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  • Problems while trying to make a query with variables in the conditions (stored procedure)

    - by pablo89
    Hi!! Im having a problem, Im trying to do a query... I remember that in the past I did something like this but today this query is returning nothing, no error, no data, just nothing... the query is something like this: SELECT field1, @variableX:=field2 FROM table WHERE (SELECT COUNT(fieldA) FROM table2 WHERE fieldB=@variableX AND fieldC=0)0 AND (SELECT COUNT(fieldA) FROM table2 WHERE fieldB=@variableX AND fieldC=4)=0; I also tried this query but it didnt work (also it gaves no error): SELECT field1, @variableX:=field2, @variableY:=(SELECT COUNT(fieldA) FROM table2 WHERE fieldB=@variableX AND fieldC=0), @variableZ:=(SELECT COUNT(fieldA) FROM table2 WHERE fieldB=@variableX AND fieldC=4) FROM table WHERE @variableY0 AND @variableZ=0; As you can see, what Im trying to do in the 1st query is use a variable in the conditions; in the 2nd query Im trying to create some variables and evaluate them in the conditions. At the end in the 2nd query the @variableY=1 AND @variableZ=0 but I dont know what the query returns an empty data What could be wrong here??? Any comment or suggest is welcome!!! thanks!!! Bye!!!

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  • Number 0 is not saving to database as a prefix in SQL Server of CHAR data type column

    - by gmhk
    I am trying to insert an value as '019393' into a table with a CHAR(10) column. It is inserting only '19393' into the database I am implementing this feature in a stored procedure, doing some manipulation like incrementing that number by 15 and saving it back with '0' as the prefix I am using SQL Server database Note: I tried CASTING that value as VARCHAR before saving to the database, but even that did not get the solution Code SELECT @fromBSB = fromBSB, @toBSB = toBSB, @type = Type FROM [dbo].[tbl_REF_SpecialBSBRanges] WHERE CAST(@inputFromBSB AS INT) BETWEEN fromBSB AND toBSB SET @RETURNVALUE = @fromBSB IF(@fromBSB = @inputFromBSB) BEGIN PRINT 'Starting Number is Equal'; DELETE FROM tbl_REF_SpecialBSBRanges WHERE Type = @type AND fromBSB = @fromBSB AND toBSB = @toBSB INSERT INTO [tbl_REF_SpecialBSBRanges] ([Type], [fromBSB], [toBSB]) VALUES(@type, CAST('0' + @fromBSB + 1 AS CHAR), @toBSB) INSERT INTO [tbl_REF_SpecialBSBRanges] ([Type], [fromBSB], [toBSB]) VALUES(@inputBSBName, @inputFromBSB, @inputToBSB) END

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  • Master Note for Generic Data Warehousing

    - by lajos.varady(at)oracle.com
    ++++++++++++++++++++++++++++++++++++++++++++++++++++ The complete and the most recent version of this article can be viewed from My Oracle Support Knowledge Section. Master Note for Generic Data Warehousing [ID 1269175.1] ++++++++++++++++++++++++++++++++++++++++++++++++++++In this Document   Purpose   Master Note for Generic Data Warehousing      Components covered      Oracle Database Data Warehousing specific documents for recent versions      Technology Network Product Homes      Master Notes available in My Oracle Support      White Papers      Technical Presentations Platforms: 1-914CU; This document is being delivered to you via Oracle Support's Rapid Visibility (RaV) process and therefore has not been subject to an independent technical review. Applies to: Oracle Server - Enterprise Edition - Version: 9.2.0.1 to 11.2.0.2 - Release: 9.2 to 11.2Information in this document applies to any platform. Purpose Provide navigation path Master Note for Generic Data Warehousing Components covered Read Only Materialized ViewsQuery RewriteDatabase Object PartitioningParallel Execution and Parallel QueryDatabase CompressionTransportable TablespacesOracle Online Analytical Processing (OLAP)Oracle Data MiningOracle Database Data Warehousing specific documents for recent versions 11g Release 2 (11.2)11g Release 1 (11.1)10g Release 2 (10.2)10g Release 1 (10.1)9i Release 2 (9.2)9i Release 1 (9.0)Technology Network Product HomesOracle Partitioning Advanced CompressionOracle Data MiningOracle OLAPMaster Notes available in My Oracle SupportThese technical articles have been written by Oracle Support Engineers to provide proactive and top level information and knowledge about the components of thedatabase we handle under the "Database Datawarehousing".Note 1166564.1 Master Note: Transportable Tablespaces (TTS) -- Common Questions and IssuesNote 1087507.1 Master Note for MVIEW 'ORA-' error diagnosis. For Materialized View CREATE or REFRESHNote 1102801.1 Master Note: How to Get a 10046 trace for a Parallel QueryNote 1097154.1 Master Note Parallel Execution Wait Events Note 1107593.1 Master Note for the Oracle OLAP OptionNote 1087643.1 Master Note for Oracle Data MiningNote 1215173.1 Master Note for Query RewriteNote 1223705.1 Master Note for OLTP Compression Note 1269175.1 Master Note for Generic Data WarehousingWhite Papers Transportable Tablespaces white papers Database Upgrade Using Transportable Tablespaces:Oracle Database 11g Release 1 (February 2009) Platform Migration Using Transportable Database Oracle Database 11g and 10g Release 2 (August 2008) Database Upgrade using Transportable Tablespaces: Oracle Database 10g Release 2 (April 2007) Platform Migration using Transportable Tablespaces: Oracle Database 10g Release 2 (April 2007)Parallel Execution and Parallel Query white papers Best Practices for Workload Management of a Data Warehouse on the Sun Oracle Database Machine (June 2010) Effective resource utilization by In-Memory Parallel Execution in Oracle Real Application Clusters 11g Release 2 (Feb 2010) Parallel Execution Fundamentals in Oracle Database 11g Release 2 (November 2009) Parallel Execution with Oracle Database 10g Release 2 (June 2005)Oracle Data Mining white paper Oracle Data Mining 11g Release 2 (March 2010)Partitioning white papers Partitioning with Oracle Database 11g Release 2 (September 2009) Partitioning in Oracle Database 11g (June 2007)Materialized Views and Query Rewrite white papers Oracle Materialized Views  and Query Rewrite (May 2005) Improving Performance using Query Rewrite in Oracle Database 10g (December 2003)Database Compression white papers Advanced Compression with Oracle Database 11g Release 2 (September 2009) Table Compression in Oracle Database 10g Release 2 (May 2005)Oracle OLAP white papers On-line Analytic Processing with Oracle Database 11g Release 2 (September 2009) Using Oracle Business Intelligence Enterprise Edition with the OLAP Option to Oracle Database 11g (July 2008)Generic white papers Enabling Pervasive BI through a Practical Data Warehouse Reference Architecture (February 2010) Optimizing and Protecting Storage with Oracle Database 11g Release 2 (November 2009) Oracle Database 11g for Data Warehousing and Business Intelligence (August 2009) Best practices for a Data Warehouse on Oracle Database 11g (September 2008)Technical PresentationsA selection of ObE - Oracle by Examples documents: Generic Using Basic Database Functionality for Data Warehousing (10g) Partitioning Manipulating Partitions in Oracle Database (11g Release 1) Using High-Speed Data Loading and Rolling Window Operations with Partitioning (11g Release 1) Using Partitioned Outer Join to Fill Gaps in Sparse Data (10g) Materialized View and Query Rewrite Using Materialized Views and Query Rewrite Capabilities (10g) Using the SQLAccess Advisor to Recommend Materialized Views and Indexes (10g) Oracle OLAP Using Microsoft Excel With Oracle 11g Cubes (how to analyze data in Oracle OLAP Cubes using Excel's native capabilities) Using Oracle OLAP 11g With Oracle BI Enterprise Edition (Creating OBIEE Metadata for OLAP 11g Cubes and querying those in BI Answers) Building OLAP 11g Cubes Querying OLAP 11g Cubes Creating Interactive APEX Reports Over OLAP 11g CubesSelection of presentations from the BIWA website:Extreme Data Warehousing With Exadata  by Hermann Baer (July 2010) (slides 2.5MB, recording 54MB)Data Mining Made Easy! Introducing Oracle Data Miner 11g Release 2 New "Work flow" GUI   by Charlie Berger (May 2010) (slides 4.8MB, recording 85MB )Best Practices for Deploying a Data Warehouse on Oracle Database 11g  by Maria Colgan (December 2009)  (slides 3MB, recording 18MB, white paper 3MB )

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  • How to use a list of values in Excel as filter in a query

    - by Luca Zavarella
    It often happens that a customer provides us with a list of items for which to extract certain information. Imagine, for example, that our clients wish to have the header information of the sales orders only for certain orders. Most likely he will give us a list of items in a column in Excel, or, less probably, a simple text file with the identification code:     As long as the given values ??are at best a dozen, it costs us nothing to copy and paste those values ??in our SSMS and place them in a WHERE clause, using the IN operator, making sure to include the quotes in the case of alphanumeric elements (the database sample is AdventureWorks2008R2): SELECT * FROM Sales.SalesOrderHeader AS SOH WHERE SOH.SalesOrderNumber IN ( 'SO43667' ,'SO43709' ,'SO43726' ,'SO43746' ,'SO43782' ,'SO43796') Clearly, the need to add commas and quotes becomes an hassle when dealing with hundreds of items (which of course has happened to us!). It’d be comfortable to do a simple copy and paste, leaving the items as they are pasted, and make sure the query works fine. We can have this commodity via a User Defined Function, that returns items in a table. Simply we’ll provide the function with an input string parameter containing the pasted items. I give you directly the T-SQL code, where comments are there to clarify what was written: CREATE FUNCTION [dbo].[SplitCRLFList] (@List VARCHAR(MAX)) RETURNS @ParsedList TABLE ( --< Set the item length as your needs Item VARCHAR(255) ) AS BEGIN DECLARE --< Set the item length as your needs @Item VARCHAR(255) ,@Pos BIGINT --< Trim TABs due to indentations SET @List = REPLACE(@List, CHAR(9), '') --< Trim leading and trailing spaces, then add a CR\LF at the end of the list SET @List = LTRIM(RTRIM(@List)) + CHAR(13) + CHAR(10) --< Set the position at the first CR/LF in the list SET @Pos = CHARINDEX(CHAR(13) + CHAR(10), @List, 1) --< If exist other chars other than CR/LFs in the list then... IF REPLACE(@List, CHAR(13) + CHAR(10), '') <> '' BEGIN --< Loop while CR/LFs are over (not found = CHARINDEX returns 0) WHILE @Pos > 0 BEGIN --< Get the heading list chars from the first char to the first CR/LF and trim spaces SET @Item = LTRIM(RTRIM(LEFT(@List, @Pos - 1))) --< If the so calulated item is not empty... IF @Item <> '' BEGIN --< ...insert it in the @ParsedList temporary table INSERT INTO @ParsedList (Item) VALUES (@Item) --(CAST(@Item AS int)) --< Use the appropriate conversion if needed END --< Remove the first item from the list... SET @List = RIGHT(@List, LEN(@List) - @Pos - 1) --< ...and set the position to the next CR/LF SET @Pos = CHARINDEX(CHAR(13) + CHAR(10), @List, 1) --< Repeat this block while the upon loop condition is verified END END RETURN END At this point, having created the UDF, our query is transformed trivially in: SELECT * FROM Sales.SalesOrderHeader AS SOH WHERE SOH.SalesOrderNumber IN ( SELECT Item FROM SplitCRLFList('SO43667 SO43709 SO43726 SO43746 SO43782 SO43796') AS SCL) Convenient, isn’t it? You can find the script DBA_SplitCRLFList.sql here. Bye!!

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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; 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    .dbd-banner p{ font-size:0.75em; padding:0 0 10px; margin:0 } .dbd-banner p span{ color:#675C6D; } .dbd-banner p:last-child{ padding:0; } @media ALL and (max-width:640px){ .dbd-banner{ background:#f0f0f0; padding:5px; color:#333; margin-top: 5px; } } -- Database delivery patterns & practices STAGE 4 AUTOMATED DEPLOYMENT If you’ve been fortunate enough to get to the stage where you’ve implemented some sort of continuous integration process for your database updates, then hopefully you’re seeing the benefits of that investment – constant feedback on changes your devs are making, advanced warning of data loss (prior to the production release on Saturday night!), a nice suite of automated tests to check business logic, so you know it’s going to work when it goes live, and so on. But what next? What can you do to improve your delivery process further, moving towards a full continuous delivery process for your database? In this article I describe some of the issues you might need to tackle on the next stage of this journey, and how to plan to overcome those obstacles before they appear. Our Database Delivery Learning Program consists of four stages, really three – source controlling a database, running continuous integration processes, then how to set up automated deployment (the middle stage is split in two – basic and advanced continuous integration, making four stages in total). If you’ve managed to work through the first three of these stages – source control, basic, then advanced CI, then you should have a solid change management process set up where, every time one of your team checks in a change to your database (whether schema or static reference data), this change gets fully tested automatically by your CI server. But this is only part of the story. Great, we know that our updates work, that the upgrade process works, that the upgrade isn’t going to wipe our 4Tb of production data with a single DROP TABLE. But – how do you get this (fully tested) release live? Continuous delivery means being always ready to release your software at any point in time. There’s a significant gap between your latest version being tested, and it being easily releasable. Just a quick note on terminology – there’s a nice piece here from Atlassian on the difference between continuous integration, continuous delivery and continuous deployment. This piece also gives a nice description of the benefits of continuous delivery. These benefits have been summed up by Jez Humble at Thoughtworks as: “Continuous delivery is a set of principles and practices to reduce the cost, time, and risk of delivering incremental changes to users” There’s another really useful piece here on Simple-Talk about the need for continuous delivery and how it applies to the database written by Phil Factor – specifically the extra needs and complexities of implementing a full CD solution for the database (compared to just implementing CD for, say, a web app). So, hopefully you’re convinced of moving on the the next stage! The next step after CI is to get some sort of automated deployment (or “release management”) process set up. But what should I do next? What do I need to plan and think about for getting my automated database deployment process set up? Can’t I just install one of the many release management tools available and hey presto, I’m ready! If only it were that simple. Below I list some of the areas that it’s worth spending a little time on, where a little planning and prep could go a long way. It’s also worth pointing out, that this should really be an evolving process. Depending on your starting point of course, it can be a long journey from your current setup to a full continuous delivery pipeline. If you’ve got a CI mechanism in place, you’re certainly a long way down that path. Nevertheless, we’d recommend evolving your process incrementally. Pages 157 and 129-141 of the book on Continuous Delivery (by Jez Humble and Dave Farley) have some great guidance on building up a pipeline incrementally: http://www.amazon.com/Continuous-Delivery-Deployment-Automation-Addison-Wesley/dp/0321601912 For now, in this post, we’ll look at the following areas for your checklist: You and Your Team Environments The Deployment Process Rollback and Recovery Development Practices You and Your Team It’s a cliché in the DevOps community that “It’s not all about processes and tools, really it’s all about a culture”. As stated in this DevOps report from Puppet Labs: “DevOps processes and tooling contribute to high performance, but these practices alone aren’t enough to achieve organizational success. The most common barriers to DevOps adoption are cultural: lack of manager or team buy-in, or the value of DevOps isn’t understood outside of a specific group”. Like most clichés, there’s truth in there – if you want to set up a database continuous delivery process, you need to get your boss, your department, your company (if relevant) onside. Why? Because it’s an investment with the benefits coming way down the line. But the benefits are huge – for HP, in the book A Practical Approach to Large-Scale Agile Development: How HP Transformed LaserJet FutureSmart Firmware, these are summarized as: -2008 to present: overall development costs reduced by 40% -Number of programs under development increased by 140% -Development costs per program down 78% -Firmware resources now driving innovation increased by a factor of 8 (from 5% working on new features to 40% But what does this mean? It means that, when moving to the next stage, to make that extra investment in automating your deployment process, it helps a lot if everyone is convinced that this is a good thing. That they understand the benefits of automated deployment and are willing to make the effort to transform to a new way of working. Incidentally, if you’re ever struggling to convince someone of the value I’d strongly recommend just buying them a copy of this book – a great read, and a very practical guide to how it can really work at a large org. I’ve spoken to many customers who have implemented database CI who describe their deployment process as “The point where automation breaks down. Up to that point, the CI process runs, untouched by human hand, but as soon as that’s finished we revert to manual.” This deployment process can involve, for example, a DBA manually comparing an environment (say, QA) to production, creating the upgrade scripts, reading through them, checking them against an Excel document emailed to him/her the night before, turning to page 29 in his/her notebook to double-check how replication is switched off and on for deployments, and so on and so on. Painful, error-prone and lengthy. But the point is, if this is something like your deployment process, telling your DBA “We’re changing everything you do and your toolset next week, to automate most of your role – that’s okay isn’t it?” isn’t likely to go down well. There’s some work here to bring him/her onside – to explain what you’re doing, why there will still be control of the deployment process and so on. Or of course, if you’re the DBA looking after this process, you have to do a similar job in reverse. You may have researched and worked out how you’d like to change your methodology to start automating your painful release process, but do the dev team know this? What if they have to start producing different artifacts for you? Will they be happy with this? Worth talking to them, to find out. As well as talking to your DBA/dev team, the other group to get involved before implementation is your manager. And possibly your manager’s manager too. As mentioned, unless there’s buy-in “from the top”, you’re going to hit problems when the implementation starts to get rocky (and what tool/process implementations don’t get rocky?!). You need to have support from someone senior in your organisation – someone you can turn to when you need help with a delayed implementation, lack of resources or lack of progress. Actions: Get your DBA involved (or whoever looks after live deployments) and discuss what you’re planning to do or, if you’re the DBA yourself, get the dev team up-to-speed with your plans, Get your boss involved too and make sure he/she is bought in to the investment. Environments Where are you going to deploy to? And really this question is – what environments do you want set up for your deployment pipeline? Assume everyone has “Production”, but do you have a QA environment? Dedicated development environments for each dev? Proper pre-production? I’ve seen every setup under the sun, and there is often a big difference between “What we want, to do continuous delivery properly” and “What we’re currently stuck with”. Some of these differences are: What we want What we’ve got Each developer with their own dedicated database environment A single shared “development” environment, used by everyone at once An Integration box used to test the integration of all check-ins via the CI process, along with a full suite of unit-tests running on that machine In fact if you have a CI process running, you’re likely to have some sort of integration server running (even if you don’t call it that!). Whether you have a full suite of unit tests running is a different question… Separate QA environment used explicitly for manual testing prior to release “We just test on the dev environments, or maybe pre-production” A proper pre-production (or “staging”) box that matches production as closely as possible Hopefully a pre-production box of some sort. But does it match production closely!? A production environment reproducible from source control A production box which has drifted significantly from anything in source control The big question is – how much time and effort are you going to invest in fixing these issues? In reality this just involves figuring out which new databases you’re going to create and where they’ll be hosted – VMs? Cloud-based? What about size/data issues – what data are you going to include on dev environments? Does it need to be masked to protect access to production data? And often the amount of work here really depends on whether you’re working on a new, greenfield project, or trying to update an existing, brownfield application. There’s a world if difference between starting from scratch with 4 or 5 clean environments (reproducible from source control of course!), and trying to re-purpose and tweak a set of existing databases, with all of their surrounding processes and quirks. But for a proper release management process, ideally you have: Dedicated development databases, An Integration server used for testing continuous integration and running unit tests. [NB: This is the point at which deployments are automatic, without human intervention. Each deployment after this point is a one-click (but human) action], QA – QA engineers use a one-click deployment process to automatically* deploy chosen releases to QA for testing, Pre-production. The environment you use to test the production release process, Production. * A note on the use of the word “automatic” – when carrying out automated deployments this does not mean that the deployment is happening without human intervention (i.e. that something is just deploying over and over again). It means that the process of carrying out the deployment is automatic in that it’s not a person manually running through a checklist or set of actions. The deployment still requires a single-click from a user. Actions: Get your environments set up and ready, Set access permissions appropriately, Make sure everyone understands what the environments will be used for (it’s not a “free-for-all” with all environments to be accessed, played with and changed by development). The Deployment Process As described earlier, most existing database deployment processes are pretty manual. The following is a description of a process we hear very often when we ask customers “How do your database changes get live? How does your manual process work?” Check pre-production matches production (use a schema compare tool, like SQL Compare). Sometimes done by taking a backup from production and restoring in to pre-prod, Again, use a schema compare tool to find the differences between the latest version of the database ready to go live (i.e. what the team have been developing). This generates a script, User (generally, the DBA), reviews the script. This often involves manually checking updates against a spreadsheet or similar, Run the script on pre-production, and check there are no errors (i.e. it upgrades pre-production to what you hoped), If all working, run the script on production.* * this assumes there’s no problem with production drifting away from pre-production in the interim time period (i.e. someone has hacked something in to the production box without going through the proper change management process). This difference could undermine the validity of your pre-production deployment test. Red Gate is currently working on a free tool to detect this problem – sign up here at www.sqllighthouse.com, if you’re interested in testing early versions. There are several variations on this process – some better, some much worse! How do you automate this? In particular, step 3 – surely you can’t automate a DBA checking through a script, that everything is in order!? The key point here is to plan what you want in your new deployment process. There are so many options. At one extreme, pure continuous deployment – whenever a dev checks something in to source control, the CI process runs (including extensive and thorough testing!), before the deployment process keys in and automatically deploys that change to the live box. Not for the faint hearted – and really not something we recommend. At the other extreme, you might be more comfortable with a semi-automated process – the pre-production/production matching process is automated (with an error thrown if these environments don’t match), followed by a manual intervention, allowing for script approval by the DBA. One he/she clicks “Okay, I’m happy for that to go live”, the latter stages automatically take the script through to live. And anything in between of course – and other variations. But we’d strongly recommended sitting down with a whiteboard and your team, and spending a couple of hours mapping out “What do we do now?”, “What do we actually want?”, “What will satisfy our needs for continuous delivery, but still maintaining some sort of continuous control over the process?” NB: Most of what we’re discussing here is about production deployments. It’s important to note that you will also need to map out a deployment process for earlier environments (for example QA). However, these are likely to be less onerous, and many customers opt for a much more automated process for these boxes. Actions: Sit down with your team and a whiteboard, and draw out the answers to the questions above for your production deployments – “What do we do now?”, “What do we actually want?”, “What will satisfy our needs for continuous delivery, but still maintaining some sort of continuous control over the process?” Repeat for earlier environments (QA and so on). Rollback and Recovery If only every deployment went according to plan! Unfortunately they don’t – and when things go wrong, you need a rollback or recovery plan for what you’re going to do in that situation. Once you move in to a more automated database deployment process, you’re far more likely to be deploying more frequently than before. No longer once every 6 months, maybe now once per week, or even daily. Hence the need for a quick rollback or recovery process becomes paramount, and should be planned for. NB: These are mainly scenarios for handling rollbacks after the transaction has been committed. If a failure is detected during the transaction, the whole transaction can just be rolled back, no problem. There are various options, which we’ll explore in subsequent articles, things like: Immediately restore from backup, Have a pre-tested rollback script (remembering that really this is a “roll-forward” script – there’s not really such a thing as a rollback script for a database!) Have fallback environments – for example, using a blue-green deployment pattern. Different options have pros and cons – some are easier to set up, some require more investment in infrastructure; and of course some work better than others (the key issue with using backups, is loss of the interim transaction data that has been added between the failed deployment and the restore). The best mechanism will be primarily dependent on how your application works and how much you need a cast-iron failsafe mechanism. Actions: Work out an appropriate rollback strategy based on how your application and business works, your appetite for investment and requirements for a completely failsafe process. Development Practices This is perhaps the more difficult area for people to tackle. The process by which you can deploy database updates is actually intrinsically linked with the patterns and practices used to develop that database and linked application. So you need to decide whether you want to implement some changes to the way your developers actually develop the database (particularly schema changes) to make the deployment process easier. A good example is the pattern “Branch by abstraction”. Explained nicely here, by Martin Fowler, this is a process that can be used to make significant database changes (e.g. splitting a table) in a step-wise manner so that you can always roll back, without data loss – by making incremental updates to the database backward compatible. Slides 103-108 of the following slidedeck, from Niek Bartholomeus explain the process: https://speakerdeck.com/niekbartho/orchestration-in-meatspace As these slides show, by making a significant schema change in multiple steps – where each step can be rolled back without any loss of new data – this affords the release team the opportunity to have zero-downtime deployments with considerably less stress (because if an increment goes wrong, they can roll back easily). There are plenty more great patterns that can be implemented – the book Refactoring Databases, by Scott Ambler and Pramod Sadalage is a great read, if this is a direction you want to go in: http://www.amazon.com/Refactoring-Databases-Evolutionary-paperback-Addison-Wesley/dp/0321774515 But the question is – how much of this investment are you willing to make? How often are you making significant schema changes that would require these best practices? Again, there’s a difference here between migrating old projects and starting afresh – with the latter it’s much easier to instigate best practice from the start. Actions: For your business, work out how far down the path you want to go, amending your database development patterns to “best practice”. It’s a trade-off between implementing quality processes, and the necessity to do so (depending on how often you make complex changes). Socialise these changes with your development group. No-one likes having “best practice” changes imposed on them, so good to introduce these ideas and the rationale behind them early.   Summary The next stages of implementing a continuous delivery pipeline for your database changes (once you have CI up and running) require a little pre-planning, if you want to get the most out of the work, and for the implementation to go smoothly. We’ve covered some of the checklist of areas to consider – mainly in the areas of “Getting the team ready for the changes that are coming” and “Planning our your pipeline, environments, patterns and practices for development”, though there will be more detail, depending on where you’re coming from – and where you want to get to. This article is part of our database delivery patterns & practices series on Simple Talk. Find more articles for version control, automated testing, continuous integration & deployment.

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  • Database Security Events in April

    - by Troy Kitch
    Wed, Apr 18, Executive Oracle Database Security Round Table - Tampa, FL Tue, Apr 24, ISC(2) Leadership Regional Event Series - San Diego, CA April 24 - May 17,  Independent Oracle Users Group Enterprise Data at Risk Seminar Series Tue, Apr 24 IOUG Enterprise Data at Risk Seminar Series - Toronto Wed, Apr 25 IOUG Enterprise Data at Risk Seminar Series - New York Thu, Apr 26 IOUG Enterprise Data at Risk Seminar Series - Boston Thu, Apr 26 ISC(2) Leadership Regional Event Series - San Jose, CA

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  • Database Activity Monitoring Part 2 - SQL Injection Attacks

    If you think through the web sites you visit on a daily basis the chances are that you will need to login to verify who you are. In most cases your username would be stored in a relational database along with all the other registered users on that web site. Hopefully your password will be encrypted and not stored in plain text.

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  • Increase application performance

    - by Prayos
    I'm writing a program for a company that will generate a daily report for them. All of the data that they use for this report is stored in a local SQLite database. For this report, the utilize pretty much every bit of the information in the database. So currently, when I query the datbase, I retrieve everything, and store the information in lists. Here's what I've got: using (var dataReader = _connection.Select(query)) { if (dataReader.HasRows) { while (dataReader.Read()) { _date.Add(Convert.ToDateTime(dataReader["date"])); _measured.Add(Convert.ToDouble(dataReader["measured_dist"])); _bit.Add(Convert.ToDouble(dataReader["bit_loc"])); _psi.Add(Convert.ToDouble(dataReader["pump_press"])); _time.Add(Convert.ToDateTime(dataReader["timestamp"])); _fob.Add(Convert.ToDouble(dataReader["force_on_bit"])); _torque.Add(Convert.ToDouble(dataReader["torque"])); _rpm.Add(Convert.ToDouble(dataReader["rpm"])); _pumpOneSpm.Add(Convert.ToDouble(dataReader["pump_1_strokes_pm"])); _pumpTwoSpm.Add(Convert.ToDouble(dataReader["pump_2_strokes_pm"])); _pullForce.Add(Convert.ToDouble(dataReader["pull_force"])); _gpm.Add(Convert.ToDouble(dataReader["flow"])); } } } I then utilize these lists for the calculations. Obviously, the more information that is in this database, the longer the initial query will take. I'm curious if there is a way to increase the performance of the query at all? Thanks for any and all help. EDIT One of the report rows is called Daily Drilling Hours. For this calculation, I use this method: // Retrieves the timestamps where measured depth == bit depth and PSI >= 50 public double CalculateDailyProjectDrillingHours(DateTime date) { var dailyTimeStamps = _time.Where((t, i) => _date[i].Equals(date) && _measured[i].Equals(_bit[i]) && _psi[i] >= 50).ToList(); return _dailyDrillingHours = Convert.ToDouble(Math.Round(TimeCalculations(dailyTimeStamps).TotalHours, 2, MidpointRounding.AwayFromZero)); } // Checks that the interval is less than 10, then adds the interval to the total time private static TimeSpan TimeCalculations(IList<DateTime> timeStamps) { var interval = new TimeSpan(0, 0, 10); var totalTime = new TimeSpan(); TimeSpan timeDifference; for (var j = 0; j < timeStamps.Count - 1; j++) { if (timeStamps[j + 1].Subtract(timeStamps[j]) <= interval) { timeDifference = timeStamps[j + 1].Subtract(timeStamps[j]); totalTime = totalTime.Add(timeDifference); } } return totalTime; }

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