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Optimizing and Simplifying Complex SQL with Advanced Grouping Presented by: Jared Still
© 2009/2010 Pythian About Me Worked with Oracle since version 7.0 Have an affinity for things Perlish, such as DBD::Oracle Working as a DBA at Pythian since Jan 2011 Hobbies and extracurricular activities usually do not involve computers or databases. Contact: email@example.com About this presentation We will explore advanced grouping functionality This presentation just skims the surface Truly understanding how to make use of advanced grouping you will need to invest some time experimenting with it and examining the results. 2
© 2009/2010 Pythian 3
Why talk about GROUP BY? Somewhat intimidating at first It seems to be underutilized The performance implications of GROUP BY are not often discussed 4
© 2009/2010 Pythian 5 GROUP BY Basics GROUP BY does not guarantee a SORT @gb_1.sql 21:00:47 SQL> select /*+ gather_plan_statistics */ deptno, count(*) 21:00:48 2 from scott.emp 21:00:48 3 group by deptno 21:00:48 4 / DEPTNO COUNT(*) ---------- 30 6 20 5 10 3 3 rows selected. Notice the execution plan step is HASH GROUP BY Inline views and/or Subfactored Queries may change results – best not to rely on that behavior. GROUP BY can be HASH or SORT – neither guarantees sorted output
© 2009/2010 Pythian 6 GROUP BY Basics GROUP BY is a SQL optimization Following does 4 full table scans of EMP @gb_2.sql select /*+ gather_plan_statistics */ distinct dname, decode( d.deptno, 10, (select count(*) from scott.emp where deptno= 10), 20, (select count(*) from scott.emp where deptno= 20), 30, (select count(*) from scott.emp where deptno= 30), (select count(*) from scott.emp where deptno not in (10,20,30)) ) dept_count from (select distinct deptno from scott.emp) d join scott.dept d2 on d2.deptno = d.deptno; DNAME DEPT_COUNT -------------- ---------- SALES 6 ACCOUNTING 3 RESEARCH 5 3 rows selected.
© 2009/2010 Pythian GROUP BY Basics Use GROUP BY to reduce IO 1 full table scan of EMP @gb_3.sql select /*+ gather_plan_statistics */ d.dname, count(empno) empcount from scott.emp e join scott.dept d on d.deptno = e.deptno group by d.dname order by d.dname; DNAME EMPCOUNT -------------- ---------- ACCOUNTING 3 RESEARCH 5 SALES 6 3 rows selected. 7
© 2009/2010 Pythian GROUP BY Basics – HAVING Not used as much as it once was – here’s why It is easily replaced by Subfactored Queries (ANSI CTE: Common Table Expressions ) select deptno,count(*) from scott.emp group by deptno having count(*) > 5; can be rewritten as: with gcount as ( select deptno,count(*) as dept_count from scott.emp group by deptno ) select * from gcount where dept_count > 5; 8
© 2009/2010 Pythian Advanced GB – CUBE() Used to generate cross tab type reports Generates all combinations of columns in cube() @gb_4 with emps as ( select /*+ gather_plan_statistics */ ename, deptno from scott.emp group by cube(ename,deptno) ) select rownum, ename, deptno from emps 9
© 2009/2010 Pythian Advanced GB – CUBE() Notice the number of rows returned? 32 Notice the #rows the raw query actually returned. 56 in GENERATE CUBE in execution plan. Superaggregate rows generated by Oracle with NULL for GROUP BY columns– these NULLS represent the set of all values (see GROUPING() docs). Re-examine output for rows with NULL. For each row, Oracle generates a row with NULL for all columns in CUBE() All but one of these rows is filtered from output with the SORT GROUP BY step. Number of rows is predictable - @gb_5.sql 10
© 2009/2010 Pythian Advanced GB – CUBE() Is CUBE() saving any work in the database? Without CUBE(), how would you do this? gb_6.sql – UNION ALL Notice the multiple TABLE ACCESS FULL steps CUBE() returned the same results with one TABLE scan 11
© 2009/2010 Pythian Advanced GB – CUBE() OK – so what good is it? Using the SALES example schema - Criteria: all sales data for the year 2001. sales summarized by product category, aggregates based on 10-year customer age ranges, income levels, summaries income level regardless of age group summaries by age group regardless of income Here’s one way to do it. @gb_7.sql 12
© 2009/2010 Pythian Advanced GB – CUBE() Use CUBE() to generate the same output @gb_8.sql UNION ALL 8 seconds 9 table scans CUBE() 4 seconds 4 table scans 2 index scans 13
© 2009/2010 Pythian Advanced GB–Discern SA NULL Look at output from previous SQL – See all those NULLS on CUST_INCOME_LEVEL and AGE_RANGE How should you handle them? Can you use NVL() ? How will you discern between NULL data and Superaggregate NULLs? @gb_9.sql Are all those NULL values generated as Superaggregate rows? 14
© 2009/2010 Pythian Advanced GB–GROUPING() Use GROUPING to discern Superaggregates @gb_10a.sql - 0 = data null, 1 = SA null Use with DECODE() or CASE to determine output @gb_10b.sql – examine the use of GROUPING() Now we can see which is NULL data and which is SA NULL, and assign appropriate text for SA NULL columns. @gb_11.sql - Put it to work in our Sales Report “ALL INCOME” and “ALL AGE” where sales are Aggregated on the income regardless of age, and age regardless of income. 15
© 2009/2010 Pythian Advanced GB–GROUPING_ID() GROUPING_ID() takes the idea behind GROUPING() up a notch GROUPING() returns 0 or 1 GROUPING_ID() evaluates expressions and returns a bit vector – arguments correspond to bit position @gb_12a.sql GROUPING_ID() generates the GID values GROUPING() illustrates binary bit vector @gb_12b.sql OK – we made a truth table. What can we do with it? 16
© 2009/2010 Pythian Advanced GB–GROUPING_ID() Use GROUPING_ID() to customize sales report Useful for customizing report without any code change Summaries only Age Range only Income level + summaries etc… Options chosen by user are assigned values that correspond to bit vector used in GROUPING_ID() @gb_13.sql – examine PL/SQL block Experiment with different values and check output What do you think will happen when all options=0? How would you create this report without advanced grouping? No, I did not write an example – too much work. 17
© 2009/2010 Pythian Advanced GB–ROLLUP() Similar to CUBE() for 1 argument ROLLUP() identical to CUBE() @gb_14a.sql for 1+N arguments ROLLUP produces fewer redundant rows @gb_14b.sql 18
© 2009/2010 Pythian Advanced GB–ROLLUP() ROLLUP() – running subtotals without UNION ALL Much like CUBE(), ROLLUP() reduces the database workload Sales Report: All customers that begin with ‘Sul’ subtotal by year per customer subtotal by product category per customer grand total @gb_14c.sql 19
© 2009/2010 Pythian Advanced GB–GROUPING SETS Use with ROLLUP() @gb_15a.sql This looks just like the CUBE() output from gb_14b.sql But, now we can do things with GROUPING SETS that cannot easily be done with CUBE() Add “Country” to generated data Total by Country and ROLLUP(Region, Group) @gb_15b.sql 20
© 2009/2010 Pythian Advanced GB–GROUPING SETS Combine what has been covered into the sales report @gb_16.sql Sometimes GROUPING SETS produces duplicate rows Last 2 lines of reports are duplicates In this case due to ROLLUP(PROD_CATEGORY) Use GROUP_ID() – its purpose is to distinguish duplicate rows caused by GROUP BY uncomment HAVING clause and rerun to see effect Performance Note: GROUPING SETS is better at reducing workload GROUPING_ID more flexible – no code changes 21
© 2009/2010 Pythian Advanced GROUP BY - Summary Greatly reduce database workload with Advance GROUP BY functionality Greatly reduce the amount of SQL to produce the same results There is a learning curve Start using it! 22
© 2009/2010 Pythian References Oracle 11g Documentation on advanced GROUP BY is quite good Pro Oracle SQL – Apress http://www.apress.com/9781430232285 http://www.apress.com/9781430232285 Advanced SQL Functions in Oracle 10g http://www.amazon.com/Advanced-SQL- Functions-Oracle-10G/dp/818333184X http://www.amazon.com/Advanced-SQL- Functions-Oracle-10G/dp/818333184X 23
© 2009/2010 Pythian 24 Grouping Glossary CUBE() GROUP_ID() GROUPING() GROUPING_ID() GROUPING_SETS() ROLLUP()
© 2009/2010 Pythian Glossary–SUPERAGGRETE ROW GROUP BY extension will generate rows that have a NULL value in place of the value of the column being operated on. The NULL represents the set of all values for that column. The GROUPING() and GROUPING_ID() functions can be used to distinguish these. 25
© 2009/2010 Pythian Glossary – CUBE() GROUP BY extension CUBE(expr1,expr2,…) returns all possible combination of columns passed Demo: gl_cube.sql 26
© 2009/2010 Pythian Glossary – GROUP_ID() Function GROUP_ID() Returns > 0 for duplicate rows Demo: gl_group_id.sql 27
© 2009/2010 Pythian Glossary – ROLLUP() GROUP BY extension ROLLUP(expr1, expr2,…) Creates summaries of GROUP BY expressions Demo: gl_rollup.sql 28
© 2009/2010 Pythian Glossary – GROUPING() Function GROUPING(expr) returns 1 for superaggregate rows returns 0 for non-superaggregate rows Demo: gl_rollup.sql Used in demo to order the rows 29
© 2009/2010 Pythian Glossary – GROUPING_ID() Function GROUPING_ID(expr) returns a number representing the GROUP BY level of a row Demo: gl_grouping_id.sql 30
© 2009/2010 Pythian Glossary – GROUPING SETS GROUP BY Extension GROUPING SETS( expr1, expr2,…) Used to create subtotals based on the expressions page Demo: gl_grouping_sets.sql 31
© 2009/2010 Pythian GROUP BY Bug Malformed GROUP BY statements that worked < 184.108.40.206 may now get ORA-979 not a GROUP BY expression Due to bug #9477688 being fixed in 220.127.116.11 Patch 10624168 can be used to re-institute previous behavior ( must be patched offline – online mode patch is broken) @group_by_malformed.sql 32
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