predicates supplied in a SQL statement. An IM storage index keeps track of minimum and maximum values for each column in each of the In-Memory Compression Unit (IMCU). In our query the WHERE clause predicate is on the lo_shipmode column. The IM storage index on the lo_shipdate column is examined to determine if our specified column value 5 exist in any IMCU by comparing the value 5 to the minimum and maximum values maintained in the Storage Index. If the value 5 is outside the minimum and maximum range for an IMCU, the scan of that IMCU is avoided. For the IMCUs where the value 5 does fall within the min, max range, an additional level of data pruning is possible via the metadata dictionary created when dictionary-based compression is used on IMCU. The dictionary contains a list of the unique column values within the IMCU. Since we have an equality predicate we can easily determine if 5 is one of the distinct column values or not. The combination of the IM storage index and dictionary based pruning, enables us to only scan the necessary IMCUs. 4. Use SIMD to apply filter predicates For the IMCU that need to be scanned Oracle takes advantage of SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. The column format used in the IM column store has been specifically designed to maximize the number of column entries that can be loaded into the vector registers on the CPU and evaluated in a single CPU instruction. SIMD vector processing enables the Oracle Database In-Memory to scan billion of rows per second per core versus the millions of rows per second per core scan rate that can be achieved in the buffer cache. I mentioned earlier in this post that in order to confirm the IM column store was used; we need to examine the session level statistics. You can monitor the session level statistics by querying the performance views v$mystat and v$statname. All of the statistics related to the In-Memory Column Store begin with IM. You can see the full list of these statistics by typing: display_name format a30 SELECT display_name FROM v$statname WHERE  display_name LIKE 'IM%'; If we check the session statistics after we execute our query the results would be as follow; SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE lo_shipmode = 5; SELECT display_name FROM v$statname WHERE  display_name IN ('IM scan CUs columns accessed',                        'IM scan segments minmax eligible',                        'IM scan CUs pruned'); As you can see, only 2 IMCUs were accessed during the scan as the majority of the IMCUs (44) in the LINEORDER table were pruned out thanks to the storage index on the lo_shipmode column. In next weeks post I will describe how you can control which queries use the IM column store and which don't. +Maria Colgan

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• Winnipeg SQL Server UG April Event &ndash; How To Do An Index Review

  - by D'Arcy Lussier

  April Event - How to Do an Index Review April 14th, 2010 5:30 - 8:00 17th Floor Conference Room, Richardson Building One Lombard Place, Winnipeg Pizza and Drinks Provided! Did you know that SQL Server 2005+ keeps query execution statistics, index usage statistics and even missing index statistics?  Learn how to access this information and use it to help you make good decisions about what your database really needs in terms of indexes in a lot less time than you might think an index review should take.  There are 6 or 7 (depending on your version of SQL server) DMVs (dynamic management views) to look at which reveal a lot about your database and how you can improve its performance. To register for this event, please click HERE to register!

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• Oracle Enterprise Manager 12c(EM12c):????????? ~??????~

  - by Kumiko Fujita

  ?????? = ?????????????????? Oracle Enterprise Manager 12c?????????????????????????????????????????????????????????????????????????????·????????????Infrastructure as a Service(IaaS)???Database as a Service(DBaaS)????????????????? ?????? ??????? 1. Consolidation Planner -??????????????????????!- Consolidation Planner?????????????????·??????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????Consolidation Planner???????????????????????????????????????????????????????????? 2. Cloud Management(Oracle VM??) -???????????????????????!- ??????·???????????????????????????????????????????????????????????????/???????????????????????????????????Cloud Management??????????????????????????Oracle VM 3.0????????????????????????????·?????????????????????????????????????????????????????????????????????????·?????????????????????? 3. Chargeback and Trending -?????????????????????????!- ??????·????????????????????????????????????????????????????????????Chargeback and Trending???????????????????????????Oracle WebLogic Server???????????????????????????????????????????????????????????????????????CPU????/?????????????????????????????????????????????????????????????????????????????????????????????????? ??????! ?????Cloud Management?(PDF) ?????????(????????????????) WMV(??) WMV(??) MP4(??) MP4(??)

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• Sharepoint SSRS: Unexpected Error Encountered

  - by Nicholas

  We are running an intranet website hosted on Sharepoint 2007, serving reports to users via SSRS. Recently, our users are experiencing error when trying to access certain reports with the error message "An unexpected error has occurred". After trying many things, to cut a long story short we manage to temporarily solve the problem by manually running UPDATE STATISTICS on the tables in the database. However, this error will periodically crop up again and again, running UPDATE STATISTICS immediately solved it. I had suggested to run a daily job in the server to auto run UPDATE STATISTICS every few hours or so but was rejected by my team lead. Is there any other workaround for this error? Why does UPDATE STATISTICS solve the problem, I still not very understand?

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• block write access to table from an application in mysql

  - by hoberion

  Hello, We have a CMS plugin that writes statistics to 1 table, this creates performance issues on the entire platform. We decided to use another statistics plugin which can connect to a different database server (the first plugin couldn't!) however we need parts of the first plugin. I want to lock the statistics table to prevent misusage (not allowed to drop it by the developer) So I was wondering if a lock table could do this or if I can implement some sort of read only table

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• JMX Based Monitoring - Part Two - JVM Monitoring

  - by Anthony Shorten

  This the second article in the series focussing on the JMX based monitoring capabilities possible with the Oracle Utilities Application Framework. In all versions of the Oracle utilities Application Framework, it is possible to use the basic JMX based monitoring available with the Java Virtual Machine to provide basic statistics ablut the JVM. In Java 5 and above, the JVM automatically allowed local monitoring of the JVM statistics from an approporiate console. When I say local I mean the monitoring tool must be executed from the same machine (and in some cases the same user that is running the JVM) to connect to the JVM directly. If you are using jconsole, for example, then you must have access to a GUI (X-Windows or Windows) to display the jconsole output. This is the easist way of monitoring without doing too much configration but is not always practical. Java offers a remote monitorig capability to allow yo to connect to a remotely executing JVM from a console (like jconsole). To use this facility additional JVM options must be added to the command line that started the JVM. Details of the additional options for the version of the Java you are running is located at the JMX information site. Typically to remotely connect to a running JVM that JVM must be configured with the following categories of options: JMX Port - The JVM must allow connections on a listening port specified on the command line Connection security - The connection to the JVM can be secured. This is recommended as JMX is not just a monitoring protocol it is a managemet protocol. It is possible to change values in a running JVM using JMX and there are NO "Are you sure?" safeguards. For a Oracle Utilities Application Framework based application there are a few guidelines when configuring and using this JMX based remote monitoring of the JVM's: Online JVM - The JVM used to run the online system is embedded within the J2EE Web Application Server. To enable JMX monitoring on this JVM you can either change the startup script that starts the Web Application Server or check whether your J2EE Web Application natively supports JVM statistics collection. Child JVM's (COBOL only) - The Child JVM's should not be monitored using this method as they are recycled regularly by the configuration and therefore statistics collected are of little value. Batch Threadpoools - Batch already has a JMX interface (which will be covered in another article). Additional monitoring can be enabled but the base supported monitoring is sufficient for most needs. If you are an Oracle Utilities Application Framework site, then you can specify the additional options for JMX Java monitoring on the OPTS paramaters supported for each component of the architecture. Just ensure the port numbers used are unique for each JVM running on any machine.

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• ZFS for Database Log Files

  - by user12620111

  I've been troubled by drop outs in CPU usage in my application server, characterized by the CPUs suddenly going from close to 90% CPU busy to almost completely CPU idle for a few seconds. Here is an example of a drop out as shown by a snippet of vmstat data taken while the application server is under a heavy workload. # vmstat 1  kthr      memory            page            disk          faults      cpu  r b w   swap  free  re  mf pi po fr de sr s3 s4 s5 s6   in   sy   cs us sy id  1 0 0 130160176 116381952 0 16 0 0 0 0  0  0  0  0  0 207377 117715 203884 70 21 9  12 0 0 130160160 116381936 0 25 0 0 0 0 0  0  0  0  0 200413 117162 197250 70 20 9  11 0 0 130160176 116381920 0 16 0 0 0 0 0  0  1  0  0 203150 119365 200249 72 21 7  8 0 0 130160176 116377808 0 19 0 0 0 0  0  0  0  0  0 169826 96144 165194 56 17 27  0 0 0 130160176 116377800 0 16 0 0 0 0  0  0  0  0  1 10245 9376 9164 2  1 97  0 0 0 130160176 116377792 0 16 0 0 0 0  0  0  0  0  2 15742 12401 14784 4 1 95  0 0 0 130160176 116377776 2 16 0 0 0 0  0  0  1  0  0 19972 17703 19612 6 2 92  14 0 0 130160176 116377696 0 16 0 0 0 0 0  0  0  0  0 202794 116793 199807 71 21 8  9 0 0 130160160 116373584 0 30 0 0 0 0  0  0 18  0  0 203123 117857 198825 69 20 11 This behavior occurred consistently while the application server was processing synthetic transactions: HTTP requests from JMeter running on an external machine. I explored many theories trying to explain the drop outs, including: Unexpected JMeter behavior Network contention Java Garbage Collection Application Server thread pool problems Connection pool problems Database transaction processing Database I/O contention Graphing the CPU %idle led to a breakthrough: Several of the drop outs were 30 seconds apart. With that insight, I went digging through the data again and looking for other outliers that were 30 seconds apart. In the database server statistics, I found spikes in the iostat "asvc_t" (average response time of disk transactions, in milliseconds) for the disk drive that was being used for the database log files. Here is an example:                     extended device statistics     r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 2053.6    0.0 8234.3  0.0  0.2    0.0    0.1   0  24 c3t60080E5...F4F6d0s0     0.0 2162.2    0.0 8652.8  0.0  0.3    0.0    0.1   0  28 c3t60080E5...F4F6d0s0     0.0 1102.5    0.0 10012.8  0.0  4.5    0.0    4.1   0  69 c3t60080E5...F4F6d0s0     0.0   74.0    0.0 7920.6  0.0 10.0    0.0  135.1   0 100 c3t60080E5...F4F6d0s0     0.0  568.7    0.0 6674.0  0.0  6.4    0.0   11.2   0  90 c3t60080E5...F4F6d0s0     0.0 1358.0    0.0 5456.0  0.0  0.6    0.0    0.4   0  55 c3t60080E5...F4F6d0s0     0.0 1314.3    0.0 5285.2  0.0  0.7    0.0    0.5   0  70 c3t60080E5...F4F6d0s0 Here is a little more information about my database configuration: The database and application server were running on two different SPARC servers. Storage for the database was on a storage array connected via 8 gigabit Fibre Channel Data storage and log file were on different physical disk drives Reliable low latency I/O is provided by battery backed NVRAM Highly available: Two Fibre Channel links accessed via MPxIO Two Mirrored cache controllers The log file physical disks were mirrored in the storage device Database log files on a ZFS Filesystem with cutting-edge technologies, such as copy-on-write and end-to-end checksumming Why would I be getting service time spikes in my high-end storage? First, I wanted to verify that the database log disk service time spikes aligned with the application server CPU drop outs, and they did: At first, I guessed that the disk service time spikes might be related to flushing the write through cache on the storage device, but I was unable to validate that theory. After searching the WWW for a while, I decided to try using a separate log device: # zpool add ZFS-db-41 log c3t60080E500017D55C000015C150A9F8A7d0 The ZFS log device is configured in a similar manner as described above: two physical disks mirrored in the storage array. This change to the database storage configuration eliminated the application server CPU drop outs: Here is the zpool configuration: # zpool status ZFS-db-41   pool: ZFS-db-41  state: ONLINE  scan: none requested config:         NAME                                     STATE         ZFS-db-41                                ONLINE           c3t60080E5...F4F6d0  ONLINE         logs           c3t60080E5...F8A7d0  ONLINE Now, the I/O spikes look like this:                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1053.5    0.0 4234.1  0.0  0.8    0.0    0.7   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1131.8    0.0 4555.3  0.0  0.8    0.0    0.7   0  76 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1167.6    0.0 4682.2  0.0  0.7    0.0    0.6   0  74 c3t60080E5...F8A7d0s0     0.0  162.2    0.0 19153.9  0.0  0.7    0.0    4.2   0  12 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1247.2    0.0 4992.6  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0     0.0   41.0    0.0   70.0  0.0  0.1    0.0    1.6   0   2 c3t60080E5...F4F6d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1241.3    0.0 4989.3  0.0  0.8    0.0    0.6   0  75 c3t60080E5...F8A7d0s0                     extended device statistics                  r/s    w/s   kr/s   kw/s wait actv wsvc_t asvc_t  %w  %b device     0.0 1193.2    0.0 4772.9  0.0  0.7    0.0    0.6   0  71 c3t60080E5...F8A7d0s0 We can see the steady flow of 4k writes to the ZIL device from O_SYNC database log file writes. The spikes are from flushing the transaction group. Like almost all problems that I run into, once I thoroughly understand the problem, I find that other people have documented similar experiences. Thanks to all of you who have documented alternative approaches. Saved for another day: now that the problem is obvious, I should try "zfs:zfs_immediate_write_sz" as recommended in the ZFS Evil Tuning Guide. References: The ZFS Intent Log Solaris ZFS, Synchronous Writes and the ZIL Explained ZFS Evil Tuning Guide: Cache Flushes ZFS Evil Tuning Guide: Tuning ZFS for Database Performance

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• SQL SERVER Disabled Index and UpdateStatistics

  When we try to update the statistics, it throws an error as if the clustered index is disabled. Now let us enable the clustered index only and attempt to update the statistics of the table right after that. Have you ever come across the situation where a conversation never gets over and it continues even [...]...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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• How do you monitor SSD wear in Windows when the drives are presented as 'generic' devices?

  - by MikeyB

  Under Linux, we can monitor SSD wear fairly easily with smartmontools whether the drive is presented as a normal block device or a generic device (which happens when the drive has been hardware RAIDed by certain controllers such as the one on the IBM HS22). How can we do the equivalent under Windows? Does anyone actually use smartmontools? Or are there other packages out there? The problem is that SCSI Generic devices just don't show up in Windows. If the drives aren't RAIDed we can see them fine. How I'd do it in Linux: sles11-live:~ # lsscsi -g [1:0:0:0] disk SMART USB-IBM 8989 /dev/sda /dev/sg0 [2:0:0:0] disk ATA MTFDDAK256MAR-1K MA44 - /dev/sg1 [2:0:1:0] disk ATA MTFDDAK256MAR-1K MA44 - /dev/sg2 [2:1:8:0] disk LSILOGIC Logical Volume 3000 /dev/sdb /dev/sg3 sles11-live:~ # smartctl -l ssd /dev/sg1 smartctl 5.42 2011-10-20 r3458 [x86_64-linux-2.6.32.49-0.3-default] (local build) Copyright (C) 2002-11 by Bruce Allen, http://smartmontools.sourceforge.net Device Statistics (GP Log 0x04) Page Offset Size Value Description 7 ===== = = == Solid State Device Statistics (rev 1) == 7 0x008 1 26~ Percentage Used Endurance Indicator |_ ~ normalized value sles11-live:~ # smartctl -l ssd /dev/sg2 smartctl 5.42 2011-10-20 r3458 [x86_64-linux-2.6.32.49-0.3-default] (local build) Copyright (C) 2002-11 by Bruce Allen, http://smartmontools.sourceforge.net Device Statistics (GP Log 0x04) Page Offset Size Value Description 7 ===== = = == Solid State Device Statistics (rev 1) == 7 0x008 1 3~ Percentage Used Endurance Indicator |_ ~ normalized value

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• Seeking on a Heap, and Two Useful DMVs

  - by Paul White

  So far in this mini-series on seeks and scans, we have seen that a simple ‘seek’ operation can be much more complex than it first appears.  A seek can contain one or more seek predicates – each of which can either identify at most one row in a unique index (a singleton lookup) or a range of values (a range scan).  When looking at a query plan, we will often need to look at the details of the seek operator in the Properties window to see how many operations it is performing, and what type of operation each one is.  As you saw in the first post in this series, the number of hidden seeking operations can have an appreciable impact on performance. Measuring Seeks and Scans I mentioned in my last post that there is no way to tell from a graphical query plan whether you are seeing a singleton lookup or a range scan.  You can work it out – if you happen to know that the index is defined as unique and the seek predicate is an equality comparison, but there’s no separate property that says ‘singleton lookup’ or ‘range scan’.  This is a shame, and if I had my way, the query plan would show different icons for range scans and singleton lookups – perhaps also indicating whether the operation was one or more of those operations underneath the covers. In light of all that, you might be wondering if there is another way to measure how many seeks of either type are occurring in your system, or for a particular query.  As is often the case, the answer is yes – we can use a couple of dynamic management views (DMVs): sys.dm_db_index_usage_stats and sys.dm_db_index_operational_stats. Index Usage Stats The index usage stats DMV contains counts of index operations from the perspective of the Query Executor (QE) – the SQL Server component that is responsible for executing the query plan.  It has three columns that are of particular interest to us: user_seeks – the number of times an Index Seek operator appears in an executed plan user_scans – the number of times a Table Scan or Index Scan operator appears in an executed plan user_lookups – the number of times an RID or Key Lookup operator appears in an executed plan An operator is counted once per execution (generating an estimated plan does not affect the totals), so an Index Seek that executes 10,000 times in a single plan execution adds 1 to the count of user seeks.  Even less intuitively, an operator is also counted once per execution even if it is not executed at all.  I will show you a demonstration of each of these things later in this post. Index Operational Stats The index operational stats DMV contains counts of index and table operations from the perspective of the Storage Engine (SE).  It contains a wealth of interesting information, but the two columns of interest to us right now are: range_scan_count – the number of range scans (including unrestricted full scans) on a heap or index structure singleton_lookup_count – the number of singleton lookups in a heap or index structure This DMV counts each SE operation, so 10,000 singleton lookups will add 10,000 to the singleton lookup count column, and a table scan that is executed 5 times will add 5 to the range scan count. The Test Rig To explore the behaviour of seeks and scans in detail, we will need to create a test environment.  The scripts presented here are best run on SQL Server 2008 Developer Edition, but the majority of the tests will work just fine on SQL Server 2005.  A couple of tests use partitioning, but these will be skipped if you are not running an Enterprise-equivalent SKU.  Ok, first up we need a database: USE master; GO IF DB_ID('ScansAndSeeks') IS NOT NULL DROP DATABASE ScansAndSeeks; GO CREATE DATABASE ScansAndSeeks; GO USE ScansAndSeeks; GO ALTER DATABASE ScansAndSeeks SET ALLOW_SNAPSHOT_ISOLATION OFF ; ALTER DATABASE ScansAndSeeks SET AUTO_CLOSE OFF, AUTO_SHRINK OFF, AUTO_CREATE_STATISTICS OFF, AUTO_UPDATE_STATISTICS OFF, PARAMETERIZATION SIMPLE, READ_COMMITTED_SNAPSHOT OFF, RESTRICTED_USER ; Notice that several database options are set in particular ways to ensure we get meaningful and reproducible results from the DMVs.  In particular, the options to auto-create and update statistics are disabled.  There are also three stored procedures, the first of which creates a test table (which may or may not be partitioned).  The table is pretty much the same one we used yesterday: The table has 100 rows, and both the key_col and data columns contain the same values – the integers from 1 to 100 inclusive.  The table is a heap, with a non-clustered primary key on key_col, and a non-clustered non-unique index on the data column.  The only reason I have used a heap here, rather than a clustered table, is so I can demonstrate a seek on a heap later on.  The table has an extra column (not shown because I am too lazy to update the diagram from yesterday) called padding – a CHAR(100) column that just contains 100 spaces in every row.  It’s just there to discourage SQL Server from choosing table scan over an index + RID lookup in one of the tests. The first stored procedure is called ResetTest: CREATE PROCEDURE dbo.ResetTest @Partitioned BIT = 'false' AS BEGIN SET NOCOUNT ON ; IF OBJECT_ID(N'dbo.Example', N'U') IS NOT NULL BEGIN DROP TABLE dbo.Example; END ; -- Test table is a heap -- Non-clustered primary key on 'key_col' CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ; IF @Partitioned = 'true' BEGIN -- Enterprise, Trial, or Developer -- required for partitioning tests IF SERVERPROPERTY('EngineEdition') = 3 BEGIN EXECUTE (' DROP TABLE dbo.Example ; IF EXISTS ( SELECT 1 FROM sys.partition_schemes WHERE name = N''PS'' ) DROP PARTITION SCHEME PS ; IF EXISTS ( SELECT 1 FROM sys.partition_functions WHERE name = N''PF'' ) DROP PARTITION FUNCTION PF ; CREATE PARTITION FUNCTION PF (INTEGER) AS RANGE RIGHT FOR VALUES (20, 40, 60, 80, 100) ; CREATE PARTITION SCHEME PS AS PARTITION PF ALL TO ([PRIMARY]) ; CREATE TABLE dbo.Example ( key_col INTEGER NOT NULL, data INTEGER NOT NULL, padding CHAR(100) NOT NULL DEFAULT SPACE(100), CONSTRAINT [PK dbo.Example key_col] PRIMARY KEY NONCLUSTERED (key_col) ) ON PS (key_col); '); END ELSE BEGIN RAISERROR('Invalid SKU for partition test', 16, 1); RETURN; END; END ; -- Non-unique non-clustered index on the 'data' column CREATE NONCLUSTERED INDEX [IX dbo.Example data] ON dbo.Example (data) ; -- Add 100 rows INSERT dbo.Example WITH (TABLOCKX) ( key_col, data ) SELECT key_col = V.number, data = V.number FROM master.dbo.spt_values AS V WHERE V.[type] = N'P' AND V.number BETWEEN 1 AND 100 ; END; GO The second stored procedure, ShowStats, displays information from the Index Usage Stats and Index Operational Stats DMVs: CREATE PROCEDURE dbo.ShowStats @Partitioned BIT = 'false' AS BEGIN -- Index Usage Stats DMV (QE) SELECT index_name = ISNULL(I.name, I.type_desc), scans = IUS.user_scans, seeks = IUS.user_seeks, lookups = IUS.user_lookups FROM sys.dm_db_index_usage_stats AS IUS JOIN sys.indexes AS I ON I.object_id = IUS.object_id AND I.index_id = IUS.index_id WHERE IUS.database_id = DB_ID(N'ScansAndSeeks') AND IUS.object_id = OBJECT_ID(N'dbo.Example', N'U') ORDER BY I.index_id ; -- Index Operational Stats DMV (SE) IF @Partitioned = 'true' SELECT index_name = ISNULL(I.name, I.type_desc), partitions = COUNT(IOS.partition_number), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; ELSE SELECT index_name = ISNULL(I.name, I.type_desc), range_scans = SUM(IOS.range_scan_count), single_lookups = SUM(IOS.singleton_lookup_count) FROM sys.dm_db_index_operational_stats ( DB_ID(N'ScansAndSeeks'), OBJECT_ID(N'dbo.Example', N'U'), NULL, NULL ) AS IOS JOIN sys.indexes AS I ON I.object_id = IOS.object_id AND I.index_id = IOS.index_id GROUP BY I.index_id, -- Key I.name, I.type_desc ORDER BY I.index_id; END; The final stored procedure, RunTest, executes a query written against the example table: CREATE PROCEDURE dbo.RunTest @SQL VARCHAR(8000), @Partitioned BIT = 'false' AS BEGIN -- No execution plan yet SET STATISTICS XML OFF ; -- Reset the test environment EXECUTE dbo.ResetTest @Partitioned ; -- Previous call will throw an error if a partitioned -- test was requested, but SKU does not support it IF @@ERROR = 0 BEGIN -- IO statistics and plan on SET STATISTICS XML, IO ON ; -- Test statement EXECUTE (@SQL) ; -- Plan and IO statistics off SET STATISTICS XML, IO OFF ; EXECUTE dbo.ShowStats @Partitioned; END; END; The Tests The first test is a simple scan of the heap table: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example'; The top result set comes from the Index Usage Stats DMV, so it is the Query Executor’s (QE) view.  The lower result is from Index Operational Stats, which shows statistics derived from the actions taken by the Storage Engine (SE).  We see that QE performed 1 scan operation on the heap, and SE performed a single range scan.  Let’s try a single-value equality seek on a unique index next: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 32'; This time we see a single seek on the non-clustered primary key from QE, and one singleton lookup on the same index by the SE.  Now for a single-value seek on the non-unique non-clustered index: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32'; QE shows a single seek on the non-clustered non-unique index, but SE shows a single range scan on that index – not the singleton lookup we saw in the previous test.  That makes sense because we know that only a single-value seek into a unique index is a singleton seek.  A single-value seek into a non-unique index might retrieve any number of rows, if you think about it.  The next query is equivalent to the IN list example seen in the first post in this series, but it is written using OR (just for variety, you understand): EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data = 32 OR data = 33'; The plan looks the same, and there’s no difference in the stats recorded by QE, but the SE shows two range scans.  Again, these are range scans because we are looking for two values in the data column, which is covered by a non-unique index.  I’ve added a snippet from the Properties window to show that the query plan does show two seek predicates, not just one.  Now let’s rewrite the query using BETWEEN: EXECUTE dbo.RunTest @SQL = 'SELECT data FROM Example WHERE data BETWEEN 32 AND 33'; Notice the seek operator only has one predicate now – it’s just a single range scan from 32 to 33 in the index – as the SE output shows.  For the next test, we will look up four values in the key_col column: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col IN (2,4,6,8)'; Just a single seek on the PK from the Query Executor, but four singleton lookups reported by the Storage Engine – and four seek predicates in the Properties window.  On to a more complex example: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WITH (INDEX([PK dbo.Example key_col])) WHERE key_col BETWEEN 1 AND 8'; This time we are forcing use of the non-clustered primary key to return eight rows.  The index is not covering for this query, so the query plan includes an RID lookup into the heap to fetch the data and padding columns.  The QE reports a seek on the PK and a lookup on the heap.  The SE reports a single range scan on the PK (to find key_col values between 1 and 8), and eight singleton lookups on the heap.  Remember that a bookmark lookup (RID or Key) is a seek to a single value in a ‘unique index’ – it finds a row in the heap or cluster from a unique RID or clustering key – so that’s why lookups are always singleton lookups, not range scans. Our next example shows what happens when a query plan operator is not executed at all: EXECUTE dbo.RunTest @SQL = 'SELECT key_col FROM Example WHERE key_col = 8 AND @@TRANCOUNT < 0'; The Filter has a start-up predicate which is always false (if your @@TRANCOUNT is less than zero, call CSS immediately).  The index seek is never executed, but QE still records a single seek against the PK because the operator appears once in an executed plan.  The SE output shows no activity at all.  This next example is 2008 and above only, I’m afraid: EXECUTE dbo.RunTest @SQL = 'SELECT * FROM Example WHERE key_col BETWEEN 1 AND 30', @Partitioned = 'true'; This is the first example to use a partitioned table.  QE reports a single seek on the heap (yes – a seek on a heap), and the SE reports two range scans on the heap.  SQL Server knows (from the partitioning definition) that it only needs to look at partitions 1 and 2 to find all the rows where key_col is between 1 and 30 – the engine seeks to find the two partitions, and performs a range scan seek on each partition. The final example for today is another seek on a heap – try to work out the output of the query before running it! EXECUTE dbo.RunTest @SQL = 'SELECT TOP (2) WITH TIES * FROM Example WHERE key_col BETWEEN 1 AND 50 ORDER BY $PARTITION.PF(key_col) DESC', @Partitioned = 'true'; Notice the lack of an explicit Sort operator in the query plan to enforce the ORDER BY clause, and the backward range scan. © 2011 Paul White email: [email protected] twitter: @SQL_Kiwi

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• Migrating SQL Server Databases – The DBA’s Checklist (Part 2)

  - by Sadequl Hussain

  Continuing from Part 1  , our Migration Checklist continues: Step 5: Update statistics It is always a good idea to update the statistics of the database that you have just installed or migrated. To do this, run the following command against the target database: sp_updatestats The sp_updatestats system stored procedure runs the UPDATE STATISTICS command against every user and system table in the database.  However, a word of caution: running the sp_updatestats against a database with a compatibility level below 90 (SQL Server 2005) will reset the automatic UPDATE STATISTICS settings for every index and statistics of every table in the database. You may therefore want to change the compatibility mode before you run the command. Another thing you should remember to do is to ensure the new database has its AUTO_CREATE_STATISTICS and AUTO_UPDATE_STATISTICS properties set to ON. You can do so using the ALTER DATABASE command or from the SSMS. Step 6: Set database options You may have to change the state of a database after it has been restored. If the database was changed to single-user or read-only mode before backup, the restored copy will also retain these settings. This may not be an issue when you are manually restoring from Enterprise Manager or the Management Studio since you can change the properties. However, this is something to be mindful of if the restore process is invoked by an automated job or script and the database needs to be written to immediately after restore. You may want to check the database’s status programmatically in such cases. Another important option you may want to set for the newly restored / attached database is PAGE_VERIFY. This option specifies how you want SQL Server to ensure the physical integrity of the data. It is a new option from SQL Server 2005 and can have three values: CHECKSUM (default for SQL Server 2005 and latter databases), TORN_PAGE_DETECTION (default when restoring a pre-SQL Server 2005 database) or NONE. Torn page detection was itself an option for SQL Server 2000 databases. From SQL Server 2005, when PAGE_VERIFY is set to CHECKSUM, the database engine calculates the checksum for a page’s contents and writes it to the page header before storing it in disk. When the page is read from the disk, the checksum is computed again and compared with the checksum stored in the header.  Torn page detection works much like the same way in that it stores a bit in the page header for every 512 byte sector. When data is read from the page, the torn page bits stored in the header is compared with the respective sector contents. When PAGE_VERIFY is set to NONE, SQL Server does not perform any checking, even if torn page data or checksums are present in the page header.  This may not be something you would want to set unless there is a very specific reason.  Microsoft suggests using the CHECKSUM page verify option as this offers more protection. Step 7: Map database users to logins A common database migration issue is related to user access. Windows and SQL Server native logins that existed in the source instance and had access to the database may not be present in the destination. Even if the logins exist in the destination, the mapping between the user accounts and the logins will not be automatic. You can use a special system stored procedure called sp_change_users_login to address these situations. The procedure needs to be run against the newly attached or restored database and can accept four parameters. Depending on what you want to do, you may be using less than four though. The first parameter, @Action, can take three values. When you specify @Action = ‘Report’, the system will provide you with a list of database users which are not mapped to any login. If you want to map a database user to an existing SQL Server login, the value for @Action will be ‘Update_One’. In this case, you will only need to provide the database user name and the login it will map to. So if your newly restored database has a user account called “bob” and there is already a SQL Server login with the same name and you want to map the user to the login, you will execute a query like the following: sp_change_users_login         @Action = ‘Update_One’,         @UserNamePattern = ‘bob’,         @LoginName = ‘bob’ If the login does not exist, you can instruct SQL Server to create the login with the same name. In this case you will need to provide a password for the login and the value of the @Action parameter will be ‘Auto_Fix’. If the login already exists, it will be automatically mapped to the user account. Unfortunately sp_change_users_login system stored procedure cannot be used to map database users to trusted logins (Windows accounts) in SQL Server. You will need to follow a manual process to re-map the database user accounts.  Continues…

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• Battery life starts at 2:30 hrs (99%), but less than 1 minute later is only 1:30 hrs (99%)

  - by zondu

  After searching this and other forums, I haven't seen this same issue listed anywhere for Ubuntu 12. Prior to installing Ubuntu 12.10, my Netbook (Acer AspireOne D250, SATA HDD) was consistently getting 2:30-3 hrs battery life under Windows XP Home, SP3. However, immediately after installing Ubuntu 12.10, the battery life starts out at 2:30 hrs (99%), but less than 1 minute later suddenly drops to 1:30 hrs (99%), which seems very odd. It could be a complete coincidence that the battery is suddenly flaky at the exact same moment that Ubuntu 12.10 was installed, but that doesn't seem likely. I'm a newbie to Ubuntu, so I don't have much experience tweaking/trouble-shooting yet. Here's what I've tried so far: enabled laptop mode (sudo su, then echo 5 /proc/sys/vm/laptop_mode) and checked that it is running when the A/C adapter is unplugged, but it doesn't seem to have made any noticeable difference in battery life, installed Jupiter, but it didn't work and messed up the system, so I had to uninstall it, disabled bluetooth (wifi is still on b/c it is necessary), set the screen to lowest brightness, etc., run through at least 1 full power cycle (running until the netbook shut itself off due to critical battery) and have been using it normally (sometimes plugged in, often unplugged until the battery gets very low) for a week since installing Ubuntu 12.10. installed powertop, but have no idea how to interpret its results. Here are the results of acpi -b: w/ A/C adapter: Battery 0: Full, 100% immediately after unplugging: Battery 0: Discharging, 99%, 02:30:20 remaining 1 minute after unplugging: Battery 0: Discharging, 99%, 01:37:49 remaining 2-3 minutes after unplugging: Battery 0: Discharging, 95%, 01:33:01 remaining 10 minutes after unplugging: Battery 0: Discharging, 85%, 01:13:38 remaining Results of cat /sys/class/power_supply/BAT0/uevent: w/ A/C adapter: POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Full POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=12136000 POWER_SUPPLY_CURRENT_NOW=773000 POWER_SUPPLY_CHARGE_FULL_DESIGN=4500000 POWER_SUPPLY_CHARGE_FULL=1956000 POWER_SUPPLY_CHARGE_NOW=1956000 POWER_SUPPLY_MODEL_NAME=UM08B32 POWER_SUPPLY_MANUFACTURER=SANYO POWER_SUPPLY_SERIAL_NUMBER= immediately after unplugging: POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Discharging POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=11886000 POWER_SUPPLY_CURRENT_NOW=773000 POWER_SUPPLY_CHARGE_FULL_DESIGN=4500000 POWER_SUPPLY_CHARGE_FULL=1956000 POWER_SUPPLY_CHARGE_NOW=1937000 POWER_SUPPLY_MODEL_NAME=UM08B32 POWER_SUPPLY_MANUFACTURER=SANYO POWER_SUPPLY_SERIAL_NUMBER= 1 minute later: POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Discharging POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=11728000 POWER_SUPPLY_CURRENT_NOW=1174000 POWER_SUPPLY_CHARGE_FULL_DESIGN=4500000 POWER_SUPPLY_CHARGE_FULL=1956000 POWER_SUPPLY_CHARGE_NOW=1937000 POWER_SUPPLY_MODEL_NAME=UM08B32 POWER_SUPPLY_MANUFACTURER=SANYO POWER_SUPPLY_SERIAL_NUMBER= 2-3 minutes later: POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Discharging POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=11583000 POWER_SUPPLY_CURRENT_NOW=1209000 POWER_SUPPLY_CHARGE_FULL_DESIGN=4500000 POWER_SUPPLY_CHARGE_FULL=1956000 POWER_SUPPLY_CHARGE_NOW=1878000 POWER_SUPPLY_MODEL_NAME=UM08B32 POWER_SUPPLY_MANUFACTURER=SANYO POWER_SUPPLY_SERIAL_NUMBER= 10 minutes later: POWER_SUPPLY_NAME=BAT0 POWER_SUPPLY_STATUS=Discharging POWER_SUPPLY_PRESENT=1 POWER_SUPPLY_TECHNOLOGY=Li-ion POWER_SUPPLY_CYCLE_COUNT=0 POWER_SUPPLY_VOLTAGE_MIN_DESIGN=10800000 POWER_SUPPLY_VOLTAGE_NOW=11230000 POWER_SUPPLY_CURRENT_NOW=1239000 POWER_SUPPLY_CHARGE_FULL_DESIGN=4500000 POWER_SUPPLY_CHARGE_FULL=1956000 POWER_SUPPLY_CHARGE_NOW=1644000 POWER_SUPPLY_MODEL_NAME=UM08B32 POWER_SUPPLY_MANUFACTURER=SANYO POWER_SUPPLY_SERIAL_NUMBER= Results of upower -i /org/freedesktop/UPower/devices/battery_BAT0: w/ A/C adapter: native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/device:02/PNP0C0A:00/power_supply/BAT0 vendor: SANYO model: UM08B32 power supply: yes updated: Tue Nov 27 15:24:58 2012 (823 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: fully-charged energy: 21.1248 Wh energy-empty: 0 Wh energy-full: 21.1248 Wh energy-full-design: 48.6 Wh energy-rate: 8.3484 W voltage: 12.173 V percentage: 100% capacity: 43.4667% technology: lithium-ion immediately after unplugging: native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/device:02/PNP0C0A:00/power_supply/BAT0 vendor: SANYO model: UM08B32 power supply: yes updated: Tue Nov 27 15:41:25 2012 (1 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: discharging energy: 20.9196 Wh energy-empty: 0 Wh energy-full: 21.1248 Wh energy-full-design: 48.6 Wh energy-rate: 8.3484 W voltage: 11.86 V time to empty: 2.5 hours percentage: 99.0286% capacity: 43.4667% technology: lithium-ion History (charge): 1354023683 99.029 discharging 1 minute later: native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/device:02/PNP0C0A:00/power_supply/BAT0 vendor: SANYO model: UM08B32 power supply: yes updated: Tue Nov 27 15:42:31 2012 (17 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: discharging energy: 20.9196 Wh energy-empty: 0 Wh energy-full: 21.1248 Wh energy-full-design: 48.6 Wh energy-rate: 13.5432 W voltage: 11.753 V time to empty: 1.5 hours percentage: 99.0286% capacity: 43.4667% technology: lithium-ion History (charge): 1354023683 99.029 discharging History (rate): 1354023751 13.543 discharging 2-3 minutes later: native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/device:02/PNP0C0A:00/power_supply/BAT0 vendor: SANYO model: UM08B32 power supply: yes updated: Tue Nov 27 15:45:06 2012 (20 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: discharging energy: 20.2824 Wh energy-empty: 0 Wh energy-full: 21.1248 Wh energy-full-design: 48.6 Wh energy-rate: 13.7484 W voltage: 11.545 V time to empty: 1.5 hours percentage: 96.0123% capacity: 43.4667% technology: lithium-ion History (charge): 1354023906 96.012 discharging 1354023844 97.035 discharging History (rate): 1354023906 13.748 discharging 1354023875 12.992 discharging 1354023844 13.284 discharging 10 minutes later: native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/device:02/PNP0C0A:00/power_supply/BAT0 vendor: SANYO model: UM08B32 power supply: yes updated: Tue Nov 27 15:54:24 2012 (28 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: discharging energy: 18.1764 Wh energy-empty: 0 Wh energy-full: 21.1248 Wh energy-full-design: 48.6 Wh energy-rate: 13.2948 W voltage: 11.268 V time to empty: 1.4 hours percentage: 86.0429% capacity: 43.4667% technology: lithium-ion History (charge): 1354024433 86.043 discharging History (rate): 1354024464 13.295 discharging 1354024433 13.662 discharging 1354024402 13.781 discharging I noticed that between #2 and #3 (0 and 1 minutes after unplugging), while the battery still reports 99% charge and drops from 2:30 hr to 1:30 hr, the energy usage goes from 8.34 W to 13.54 W and the current_now increases, but shouldn't it be using less energy in battery mode since the screen is much dimmer and it's in power saving mode? (or is that normal behavior?) It also seems to drain more quickly than what it predicts, especially with the 1-1.25 hour drop in the first minute of being unplugged, which seems odd. What really concerns me is that Ubuntu 12.10 may not be properly managing the battery (with the sudden change in charge/life from 2:30 to 1:30 or 1:15 within a minute of unplugging), and that a new battery may quickly die under Ubuntu 12.10. I'd greatly appreciate any advice/suggestions on what to do, and especially whether there's a way to get back the 1-1.5 hrs of battery life that were suddenly lost when changing from WinXp to Ubuntu 12.10. Thanks :)

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• 256 Windows Azure Worker Roles, Windows Kinect and a 90's Text-Based Ray-Tracer

  - by Alan Smith

  For a couple of years I have been demoing a simple render farm hosted in Windows Azure using worker roles and the Azure Storage service. At the start of the presentation I deploy an Azure application that uses 16 worker roles to render a 1,500 frame 3D ray-traced animation. At the end of the presentation, when the animation was complete, I would play the animation delete the Azure deployment. The standing joke with the audience was that it was that it was a “$2 demo”, as the compute charges for running the 16 instances for an hour was $1.92, factor in the bandwidth charges and it’s a couple of dollars. The point of the demo is that it highlights one of the great benefits of cloud computing, you pay for what you use, and if you need massive compute power for a short period of time using Windows Azure can work out very cost effective. The “$2 demo” was great for presenting at user groups and conferences in that it could be deployed to Azure, used to render an animation, and then removed in a one hour session. I have always had the idea of doing something a bit more impressive with the demo, and scaling it from a “$2 demo” to a “$30 demo”. The challenge was to create a visually appealing animation in high definition format and keep the demo time down to one hour.  This article will take a run through how I achieved this. Ray Tracing Ray tracing, a technique for generating high quality photorealistic images, gained popularity in the 90’s with companies like Pixar creating feature length computer animations, and also the emergence of shareware text-based ray tracers that could run on a home PC. In order to render a ray traced image, the ray of light that would pass from the view point must be tracked until it intersects with an object. At the intersection, the color, reflectiveness, transparency, and refractive index of the object are used to calculate if the ray will be reflected or refracted. Each pixel may require thousands of calculations to determine what color it will be in the rendered image. Pin-Board Toys Having very little artistic talent and a basic understanding of maths I decided to focus on an animation that could be modeled fairly easily and would look visually impressive. I’ve always liked the pin-board desktop toys that become popular in the 80’s and when I was working as a 3D animator back in the 90’s I always had the idea of creating a 3D ray-traced animation of a pin-board, but never found the energy to do it. Even if I had a go at it, the render time to produce an animation that would look respectable on a 486 would have been measured in months. PolyRay Back in 1995 I landed my first real job, after spending three years being a beach-ski-climbing-paragliding-bum, and was employed to create 3D ray-traced animations for a CD-ROM that school kids would use to learn physics. I had got into the strange and wonderful world of text-based ray tracing, and was using a shareware ray-tracer called PolyRay. PolyRay takes a text file describing a scene as input and, after a few hours processing on a 486, produced a high quality ray-traced image. The following is an example of a basic PolyRay scene file. background Midnight_Blue   static define matte surface { ambient 0.1 diffuse 0.7 } define matte_white texture { matte { color white } } define matte_black texture { matte { color dark_slate_gray } } define position_cylindrical 3 define lookup_sawtooth 1 define light_wood <0.6, 0.24, 0.1> define median_wood <0.3, 0.12, 0.03> define dark_wood <0.05, 0.01, 0.005>     define wooden texture { noise surface { ambient 0.2  diffuse 0.7  specular white, 0.5 microfacet Reitz 10 position_fn position_cylindrical position_scale 1  lookup_fn lookup_sawtooth octaves 1 turbulence 1 color_map( [0.0, 0.2, light_wood, light_wood] [0.2, 0.3, light_wood, median_wood] [0.3, 0.4, median_wood, light_wood] [0.4, 0.7, light_wood, light_wood] [0.7, 0.8, light_wood, median_wood] [0.8, 0.9, median_wood, light_wood] [0.9, 1.0, light_wood, dark_wood]) } } define glass texture { surface { ambient 0 diffuse 0 specular 0.2 reflection white, 0.1 transmission white, 1, 1.5 }} define shiny surface { ambient 0.1 diffuse 0.6 specular white, 0.6 microfacet Phong 7  } define steely_blue texture { shiny { color black } } define chrome texture { surface { color white ambient 0.0 diffuse 0.2 specular 0.4 microfacet Phong 10 reflection 0.8 } }   viewpoint {     from <4.000, -1.000, 1.000> at <0.000, 0.000, 0.000> up <0, 1, 0> angle 60     resolution 640, 480 aspect 1.6 image_format 0 }       light <-10, 30, 20> light <-10, 30, -20>   object { disc <0, -2, 0>, <0, 1, 0>, 30 wooden }   object { sphere <0.000, 0.000, 0.000>, 1.00 chrome } object { cylinder <0.000, 0.000, 0.000>, <0.000, 0.000, -4.000>, 0.50 chrome }   After setting up the background and defining colors and textures, the viewpoint is specified. The “camera” is located at a point in 3D space, and it looks towards another point. The angle, image resolution, and aspect ratio are specified. Two lights are present in the image at defined coordinates. The three objects in the image are a wooden disc to represent a table top, and a sphere and cylinder that intersect to form a pin that will be used for the pin board toy in the final animation. When the image is rendered, the following image is produced. The pins are modeled with a chrome surface, so they reflect the environment around them. Note that the scale of the pin shaft is not correct, this will be fixed later. Modeling the Pin Board The frame of the pin-board is made up of three boxes, and six cylinders, the front box is modeled using a clear, slightly reflective solid, with the same refractive index of glass. The other shapes are modeled as metal. object { box <-5.5, -1.5, 1>, <5.5, 5.5, 1.2> glass } object { box <-5.5, -1.5, -0.04>, <5.5, 5.5, -0.09> steely_blue } object { box <-5.5, -1.5, -0.52>, <5.5, 5.5, -0.59> steely_blue } object { cylinder <-5.2, -1.2, 1.4>, <-5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, -1.2, 1.4>, <5.2, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <-5.2, 5.2, 1.4>, <-5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <5.2, 5.2, 1.4>, <5.2, 5.2, -0.74>, 0.2 steely_blue } object { cylinder <0, -1.2, 1.4>, <0, -1.2, -0.74>, 0.2 steely_blue } object { cylinder <0, 5.2, 1.4>, <0, 5.2, -0.74>, 0.2 steely_blue }   In order to create the matrix of pins that make up the pin board I used a basic console application with a few nested loops to create two intersecting matrixes of pins, which models the layout used in the pin boards. The resulting image is shown below. The pin board contains 11,481 pins, with the scene file containing 23,709 lines of code. For the complete animation 2,000 scene files will be created, which is over 47 million lines of code. Each pin in the pin-board will slide out a specific distance when an object is pressed into the back of the board. This is easily modeled by setting the Z coordinate of the pin to a specific value. In order to set all of the pins in the pin-board to the correct position, a bitmap image can be used. The position of the pin can be set based on the color of the pixel at the appropriate position in the image. When the Windows Azure logo is used to set the Z coordinate of the pins, the following image is generated. The challenge now was to make a cool animation. The Azure Logo is fine, but it is static. Using a normal video to animate the pins would not work; the colors in the video would not be the same as the depth of the objects from the camera. In order to simulate the pin board accurately a series of frames from a depth camera could be used. Windows Kinect The Kenect controllers for the X-Box 360 and Windows feature a depth camera. The Kinect SDK for Windows provides a programming interface for Kenect, providing easy access for .NET developers to the Kinect sensors. The Kinect Explorer provided with the Kinect SDK is a great starting point for exploring Kinect from a developers perspective. Both the X-Box 360 Kinect and the Windows Kinect will work with the Kinect SDK, the Windows Kinect is required for commercial applications, but the X-Box Kinect can be used for hobby projects. The Windows Kinect has the advantage of providing a mode to allow depth capture with objects closer to the camera, which makes for a more accurate depth image for setting the pin positions. Creating a Depth Field Animation The depth field animation used to set the positions of the pin in the pin board was created using a modified version of the Kinect Explorer sample application. In order to simulate the pin board accurately, a small section of the depth range from the depth sensor will be used. Any part of the object in front of the depth range will result in a white pixel; anything behind the depth range will be black. Within the depth range the pixels in the image will be set to RGB values from 0,0,0 to 255,255,255. A screen shot of the modified Kinect Explorer application is shown below. The Kinect Explorer sample application was modified to include slider controls that are used to set the depth range that forms the image from the depth stream. This allows the fine tuning of the depth image that is required for simulating the position of the pins in the pin board. The Kinect Explorer was also modified to record a series of images from the depth camera and save them as a sequence JPEG files that will be used to animate the pins in the animation the Start and Stop buttons are used to start and stop the image recording. En example of one of the depth images is shown below. Once a series of 2,000 depth images has been captured, the task of creating the animation can begin. Rendering a Test Frame In order to test the creation of frames and get an approximation of the time required to render each frame a test frame was rendered on-premise using PolyRay. The output of the rendering process is shown below. The test frame contained 23,629 primitive shapes, most of which are the spheres and cylinders that are used for the 11,800 or so pins in the pin board. The 1280x720 image contains 921,600 pixels, but as anti-aliasing was used the number of rays that were calculated was 4,235,777, with 3,478,754,073 object boundaries checked. The test frame of the pin board with the depth field image applied is shown below. The tracing time for the test frame was 4 minutes 27 seconds, which means rendering the2,000 frames in the animation would take over 148 hours, or a little over 6 days. Although this is much faster that an old 486, waiting almost a week to see the results of an animation would make it challenging for animators to create, view, and refine their animations. It would be much better if the animation could be rendered in less than one hour. Windows Azure Worker Roles The cost of creating an on-premise render farm to render animations increases in proportion to the number of servers. The table below shows the cost of servers for creating a render farm, assuming a cost of $500 per server. Number of Servers Cost 1 $500 16 $8,000 256 $128,000   As well as the cost of the servers, there would be additional costs for networking, racks etc. Hosting an environment of 256 servers on-premise would require a server room with cooling, and some pretty hefty power cabling. The Windows Azure compute services provide worker roles, which are ideal for performing processor intensive compute tasks. With the scalability available in Windows Azure a job that takes 256 hours to complete could be perfumed using different numbers of worker roles. The time and cost of using 1, 16 or 256 worker roles is shown below. Number of Worker Roles Render Time Cost 1 256 hours $30.72 16 16 hours $30.72 256 1 hour $30.72   Using worker roles in Windows Azure provides the same cost for the 256 hour job, irrespective of the number of worker roles used. Provided the compute task can be broken down into many small units, and the worker role compute power can be used effectively, it makes sense to scale the application so that the task is completed quickly, making the results available in a timely fashion. The task of rendering 2,000 frames in an animation is one that can easily be broken down into 2,000 individual pieces, which can be performed by a number of worker roles. Creating a Render Farm in Windows Azure The architecture of the render farm is shown in the following diagram. The render farm is a hybrid application with the following components: ·         On-Premise o   Windows Kinect – Used combined with the Kinect Explorer to create a stream of depth images. o   Animation Creator – This application uses the depth images from the Kinect sensor to create scene description files for PolyRay. These files are then uploaded to the jobs blob container, and job messages added to the jobs queue. o   Process Monitor – This application queries the role instance lifecycle table and displays statistics about the render farm environment and render process. o   Image Downloader – This application polls the image queue and downloads the rendered animation files once they are complete. ·         Windows Azure o   Azure Storage – Queues and blobs are used for the scene description files and completed frames. A table is used to store the statistics about the rendering environment.   The architecture of each worker role is shown below.   The worker role is configured to use local storage, which provides file storage on the worker role instance that can be use by the applications to render the image and transform the format of the image. The service definition for the worker role with the local storage configuration highlighted is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceDefinition name="CloudRay" >   <WorkerRole name="CloudRayWorkerRole" vmsize="Small">     <Imports>     </Imports>     <ConfigurationSettings>       <Setting name="DataConnectionString" />     </ConfigurationSettings>     <LocalResources>       <LocalStorage name="RayFolder" cleanOnRoleRecycle="true" />     </LocalResources>   </WorkerRole> </ServiceDefinition>     The two executable programs, PolyRay.exe and DTA.exe are included in the Azure project, with Copy Always set as the property. PolyRay will take the scene description file and render it to a Truevision TGA file. As the TGA format has not seen much use since the mid 90’s it is converted to a JPG image using Dave's Targa Animator, another shareware application from the 90’s. Each worker roll will use the following process to render the animation frames. 1.       The worker process polls the job queue, if a job is available the scene description file is downloaded from blob storage to local storage. 2.       PolyRay.exe is started in a process with the appropriate command line arguments to render the image as a TGA file. 3.       DTA.exe is started in a process with the appropriate command line arguments convert the TGA file to a JPG file. 4.       The JPG file is uploaded from local storage to the images blob container. 5.       A message is placed on the images queue to indicate a new image is available for download. 6.       The job message is deleted from the job queue. 7.       The role instance lifecycle table is updated with statistics on the number of frames rendered by the worker role instance, and the CPU time used. The code for this is shown below. public override void Run() {     // Set environment variables     string polyRayPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), PolyRayLocation);     string dtaPath = Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), DTALocation);       LocalResource rayStorage = RoleEnvironment.GetLocalResource("RayFolder");     string localStorageRootPath = rayStorage.RootPath;       JobQueue jobQueue = new JobQueue("renderjobs");     JobQueue downloadQueue = new JobQueue("renderimagedownloadjobs");     CloudRayBlob sceneBlob = new CloudRayBlob("scenes");     CloudRayBlob imageBlob = new CloudRayBlob("images");     RoleLifecycleDataSource roleLifecycleDataSource = new RoleLifecycleDataSource();       Frames = 0;       while (true)     {         // Get the render job from the queue         CloudQueueMessage jobMsg = jobQueue.Get();           if (jobMsg != null)         {             // Get the file details             string sceneFile = jobMsg.AsString;             string tgaFile = sceneFile.Replace(".pi", ".tga");             string jpgFile = sceneFile.Replace(".pi", ".jpg");               string sceneFilePath = Path.Combine(localStorageRootPath, sceneFile);             string tgaFilePath = Path.Combine(localStorageRootPath, tgaFile);             string jpgFilePath = Path.Combine(localStorageRootPath, jpgFile);               // Copy the scene file to local storage             sceneBlob.DownloadFile(sceneFilePath);               // Run the ray tracer.             string polyrayArguments =                 string.Format("\"{0}\" -o \"{1}\" -a 2", sceneFilePath, tgaFilePath);             Process polyRayProcess = new Process();             polyRayProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), polyRayPath);             polyRayProcess.StartInfo.Arguments = polyrayArguments;             polyRayProcess.Start();             polyRayProcess.WaitForExit();               // Convert the image             string dtaArguments =                 string.Format(" {0} /FJ /P{1}", tgaFilePath, Path.GetDirectoryName (jpgFilePath));             Process dtaProcess = new Process();             dtaProcess.StartInfo.FileName =                 Path.Combine(Environment.GetEnvironmentVariable("RoleRoot"), dtaPath);             dtaProcess.StartInfo.Arguments = dtaArguments;             dtaProcess.Start();             dtaProcess.WaitForExit();               // Upload the image to blob storage             imageBlob.UploadFile(jpgFilePath);               // Add a download job.             downloadQueue.Add(jpgFile);               // Delete the render job message             jobQueue.Delete(jobMsg);               Frames++;         }         else         {             Thread.Sleep(1000);         }           // Log the worker role activity.         roleLifecycleDataSource.Alive             ("CloudRayWorker", RoleLifecycleDataSource.RoleLifecycleId, Frames);     } }     Monitoring Worker Role Instance Lifecycle In order to get more accurate statistics about the lifecycle of the worker role instances used to render the animation data was tracked in an Azure storage table. The following class was used to track the worker role lifecycles in Azure storage.   public class RoleLifecycle : TableServiceEntity {     public string ServerName { get; set; }     public string Status { get; set; }     public DateTime StartTime { get; set; }     public DateTime EndTime { get; set; }     public long SecondsRunning { get; set; }     public DateTime LastActiveTime { get; set; }     public int Frames { get; set; }     public string Comment { get; set; }       public RoleLifecycle()     {     }       public RoleLifecycle(string roleName)     {         PartitionKey = roleName;         RowKey = Utils.GetAscendingRowKey();         Status = "Started";         StartTime = DateTime.UtcNow;         LastActiveTime = StartTime;         EndTime = StartTime;         SecondsRunning = 0;         Frames = 0;     } }     A new instance of this class is created and added to the storage table when the role starts. It is then updated each time the worker renders a frame to record the total number of frames rendered and the total processing time. These statistics are used be the monitoring application to determine the effectiveness of use of resources in the render farm. Rendering the Animation The Azure solution was deployed to Windows Azure with the service configuration set to 16 worker role instances. This allows for the application to be tested in the cloud environment, and the performance of the application determined. When I demo the application at conferences and user groups I often start with 16 instances, and then scale up the application to the full 256 instances. The configuration to run 16 instances is shown below. <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="16" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     About six minutes after deploying the application the first worker roles become active and start to render the first frames of the animation. The CloudRay Monitor application displays an icon for each worker role instance, with a number indicating the number of frames that the worker role has rendered. The statistics on the left show the number of active worker roles and statistics about the render process. The render time is the time since the first worker role became active; the CPU time is the total amount of processing time used by all worker role instances to render the frames.   Five minutes after the first worker role became active the last of the 16 worker roles activated. By this time the first seven worker roles had each rendered one frame of the animation.   With 16 worker roles u and running it can be seen that one hour and 45 minutes CPU time has been used to render 32 frames with a render time of just under 10 minutes.     At this rate it would take over 10 hours to render the 2,000 frames of the full animation. In order to complete the animation in under an hour more processing power will be required. Scaling the render farm from 16 instances to 256 instances is easy using the new management portal. The slider is set to 256 instances, and the configuration saved. We do not need to re-deploy the application, and the 16 instances that are up and running will not be affected. Alternatively, the configuration file for the Azure service could be modified to specify 256 instances.   <?xml version="1.0" encoding="utf-8"?> <ServiceConfiguration serviceName="CloudRay" xmlns="http://schemas.microsoft.com/ServiceHosting/2008/10/ServiceConfiguration" osFamily="1" osVersion="*">   <Role name="CloudRayWorkerRole">     <Instances count="256" />     <ConfigurationSettings>       <Setting name="DataConnectionString"         value="DefaultEndpointsProtocol=https;AccountName=cloudraydata;AccountKey=..." />     </ConfigurationSettings>   </Role> </ServiceConfiguration>     Six minutes after the new configuration has been applied 75 new worker roles have activated and are processing their first frames.   Five minutes later the full configuration of 256 worker roles is up and running. We can see that the average rate of frame rendering has increased from 3 to 12 frames per minute, and that over 17 hours of CPU time has been utilized in 23 minutes. In this test the time to provision 140 worker roles was about 11 minutes, which works out at about one every five seconds.   We are now half way through the rendering, with 1,000 frames complete. This has utilized just under three days of CPU time in a little over 35 minutes.   The animation is now complete, with 2,000 frames rendered in a little over 52 minutes. The CPU time used by the 256 worker roles is 6 days, 7 hours and 22 minutes with an average frame rate of 38 frames per minute. The rendering of the last 1,000 frames took 16 minutes 27 seconds, which works out at a rendering rate of 60 frames per minute. The frame counts in the server instances indicate that the use of a queue to distribute the workload has been very effective in distributing the load across the 256 worker role instances. The first 16 instances that were deployed first have rendered between 11 and 13 frames each, whilst the 240 instances that were added when the application was scaled have rendered between 6 and 9 frames each.   Completed Animation I’ve uploaded the completed animation to YouTube, a low resolution preview is shown below. Pin Board Animation Created using Windows Kinect and 256 Windows Azure Worker Roles   The animation can be viewed in 1280x720 resolution at the following link: http://www.youtube.com/watch?v=n5jy6bvSxWc Effective Use of Resources According to the CloudRay monitor statistics the animation took 6 days, 7 hours and 22 minutes CPU to render, this works out at 152 hours of compute time, rounded up to the nearest hour. As the usage for the worker role instances are billed for the full hour, it may have been possible to render the animation using fewer than 256 worker roles. When deciding the optimal usage of resources, the time required to provision and start the worker roles must also be considered. In the demo I started with 16 worker roles, and then scaled the application to 256 worker roles. It would have been more optimal to start the application with maybe 200 worker roles, and utilized the full hour that I was being billed for. This would, however, have prevented showing the ease of scalability of the application. The new management portal displays the CPU usage across the worker roles in the deployment. The average CPU usage across all instances is 93.27%, with over 99% used when all the instances are up and running. This shows that the worker role resources are being used very effectively. Grid Computing Scenarios Although I am using this scenario for a hobby project, there are many scenarios where a large amount of compute power is required for a short period of time. Windows Azure provides a great platform for developing these types of grid computing applications, and can work out very cost effective. ·         Windows Azure can provide massive compute power, on demand, in a matter of minutes. ·         The use of queues to manage the load balancing of jobs between role instances is a simple and effective solution. ·         Using a cloud-computing platform like Windows Azure allows proof-of-concept scenarios to be tested and evaluated on a very low budget. ·         No charges for inbound data transfer makes the uploading of large data sets to Windows Azure Storage services cost effective. (Transaction charges still apply.) Tips for using Windows Azure for Grid Computing Scenarios I found the implementation of a render farm using Windows Azure a fairly simple scenario to implement. I was impressed by ease of scalability that Azure provides, and by the short time that the application took to scale from 16 to 256 worker role instances. In this case it was around 13 minutes, in other tests it took between 10 and 20 minutes. The following tips may be useful when implementing a grid computing project in Windows Azure. ·         Using an Azure Storage queue to load-balance the units of work across multiple worker roles is simple and very effective. The design I have used in this scenario could easily scale to many thousands of worker role instances. ·         Windows Azure accounts are typically limited to 20 cores. If you need to use more than this, a call to support and a credit card check will be required. ·         Be aware of how the billing model works. You will be charged for worker role instances for the full clock our in which the instance is deployed. Schedule the workload to start just after the clock hour has started. ·         Monitor the utilization of the resources you are provisioning, ensure that you are not paying for worker roles that are idle. ·         If you are deploying third party applications to worker roles, you may well run into licensing issues. Purchasing software licenses on a per-processor basis when using hundreds of processors for a short time period would not be cost effective. ·         Third party software may also require installation onto the worker roles, which can be accomplished using start-up tasks. Bear in mind that adding a startup task and possible re-boot will add to the time required for the worker role instance to start and activate. An alternative may be to use a prepared VM and use VM roles. ·         Consider using the Windows Azure Autoscaling Application Block (WASABi) to autoscale the worker roles in your application. When using a large number of worker roles, the utilization must be carefully monitored, if the scaling algorithms are not optimal it could get very expensive!

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• Different boot time for the same computer by different commands

  - by andrej

  As far as I am aware, there are 3 ways to check the computer boot time in windows. And they should give the same time, just in different formats. Why do I get different times, where do these commands get their time? wmic os get lastBootUpTime | find "+120" 20140823002317.596695+120 systeminfo | find /i "boot time" System Boot Time: 23.8.2014, 0:23:17 net statistics server | find /i "statistics since" Statistics since 22.8.2014 18:21:30 The first two are the same (0:23), but the third is different (18:21), and also accurate. Why? At boot, all tree show the same, but at some point, they change. I am using windows 7 ultimate, 64bit.

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• What is the difference between sar -B verses sar -W

  - by Mark

  I am trying to understand why my system is running slowly. I found the sar command, but wanted to know the difference between sar -B and sar -W I read the man page, and I understand that -B gives me the paging statistics and -W gives me the swapping statistics. What I would like to understand is the following: What is the correlation between the two sets of statistics. When should I be concerned about -B and when about -W? ie, what values from each command should I be concerned with? Which statistic is more closely related to system performance Thanks

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• JRuby wrong element type class java.lang.String(array contains char) related to JAVA_HOME

  - by Daryl

  I am on Ubuntu x64 bit running: java version "1.6.0_18" OpenJDK Runtime Environment (IcedTea6 1.8) (6b18-1.8-0ubuntu1) OpenJDK 64-Bit Server VM (build 14.0-b16, mixed mode) and jruby 1.4.0 (ruby 1.8.7 patchlevel 174) (2010-02-11 6586) (OpenJDK 64-Bit Server VM 1.6.0_18) [amd64-java] I have this code running on my Windows 7 computer at home. I recently copied over my whole folder over to Ubuntu, installed java, jruby, and associated gems but I get this error when I run my main file: jruby run.rb test =================Processing FREDERICKSBURG_1.1======================= ERROR IN TESTING wrong element type class java.lang.String(array contains char) /home/daryl/Desktop/work/Code/geografikos/lib/sentence_splitter/splitter.rb:21:in `to_java' /home/daryl/Desktop/work/Code/geografikos/lib/sentence_splitter/splitter.rb:21:in `split' /home/daryl/Desktop/work/Code/geografikos/lib/models/page.rb:103:in `sentences' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/lingpipe_svm.rb:34:in `extract' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/geo_controller.rb:9:in `process' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/geo_controller.rb:8:in `each' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/geo_controller.rb:8:in `process' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/geo_controller.rb:6:in `each' /home/daryl/Desktop/work/Code/geografikos/lib/extractor/geo_controller.rb:6:in `process' /home/daryl/Desktop/work/Code/geografikos/lib/statistics.rb:111:in `generate_all' /home/daryl/Desktop/work/Code/geografikos/lib/statistics.rb:105:in `each' /home/daryl/Desktop/work/Code/geografikos/lib/statistics.rb:105:in `generate_all' run.rb:56 The focus of the error is: ERROR IN TESTING wrong element type class java.lang.String(array contains char) Everything works fine on my windows machine. I figured I was getting this error because I did not have JAVA_HOME set however I added this to bashrc as: export JAVA_HOME=/usr/lib/jvm/java-1.6.0-openjdk and have confirmed: echo $JAVA_HOME /usr/lib/jvm/java-1.6.0-openjdk I can produce a similar error by removing my JAVA_HOME variable on windows: =================Processing FREDERICKSBURG_1.3======================= ERROR IN TESTING cannot convert instance of class org.jruby.RubyString to char C:/work/Code/geografikos/lib/sentence_splitter/splitter.rb:21:in `to_java' C:/work/Code/geografikos/lib/sentence_splitter/splitter.rb:21:in `split' C:/work/Code/geografikos/lib/models/page.rb:103:in `sentences' C:/work/Code/geografikos/lib/extractor/lingpipe_svm.rb:34:in `extract' C:/work/Code/geografikos/lib/extractor/geo_controller.rb:9:in `process' C:/work/Code/geografikos/lib/extractor/geo_controller.rb:8:in `each' C:/work/Code/geografikos/lib/extractor/geo_controller.rb:8:in `process' C:/work/Code/geografikos/lib/extractor/geo_controller.rb:6:in `each' C:/work/Code/geografikos/lib/extractor/geo_controller.rb:6:in `process' C:/work/Code/geografikos/lib/statistics.rb:111:in `generate_all' C:/work/Code/geografikos/lib/statistics.rb:105:in `each' C:/work/Code/geografikos/lib/statistics.rb:105:in `generate_all' run.rb:56 It is obviously not exactly the same but I have a feeling this has to do with the java path. You can probably derive from the error that I am just trying to convert a ruby variable to java using to_java. This works fine on my windows machine and I have confirmed the gems are the same but I don't think this has to do with gems. I lied. I changed my JAVA_HOME back on my windows machine and this error still occurs. So now the code doesn't run on either machine. I recently installed git on my windows machine and added the code to a repository. But I haven't really done anything with it. All it said was it will convert all LF to CRLF...That shouldn't change anything though should it? Any ideas on why I am now getting these errors? I haven't changed anything on my windows machine in months except for installing git.

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• JMX Based Monitoring - Part One

  - by Anthony Shorten

  In all versions of the Oracle Utilities Application Framework there is an ability to use Java Management eXtensions (JMX) to both manage and monitor the various components of the product. This means that sites can use a JSR120 compliant JMX browser or JMX console to view or manage the components of the product with little or no configuration required. In each version we have progressively added JMX capabilities to allow IT groups more detailed information. In Oracle Utilities Application Framework V2.1 and above it was possible to use JMX on the Web Application Server provided Mbeans to allow you to monitor the online component of the product as well as manage the configuration. Also with a few additional java options it is possible to get a good level of detail about the Java Virtual machine including memory and thread usage. In Oracle Utilities Application Framework V2.2 and above, we added support for Java 5 statistics (Java enabled them by default), database pool statistics and also added the ability to manage and moinitor the batch component of the architecture. Now, in Oracle Utilities Application Framework V4 and above, we added support for Java 6 MXBeans, online management of the cache using JMX, additional JVM information and Performance monitoring using JMX. JMX allows the product to be managed from a common console such as Oracle Enterprise Manager, Tivoli, HP OpenView (and a lot more). Over the next week or so I will be compiling a set of blog entries discussing what is available (in summary format) using JMX and how to get access to the JMX statistics for your version of the product.

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• SQL SERVER – 2012 RC0 Various Resources and Downloads

  - by pinaldave

  Microsoft SQL Server 2012 Release Candidate 0 (RC0) Microsoft SQL Server 2012 RC0 enables a cloud-ready information platform that will help organizations unlock breakthrough insights across the organization. Microsoft SQL Server 2012 Express RC Microsoft SQL Server 2012 Express RC0 is a powerful and reliable free data management system that delivers a rich set of features, data protection, and performance for embedded applications, lightweight Web Sites, applications, and local data stores. Microsoft SQL Server 2012 Semantic Language Statistics RC0 The Semantic Language Statistics Database is a required component for the Statistical Semantic Search feature in Microsoft SQL Server 2012 Semantic Language Statistics RC0. Microsoft SQL Server 2012 Release Candidate 0 (RC0) Manageability Tool Kit The Microsoft SQL Server 2012 Release Candidate 0 (RC0) Manageability Tool Kit is a collection of stand-alone packages which provide additional value for Microsoft SQL Server 2012 Release Candidate 0 (RC0). Microsoft SQL Server 2012 PowerPivot for Microsoft Excel 2010 Release Candidate 0 (RC0) Microsoft PowerPivot for Microsoft Excel 2010 provides ground-breaking technology; fast manipulation of large data sets, streamlined integration of data, and the ability to effortlessly share your analysis through Microsoft SharePoint Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Database, PostADay, SQL, SQL Authority, SQL Documentation, SQL Download, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology

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• Netbook performs hard shutdown without warning on low battery power

  - by Steve Kroon

  My Asus EEE netbook performs a hard shutdown when it reaches low battery power, without giving any warning - i.e. the power just goes off, without any shutdown process. I can't find anything in the syslog, and no error messages are printed before it happens. I've had this problem on previous (K)Ubuntu versions, and hoped updating to Ubuntu Precise would help resolve the issue, but it hasn't. The option in the Power application for "when power is critically low" is currently blank - the only options are a (grayed-out) hibernate and "Power off". I have re-installed indicator-power to no effect. The time remaining reported by acpi is unstable, as is the time remaining reported by gnome-power-statistics. (For example, running acpi twice in succession, I got 2h16min, and then 3h21min remaining. These sorts of jumps in the remaining time are also in the gnome-power-statistics graphs.) It might be possible to write a script to give me advance warning (as per @RanRag's comment below), but I would prefer to isolate why I don't get a critical battery notification from the system before this happens, so that I can take action as appropriate (suspend/shutdown/plug in power) when I get a notification. Some additional information on the battery: kroon@minia:~$ upower -i /org/freedesktop/UPower/devices/battery_BAT0 native-path: /sys/devices/LNXSYSTM:00/device:00/PNP0A08:00/PNP0C0A:00/power_supply/BAT0 vendor: ASUS model: 1005P power supply: yes updated: Fri Aug 17 07:31:23 2012 (9 seconds ago) has history: yes has statistics: yes battery present: yes rechargeable: yes state: charging energy: 33.966 Wh energy-empty: 0 Wh energy-full: 34.9272 Wh energy-full-design: 47.52 Wh energy-rate: 3.7692 W voltage: 12.61 V time to full: 15.3 minutes percentage: 97.248% capacity: 73.5% technology: lithium-ion History (charge): 1345181483 97.248 charging 1345181453 97.155 charging 1345181423 97.062 charging 1345181393 96.970 charging History (rate): 1345181483 3.769 charging 1345181453 3.899 charging 1345181423 4.061 charging 1345181393 4.201 charging kroon@minia:~$ cat /proc/acpi/battery/BAT0/state present: yes capacity state: ok charging state: charging present rate: 332 mA remaining capacity: 3149 mAh present voltage: 12612 mV kroon@minia:~$ cat /proc/acpi/battery/BAT0/info present: yes design capacity: 4400 mAh last full capacity: 3209 mAh battery technology: rechargeable design voltage: 10800 mV design capacity warning: 10 mAh design capacity low: 5 mAh cycle count: 0 capacity granularity 1: 44 mAh capacity granularity 2: 44 mAh model number: 1005P serial number: battery type: LION OEM info: ASUS

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• Ubuntu 12.04 connected to wireless network but internet not working

  - by A.J.

  I can connect to my house's wireless network just fine, but when I'm connected I can't browse the web. Firefox starts connecting to a site and then just poops out. This doesn't happen on my roommates' computers (running Windows) or on our 3DSes, so I know it's just my laptop. I already tried sudo dhclient -r sudo dhclient sudo ifconfig eth0 down sudo ifconfig eth0 up Results of a few commands I was asked to run in comments: ping -c 2 4.2.2.2 PING 4.2.2.2 (4.2.2.2) 56(84) bytes of data. ^C --- 4.2.2.2 ping statistics --- 2 packets transmitted, 0 received, 100% packet loss, time 1007ms ping -c 2 google.com PING google.com (173.194.33.38) 56(84) bytes of data. --- google.com ping statistics --- 2 packets transmitted, 0 received, 100% packet loss, time 1006ms nm-tool NetworkManager Tool State: connected (global) - Device: eth0 ----------------------------------------------------------------- Type: Wired Driver: atl1c State: unavailable Default: no HW Address: 88:AE:1D:6B:4E:E7 Capabilities: Carrier Detect: yes Speed: 100 Mb/s Wired Properties Carrier: off - Device: wlan0 [JUSTICE] ----------------------------------------------------- Type: 802.11 WiFi Driver: ath9k State: connected Default: yes HW Address: 1C:65:9D:65:C6:31 Capabilities: Speed: 1 Mb/s Wireless Properties WEP Encryption: yes WPA Encryption: yes WPA2 Encryption: yes Wireless Access Points (* = current AP) HOME-9B18: Infra, 00:26:F3:53:9B:18, Freq 2412 MHz, Rate 54 Mb/s, Strength 34 WPA WPA2 cougdad48 Network: Infra, 60:33:4B:E4:C4:5D, Freq 2437 MHz, Rate 54 Mb/s, Strength 22 WPA2 cougdad48 Guest Network: Infra, 66:33:4B:E4:C4:5D, Freq 2437 MHz, Rate 54 Mb/s, Strength 20 WPA2 belkin.ade: Infra, 94:44:52:FF:8A:DE, Freq 2457 MHz, Rate 54 Mb/s, Strength 20 WPA WPA2 *JUSTICE: Infra, 00:24:01:7B:9F:7E, Freq 2462 MHz, Rate 54 Mb/s, Strength 88 WEP CenturyLink: Infra, B2:B2:DC:8E:E2:58, Freq 2462 MHz, Rate 54 Mb/s, Strength 17 WPA WPA2 IPv4 Settings: Address: 192.168.0.11 Prefix: 24 (255.255.255.0) Gateway: 192.168.0.1 DNS: 192.168.0.1 (JUSTICE is my home's network.) ping -c 2 198.168.0.1 PING 198.168.0.1 (198.168.0.1) 56(84) bytes of data. --- 198.168.0.1 ping statistics --- 2 packets transmitted, 0 received, 100% packet loss, time 1007ms

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