Filter predicates after MIN aggregation

One of OLTP SQL on Exadata was consuming lot of CPU and was running for about 8 seconds per execution. Most of CPU activity was towards bottom half of the SQL as shown in real time SQL monitoring report below.

 

 

To demonstrate this I will created simple test case using following code. PCTFREE, c3 and 90000 rows for multiple iterations are used to create large table:

SQL> drop table tab purge;
drop table tab purge
           *
ERROR at line 1:
ORA-00942: table or view does not exist


SQL> create table tab (c1 number, c2 date, c3 char(2000)) pctfree 50;

Table created.

SQL> insert into tab select rownum, sysdate-rownum, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> insert into tab select rownum, sysdate-rownum-3, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> insert into tab select rownum, sysdate-rownum-2, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> insert into tab select rownum, sysdate-rownum-1, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> insert into tab select rownum, sysdate-rownum+2, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> insert into tab select rownum, sysdate-rownum+2, 'X' from dual connect by level <=90000;

90000 rows created.

SQL> commit;

Commit complete.

SQL> create index i_c1 on tab(c1);

Index created.

SQL> create index i_c2 on tab(c2);

Index created.

SQL> exec dbms_stats.gather_table_stats(USER,'TAB',cascade=>true,estimate_percent=>100);

PL/SQL procedure successfully completed.

Original SQL was calculating MIN of date column grouped by column c1 and then was applying filter against MIN column for narrow date range. So majority of rows were filtered out after full table scan and aggregation. As you can see it was doing 540k buffer gets to return 5 records. As this database is on Exadata, it does 540K physical reads very fast due to smart scan but in original SQL 43% of CPU activity was against full table scan operation.

 

SQL> alter session set statistics_level=all;
SQL>
SQL> select c1, c2 from
  2  (select c1,min(c2) c2
  3  from tab
  4  group by c1)
  5  where c2>=sysdate-15 and c2<=sysdate-10
  6  order by 1,2;

        C1 C2
---------- ---------
         7 01-MAY-15
         8 30-APR-15
         9 29-APR-15
        10 28-APR-15
        11 27-APR-15
SQL>
SQL> select * from table(dbms_xplan.display_cursor(format=>'allstats last'));

PLAN_TABLE_OUTPUT
-----------------------------------------------------------------------------------------------------------------------------------------
SQL_ID  58qhpx48c18v9, child number 0
-------------------------------------
select c1, c2 from (select c1,min(c2) c2 from tab group by c1) where
c2>=sysdate-15 and c2<=sysdate-10 order by 1,2

Plan hash value: 443431404

-----------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |  OMem |  1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |      |      1 |        |      5 |00:00:00.69 |     540K|    540K|       |       |          |
|   1 |  SORT ORDER BY               |      |      1 |   4500 |      5 |00:00:00.69 |     540K|    540K|  2048 |  2048 | 2048  (0)|
|*  2 |   FILTER                     |      |      1 |        |      5 |00:00:00.69 |     540K|    540K|       |       |          |
|   3 |    HASH GROUP BY             |      |      1 |   4500 |  90000 |00:00:00.68 |     540K|    540K|    31M|  8462K| 5717K (0)|
|   4 |     TABLE ACCESS STORAGE FULL| TAB  |      1 |    540K|    540K|00:00:00.47 |     540K|    540K|  1025K|  1025K| 7199K (0)|
-----------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - filter((MIN("C2")>=SYSDATE@!-15 AND MIN("C2")<=SYSDATE@!-10))

 

Column C1 was not unique so we cannot just apply filter prior to MIN. As you can see from below that consistent gets have gone down significantly due to index range scan but SQL is not returning same output. If filter predicate is applied first its returning records that we need and records that we don’t (false positives).

SQL> select c1,min(c2)
  2  from tab
  3  where c2>=sysdate-15 and c2<=sysdate-10
  4  group by c1
  5  order by 1,2;

        C1 MIN(C2)
---------- -----------------
         7 05/01/15 18:42:40
         8 04/30/15 18:42:40
         9 04/29/15 18:42:40
        10 04/28/15 18:42:40
        11 04/27/15 18:42:40
        12 04/27/15 18:43:15
        13 04/27/15 18:43:50
        14 04/27/15 18:42:04
        15 04/28/15 18:44:25
        16 04/27/15 18:44:25
SQL> select * from table(dbms_xplan.display_cursor(format=>'allstats l

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------------------------
SQL_ID  7fwwz4v1a9fx4, child number 0
-------------------------------------
select c1,min(c2) from tab where c2>=sysdate-15 and c2<=sysdate-10
group by c1 order by 1,2

Plan hash value: 1451778582

-------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |      |      1 |        |     10 |00:00:00.01 |      33 |
|   1 |  SORT ORDER BY                 |      |      1 |     32 |     10 |00:00:00.01 |      33 |
|   2 |   HASH GROUP BY                |      |      1 |     32 |     10 |00:00:00.01 |      33 |
|*  3 |    FILTER                      |      |      1 |        |     30 |00:00:00.01 |      33 |
|   4 |     TABLE ACCESS BY INDEX ROWID| TAB  |      1 |     32 |     30 |00:00:00.01 |      33 |
|*  5 |      INDEX RANGE SCAN          | I_C2 |      1 |     32 |     30 |00:00:00.01 |       3 |
-------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   3 - filter(SYSDATE@!-10>=SYSDATE@!-15)
   5 - access("C2">=SYSDATE@!-15 AND "C2"<=SYSDATE@!-10)

For such construct of SQL, we can still apply filter predicates first and remove records that should not be returned (false positives) using sub query as shown below. As you can see from below the consistent gets have gone down, indexes are being used, response time has improved, CPU activity has also gone down and we are getting same output.

 

SQL> select  t1.c1, t1.c2 from
  2  (select c1,min(c2) c2
  3  from tab
  4  where c2>=sysdate-15 and c2<=sysdate-10
  5  group by c1) t1
  6  where not exists (select c1 from tab t2 where t1.c1 = t2.c1 and t1.c2 > t2.c2 and t2.c2<=sysdate-10)
  7  order by 1,2;

        C1 C2
---------- -----------------
         7 05/01/15 18:42:40
         8 04/30/15 18:42:40
         9 04/29/15 18:42:40
        10 04/28/15 18:42:40
        11 04/27/15 18:42:40
SQL>
SQL> select * from table(dbms_xplan.display_cursor(format=>'allstats last'));

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------------------------------------------
SQL_ID  g0agfd3mm02qk, child number 0
-------------------------------------
select  t1.c1, t1.c2 from (select c1,min(c2) c2 from tab where
c2>=sysdate-15 and c2<=sysdate-10 group by c1) t1 where not exists
(select c1 from tab t2 where t1.c1 = t2.c1 and t1.c2 > t2.c2 and
t2.c2<=sysdate-10) order by 1,2

Plan hash value: 327191261

-------------------------------------------------------------------------------------------------------------------
| Id  | Operation                        | Name | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem |
-------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                 |      |      1 |        |      5 |00:00:00.01 |      84 |    	  |       |
|   1 |  SORT ORDER BY                   |      |      1 |     30 |      5 |00:00:00.01 |      84 |  2048 |  2048 |
|   2 |   NESTED LOOPS ANTI              |      |      1 |     30 |      5 |00:00:00.01 |      84 |       |       |
|   3 |    VIEW                          |      |      1 |     32 |     10 |00:00:00.01 |      33 |       |       |
|   4 |     HASH GROUP BY                |      |      1 |     32 |     10 |00:00:00.01 |      33 |  3331K|  3331K|
|*  5 |      FILTER                      |      |      1 |        |     30 |00:00:00.01 |      33 |       |       |
|   6 |       TABLE ACCESS BY INDEX ROWID| TAB  |      1 |     32 |     30 |00:00:00.01 |      33 |       |       |
|*  7 |        INDEX RANGE SCAN          | I_C2 |      1 |     32 |     30 |00:00:00.01 |       3 |  1025K|  1025K|
|*  8 |    TABLE ACCESS BY INDEX ROWID   | TAB  |     10 |  26996 |      5 |00:00:00.01 |      51 |       |       |
|*  9 |     INDEX RANGE SCAN             | I_C1 |     10 |      6 |     38 |00:00:00.01 |      13 |  1025K|  1025K|
-------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   5 - filter(SYSDATE@!-10>=SYSDATE@!-15)
   7 - access("C2">=SYSDATE@!-15 AND "C2"<=SYSDATE@!-10)
   8 - filter(("T2"."C2"<=SYSDATE@!-10 AND "T1"."C2">"T2"."C2"))
   9 - access("T1"."C1"="T2"."C1")

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SQL Plan Tree Visualization

Real Time SQL Monitor Graphical plan from Cloud Control at times is not legible. Using google chart and visualization techniques that I learned from Accenture Enkitec’s and Iggy’s blog, I have create three scripts to quickly display plan in graphical Btree format. The one from real time sql monitoring displays few more metrics such as cardinality, IO and time statistics and I have added some color logic such that if time spend in one plan line id is more than 10% of total elapsed time then display Timeline in red otherwise green.

@getplan_cursor_tree.sql – Display graphical btree plan from cache based on SQL ID and plan_hash_value

@getplan_awr_tree.sql – Display graphical btree plan from AWR metadata based on SQL ID and plan_hash_value

@getplan_realsqlmonitor_tree.sql – Display graphical btree plan from Real Time SQL monitoring based on SQL ID, sql_exec_id etc.

Modify hard coded lines in script:

SPO logs\getplan_realsqlmonitor_tree_&tmenvs..html;

host start chrome C:\Users\U267399\Desktop\Tools\scripts\logs\getplan_realsqlmonitor_tree_&tmenvs..html

Sample output from OEM:

 

 

Sample html output using getplan_realsqlmonitor_tree.sql: 

Full html file https://dl.dropboxusercontent.com/u/48791336/getplan_realsqlmonitor_tree_031615_170343.html

Download

References:

1) http://mauro-pagano.com/2015/03/10/trouble-reading-an-execution-plan-maybe-sqld360-can-help/

2) https://iggyfernandez.wordpress.com/2010/11/26/explaining-the-explain-plan-using-pictures/

SQL Performance changing over time

Here is another visualization to analyze sql performance changing over period of time using Excel.

Input Screen:

Enter the details such as username, password, tns, Days, Interval and SQL ID and click on run button.

Output Screen:

As you can see from above, IO Wait times on 8/16 and 9/2 were higher compared to 8/19, 8/21 and 8/26. Number of executions were more or less same on those days. On 9/9 and 9/10, IO wait times were at peak but that could be due corresponding increase in number of executions.

 

Data 2 worksheet will show more details about other SQL statistics. Units for other statistics are different so its difficult to show these statistics in two dimensional chart format.

Formatting for some combination by Day/Interval may not format excel chart properly. You may have to adjust chart manually to make it legible.

Download (Will be updated on 11/08)

Build Bind Variables from v$sql_monitor binds_xml column

One of my client had interesting SQL performance issue. Whenever client executes query from ab initio it takes forever and same SQL code from sqlplus runs in less than a minute. Both tools were producing different plan_hash_value. I looked at v$ses_optimizer_env to ensure there were no session level parameters set for ab initio session.

I wanted to run exact SQL from sqlplus, create sql plan baseline so that when SQL is submitted from AI it can use good plan. AI was generating SQL dynamically so I decided get exact SQL text, bind variables and values from v$sql_monitor. I defined bind variables, assigned values and ran SQL from sqlplus and created profile which was eventually used by AI. To automate constructing bind variables and SQL Text from v$sql_monitor, I wrote script called build_bind_sqlmontior.sql.

Script will prompt for Key, SID and SQL_ID. Enter value for at least on prompt and later on enter key value to construct bind variables and SQL Text as shown below:

SQL> @build_bind_sqlmonitor.sql
Please enter the value for Key if known       :
Please enter the value for Sid if known       : 986
Please enter the value for sql_id if known    :

             KEY STATUS              USERNAME   MODULE                      SID SQL_ID        FIRST_REFRESH_TIME   PROGRAM         SQL_TEXT
---------------- ------------------- ---------- -------------------- ---------- ------------- -------------------- --------------- --------------------
   6996503228544 DONE (ALL ROWS)     SYS        sqlplus.exe                 986 8wy5mjmh53vxf 01/24/14 10:41:56    sqlplus.exe     select /*+ monitor f
   8362302828663 DONE (ERROR)        SYS        sqlplus.exe                 986 4bkxr36kasm7s 01/24/14 10:40:51    sqlplus.exe     select /*+ monitor f
  16471201083509 DONE (ERROR)        SYS        sqlplus.exe                 986 azb329svj1ajd 01/24/14 10:39:36    sqlplus.exe     select /*+ monitor f
   7082402574450 DONE (ERROR)        SYS        sqlplus.exe                 986 3036w6mx13rq5 01/24/14 10:38:36    sqlplus.exe     select /*+ monitor f
   5845451993201 DONE (ERROR)        SYS        sqlplus.exe                 986 20fmy44010wvw 01/24/14 10:38:00    sqlplus.exe     select /*+ monitor f
  11643657842205 DONE (ALL ROWS)                                            986 bfjf698jtp6n1 01/24/14 03:10:14
   7421704990227 DONE                                                       986 2vn8ztuzaq3k4 01/24/14 03:10:11
  14856293379585 DONE (ALL ROWS)                                            986 53afdbcv9637w 01/24/14 03:10:10
.....
.....
Please enter Key from above       : 6996503228544

variable SYS_B_0 VARCHAR2(32);
variable SYS_B_1 VARCHAR2(32);
variable SYS_B_2 NUMBER;

exec :SYS_B_0 := '31-OCT-12 00.00.00';
exec :SYS_B_1 := 'DD-MON-YY HH24:MI:SS';
exec :SYS_B_2 := 30205578;

select /*+ monitor full(ism) */ *
from ism.table_name ism
where col1=to_date(:"SYS_B_0",:"SYS_B_1")
and   col2=:"SYS_B_2";

SQL> variable SYS_B_0 VARCHAR2(32);
SQL> variable SYS_B_1 VARCHAR2(32);
SQL> variable SYS_B_2 NUMBER;
SQL>
SQL> exec :SYS_B_0 := '31-OCT-12 00.00.00';

PL/SQL procedure successfully completed.

SQL> exec :SYS_B_1 := 'DD-MON-YY HH24:MI:SS';

PL/SQL procedure successfully completed.

SQL> exec :SYS_B_2 := 30205578;

PL/SQL procedure successfully completed.

SQL> select /*+ monitor full(ism) */ *
  2  from ism.table_name ism
  3  where col1=to_date(:"SYS_B_0",:"SYS_B_1")
  4  and   col2=:"SYS_B_2";

.....

1 row selected.

Download Script

If there is any data type conversion script may not produce reliable output.

Bloom Filter and function in join condition

One of my data warehouse user wanted to compare data between two Quarters. Original SQL is slightly complicated so I have created test case to demonstrate the issue.

DROP TABLE t1;

CREATE TABLE t1
  (
    id       NUMBER NOT NULL,
    datecol  DATE NOT NULL,
    comments VARCHAR2(500)
  )
  PARTITION BY RANGE
  (
    datecol
  )
  (
    PARTITION q3 VALUES LESS THAN (TO_DATE('10/10/2013', 'DD/MM/YYYY')) TABLESPACE users,
    PARTITION q4 VALUES LESS THAN (TO_DATE('01/01/2043', 'DD/MM/YYYY')) TABLESPACE users
  );
  
INSERT INTO t1
SELECT
  rownum,
  to_date('31/12/2013','DD/MM/YYYY'),
  dbms_random.string('X', '500')
FROM dual a
  CONNECT BY level <= 5000;
  
INSERT INTO t1
SELECT
  rownum,
  to_date('30/09/2013','DD/MM/YYYY'),
  dbms_random.string('X', '500')
FROM dual a
  CONNECT BY level <= 5000;

For sake of simplicity, I have added only two partitions here but original fact table had 10 years of data. Below is subset of SQL statement that user was running to compare data across quarters.

select /*+ leading(xa,xb) parallel(xa,4) parallel(xb,4) */ sum(xa.id),count( distinct xb.comments)
from t1 xa ,t1 xb
where xa.datecol = add_months(xb.datecol,-3)
and   xa.datecol = '30-SEP-13';

------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                        | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                 |          |     1 |   265 |   367  (74)| 00:00:01 |       |       |        |      |            |
|   1 |  SORT AGGREGATE                  |          |     1 |   265 |            |          |       |       |        |      |            |
|   2 |   PX COORDINATOR                 |          |       |       |            |          |       |       |        |      |            |
|   3 |    PX SEND QC (RANDOM)           | :TQ10001 |     1 |   265 |            |          |       |       |  Q1,01 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE               |          |     1 |   265 |            |          |       |       |  Q1,01 | PCWP |            |
|   5 |      VIEW                        | VW_DAG_0 |    34M|  8774M|   367  (74)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   6 |       HASH GROUP BY              |          |    34M|  9370M|   367  (74)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   7 |        PX RECEIVE                |          |    34M|  9370M|   367  (74)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   8 |         PX SEND HASH             | :TQ10000 |    34M|  9370M|   367  (74)| 00:00:01 |       |       |  Q1,00 | P->P | HASH       |
|   9 |          HASH GROUP BY           |          |    34M|  9370M|   367  (74)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
|* 10 |           HASH JOIN              |          |    34M|  9370M|   122  (20)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
|  11 |            PARTITION RANGE SINGLE|          |  5501 |   118K|    33   (0)| 00:00:01 |   KEY |   KEY |  Q1,00 | PCWC |            |
|* 12 |             TABLE ACCESS FULL    | T1       |  5501 |   118K|    33   (0)| 00:00:01 |   KEY |   KEY |  Q1,00 | PCWP |            |
|  13 |            PX BLOCK ITERATOR     |          |  6311 |  1608K|    65   (0)| 00:00:01 |     1 |     2 |  Q1,00 | PCWC |            |
|* 14 |             TABLE ACCESS FULL    | T1       |  6311 |  1608K|    65   (0)| 00:00:01 |     1 |     2 |  Q1,00 | PCWP |            |
------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

  10 - access("XA"."DATECOL"=ADD_MONTHS(INTERNAL_FUNCTION("XB"."DATECOL"),-3))
  12 - filter("XA"."DATECOL"='30-SEP-13')
  14 - filter(ADD_MONTHS(INTERNAL_FUNCTION("XB"."DATECOL"),-3)='30-SEP-13')

Note
-----
   - dynamic statistics used: dynamic sampling (level=2)

As you can see from line 14, table T1 (XB) was doing full table scan and was accessing both the partitions (pstart 1 and pstop 2). In original SQL it was accessing all partitions for last 10 years.

I asked user to add additional filter for T1 (XB) so that it will scan required partition as shown below. But user was not satisfied (as it would require calculating dates manually every time they run this SQL and sometimes they run this SQL for multiple Quarters). Other option was to pass filter as xb.datecol = (select add_months(‘30-SEP-13’,3) from dual) but I was curios to find out more about bloom filters.

select /*+ leading(xa,xb) parallel(xa,4) parallel(xb,4) */ sum(xa.id),count( distinct xb.comments)
from t1 xa ,t1 xb
where xa.datecol = add_months(xb.datecol,-3)
and   xa.datecol = '30-SEP-13'
and   xb.datecol = '31-DEC-13';

I was wondering whether Oracle would create bloom filter after scanning T1 (XA) and apply that to T2 (XB) and do partition scan on T2(XB) instead of scanning all partitions but the problem was add_months function in join condition.

Original Plan:

Recommended SQL and plan (bloom filter in action):

select /*+ leading(xa,xb) parallel(xa,4) parallel(xb,4) */ sum(xa.id),count( distinct xb.comments)
from t1 xa ,t1 xb
where add_months(xa.datecol,+3) = xb.datecol
and   xa.datecol = '30-SEP-13';

-------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                         | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
-------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                  |          |     1 |   265 |   315  (69)| 00:00:01 |       |       |        |      |            |
|   1 |  SORT AGGREGATE                   |          |     1 |   265 |            |          |       |       |        |      |            |
|   2 |   PX COORDINATOR                  |          |       |       |            |          |       |       |        |      |            |
|   3 |    PX SEND QC (RANDOM)            | :TQ10001 |     1 |   265 |            |          |       |       |  Q1,01 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE                |          |     1 |   265 |            |          |       |       |  Q1,01 | PCWP |            |
|   5 |      VIEW                         | VW_DAG_0 |    28M|  7145M|   315  (69)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   6 |       HASH GROUP BY               |          |    28M|  7630M|   315  (69)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   7 |        PX RECEIVE                 |          |    28M|  7630M|   315  (69)| 00:00:01 |       |       |  Q1,01 | PCWP |            |
|   8 |         PX SEND HASH              | :TQ10000 |    28M|  7630M|   315  (69)| 00:00:01 |       |       |  Q1,00 | P->P | HASH       |
|   9 |          HASH GROUP BY            |          |    28M|  7630M|   315  (69)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
|* 10 |           HASH JOIN               |          |    28M|  7630M|   118  (17)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
|  11 |            PART JOIN FILTER CREATE| :BF0000  |  5501 |   118K|    33   (0)| 00:00:01 |       |       |  Q1,00 | PCWP |            |
|  12 |             PARTITION RANGE SINGLE|          |  5501 |   118K|    33   (0)| 00:00:01 |   KEY |   KEY |  Q1,00 | PCWC |            |
|* 13 |              TABLE ACCESS FULL    | T1       |  5501 |   118K|    33   (0)| 00:00:01 |   KEY |   KEY |  Q1,00 | PCWP |            |
|  14 |            PX BLOCK ITERATOR      |          | 10278 |  2619K|    65   (0)| 00:00:01 |:BF0000|:BF0000|  Q1,00 | PCWC |            |
|  15 |             TABLE ACCESS FULL     | T1       | 10278 |  2619K|    65   (0)| 00:00:01 |:BF0000|:BF0000|  Q1,00 | PCWP |            |
-------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

  10 - access("XB"."DATECOL"=ADD_MONTHS(INTERNAL_FUNCTION("XA"."DATECOL"),3))
  13 - filter("XA"."DATECOL"='30-SEP-13')

As you can see from above, just by flipping the add_months function Oracle now creates and uses bloom filter to scan required partition for T1 (XB). So from this behavior it looks like bloom filters cannot be used for join column surrounded by function. It sounds very similar to having index on column x, scanning table with function(x)=y will not use index on column x. May be in future Oracle will add function based bloom filters.

Outer Join, Ordered hint and SWAP_JOIN_INPUTS

Outer joins access tables in following order:

from t1, t2 where t1.col1(+)=t2.col1 –> table will be accessed in order t2, t1

from t1, t2 where t1.col1=t2.col1(+) –> table will be accessed in order t1,t2

One of my developer had to do some data cleanup so he quickly staged data in table t1, t2 was our large fact table and ran following query (test case). Query ran for hours on Exadata and did not finish.

select /*+ ordered full(t1) full(t2) parallel(t1,4) parallel(t2,4) */ t1.*, t2.* from t1,t2 where t1.col1(+)=t2.col1;

When I looked at SQL monitoring report driving table was t2 (large fact table). Below is the output from test case:

SQL> create table t1(c1, c2)
  2  as
  3  select level, rpad('x',10)
  4  from dual
  5  connect by level <= 200000
  6  ;

Table created.

SQL>
SQL> create table t2(c1, c2)
  2  as
  3  select level, rpad('x',10)
  4  from dual
  5  connect by level <= 100
  6  ;

Table created.

SQL>
SQL> exec dbms_stats.gather_table_stats(user, 'T1');

PL/SQL procedure successfully completed.

SQL>
SQL> exec dbms_stats.gather_table_stats(user, 'T2');

PL/SQL procedure successfully completed.


SQL> explain plan for
  2  select /*+ ordered */ t2.*,t1.*
  3  from t2, t1
  4  where t1.c1 = t2.c1(+);

Explained.

SQL>
SQL> @utlxpls.sql

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------
Plan hash value: 1823443478

-------------------------------------------------------------------------------------------
| Id  | Operation                  | Name | Rows  | Bytes |TempSpc| Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |   200K|  5859K|       |   436   (1)| 00:00:06 |
|*  1 |  HASH JOIN OUTER           |      |   200K|  5859K|  5472K|   436   (1)| 00:00:06 |
|   2 |   TABLE ACCESS STORAGE FULL| T1   |   200K|  3125K|       |   165   (1)| 00:00:02 |
|   3 |   TABLE ACCESS STORAGE FULL| T2   |   100 |  1400 |       |     3   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   1 - access("T1"."C1"="T2"."C1"(+))

22 rows selected.

SQL> explain plan for
  2  select t2.*,t1.*
  3  from t2, t1
  4  where t1.c1 = t2.c1(+);

Explained.

SQL>
SQL> @utlxpls.sql

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------
Plan hash value: 312430291

-----------------------------------------------------------------------------------
| Id  | Operation                  | Name | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |      |   200K|  5859K|   169   (2)| 00:00:03 |
|*  1 |  HASH JOIN RIGHT OUTER     |      |   200K|  5859K|   169   (2)| 00:00:03 |
|   2 |   TABLE ACCESS STORAGE FULL| T2   |   100 |  1400 |     3   (0)| 00:00:01 |
|   3 |   TABLE ACCESS STORAGE FULL| T1   |   200K|  3125K|   165   (1)| 00:00:02 |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   1 - access("T1"."C1"="T2"."C1"(+))

When ORDERED hint was added it used t1 (large table as driving table) with hash join outer and when ORDERED hint was NOT used it used t2 (small table as driving table) with hash join right outer.

Output on left is without ORDERED hint and on right is with ORDERED hint.

 

So based on above output looks like that when ORDERED hint was used with OUTER JOIN, Oracle optimizer does not go through join permutations (in this particular case swapping join inputs) and hence choose T1 (larger table) as driving table. When ORDERED hint was removed, Oracle optimizer computes cost of swapping join inputs and choose T2 (smaller table) as driving table. Why Oracle considered join order as t1, t2 with ordered hint? I think because of outer join.

For my production issue, I asked ETL/development team to remove ORDERED hint and query ran in 12 minutes.

Calculate Median from Frequency in SQL

If numbers are stored sequentially then median function can be used to calculate median. But what if you want to calculate median based on frequencies? Either you can convert frequencies into multiple values in rows format and then use median function or calculate median by using frequencies as described by http://www.onlinemathlearning.com/median-frequency-table.html

Here is how I put that into SQL format:

SQL> set feed on
SQL> set echo on
SQL> drop table v_median purge;

Table dropped.

SQL> create table v_median (id number, cnt number);

Table created.

SQL> delete from v_median;

0 rows deleted.

SQL> insert into v_median values (1,2);

1 row created.

SQL> insert into v_median values (2,5);

1 row created.

SQL> insert into v_median values (3,1);

1 row created.

SQL> insert into v_median values (4,3);

1 row created.

SQL> insert into v_median values (5,2);

1 row created.

SQL> commit;

Commit complete.

SQL> WITH qb1 AS
  2    (SELECT id,
  3      cnt,
  4      SUM(cnt) over (order by id rows unbounded preceding) tot
  5    FROM v_median
  6    ORDER BY id
  7    ),
  8    qb2 AS
  9    (SELECT MAX(tot) AS n ,
 10      CASE
 11        WHEN mod(MAX(tot),2) = 0
 12        THEN 'even'
 13        WHEN mod(MAX(tot),2) = 1
 14        THEN 'odd'
 15      END AS oe
 16    FROM
 17      (SELECT id,
 18        cnt,
 19        SUM(cnt) over (order by id rows unbounded preceding) tot
 20      FROM v_median
 21      ORDER BY id
 22      )
 23    )
 24  SELECT median
 25  FROM
 26    (SELECT DISTINCT
 27      CASE
 28        WHEN (qb2.oe    ='odd'
 29        AND (qb2.n+1)/2 = qb1.tot)
 30        THEN qb1.id
 31        WHEN (qb2.oe='even')
 32        THEN
 33          (SELECT median(id)
 34          FROM
 35            ( SELECT DISTINCT id
 36            FROM
 37              (SELECT id
 38              FROM
 39                (SELECT id FROM qb1,qb2 WHERE tot>((qb2.n/2)+1)
 40                )
 41              WHERE rownum=1
 42              UNION
 43              SELECT id
 44              FROM
 45                (SELECT id FROM qb1,qb2 WHERE tot<((qb2.n/2)+1)ORDER BY tot DESC
 46                )
 47              WHERE rownum=1
 48              )
 49            )
 50          )
 51      END AS median
 52    FROM qb1,
 53      qb2
 54    )
 55  WHERE median IS NOT NULL;

    MEDIAN                                                                                                                                                                                              
----------                                                                                                                                                                                              
         2                                                                                                                                                                                              

1 row selected.

SQL> delete from v_median;

5 rows deleted.

SQL> insert into v_median values (0,11);

1 row created.

SQL> insert into v_median values (1,9);

1 row created.

SQL> insert into v_median values (2,5);

1 row created.

SQL> insert into v_median values (3,10);

1 row created.

SQL> insert into v_median values (4,15);

1 row created.

SQL> commit;

Commit complete.

SQL> WITH qb1 AS
  2    (SELECT id,
  3      cnt,
  4      SUM(cnt) over (order by id rows unbounded preceding) tot
  5    FROM v_median
  6    ORDER BY id
  7    ),
  8    qb2 AS
  9    (SELECT MAX(tot) AS n ,
 10      CASE
 11        WHEN mod(MAX(tot),2) = 0
 12        THEN 'even'
 13        WHEN mod(MAX(tot),2) = 1
 14        THEN 'odd'
 15      END AS oe
 16    FROM
 17      (SELECT id,
 18        cnt,
 19        SUM(cnt) over (order by id rows unbounded preceding) tot
 20      FROM v_median
 21      ORDER BY id
 22      )
 23    )
 24  SELECT median
 25  FROM
 26    (SELECT DISTINCT
 27      CASE
 28        WHEN (qb2.oe    ='odd'
 29        AND (qb2.n+1)/2 = qb1.tot)
 30        THEN qb1.id
 31        WHEN (qb2.oe='even')
 32        THEN
 33          (SELECT median(id)
 34          FROM
 35            ( SELECT DISTINCT id
 36            FROM
 37              (SELECT id
 38              FROM
 39                (SELECT id FROM qb1,qb2 WHERE tot>((qb2.n/2)+1)
 40                )
 41              WHERE rownum=1
 42              UNION
 43              SELECT id
 44              FROM
 45                (SELECT id FROM qb1,qb2 WHERE tot<((qb2.n/2)+1)ORDER BY tot DESC
 46                )
 47              WHERE rownum=1
 48              )
 49            )
 50          )
 51      END AS median
 52    FROM qb1,
 53      qb2
 54    )
 55  WHERE median IS NOT NULL;

    MEDIAN                                                                                                                                                                                              
----------                                                                                                                                                                                              
       2.5                                                                                                                                                                                              

1 row selected.

SQL> spool off

Oracle CHAR data type comparisons

I received below test case from my pl/sql developer.

dev@oradb > drop table test_3;

Table dropped.

dev@oradb > create table test_3  (col1 char(2));

Table created.
 
dev@oradb > insert into test_3 values('  ');

1 row created.

dev@oradb > insert into test_3 values('~ ');

1 row created.

dev@oradb > insert into test_3 values('~');

1 row created.

dev@oradb > insert into test_3 values(' ~');

1 row created.

dev@oradb > commit;

Commit complete.

dev@oradb > select * from test_3;

CO
--

~
~
 ~


4 rows selected.

Developer wanted to find record with column value = ‘~’ and he used below query:

Test 1: --I would expect no records since it is a char field
dev@oradb > select * from test_3 where col1='~';

CO
--
~
~

2 rows selected.

Developer was expecting that above query will return 0 rows as col1 is defined as char(2) so ‘~’ will be stored as ‘~ ‘ and ‘~’ <> ‘~ ‘ but query returned 2 rows  instead of 0 rows.

As per Oracle documentation http://docs.oracle.com/cd/B28359_01/server.111/b28286/sql_elements002.htm:

With blank-padded semantics, if the two values have different lengths, then Oracle first adds blanks to the end of the shorter one so their lengths are equal. Oracle then compares the values character by character up to the first character that differs. Oracle uses blank-padded comparison semantics only when both values in the comparison are either expressions of datatype CHAR, NCHAR, text literals, or values returned by the USER function.

So now we know how CHAR data types with different lengths are compared how do we know what is the data type of ‘~’ on right side of equality operator? It’s char but lets prove it.

dba@oradb >  create table datatype_test as select '~' as db from dual;

Table created.

dba@oradb > desc  datatype_test
 Name                                            Null?    Type
 ----------------------------------------------- -------- --------------------------------
 DB                                                       CHAR(1)

dba@oradb > select col1,ora_hash(col1),ora_hash('~'),ora_hash('~ ') as "ora_hash('~ ')" from test_3;

CO ORA_HASH(COL1) ORA_HASH('~') ora_hash('~ ')
-- -------------- ------------- --------------
        190699439     216329699     4123278633
~      4123278633     216329699     4123278633
~      4123278633     216329699     4123278633
 ~      999748150     216329699     4123278633
 
4 rows selected.

From above its clear that data type of ‘~’ on right side of equality operator is CHAR and hence select query return 2 records (record 2 and record 3) instead of 0 rows. Visual comparison will look something like following:

Expected Comparison

Actual Comparison

‘  ‘	‘~’	‘  ‘	‘~ ‘
‘~ ‘	‘~’	‘~ ‘	‘~ ‘
‘~ ’	‘~’	‘~ ‘	‘~ ‘
‘ ~’	‘~’	‘ ~’	‘~ ‘

Hash value returned by ora_hash function also matches as shown above.

Developer tried to use different work around which produced different results and I used same queries to prove why similar workarounds are producing different results.

Test 2: --above query works then why not this query? all im doing is replacing a ~ with nvl(trim('  '),'~')

dev@oradb > select * from test_3 where col1 =nvl(trim('  '),'~');

no rows selected

dba@oradb > select col1,ora_hash(col1),ora_hash(nvl(trim('  '),'~')) as "ora_hash(nvl(trim('  '),'~'))" from test_3;

CO ORA_HASH(COL1) ora_hash(nvl(trim('  '),'~'))
-- -------------- -----------------------------
        190699439                     216329699
~      4123278633                     216329699
~      4123278633                     216329699
 ~      999748150                     216329699

dba@oradb > create table datatype_test as select nvl(trim('  '),'~') as db from dual;

Table created.

dba@oradb > desc  datatype_test
 Name                                            Null?    Type
 ----------------------------------------------- -------- --------------------------------
 DB                                                       VARCHAR2(1)

Data type of nvl(trim(‘  ‘),’~’) is VARCHAR2 so Oracle will not pad blanks. Hash values returned are also different hence query returned 0 records.

Test 3: --works user defined function

dev@oradb > CREATE OR REPLACE FUNCTION test_fn (v_col1 VARCHAR2) RETURN VARCHAR2
  2  AS
  3   col1 CHAR(2);
  4  BEGIN
  5  col1:=  nvl(trim('  '),'~');
  6    RETURN col1;
  7  END;
  8  /

Function created.

dev@oradb > select * from test_3 where col1 =test_fn('  ');

CO
--
~
~

2 rows selected.

dba@oradb > create table datatype_test as select test_fn('  ') as dt from dual;

Table created.

dba@oradb > desc  datatype_test
 Name                                            Null?    Type
 ----------------------------------------------- -------- --------------------------------
 DT                                                       VARCHAR2(4000)

dba@oradb >  select length(dt) from datatype_test;

LENGTH(DT)
----------
         2

dba@oradb > select dt from  datatype_test;

DT
---------------------------------------------------------------
~

dba@oradb > select col1,ora_hash(col1),ora_hash(test_fn('  ')) as "ora_hash(test_fn('  '))" from test_3;

CO ORA_HASH(COL1) ora_hash(test_fn('  '))
-- -------------- -----------------------
        190699439               216329699
~      4123278633              4123278633
~      4123278633              4123278633
 ~      999748150               216329699

Data type of return variable is VARCHAR but col1 is defined as CHAR in function hence during assignment col1 will store ‘~’ as ‘~ ‘ (return value) which can also be confirmed by length function. Hash values returned for record 2 and record 3 match hence query returns 2 records.

Test 4: case also works

dev@oradb > select * from test_3 where col1 = case when trim('  ') is null then '~'   else '1' end;

CO
--
~
~

2 rows selected.

dba@oradb > select col1,ora_hash(col1),ora_hash(case when trim('  ') is null then '~'   else '1' end) as "ora_hash_on_case" from test_3;

CO ORA_HASH(COL1) ora_hash_on_case
-- -------------- ----------------
        190699439        216329699
~      4123278633        216329699
~      4123278633        216329699
 ~      999748150        216329699

 
dba@oradb > create table datatype_test as select case when trim('  ') is null then '~'   else '1' end as dt from dual;

Table created.


dba@oradb > desc  datatype_test
 Name                                            Null?    Type
 ----------------------------------------------- -------- --------------------------------
 DT                                                       CHAR(1)

Hash value doesn’t match but as the data type of return variable of CASE is CHAR oracle will pad blank and hence return 2 records.

Developer finally asked is there any way to select only record 3 using where condition? Once the data is inserted I don’t think there is way to differentiate between record 2 (‘~ ‘) and record 3 (‘~’).

Below is the data block dump of record 2 and record 3:

tab 0, row 1, @0x3f8c
tl: 6 fb: --H-FL-- lb: 0x1  cc: 1
col  0: [ 2]  7e 20
tab 0, row 2, @0x3f86
tl: 6 fb: --H-FL-- lb: 0x1  cc: 1
col  0: [ 2]  7e 20

.

11g pivot unpivot and Database Design

Oracle 11g pivot and unpivot function can help if database tables are not in second normal form. I will try to unveil this using a simple example:

Table Structures

CREATE TABLE emp1
  (
    empno   NUMBER PRIMARY KEY,
    empname VARCHAR2(1000),
    job     VARCHAR2(100),
    hiredate DATE
  );

CREATE TABLE sal1
  (
    empno   NUMBER,
    sal2000 NUMBER,
    sal2001 NUMBER,
    sal2002 NUMBER,
    CONSTRAINT fk_sal1_emp1 FOREIGN KEY (empno) REFERENCES emp1(empno)
  );

--Inserted few records into both emp1 and sal1 table.

We have seen table structures like sal1 (sal2000,sal2001,sal2002 …….). If we have to write a salary report as shown below we have to join SAL1 table multiple times. I used union but there are different ways to write similar query by joining SAL1 multiple times.

10g SQL

SELECT a.empno,
  a.empname,
  a.job,
  'sal2000' AS SALYEAR ,
  b1.sal2000 sal2000to2002
FROM emp1 a,
  sal1 b1
WHERE a.empno=b1.empno
UNION
SELECT a.empno,
  a.empname,
  a.job,
  'sal2001' AS SALYEAR ,
  b1.sal2001 sal2000to2002
FROM emp1 a,
  sal1 b1
WHERE a.empno=b1.empno
UNION
SELECT a.empno,
  a.empname,
  a.job,
  'sal2002' AS SALYEAR ,
  b1.sal2002 sal2000to2002
FROM emp1 a,
  SAL1 B1
WHERE a.empno=b1.empno;

    EMPNO EMPNAME                        JOB                            SALYEAR SAL2000TO2002
---------- ------------------------------ ------------------------------ ------- -------------
      7369 SMITH                          CLERK                          sal2000            10
      7369 SMITH                          CLERK                          sal2001            11
      7369 SMITH                          CLERK                          sal2002            12
      7499 ALLEN                          SALESMAN                       sal2000            20
      7499 ALLEN                          SALESMAN                       sal2001            21
      7499 ALLEN                          SALESMAN                       sal2002            22
      7521 WARD                           SALESMAN                       sal2000            21
      7521 WARD                           SALESMAN                       sal2001            22
      7521 WARD                           SALESMAN                       sal2002            23
      7654 MARTIN                         SALESMAN                       sal2000            23
      7654 MARTIN                         SALESMAN                       sal2001            24
      7654 MARTIN                         SALESMAN                       sal2002            25
      7698 BLAKE                          MANAGER                        sal2000            31
      7698 BLAKE                          MANAGER                        sal2001            32
      7698 BLAKE                          MANAGER                        sal2002            33
      7782 CLARK                          MANAGER                        sal2000            31
      7782 CLARK                          MANAGER                        sal2001            32
      7782 CLARK                          MANAGER                        sal2002            33
      7788 SCOTT                          ANALYST                        sal2000            25
      7788 SCOTT                          ANALYST                        sal2001            25
      7788 SCOTT                          ANALYST                        sal2002            25
      7839 KING                           PRESIDENT                      sal2000           100
      7839 KING                           PRESIDENT                      sal2001           110
      7839 KING                           PRESIDENT                      sal2002           120
      7844 TURNER                         SALESMAN                       sal2000            10
      7844 TURNER                         SALESMAN                       sal2001            11
      7844 TURNER                         SALESMAN                       sal2002            12
      7876 ADAMS                          CLERK                          sal2000            10
      7876 ADAMS                          CLERK                          sal2001            11
      7876 ADAMS                          CLERK                          sal2002            12
      7900 JAMES                          CLERK                          sal2000            17
      7900 JAMES                          CLERK                          sal2001            18
      7900 JAMES                          CLERK                          sal2002            19
      7902 FORD                           ANALYST                        sal2000            25
      7902 FORD                           ANALYST                        sal2001            26
      7902 FORD                           ANALYST                        sal2002            27
      7934 MILLER                         CLERK                          sal2000            10
      7934 MILLER                         CLERK                          sal2001            11
      7934 MILLER                         CLERK                          sal2002            12

39 rows selected.

Execution Plan
----------------------------------------------------------
   0      SELECT STATEMENT Optimizer=ALL_ROWS (Cost=18 Card=39 Bytes=23361)
   1    0   SORT (UNIQUE) (Cost=18 Card=39 Bytes=23361)
   2    1     UNION-ALL
   3    2       HASH JOIN (Cost=7 Card=13 Bytes=7709)
   4    3         TABLE ACCESS (FULL) OF 'SAL1' (TABLE) (Cost=3 Card=13 Bytes=338)
   5    3         TABLE ACCESS (FULL) OF 'EMP1' (TABLE) (Cost=3 Card=14 Bytes=7938)
   6    2       HASH JOIN (Cost=10 Card=26 Bytes=15652)
   7    6         TABLE ACCESS (FULL) OF 'EMP1' (TABLE) (Cost=3 Card=14 Bytes=7938)
   8    6         VIEW OF 'VW_JF_SET$7FC4F042' (VIEW) (Cost=6 Card=26 Bytes=910)
   9    8           SORT (UNIQUE) (Cost=6 Card=26 Bytes=676)
  10    9             UNION-ALL
  11   10               TABLE ACCESS (FULL) OF 'SAL1' (TABLE) (Cost=3  Card=13 Bytes=338)
  12   10               TABLE ACCESS (FULL) OF 'SAL1' (TABLE) (Cost=3  Card=13 Bytes=338)

Statistics
----------------------------------------------------------
          0  recursive calls
          0  db block gets
         35  consistent gets
          0  physical reads
          0  redo size
       1563  bytes sent via SQL*Net to client
        457  bytes received via SQL*Net from client
          4  SQL*Net roundtrips to/from client
          2  sorts (memory)
          0  sorts (disk)
         39  rows processed

As you can see from query and execution plan we scanned table SAL1 three times. If we have to get report for 10 years we will have to scan SAL1 for ten times and so on.

11g SQL

SELECT a.empno,
  a.empname,
  a.job,
  c.property SALYEAR,
  c.value SAL2000TO2002
FROM emp1 a,
  (SELECT empno,
    property,
    value
  FROM sal1 unpivot exclude nulls (value FOR property IN (sal2000,sal2001,sal2002))
  ) c
WHERE a.empno=c.empno;

      EMPNO EMPNAME                        JOB                            SALYEAR SAL2000TO2002
---------- ------------------------------ ------------------------------ ------- -------------
      7369 SMITH                          CLERK                          SAL2000            10
      7369 SMITH                          CLERK                          SAL2001            11
      7369 SMITH                          CLERK                          SAL2002            12
      7499 ALLEN                          SALESMAN                       SAL2000            20
      7499 ALLEN                          SALESMAN                       SAL2001            21
      7499 ALLEN                          SALESMAN                       SAL2002            22
      7521 WARD                           SALESMAN                       SAL2000            21
      7521 WARD                           SALESMAN                       SAL2001            22
      7521 WARD                           SALESMAN                       SAL2002            23
      7654 MARTIN                         SALESMAN                       SAL2000            23
      7654 MARTIN                         SALESMAN                       SAL2001            24
      7654 MARTIN                         SALESMAN                       SAL2002            25
      7698 BLAKE                          MANAGER                        SAL2000            31
      7698 BLAKE                          MANAGER                        SAL2001            32
      7698 BLAKE                          MANAGER                        SAL2002            33
      7782 CLARK                          MANAGER                        SAL2000            31
      7782 CLARK                          MANAGER                        SAL2001            32
      7782 CLARK                          MANAGER                        SAL2002            33
      7788 SCOTT                          ANALYST                        SAL2000            25
      7788 SCOTT                          ANALYST                        SAL2001            25
      7788 SCOTT                          ANALYST                        SAL2002            25
      7839 KING                           PRESIDENT                      SAL2000           100
      7839 KING                           PRESIDENT                      SAL2001           110
      7839 KING                           PRESIDENT                      SAL2002           120
      7844 TURNER                         SALESMAN                       SAL2000            10
      7844 TURNER                         SALESMAN                       SAL2001            11
      7844 TURNER                         SALESMAN                       SAL2002            12
      7876 ADAMS                          CLERK                          SAL2000            10
      7876 ADAMS                          CLERK                          SAL2001            11
      7876 ADAMS                          CLERK                          SAL2002            12
      7900 JAMES                          CLERK                          SAL2000            17
      7900 JAMES                          CLERK                          SAL2001            18
      7900 JAMES                          CLERK                          SAL2002            19
      7902 FORD                           ANALYST                        SAL2000            25
      7902 FORD                           ANALYST                        SAL2001            26
      7902 FORD                           ANALYST                        SAL2002            27
      7934 MILLER                         CLERK                          SAL2000            10
      7934 MILLER                         CLERK                          SAL2001            11
      7934 MILLER                         CLERK                          SAL2002            12

39 rows selected.

Execution Plan
----------------------------------------------------------
   0      SELECT STATEMENT Optimizer=ALL_ROWS (Cost=13 Card=39 Bytes=23478)
   1    0   HASH JOIN (Cost=13 Card=39 Bytes=23478)
   2    1     TABLE ACCESS (FULL) OF 'EMP1' (TABLE) (Cost=3 Card=14 Bytes=7938)
   3    1     VIEW (Cost=9 Card=39 Bytes=1365)
   4    3       UNPIVOT
   5    4         TABLE ACCESS (FULL) OF 'SAL1' (TABLE) (Cost=3 Card=13 Bytes=676)

Statistics
----------------------------------------------------------
          0  recursive calls
          0  db block gets
         17  consistent gets
          0  physical reads
          0  redo size
       1654  bytes sent via SQL*Net to client
        457  bytes received via SQL*Net from client
          4  SQL*Net roundtrips to/from client
          0  sorts (memory)
          0  sorts (disk)
         39  rows processed

With unpivot function we scanned SAL1 only once and that means less resource utilization and better performance. So can we just forget about design? No as we have to maintain code every time we add another column to table. For eg we have to change code if we add sal2003, sal2004 column and so on i.e. [FROM sal1 unpivot exclude nulls (value FOR property IN (sal2000,sal2001,sal2002,sal2003,sal2004…..))]