Beacon Ora Perf Class 4

2007-11-251

Beacon Infotech Corporation

www.oracleact.com

Oracle Performance Tuning

Tamilselvan G

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Index

1. Aggregate Functions

Rollup

Cube

2 Analytic Functions

ROW_NUMBER SUM

RANK FIRST_VALUE

DENSE_RANK LAST_VALUE

COUNT Ratio_To_Report

LEAD LAG

NTILE AVG

3. Most Influential Init.ora Parameters

OPTIMIZER_INDEX_CACHING

OPTIMIZER_INDEX_COST_ADJ

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Overview of Rollup and Cube

Rollup Details:

ROLLUP's action is straightforward: it creates subtotals which "roll up" from the most

detailed level to a grand total, following a grouping list specified in the ROLLUP clause.

ROLLUP takes as its argument an ordered list of grouping columns. First, it calculates

the standard aggregate values specified in the GROUP BY clause. Then, it creates

progressively higher-level subtotals, moving from right to left through the list of grouping

columns. Finally, it creates a grand total.

CUBE Details:

CUBE takes a specified set of grouping columns and creates subtotals for all possible

combinations of them. In terms of multi-dimensional analysis, CUBE generates all the

subtotals that could be calculated for a data cube with the specified dimensions. If you

have specified CUBE(col1, col2, col3 ), the result set will include all the values that would

be included in an equivalent ROLLUP statement plus additional combinations.

Both the functions were introduced in Oracle 8.1.5

Oracle Performance Tuning 1. Rollup and Cube

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Difference between Rollup and Cube


SQL> select decode(grouping(state),1,'ALL STATES',STATE),

2 decode(grouping(product),1,'ALL PRODUCTS',PRODUCT),

3 SUM(SALES)

4 from abc_sales group by rollup(state,product)

SQL> /

DECODE(GRO DECODE(GROUP SUM(SALES)

---------- ------------ ----------

FL TV 200

FL COMP 220

FL PHONE 20

FL ALL PRODUCTS 440

GA TV 100

GA COMP 200

GA PHONE 70

GA ALL PRODUCTS 370

ALL STATES ALL PRODUCTS 810

9 rows selected.

SQL> select * from abc_sales;

SDATE ST PRODUCT SALES

-------------------- -- ---------- ----------

07-MAY-2006 21:27:18 GA TV 100

06-MAY-2006 21:27:18 GA PHONE 40

05-MAY-2006 21:27:18 GA PHONE 30

05-MAY-2006 21:27:18 GA COMP 200

04-MAY-2006 21:27:18 FL TV 200

02-MAY-2006 21:27:18 FL PHONE 20

03-MAY-2006 21:27:18 FL COMP 220

7 rows selected.

2007-11-255


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Difference between Rollup and Cube


SQL> select decode(grouping(state),1,'ALL STATES',STATE),

2 decode(grouping(product),1,'ALL PRODUCTS',PRODUCT),

3 SUM(SALES)

4 from abc_sales group by cube(state,product)

SQL> /

DECODE(GRO DECODE(GROUP SUM(SALES)

---------- ------------ ----------

ALL STATES ALL PRODUCTS 810

ALL STATES TV 300 ------- Extra row

ALL STATES COMP 420 ------- Extra row

ALL STATES PHONE 90 ------- Extra row

FL ALL PRODUCTS 440

FL TV 200

FL COMP 220

FL PHONE 20

GA ALL PRODUCTS 370

GA TV 100

GA COMP 200

GA PHONE 70

12 rows selected.

In cube, we will

get the total of

2

nd

columns

used in the

cube function.

2007-11-256


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Rollup Example


SQL> select * from cust_order where rownum < 10;

ORDER_ID ORDER_DAT CUST_ID ORDER_QTY SUPPLIED_QTY ORDER_MESS

---------- --------- ---------- ---------- ------------ ----------

1 18_JAN-05 1001 16 5 X

2 17_JAN-05 1001 14 5 X

3 01_JAN-02 1001 20 5 X

4 01_JAN-03 1001 15 5 X

5 01_JAN-04 1001 12 5 X

6 01_JAN-02 1001 17 5 X

7 12_JAN-05 1001 10 5 X

8 01_JAN-03 1001 16 5 X

9 01_JAN-02 1001 13 5 X

..

SQL> select count(*) from cust_order ;

COUNT(*)

----------

127,791

SQL> select table_name, blocks, avg_row_len from user_tables

where table_name like 'CUS%' ;

TABLE_NAME BLOCKS AVG_ROW_LEN

------------------------------ ---------- -----------------------

CUSTOMER 1 19

CUST_ORDER 14200 729

SQL> DESC CUSTOMER

Name Null? Type

-------------------------- -------------- ------------------

CUST_ID NOT NULL NUMBER(38)

CUST_FNAME VARCHAR2(20)

CUST_LNAME VARCHAR2(20)

SQL> select * from customer;

CUST_ID CUST_FNAME CUST_LNAME

---------- -------------------- --------------------

1001 SCOTT TOM

1002 PETER DAVID

1003 ELLISON BOBBY

SQL> DESC CUST_ORDER

Name Null? Type

-------------------------- -------- ------------------

ORDER_ID NUMBER(38)

ORDER_DATE DATE

CUST_ID NUMBER(38)

ORDER_QTY NUMBER(38)

SUPPLIED_QTY NUMBER(38)

ORDER_MESS CHAR(700)

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select *

from ( select /*+ ordered */

cust_fname, cust_lname, to_char(b.cust_id) as cust,

sum(order_qty) ord_tot, sum(supplied_qty) ord_supp

from cust_order a, customer b

where b.cust_id = a.cust_id

group by cust_fname, cust_lname, to_char(b.cust_id)

UNION

select /*+ Ordered */ '', '', 'Report Total' as cust,

sum(order_qty) ord_tot, sum(supplied_qty) ord_supp



)

order by 1 NULLS LAST

CUST_FNAME CUST_LNAME CUST ORD_TOT ORD_SUPP

-------------------- -------------------- ----------------- ---------- ----------

ELLISON BOBBY 1003 639105 127791

PETER DAVID 1002 638318 170388

SCOTT TOM 1001 639977 212985

Report Total 1917400 511164

# of LIO = 28,409 Elapsed Time = 1.49 Seconds

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select *

From (

select decode(grouping(cust_fname),1, '*All First Name', cust_fname) FNAME,

decode(grouping(cust_lname),1, '*All Last Name', cust_lname) LNAME,

decode(grouping(b.cust_id), 1, '*Report Total', b.cust_id) custid,

sum(order_qty) ord_tot,

sum(supplied_qty) ord_supp



group by rollup(cust_fname, cust_lname, b.cust_id)

) c

where (c.custid = '*Report Total' and

c.lname = '*All Last Name' and

c.fname = '*All First Name' ) OR

(c.custid != '*Report Total' )

FNAME LNAME CUSTID ORD_TOT ORD_SUPP

-------------------- -------------------- --------------- ---------- ----------

PETER DAVID 1002 638318 170388

SCOTT TOM 1001 639977 212985

ELLISON BOBBY 1003 639105 127791

*All First Name *All Last Name *Report Total 1917400 511164

# of LIO = 14,474 Elapsed Time = 0.70 Seconds

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Even though

I reduced the

elapsed time

by half, I am

not satisfied.

Can any one

further

reduce the

elapsed time

by half?

2007-11-259


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Overview of Analytic Functions

One of the coolest things happened in Oracle 8.1.6 was the introduction

of Analytic Function.

Prior to Oracle 8.1.6, Developers wrote complex SQL statements for some

of the following reports:

1. TOP-N Sales Reps by States

2. Ratio to Total

3. Moving Average (Moving Window calculation)

4. Accessing previous and next rows in a group

5. First/Last Row within a Group (Window)

6. Ranking and Percentile

7. Liner Regression Statistics

With the help of new Analytic Functions, today we can write more

scalable application code that not only outperform the old SQL code,

but also solve complex business requirements.

Oracle Performance Tuning 2. Analytic Functions

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How it works


Joins, WHERE, GROUP BY and Having

Partitions Created;

Analytic Function applied for each row on

each partition

Final ORDER BY

Example: Slicing the rows into 4 groups

select state, sale_amt, ntile(4) over (order by sum(sale_amt)) ntile4

from ana_sales

group by state, sale_amt

/

Output is:

ST SALE_AMT NTILE4

-- ---------- ----------

AL 0 1

FL 10 1

NY 80 1

GA 100 1

NY 120 2

GA 200 2

FL 220 2

GA 300 2

FL 330 3

GA 400 3

FL 450 3

GA 500 3

FL 510 4

NY 910 4

NY 730 4

15 rows selected.

Processing Order

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How it works


Syntax : Analytic_Function(argument1, argument2.)

OVER ( PARTITION clause ORDER BY clause WINDOW clause)

Window Clause

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How it works


Where

Analytic_Function is the name of the function such MIN, MAX,ROW_NUMBER, RANK,

DENSE_RANK etc.

Arguments can be 0,1,2,3. (No more than 3 arguments is allowed. )

query_partition_clause

Use the PARTITION BY clause to partition the query result set into groups based on one or more

value_expr. If you omit this clause, then the function treats all rows of the query result set as a

single group.

Order_by_clause

Use the order_by_clause to specify how data is ordered within a partition. For all analytic functions

except PERCENTILE_CONT and PERCENTILE_DISC (which take only a single key), you can order

the values in a partition on multiple keys, each defined by a value_expr and each qualified by an

ordering sequence.

Within each function, you can specify multiple ordering expressions. Doing so is especially useful

when using functions that rank values, because the second expression can resolve ties between

identical values for the first expression.

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How it works


windowing_clause

Some analytic functions allow the windowing_clause.

ROWS | RANGE These keywords define for each row a window (a physical or logical set of rows)

used for calculating the function result. The function is then applied to all the rows in the window.

The window moves through the query result set or partition from top to bottom.

ROWS specifies the window in physical units (rows).

RANGE specifies the window as a logical offset.

You cannot specify this clause unless you have specified the order_by_clause. Some window

boundaries defined by the RANGE clause let you specify only one expression in the

order_by_clause.

The value returned by an analytic function with a logical offset is always deterministic. However,

the value returned by an analytic function with a physical offset may produce nondeterministic

results unless the ordering expression results in a unique ordering. You may have to specify

multiple columns in the order_by_clause to achieve this unique ordering.

BETWEEN ... AND Use the BETWEEN ... AND clause to specify a start point and end point for the

window. The first expression (before AND) defines the start point and the second expression (after

AND) defines the end point.

If you omit BETWEEN and specify only one end point, then Oracle considers it the start point, and the end

point defaults to the current row.

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How it works


windowing_clause

UNBOUNDED PRECEDING Specify UNBOUNDED PRECEDING to indicate that the window starts at

the first row of the partition. This is the start point specification and cannot be used as an end point

specification.

UNBOUNDED FOLLOWING Specify UNBOUNDED FOLLOWING to indicate that the window ends at

the last row of the partition. This is the end point specification and cannot be used as a start point

specification.

CURRENT ROW As a start point, CURRENT ROW specifies that the window begins at the current

row or value (depending on whether you have specified ROW or RANGE, respectively). In this case

the end point cannot be value_expr PRECEDING.

As an end point, CURRENT ROW specifies that the window ends at the current row or value

(depending on whether you have specified ROW or RANGE, respectively). In this case the start

point cannot be value_expr FOLLOWING.

value_expr PRECEDING or value_expr FOLLOWING For RANGE or ROW:

If value_expr FOLLOWING is the start point, then the end point must be value_expr FOLLOWING.

If value_expr PRECEDING is the end point, then the start point must be value_expr PRECEDING.

If you are defining a logical window defined by an interval of time in numeric format, then you may

need to use conversion functions.

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FAQ

Frequently asked questions are:

How can I get TOP-N rows by some set of columns?

How can I get 2

nd

highest paid sales rep in our company?

How can I print total of the report along with each row?

When analytic functions were not available, these questions were

difficult to answer.

Now, Analytic Functions can answer all types of those questions

very easily.

2007-11-2516


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1.1 TOP-N Rows --- Understand the question clearly.

Consider the following request:

ABC corporation had many sales reps in many states and wanted to give bonus to its

top 3 sales reps by state.

This is a ambiguous question because of repeated values, there may be 4 or 5 Sales

Reps who all made same figures. In that situation what shall the developer do?

I can come out with many interpretations with this request:

1. What Shall I do if all the reps in a state made 0 sales? Can I include those guys in

the report?

2. List out set of sales reps who made the top 3 sales by state

3. List out up to 3 people who made the top sales. If 4 or more reps happen to make

the largest sales, the answer would be no rows. If 2 reps make the highest sales

and 2 reps make the same second highest, the answer will be only 2 reps

(highest). Or does the ABC Corp want all 4 reps?

4. Sort the sales reps by salary from greatest to lowest with in a state. List out the

first 3 reps (or less if less than 3 reps work in a state).

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1.1 TOP-N Rows --- Case 1

What Shall I do if all the reps in a state made 0 sales?

I need to know before I start writing a SQL statement. So, let us worry about it now.

1.1 TOP-N Rows - Case 2 List out set of

sales reps who made the top 3

sales by state

Let us build the table.

SQL> desc ana_sales

Name Null? Type

------------------- -------- --------------

STATE CHAR(2)

ENAME VARCHAR2(10)

SALE_AMT NUMBER(6,2)

SOMETXT1 VARCHAR2(3000)

SOMETXT2 VARCHAR2(3000)

SQL> SELECT STATE, ENAME, SALE_AMT FROM ANA_SALES;

ST ENAME SALE_AMT

-- ---------- ----------

GA Tamil 100

GA Tom 200

GA Scott 300

GA Veera 400

GA Kumar 500

FL Bush 10

FL Gore 220

FL Ellison 330

FL Gates 450

FL Ravi 510

NY Alex 80

NY Mike 120

NY Lux 730

NY Mary 730

NY Kristine 910

AL Ann 0

AL Arasu 0

AL Ram 0

AL Gopi 0

19 rows selected.

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SQL> get top-1

1 select *

2 from (select state, ename, sale_amt,

3 dense_rank()

4 over ( partition by state

5 order by sale_amt desc) dr

6 from ana_sales

7 where sale_amt > 0 )

8 where dr

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List out up to 3 people who made the top sales. If 4 or more reps happen to make

the largest sales, the answer would be no rows. If 2 reps make the highest sales

and 2 reps make the same second highest, the answer will be only 2 reps

(highest). Or does the ABC Corp want all 4 reps?

SQL> get top-3

1 select *


3 count(*)


5 order by sale_amt desc

6 range unbounded preceding) cnt

7 from ana_sales


9 where cnt

2007-11-2520


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Sort the sales reps by salary from greatest to lowest with in a state. List

out the first 3 reps (or less if less than 3 reps work in a state).

I updated the sale_amt to 500 for all guys in NY state and see what we get .

SQL> get top-4

1 select *


3 row_number()


5 order by sale_amt desc

6 ) rn

7 from ana_sales


9 where rn

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1.2 ROWNUM , RANK , and DENSE_RANK

SQL> select state, ename, sale_amt,

2 dense_rank() over ( partition by state order by sale_amt desc) dr ,

3 rank() over ( partition by state order by sale_amt desc) ra ,

4 row_number() over ( partition by state order by sale_amt desc) rn

5* from ana_sales where sale_amt > 0

6 /

ST ENAME SALE_AMT DR RA RN

-- ---------- ---------- ------- ------ --------

FL Ravi 510 1 1 1

FL Gates 450 2 2 2

FL Ellison 330 3 3 3

FL Gore 220 4 4 4

FL Bush 10 5 5 5

GA Kumar 500 1 1 1

GA Veera 400 2 2 2

GA Scott 300 3 3 3

GA Tom 200 4 4 4

GA Tamil 100 5 5 5

NY Lux 500 1 1 1

NY Mary 500 1 1 2

NY Kristine 500 1 1 3

NY Alex 300 2 4 4

NY Mike 300 2 4 5

15 rows selected.

The difference between RANK

and DENS_RANK:

RANK will skip number if

repeated values are found

within a group.

Dense_Rank will assign

continuous number.

2007-11-2522


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1.3 LAG & LEAD - Accessing Previous row and next row

Most useful analytic functions.

SQL> select rdate, faren,

2 lag(faren) over(order by rdate ) lag,

3 lead(faren) over(order by rdate ) lead

4 from ana_lag_lead

5* order by rdate

SQL> /

RDATE FAREN LAG LEAD

-------------------- ------ ---- -----

07-MAY-2006 00:00:00 55 56

08-MAY-2006 00:00:00 56 55 52

09-MAY-2006 00:00:00 52 56 53

10-MAY-2006 00:00:00 53 52 59

11-MAY-2006 00:00:00 59 53 50

12-MAY-2006 00:00:00 50 59 52

13-MAY-2006 00:00:00 52 50 51

14-MAY-2006 00:00:00 51 52 45

15-MAY-2006 00:00:00 45 51

9 rows selected.

SQL> select * from ana_lag_lead ;

RDATE FAREN

-------------------- ------

07-MAY-2006 00:00:00 55

08-MAY-2006 00:00:00 56

09-MAY-2006 00:00:00 52

10-MAY-2006 00:00:00 53

11-MAY-2006 00:00:00 59

12-MAY-2006 00:00:00 50

13-MAY-2006 00:00:00 52

14-MAY-2006 00:00:00 51

15-MAY-2006 00:00:00 45

9 rows selected.

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1.4 AVG and RANGE Average Most useful analytic functions.

SQL> select rdate, price,

2 avg(price) over(order by rdate RANGE between 1 preceding and 1 following) range_avg,

3 avg(price) over(order by rdate ROWS between 2 preceding and 2 following) rows_avg,

4 sum(price) over(order by rdate ROWS UNBOUNDED PRECEDING) CUM_SUM

5 from GOLD_PRICE

6* order by rdate

SQL> /

RDATE PRICE RANGE_AVG ROWS_AVG CUM_SUM

-------------------- ---------- --------- -------- ----------

07-MAY-2006 00:00:00 720 725.00 736.67 720

08-MAY-2006 00:00:00 730 736.67 757.50 1450

09-MAY-2006 00:00:00 760 770.00 760.00 2210

10-MAY-2006 00:00:00 820 783.33 780.00 3030

11-MAY-2006 00:00:00 770 803.33 786.00 3800

12-MAY-2006 00:00:00 820 783.33 788.00 4620

13-MAY-2006 00:00:00 760 783.33 772.00 5380

14-MAY-2006 00:00:00 770 756.67 772.50 6150

15-MAY-2006 00:00:00 740 755.00 756.67 6890

9 rows selected.

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1.5 FIRST_VALUE and LAST_VALUE

The following example illustrates the use of LAST_VALUE function:

select emplid, pay_date,

last_value(pay_date) over

(partition by emplid

order by emplid, pay_date

rows between CURRENT ROW AND UNBOUNDED FOLLOWING) AS lv

from t1 order by emplid, pay_date;

EMPLID PAY_DATE LV

A1 31-JAN-2000 31-MAY-2000

A1 28-FEB-2000 31-MAY-2000

A1 31-MAR-2000 31-MAY-2000

A1 31-MAY-2000 31-MAY-2000

B1 31-JAN-2000 31-JUL-2000

B1 31-MAR-2000 31-JUL-2000

B1 31-JUL-2000 31-JUL-2000

C1 31-JAN-2000 31-DEC-2000

C1 28-FEB-2000 31-DEC-2000

C1 31-MAR-2000 31-DEC-2000

C1 31-DEC-2000 31-DEC-2000

D1 31-JAN-2000 31-OCT-2000

D1 31-MAR-2000 31-OCT-2000

D1 31-OCT-2000 31-OCT-2000

SELECT * FROM T1 ;

EMPLID PAY_DATE

--------------- -----------

A1 31-JAN-2000

A1 28-FEB-2000

A1 31-MAR-2000

A1 31-MAY-2000

B1 31-JAN-2000

B1 31-MAR-2000

B1 31-JUL-2000

C1 31-JAN-2000

C1 28-FEB-2000

C1 31-MAR-2000

C1 31-DEC-2000

D1 31-JAN-2000

D1 31-MAR-2000

D1 31-OCT-2000

14 rows selected.

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1.6 Ratio To Report

SELECT PRODUCT, SALES ,

TO_CHAR((RATIO_TO_REPORT(SALES) OVER())*100,999.99)

AS SALES_PER

FROM SALES ;

PRODUCT SALES SALES_PER

-------------------- ---------- -------

P1 280 16.67

P2 400 23.81

P3 200 11.90

P4 300 17.86

P5 190 11.31

P6 310 18.45

SQL>DESC SALES

Name Null? Type

------------ -------- ---------------------

PRODUCT VARCHAR2(20)

SALES NUMBER

SQL>SELECT * FROM SALES ;

PRODUCT SALES

-------------------- ----------

P1 280

P2 400

P3 200

P4 300

P5 190

P6 310

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1.6 Some Practical Example - Getting 3 consecutive rows

MIKE 15-JUN-2005 00:00:00 CHEMISTRY 30

MIKE 15-JUL-2005 00:00:00 CHEMISTRY 20

MIKE 15-AUG-2005 00:00:00 CHEMISTRY 35

MIKE 15-SEP-2005 00:00:00 CHEMISTRY 26

MIKE 15-OCT-2005 00:00:00 CHEMISTRY 72

MIKE 15-NOV-2005 00:00:00 CHEMISTRY 40

MIKE 15-DEC-2005 00:00:00 CHEMISTRY 50

21 rows selected.

School Policy:

The principal of a school declared that

any student scored less than 40 in 3

consecutive months would be declared

Failed in that academic year. He asked

the teacher to prepare a list of students.

SQL> select * from marks;

NAME TEST_DATE SUBJECT MARKS

---------- -------------------- --------- ----------

DAVID 15-JUN-2005 00:00:00 CHEMISTRY 60

DAVID 15-JUL-2005 00:00:00 CHEMISTRY 30

DAVID 15-AUG-2005 00:00:00 CHEMISTRY 35

DAVID 15-SEP-2005 00:00:00 CHEMISTRY 36

DAVID 15-OCT-2005 00:00:00 CHEMISTRY 62

DAVID 15-NOV-2005 00:00:00 CHEMISTRY 50

DAVID 15-DEC-2005 00:00:00 CHEMISTRY 40

SCOTT 15-JUN-2005 00:00:00 CHEMISTRY 60

SCOTT 15-JUL-2005 00:00:00 CHEMISTRY 70

SCOTT 15-AUG-2005 00:00:00 CHEMISTRY 45

SCOTT 15-SEP-2005 00:00:00 CHEMISTRY 66

SCOTT 15-OCT-2005 00:00:00 CHEMISTRY 62

SCOTT 15-NOV-2005 00:00:00 CHEMISTRY 30

SCOTT 15-DEC-2005 00:00:00 CHEMISTRY 35

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1.6 Some Practical Example - Getting 3 consecutive rows using count()

1 select distinct name , subject

2 from (

3 select name, subject, test_date, marks,

4 count(fail_ind)

5 over(partition by name, subject order by test_date

6 rows between 2 preceding and current row) as preceding,

7 count(fail_ind)


9 rows between current row and 2 following) as following

10 from (


12 (CASE when marks < 40 then 1 END ) fail_ind

13 from marks)

14 )

15 where preceding = 3 or following = 3

16 /

NAME SUBJECT

---------- ---------

DAVID CHEMISTRY

MIKE CHEMISTRY

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SQL> select name, subject, test_date, marks,

2 count(fail_ind)


4 rows between 2 preceding and current row) as preceding,

5 count(fail_ind)


7 rows between current row and 2 following) as following

8 from (


10 (CASE when marks < 40 then 1 END ) fail_ind

11* from marks)

SQL> /

Let us see the output of the SQL statement.

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NAME SUBJECT TEST_DATE MARKS PRECEDING FOLLOWING

---------- --------- --------- ---------- ---------- ----------

DAVID CHEMISTRY 15-JUN-05 60 0 2

DAVID CHEMISTRY 15-JUL-05 30 1 3

DAVID CHEMISTRY 15-AUG-05 35 2 2

DAVID CHEMISTRY 15-SEP-05 36 3 1

DAVID CHEMISTRY 15-OCT-05 62 2 0

DAVID CHEMISTRY 15-NOV-05 50 1 0

DAVID CHEMISTRY 15-DEC-05 40 0 0

MIKE CHEMISTRY 15-JUN-05 30 1 3

MIKE CHEMISTRY 15-JUL-05 20 2 3

MIKE CHEMISTRY 15-AUG-05 35 3 2

MIKE CHEMISTRY 15-SEP-05 26 3 1

MIKE CHEMISTRY 15-OCT-05 72 2 0

MIKE CHEMISTRY 15-NOV-05 40 1 0

MIKE CHEMISTRY 15-DEC-05 50 0 0

SCOTT CHEMISTRY 15-JUN-05 60 0 0

SCOTT CHEMISTRY 15-JUL-05 70 0 0

SCOTT CHEMISTRY 15-AUG-05 45 0 0

SCOTT CHEMISTRY 15-SEP-05 66 0 1

SCOTT CHEMISTRY 15-OCT-05 62 0 2

SCOTT CHEMISTRY 15-NOV-05 30 1 2

SCOTT CHEMISTRY 15-DEC-05 35 2 1

21 rows selected.

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1.6 Some Practical Example - Getting 3 consecutive rows using lag and lead

1 select distinct name, subject

2 from (


4 lag(marks,1) over (partition by name, subject order by test_date) pr_marks,

5 lag(marks,2) over (partition by name, subject order by test_date) pr_to_pr_marks,

6 lead(marks,1) over (partition by name, subject order by test_date) ne_marks,

7 lead(marks,2) over (partition by name, subject order by test_date) ne_to_ne_marks

8 from marks

9 )

10 where (marks < 40 and pr_marks < 40 and pr_to_pr_marks < 40)

11 OR

12 (marks < 40 and ne_marks < 40 and ne_to_ne_marks < 40)

13 /

NAME SUBJECT

---------- ---------

DAVID CHEMISTRY

MIKE CHEMISTRY

2007-11-2531


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1.7 Other Functions available

1. CUME_DIST - The CUME_DIST function (defined as the inverse of percentile in some

statistical books) computes the position of a specified value relative to a set of values. The

order can be ascending or descending. Ascending is the default. The range of values for

CUME_DIST is from greater than 0 to 1. To compute the CUME_DIST of a value x in a set S

of size N, you use the formula:

2 PERCENT_RANK - PERCENT_RANK is similar to CUME_DIST, but it uses rank values

rather than row counts in its numerator. Therefore, it returns the percent rank of a value

relative to a group of values. The function is available in many popular spreadsheets.

PERCENT_RANK of a row is calculated as:

(rank of row in its partition - 1) / (number of rows in the partition - 1)

3 Linear Regression Functions

The regression functions support the fitting of an ordinary-least-squares regression line to a set of

number pairs. You can use them as both aggregate functions or windowing or reporting

functions.

The functions are as follows:

REGR_COUNT Function , REGR_AVGY and REGR_AVGX Functions

REGR_SLOPE and REGR_INTERCEPT Functions , REGR_R2 Function

REGR_SXX, REGR_SYY, and REGR_SXY Functions

4. A LOT OF STATISTICAL Functions MEDIAN, STATS_MOD, ANOVA, COVAR, STD_DEV etc

2007-11-2532


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Oracle Performance Tuning 3. Most Influential Init.ora Parameters

OPTIMIZER_INDEX_CACHING and OPTIMIZER_INDEX_COST_ADJ are the most important

parameters among all the init.ora parameters. The Cost based optimizer extensively uses

these 2 parameters values during the cost calculation for arriving the optimum execution

plan.

Depending upon the type of the work (OLTP, DSS , DW) , you can change these values either at the

instance level or at the session level, and the impact is highly visible.

Oracle Database Performance Tuning Guide 10g Release 2 (10.2) Part Number B14211-01

OPTIMIZER_INDEX_CACHING

This parameter controls the costing of an index probe in conjunction with a nested loop. The

range of values 0 to 100 for OPTIMIZER_INDEX_CACHING indicates percentage of index

blocks in the buffer cache, which modifies the optimizer's assumptions about index caching

for nested loops and IN-list iterators. A value of 100 infers that 100% of the index blocks are

likely to be found in the buffer cache and the optimizer adjusts the cost of an index probe or

nested loop accordingly. Use caution when using this parameter because execution plans

can change in favor of index caching.

OPTIMIZER_INDEX_COST_ADJ

This parameter can be used to adjust the cost of index probes. The range of values is 1 to 10000.

The default value is 100, which means that indexes are evaluated as an access path based on

the normal costing model. A value of 10 means that the cost of an index access path is one-

tenth the normal cost of an index access path.

2007-11-2533


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It is very easy to understand the impact of the first parameter, OPTIMIZER_INDEX_CACHING. It

tells the optimizer what percentage of index blocks are available in the buffer cache. And it

directly affects the cost calculation.

However, the 2nd parameter, OPTIMIZER_INDEX_COST_ADJ confuses many DBAs and

developers because the range of values accepted is 1 through 10000. Many experts interpret

the meaning in a different way. Any change in OPTIMIZER_INDEX_COST_ADJ results in

table access cost being scaled down (up) to the current value divided by 100.

In other words, I would say, if the value is less than 100, then the Optimizer may choose

indexed access path OR if the value is equal to 100 (or greater than 100), then Optimizer

may choose FULL TABLE SCAN access path.

You can also interpret the meaning in this way: low value for OPTIMIZER_INDEX_COST_ADJ

indicates single block read time is cheaper; high value for OPTIMIZER_INDEX_COST_ADJ

indicates single block read time costlier.

1.10..20..30....100200..50010004000.800010000

Note:

If OPTIMIZER_INDEX_CACHING is for index blocks, then

OPTIMIZER_INDEX_COST_ADJ is for table blocks available in the buffer cache.

2007-11-2534


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SQL> desc state

Name Null? Type

--------------------- -------- ------------------

STATEID NOT NULL CHAR(2)

NAME VARCHAR2(30)

SOMETXT VARCHAR2(3900)

SQL> select stateid, name from state ;

ST NAME

-- ------------------------------

GA Georgia

CA California

NY Newyork

AL Alabama

TX Texas

MA Massachucsetts

SC South Carolina

NC North Carolina

AZ Arizona

9 rows selected. ------------ Small Table to filter rows

based on the STATE code

Note: All the tests are done in 9i. You may get different

execution plans in your system because the cost is

affected by many parameters settings.

Among the 2 parameters, Optimizer_Index_Cost_Adj is

more aggressive than OPIMIZER_INDEX_CACHING.

First let us set up 2 tables to test the theory.

SQL> desc sales

Name Null? Type

----------------- -------- ------------------

CUST_ID NOT NULL NUMBER(12)

STATE NOT NULL CHAR(2)

SALE_YEAR NUMBER(4)

SALE_MONTH NUMBER(2)

PROD_ID NUMBER

QTY NUMBER

AMT NUMBER

COMM_1 VARCHAR2(50)

COMM_2 VARCHAR2(200)

SQL> select /*+ full(a) parallel(a,16) */ count(*) from sales a ;

COUNT(*)

----------

14,336,064 ----------- Big table to test different values.

2007-11-2535


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Case 1

Let us first see what will be the cost for a query with default values of 0 and 100 for

optimizer_index_caching and optimizer_index_cost_adj respectively.

SQL> alter session set optimizer_index_caching = 0;

Session altered.

SQL> alter session set optimizer_index_cost_adj = 100 ;

Session altered.

SQL> explain plan for

2 select a.cust_id , b.name

3 from sales a, state b

4 where a.state = b.stateid and b.stateid = 'GA' ;

Explained.

SQL> select * from table(dbms_xplan.display);

PLAN_TABLE_OUTPUT

----------------------------------------------------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost |

-----------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 573K | 15M | 45551 |

| 1 | NESTED LOOPS | | 573K | 15M | 45551 |

| 2 | TABLE ACCESS BY INDEX ROWID | STATE | 1 | 21 | 1 |

|* 3 | INDEX UNIQUE SCAN | STATE_PK | 1 | | |

|* 4 | TABLE ACCESS FULL | SALES | 573K | 4480K | 45550 |

---------------------------------------------------------------------------------------------------------------------

So, the total cost

of the query is

45,551 IO. Note

also that when

CPU costing is

off, then the

COST is nothing

but total number

of IO.

This is our base

line data that will

be compared

with the

remaining tests.

2007-11-2536


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Case 2

I changed the OPTIMIZER_INDEX_COST_ADJ to 10 but kept the default value for OPTIMIZER_INDEX_CACHING.

SQL> alter session set optimizer_index_caching = 0 ;

Session altered.


Session altered.


select a.cust_id , b.name from sales a, state b

where a.state = b.stateid and b.stateid = 'GA' ;

Explained.


PLAN_TABLE_OUTPUT

--------------------------------------------------------------------------------------------------------------------------------------------

| Id | Operation | Name | Rows | Bytes | Cost (%CPU)|

---------------------------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 573K| 15M | 37683 (1) |

| 1 | NESTED LOOPS | | 573K| 15M | 37683 (1) |

| 2 | TABLE ACCESS BY INDEX ROWID | STATE | 1 | 21 | 2 (50) |


| 4 | TABLE ACCESS BY INDEX ROWID | SALES | 573K | 4480K | 37682 (1) |

|* 5 | INDEX RANGE SCAN | SALES_IDX_2 | 573K | | 30095 (0) |

------------------------------------------------------------------------------------------------------------------------------------------

The Query plan is completely changed from full table scan to indexed access on the SALES

table. The total cost of the query came down to 37,683 from 45,551, the TABLE ACCESS BY

INDEX ROWID cost is 37,682 that includes the cost of INDEX RANGE SCAN for

SALES_IDX_2 30,095.

2007-11-2537


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Case 3

In real life situation some amount of index leaf blocks are always cached in the

memory.

Now I tell Oracle that 50 % of index blocks are cached in the SGA by changing the

OPTIMIZER_INDEX_CACHING parameter.


Session altered.


Session altered.





Explained.


PLAN_TABLE_OUTPUT

---------------------------------------------------------------------------------------------------------------------------


----------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 573K| 15M | 36207 (0) |

| 1 | NESTED LOOPS | | 573K| 15M | 36207 (0) |



| 4 | TABLE ACCESS BY INDEX ROWID | SALES | 573K| 4480K | 36206 (0) |

|* 5 | INDEX RANGE SCAN | SALES_IDX_2 | 573K| | 15344 (0) |

---------------------------------------------------------------------------------------------------------------------------

In Case -2 , the cost

for INDEX RANGE

SCAN for

SALES_IDX_2 was

30095, after

changing the

parameter

OPTIMIZER_INDEX_

CACHING to 50, it

came to 15344, just

half of what it was

before.

The TABLE

ACCESS BY INDEX

ROWID for SALES

table came down to

36206 from 37682.

Finally the total

cost came down to

36,207 from 37,683.

2007-11-2538


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Case 4

If you further increase the parameter OPTIMIZER_INDEX_CACHING, then the cost of INDEX SCAN will further

come down.

Let us test this concept with a new value, 90.


Session altered.


Session altered.





Explained.


PLAN_TABLE_OUTPUT

------------------------------------------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------------------------------------------

| 0 | SELECT STATEMENT | | 573K | 15M | 34980 (0) |

| 1 | NESTED LOOPS | | 573K | 15M | 34980 (0) |



| 4 | TABLE ACCESS BY INDEX ROWID | SALES | 573K | 4480K | 34979 (0) |

|* 5 | INDEX RANGE SCAN | SALES_IDX_2 | 573K | | 3069 (0) |

--------------------------------------------------------------------------------------------------------------------------------------

The cost of INDEX RANGE SCAN has been reduced to 3069.

2007-11-2539


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In conclusion

When the SYSTEM statistics is off:

One thing you should remember, the above tests and various costs obtained did not

tell you which query will run faster.

I just demonstrated the impact of changing those 2 parameters.

There is no rule of thumb to fix those 2 values. How ever, One thing is sure that the

default value 0 and 100 for OPTIMIZER_INDEX_CACHING and

OPTIMIZER_INDEX_COST_ADJ respectively are set for Data Warehouse System.

If your system is OLTP, then these 2 parameters values should be changed. I would

test the sytem with different values before choosing the correct values.

Setting 90 to OPTIMIZER_INDEX_CACHING and 10 (or 15) to

OPTIMIZER_INDEX_COST_ADJ will perform good for OLTP system.

When the SYSTEM statistics is on:

Starting from 9i and 10g You can collect system statistics (CPU & IO). When those

statistics are available, then the optimizer uses those statistics in the execution

plan and ignores OPTIMIZER_INDEX_CACHING and

OPTIMIZER_INDEX_COST_ADJ values.

2007-11-2540


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