Oracle Database Advanced Querying

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Oracle Database
Advanced Querying
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Zohar Elkayam
CTO, Brillix
Zohar@Brillix.co.il
www.realdbamagic.com
Twitter: @realmgic

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• Zohar Elkayam, CTO/COO at Brillix-DBAces
• Oracle ACE Associate
• DBA, team leader, Oracle University instructor, public
speaker, and a senior consultant for over 18 years
• Editor of ilDBA – Israel Database Community website
• Blogger – www.realdbamagic.com
Who am I?

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About Brillix

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• Aggregative and advanced grouping options
• Analytic functions, ranking and pagination
• Hierarchical and recursive queries
• Oracle 12c new rows pattern matching feature
• XML and JSON handling with SQL
• Regular Expressions
• SQLcl – a new replacement tool for SQL*Plus
from Oracle
Agenda

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•‫הספר‬"Oracle SQL–‫יכולות‬
‫מתקדמות‬,‫לשולף‬ ‫מדריך‬
‫המהיר‬"‫ב‬ ‫פורסם‬-2011
•‫ה‬ ‫ספר‬ ‫זה‬-SQL‫הראשון‬
‫בעברית‬ ‫שנכתב‬ ‫והיחיד‬
‫סופו‬ ‫ועד‬ ‫מתחילתו‬
•‫עמיאל‬ ‫ידי‬ ‫על‬ ‫נכתב‬ ‫הספר‬
‫שלי‬ ‫טכנית‬ ‫עריכה‬ ‫ועבר‬ ‫דיוויס‬
‫אודות‬Oracle SQL–‫מתקדמות‬ ‫יכולות‬

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The REAL Agenda
10:30-10:45‫הפסקה‬
12:30-13:30‫משתתפ‬ ‫לכל‬ ‫צהריים‬ ‫ארוחת‬‫הכנס‬ ‫י‬
15:00-15:15‫מתוקה‬ ‫הפסקה‬
16:30‫הביתה‬ ‫הולכים‬

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• SQL was invented in 1970 by Dr. E. F. Codd
• Each vendor had its own flavor of SQL
• Standardized by ASNI since 1986
• Current stable standard is ANSI SQL:2011/2008
• Oracle 12c is fully compliant to CORE SQL:2011
• Oracle 11g is compliant to SQL:2008
ANSI SQL

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• In this seminar we will only talk about Queries
Queries

Group Functions
More than just group by…
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• Using SQL for aggregation:
– Group functions basics
– The CUBE and ROLLUP extensions to the GROUP
BY clause
– The GROUPING functions
– The GROUPING SETS expression
• Working with composite columns
• Using concatenated groupings
Group Function and SQL

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• Group functions will return a single row for
each group
• The group by clause groups rows together and
allows group functions to be applied
• Common group functions: SUM, MIN, MAX,
AVG, etc.
Basics

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Group Functions Syntax
SELECT [column,] group_function(column). . .
FROM table
[WHERE condition]
[GROUP BY group_by_expression]
[ORDER BY column];
SELECT AVG(salary), STDDEV(salary),
COUNT(commission_pct),MAX(hire_date)
FROM hr.employees
WHERE job_id LIKE 'SA%';

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SELECT department_id, job_id, SUM(salary),
COUNT(employee_id)
FROM hr.employees
GROUP BY department_id, job_id
Order by department_id;
The GROUP BY Clause
SELECT [column,] group_function(column)
FROM table
[WHERE condition]
[ORDER BY column];

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• Use the HAVING clause to specify which
groups are to be displayed
• You further restrict the groups on the basis of
a limiting condition
The HAVING Clause
SELECT [column,] group_function(column)...
FROM table
[WHERE condition]
[HAVING having_expression]
[ORDER BY column];

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• Use ROLLUP or CUBE with GROUP BY to
produce superaggregate rows by cross-
referencing columns
• ROLLUP grouping produces a result set
containing the regular grouped rows and the
subtotal and grand total values
• CUBE grouping produces a result set
containing the rows from ROLLUP and cross-
tabulation rows
GROUP BY with the
ROLLUP and CUBE Operators

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• ROLLUP is an extension of the GROUP BY
clause
• Use the ROLLUP operation to produce
cumulative aggregates, such as subtotals
Using the ROLLUP Operator
SELECT [column,] group_function(column). . .
FROM table
[WHERE condition]
[GROUP BY [ROLLUP] group_by_expression]
[HAVING having_expression];
[ORDER BY column];

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Using the ROLLUP Operator: Example
SELECT department_id, job_id, SUM(salary)
FROM hr.employees
WHERE department_id < 60
GROUP BY ROLLUP(department_id, job_id);
1
2
3
Total by DEPARTMENT_ID
and JOB_ID
Grand total

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• CUBE is an extension of the GROUP BY clause
• You can use the CUBE operator to produce
cross-tabulation values with a single SELECT
statement
Using the CUBE Operator
SELECT [column,] group_function(column)...
FROM table
[WHERE condition]
[GROUP BY [CUBE] group_by_expression]
[ORDER BY column];

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1
2
3
4
Grand total
Total by JOB_ID
and JOB_ID
SELECT department_id, job_id, SUM(salary)
FROM hr.employees
GROUP BY CUBE (department_id, job_id);
. . .
Using the CUBE Operator: Example

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SELECT [column,] group_function(column) .. ,
GROUPING(expr)
FROM table
[WHERE condition]
[GROUP BY [ROLLUP][CUBE] group_by_expression]
[ORDER BY column];
• The GROUPING function:
– Is used with the CUBE or ROLLUP operator
– Is used to find the groups forming the subtotal in a row
– Is used to differentiate stored NULL values from NULL
values created by ROLLUP or CUBE
– Returns 0 or 1
Working with the GROUPING Function

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SELECT department_id DEPTID, job_id JOB,
SUM(salary),
GROUPING(department_id) GRP_DEPT,
GROUPING(job_id) GRP_JOB
FROM hr.employees
GROUP BY ROLLUP(department_id, job_id);
Working with the GROUPING Function: Example
1
2
3

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• Extension to the GROUPING function
• GROUPING_ID returns a number
corresponding to the GROUPING bit vector
associated with a row
• Useful for understanding what level the row is
aggregated at and filtering those rows
Working with GROUPING_ID Function

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GROUPING_ID Function Example
SUM(salary),
GROUPING_ID(department_id,job_id) GRP_ID
FROM hr.employees
GROUP BY CUBE(department_id, job_id);
DEPTID JOB SUM(SALARY) GRP_ID
---------- ---------- ----------- ----------
48300 3
MK_MAN 13000 2
MK_REP 6000 2
PU_MAN 11000 2
AD_ASST 4400 2
PU_CLERK 13900 2
10 4400 1
10 AD_ASST 4400 0
20 19000 1
20 MK_MAN 13000 0
20 MK_REP 6000 0
30 24900 1
30 PU_MAN 11000 0
30 PU_CLERK 13900 0

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• GROUP_ID distinguishes duplicate groups
resulting from a GROUP BY specification
• A Unique group will be assigned 0, the non
unique will be assigned 1 to n-1 for n
duplicate groups
• Useful in filtering out duplicate groupings
from the query result
Working with GROUP_ID Function

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GROUP_ID Function Example
SUM(salary),
GROUP_ID() UNIQ_GRP_ID
FROM hr.employees
GROUP BY department_id, CUBE(department_id, job_id);
DEPTID JOB SUM(SALARY) UNIQ_GRP_ID
---------- ---------- ----------- -----------
10 AD_ASST 4400 0
20 MK_MAN 13000 0
20 MK_REP 6000 0
30 PU_MAN 11000 0
30 PU_CLERK 13900 0
10 AD_ASST 4400 1
20 MK_MAN 13000 1
20 MK_REP 6000 1
30 PU_MAN 11000 1
30 PU_CLERK 13900 1
10 4400 0
20 19000 0
30 24900 0
10 4400 1
20 19000 1
30 24900 1

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• The GROUPING SETS syntax is used to define
multiple groupings in the same query.
• All groupings specified in the GROUPING SETS
clause are computed and the results of individual
groupings are combined with a UNION ALL
operation.
• Grouping set efficiency:
– Only one pass over the base table is required.
– There is no need to write complex UNION statements.
– The more elements GROUPING SETS has, the
greater the performance benefit.
GROUPING SETS

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SELECT department_id, job_id,
manager_id, AVG(salary)
FROM hr.employees
GROUP BY GROUPING SETS
((department_id,job_id), (job_id,manager_id));
GROUPING SETS: Example
. . .
. . .
1
2

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• A composite column is a collection of columns
that are treated as a unit.
ROLLUP (a,(b,c), d)
• Use parentheses within the GROUP BY clause to
group columns, so that they are treated as a unit
while computing ROLLUP or CUBE operators.
• When used with ROLLUP or CUBE, composite
columns require skipping aggregation across
certain levels.
Composite Columns

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SELECT department_id, job_id, manager_id,
SUM(salary)
FROM hr.employees
GROUP BY ROLLUP( department_id,(job_id, manager_id));
Composite Columns: Example
…
1
2
3
4

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• Concatenated groupings offer a concise way to
generate useful combinations of groupings.
• To specify concatenated grouping sets, you separate
multiple grouping sets, ROLLUP, and CUBE operations
with commas so that the Oracle server combines them
into a single GROUP BY clause.
• The result is a cross-product of groupings from each
GROUPING SET.
Concatenated Groupings
GROUP BY GROUPING SETS(a, b), GROUPING SETS(c, d)

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SELECT department_id, job_id, manager_id,
SUM(salary)
FROM hr.employees
GROUP BY department_id,
ROLLUP(job_id),
CUBE(manager_id);
Concatenated Groupings: Example
…
…
…
1
3
4
5
6
2
7
…
…

Pivot and Unpivot
Turning things around!
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PIVOT and UNPIVOT Clauses
of the SELECT Statement
• You can use the PIVOT operator of the
SELECT statement to write cross-tabulation
queries that rotate the column values into new
columns, aggregating data in the process.
• You can use the UNPIVOT operator of the
SELECT statement to rotate columns into
values of a column.
PIVOT UNPIVOT

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Pivoting on the QUARTER
Column: Conceptual Example
30,000
40,000
60,000
30,000
40,000
20,000
AMOUNT_
SOLD
2,500Q1IUSAKids Jeans
2,000Q2CJapanKids Jeans
2,000Q3SUSAShorts
I
P
C
CHANNEL
Kids Jeans
Shorts
Shorts
PRODUCT
1,000Q2Germany
1,500Q4USA
Q2
QUARTER
2,500Poland
QUANTITY_
SOLD
COUNTRY
2,000
Q3
Kids Jeans
Shorts
PRODUCT
3,500
2,000
Q2
1,5002,500
Q4Q1

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• Pivoting the data before 11g was a complex
query which required the use of the CASE or
DECODE functions
Pivoting Before Oracle 11g
select product,
sum(case when quarter = 'Q1' then amount_sold else null end) Q1,
sum(case when quarter = 'Q4' then amount_sold else null end) Q4
from sales
group by product;

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Pivot Clause Syntax
table_reference PIVOT [ XML ]
( aggregate_function ( expr ) [[AS] alias ]
[, aggregate_function ( expr ) [[AS] alias ] ]...
pivot_for_clause
pivot_in_clause )
-- Specify the column(s) to pivot whose values are to
-- be pivoted into columns.
pivot_for_clause =
FOR { column |( column [, column]... ) }
-- Specify the pivot column values from the columns you
-- specified in the pivot_for_clause.
pivot_in_clause =
IN ( { { { expr | ( expr [, expr]... ) } [ [ AS] alias] }...
| subquery | { ANY | ANY [, ANY]...} } )

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Creating a New View: Example
CREATE OR REPLACE VIEW sales_view AS
SELECT
prod_name AS product,
country_name AS country,
channel_id AS channel,
SUBSTR(calendar_quarter_desc, 6,2) AS quarter,
SUM(amount_sold) AS amount_sold,
SUM(quantity_sold) AS quantity_sold
FROM sales, times, customers, countries, products
WHERE sales.time_id = times.time_id AND
sales.prod_id = products.prod_id AND
sales.cust_id = customers.cust_id AND
customers.country_id = countries.country_id
GROUP BY prod_name, country_name, channel_id,
SUBSTR(calendar_quarter_desc, 6, 2);

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Selecting the SALES VIEW Data
SELECT product, country, channel, quarter, quantity_sold
FROM sales_view;
PRODUCT COUNTRY CHANNEL QUARTER QUANTITY_SOLD
------------ ------------ ---------- -------- -------------
Y Box Italy 4 01 21
Y Box Italy 4 02 17
Y Box Italy 4 03 20
. . .
Y Box Japan 2 01 35
Y Box Japan 2 02 39
Y Box Japan 2 03 36
Y Box Japan 2 04 46
Y Box Japan 3 01 65
. . .
Bounce Italy 2 01 34
Bounce Italy 2 02 43
. . .
9502 rows selected.

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Pivoting the QUARTER Column
in the SH Schema: Example
SELECT *
FROM
(SELECT product, quarter, quantity_sold
FROM sales_view) PIVOT (sum(quantity_sold)
FOR quarter IN ('01', '02', '03', '04'))
ORDER BY product DESC;
. . .

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Unpivoting the QUARTER Column:
Conceptual Example
2,000
Q3
Kids Jeans
Shorts
PRODUCT
3,500
2,000
Q2
1,5002,500
Q4Q1
2,500Q1Kids Jeans
2,000Q2Kids Jeans
3,500Q2Shorts
1,500Q4Kids Jeans
Q3
QUARTER
2,000Shorts
SUM_OF_QUANTITYPRODUCT

4848
• Univoting the data before 11g requires
multiple queries on the table using the
UNION ALL operator
Unpivoting Before Oracle 11g
SELECT *
FROM (
SELECT product, '01' AS quarter, Q1_value FROM sales
UNION ALL
UNION ALL
UNION ALL
);

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• An UNPIVOT operation does not reverse a
PIVOT operation; instead, it rotates data found
in multiple columns of a single row into multiple
rows of a single column.
• If you are working with pivoted data, UNPIVOT
cannot reverse any aggregations that have been
made by PIVOT or any other means.
Using the UNPIVOT Operator
UNPIVOT

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• The UNPIVOT clause rotates columns from a
previously pivoted table or a regular table into
rows. You specify:
– The measure column or columns to be unpivoted
– The name or names for the columns that result from
the UNPIVOT operation
– The columns that are unpivoted back into values of
the column specified in pivot_for_clause
• You can use an alias to map the column name to
another value.
Using the UNPIVOT Clause

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UNPIVOT Clause Syntax
table_reference UNPIVOT [{INCLUDE|EXCLUDE} NULLS]
-- specify the measure column(s) to be unpivoted.
( { column | ( column [, column]... ) }
unpivot_for_clause
unpivot_in_clause )
-- Specify one or more names for the columns that will
-- result from the unpivot operation.
unpivot_for_clause =
FOR { column | ( column [, column]... ) }
-- Specify the columns that will be unpivoted into values of
-- the column specified in the unpivot_for_clause.
unpivot_in_clause =
( { column | ( column [, column]... ) }
[ AS { constant | ( constant [, constant]... ) } ]
[, { column | ( column [, column]... ) }
[ AS { constant | ( constant [, constant]...) } ] ]...)

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Creating a New Pivot Table: Example
. . .
CREATE TABLE pivotedtable AS
SELECT *
FROM
(SELECT product, quarter, quantity_sold
FROM sales_view) PIVOT (sum(quantity_sold)
FOR quarter IN ('01' AS Q1, '02' AS Q2,
'03' AS Q3, '04' AS Q4));
SELECT * FROM pivotedtable
ORDER BY product DESC;

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Unpivoting the QUARTER Column
in the SH Schema: Example
SELECT *
FROM pivotedtable
UNPIVOT (quantity_sold For Quarter IN (Q1, Q2, Q3, Q4))
ORDER BY product DESC, quarter;
. . .

5454
• More information and examples could be
found on my Blog:
http://www.realdbamagic.com/he/pivot-a-table/
More Examples…

Analytic Functions
Let’s analyze our data!
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• Oracle has enhanced SQL's analytical processing
capabilities by introducing a new family of analytic SQL
functions.
• These analytic functions enable you to calculate and
perform:
– Rankings and percentiles
– Pivoting operations
– Moving window calculations
– LAG/LEAD analysis
– FIRST/LAST analysis
– Linear regression statistics
Overview of SQL for Analysis and Reporting

5757
• Ability to see one row from another row in the
results
• Avoid self-join queries
• Summary data in detail rows
• Slice and dice within the results
Why Use Analytic Functions?

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Using the Analytic Functions
Function type Used for
Ranking Calculating ranks, percentiles, and n-tiles of the values in a
result set
Windowing Calculating cumulative and moving aggregates, works with
functions such as SUM, AVG, MIN, and so on
Reporting Calculating shares such as market share, works with
functions such as SUM, AVG, MIN, MAX, COUNT, VARIANCE,
STDDEV, RATIO_TO_REPORT, and so on
LAG/LEAD Finding a value in a row or a specified number of rows
from a current row
FIRST/LAST First or last value in an ordered group
Linear Regression Calculating linear regression and other statistics

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• Result set partitions: These are created and available
to any aggregate results such as sums and averages.
The term “partitions” is unrelated to the table
partitions feature.
• Window: For each row in a partition, you can define a
sliding window of data, which determines the range of
rows used to perform the calculations for the current
row.
• Current row: Each calculation performed with an
analytic function is based on a current row within a
partition. It serves as the reference point determining
the start and end of the window.
Concepts Used in Analytic Functions

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• We can use aggregative functions as analytic
functions (i.e. SUM, AVG, MIN, MAX, COUNT
etc.)
• Each row will get the aggregative value for a
given partition without the need for group by
clause
Reporting Functions

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• We can have multiple group by on the same row
• Getting the raw data along with the aggregated
value
Reporting Example
SELECT last_name, salary,
ROUND(AVG(salary) OVER (PARTITION BY department_id),2),
COUNT(*) OVER (PARTITION BY manager_id),
SUM(salary) OVER (PARTITION BY department_id ORDER BY salary),
MAX(salary) OVER ()
FROM hr.employees;

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• A ranking function computes the rank of a
record compared to other records in the data
set based on the values of a set of measures.
The types of ranking function are:
– RANK and DENSE_RANK functions
– PERCENT_RANK function
– ROW_NUMBER function
– NTILE function
– CUME_DIST function
Using the Ranking Functions

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• The RANK function calculates the rank of a value in a group
of values, which is useful for top-N and bottom-N reporting.
• For example, you can use the RANK function to find the top
ten products sold in Boston last year.
• When using the RANK function, ascending is the default
sort order, which you can change to descending.
• Rows with equal values for the ranking criteria receive the
same rank.
• Oracle Database then adds the number of tied rows to the
tied rank to calculate the next rank.
Working with the RANK Function
RANK ( ) OVER ( [query_partition_clause] order_by_clause )

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Using the RANK Function: Example
SELECT department_id, last_name, salary,
RANK() OVER (PARTITION BY department_id
ORDER BY salary DESC) "Rank"
FROM employees
WHERE department_id = 60
ORDER BY department_id, "Rank", salary;

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• The RANK function can be made to operate
within groups—that is, the rank gets reset
whenever the group changes.
• This is accomplished with the PARTITION BY
clause.
• The group expressions in the PARTITION BY
subclause divide the data set into groups within
which RANK operates.
• For example, to rank products within each
channel by their dollar sales, you could issue a
statement similar to the one in the next slide.
Per-Group Ranking

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Per-Group Ranking: Example
SELECT channel_desc, calendar_month_desc, TO_CHAR(SUM(amount_sold),
'9,999,999,999') SALES$, RANK() OVER (PARTITION BY channel_desc
ORDER BY SUM(amount_sold) DESC) AS RANK_BY_CHANNEL
FROM sales, products, customers, times, channels
WHERE sales.prod_id = products.prod_id
AND sales.cust_id = customers.cust_id
AND sales.time_id = times.time_id
AND sales.channel_Id = channels.channel_id
AND times.calendar_month_desc IN ('2000-08', '2000-09', '2000-
10', '2000-11')
AND channels.channel_desc IN ('Direct Sales', 'Internet')
GROUP BY channel_desc, calendar_month_desc;

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RANK and DENSE_RANK Functions: Example
RANK() OVER (PARTITION BY department_id
ORDER BY salary DESC) "Rank",
DENSE_RANK() over (partition by department_id
ORDER BY salary DESC) "Drank"
FROM employees
WHERE department_id = 60
ORDER BY department_id, last_name, salary DESC, "Rank"
DESC;
DENSE_RANK ( ) OVER ([query_partition_clause] order_by_clause)

70
Per-Cube and Rollup Group Ranking
SELECT channel_desc, country_iso_code,
TO_CHAR(SUM(amount_sold), '9,999,999,999')SALES$,
RANK() OVER
(PARTITION BY GROUPING_ID(channel_desc, country_iso_code)
ORDER BY SUM(amount_sold) DESC) AS RANK_PER_GROUP
FROM sales, customers, times, channels, countries
WHERE sales.time_id = times.time_id AND
sales.cust_id=customers.cust_id AND
sales.channel_id = channels.channel_id AND
channels.channel_desc IN ('Direct Sales', 'Internet') AND
times.calendar_month_desc='2000-09' AND
country_iso_code IN ('GB', 'US', 'JP')
GROUP BY CUBE(channel_desc, country_iso_code);

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• Uses rank values in its numerator and returns the
percent rank of a value relative to a group of values
• PERCENT_RANK of a row is calculated as follows:
• The range of values returned by PERCENT_RANK is 0
to 1, inclusive. The first row in any set has a
PERCENT_RANK of 0. The return value is NUMBER. Its
syntax is:
Using the PERCENT_RANK Function
(rank of row in its partition - 1) / (number of rows in
the partition - 1)
PERCENT_RANK () OVER ([query_partition_clause]
order_by_clause)

72
Using the PERCENT_RANK Function: Example
SELECT department_id, last_name, salary, PERCENT_RANK()
OVER (PARTITION BY department_id ORDER BY salary DESC)
AS pr
FROM hr.employees
ORDER BY department_id, pr, salary;
. . .

73
• The ROW_NUMBER function calculates a
sequential number of a value in a group of
values.
• When using the ROW_NUMBER function,
ascending is the default sort order, which you
can change to descending.
• Rows with equal values for the ranking criteria
receive a different number.
Working with the ROW_NUMBER Function
ROW_NUMBER ( ) OVER ( [query_partition_clause] order_by_clause )

7474
• ROWNUM is a pseudo column, ROW_NUMBER
is an actual function
• ROWNUM requires sorting of the entire dataset
in order to return ordered list
• ROW_NUMBER will only sort the required rows
thus giving better performance
ROW_NUMBER vs. ROWNUM

7575
• Not really a rank function
• Divides an ordered data set into a number of
buckets indicated by expr and assigns the
appropriate bucket number to each row
• The buckets are numbered 1 through expr
Working with the NTILE Function
NTILE ( expr ) OVER ([query_partition_clause] order_by_clause)

7676
• Different ranking functions may return
different results if the data has ties
• For example:
Summary of Ranking Functions
SELECT last_name, salary,
ROW_NUMBER() OVER (PARTITION BY department_id ORDER BY salary DESC),
RANK() OVER (PARTITION BY department_id ORDER BY salary DESC),
DENSE_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC),
PERCENT_RANK() OVER (PARTITION BY department_id ORDER BY salary DESC),
NTILE(4) OVER (PARTITION BY department_id ORDER BY salary DESC)
FROM hr.employees;

Inter-row Analytic Functions
77

78
• LAG provides access to more than one row of a table
at the same time without a self-join.
• Given a series of rows returned from a query and a
position of the cursor, LAG provides access to a row at
a given physical offset before that position.
• If you do not specify the offset, its default is 1.
• If the offset goes beyond the scope of the window, the
optional default value is returned. If you do not specify
the default, its value is NULL.
Using the LAG and LEAD Analytic Functions
{LAG | LEAD}(value_expr [, offset ] [, default ])
OVER ([ query_partition_clause ] order_by_clause)

79
Using the LAG and LEAD Analytic Functions:
Example
SELECT time_id, TO_CHAR(SUM(amount_sold),'9,999,999') AS
SALES,
TO_CHAR(LAG(SUM(amount_sold),1) OVER (ORDER BY
time_id),'9,999,999') AS LAG1,
TO_CHAR(LEAD(SUM(amount_sold),1) OVER (ORDER BY
time_id),'9,999,999') AS LEAD1
FROM sales
WHERE time_id >= TO_DATE('10-OCT-2000') AND
time_id <= TO_DATE('14-OCT-2000')
GROUP BY time_id;

8080
• For a specified measure, LISTAGG orders data
within each group specified in the ORDER BY
clause and then concatenates the values of
the measure column
• Limited to output of 4000 chars
Using the LISTAGG Function
LISTAGG(measure_expr [, 'delimiter'])
WITHIN GROUP (order_by_clause) [OVER
query_partition_clause]

8181
Using the LISTAGG Function Example
SELECT department_id "Dept", hire_date "Date",
last_name "Name",
LISTAGG(last_name, ', ') WITHIN GROUP (ORDER BY
hire_date, last_name)
OVER (PARTITION BY department_id) as "Emp_list"
FROM hr.employees
WHERE hire_date < '01-SEP-2003'
ORDER BY "Dept", "Date", "Name";

8282
• Both are aggregate and analytic functions
• Used to retrieve a value from the first or last row
of a sorted group, but the needed value is not the
sort key
• FIRST and LAST functions eliminate the need for
self-joins or views and enable better performance
Using the FIRST and LAST Functions
aggregate_function KEEP
(DENSE_RANK FIRST ORDER BY
expr [ DESC | ASC ][ NULLS { FIRST | LAST } ]
[, expr [ DESC | ASC ] [ NULLS { FIRST | LAST } ]
]...
)
[ OVER query_partition_clause ]

8383
FIRST and LAST Aggregate Example
SELECT department_id,
MIN(salary) KEEP (DENSE_RANK FIRST ORDER BY commission_pct)
"Worst",
MAX(salary) KEEP (DENSE_RANK LAST ORDER BY commission_pct)
"Best"
FROM employees
GROUP BY department_id
ORDER BY department_id;

8484
FIRST and LAST Analytic Example
SELECT last_name, department_id, salary,
MIN(salary) KEEP (DENSE_RANK FIRST ORDER BY
commission_pct) OVER (PARTITION BY department_id)
“Worst",
MAX(salary) KEEP (DENSE_RANK LAST ORDER BY commission_pct)
OVER (PARTITION BY department_id) "Best"
FROM employees
ORDER BY department_id, salary, last_name;

8585
• Returns the first value in an ordered set of
values
• If the first value in the set is null, then the
function returns NULL unless you specify
IGNORE NULLS. This setting is useful for data
densification.
Using FIRST_VALUE Analytic Function
FIRST_VALUE (expr [ IGNORE NULLS ]) OVER (analytic_clause)

8686
Using FIRST_VALUE Analytic Function
Example
FIRST_VALUE(last_name) OVER (ORDER BY salary ASC ROWS
UNBOUNDED PRECEDING) AS lowest_sal
FROM (SELECT * FROM employees WHERE department_id = 30
ORDER BY employee_id)
ORDER BY department_id, last_name, salary, lowest_sal;

8787
• Returns the last value in an ordered set of
values.
Using LAST_VALUE Analytic Function
LAST_VALUE (expr [ IGNORE NULLS ]) OVER (analytic_clause)

8888
• Returns the N-th values in an ordered set of
values
• Different default window: RANGE BETWEEN
UNBOUNDED PRECEDING AND CURRENT
ROW
Using NTH_VALUE Analytic Function
NTH_VALUE (measure_expr, n)
[ FROM { FIRST | LAST } ][ { RESPECT | IGNORE } NULLS ]
OVER (analytic_clause)

8989
Using NTH_VALUE Analytic Function
Example
SELECT prod_id, channel_id, MIN(amount_sold),
NTH_VALUE ( MIN(amount_sold), 2) OVER (PARTITION BY
prod_id ORDER BY channel_id
ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED
FOLLOWING) nv
FROM sh.sales
WHERE prod_id BETWEEN 13 and 16
GROUP BY prod_id, channel_id;

9191
• The windowing_clause gives some
analytic functions a further degree of control
over this window within the current partition
• The windowing_clause can only be used
if an order_by_clause is present
Window Functions

9292
Windows can be by RANGE or ROWS
Possible values for start_point and end_point
UNBOUNDED PRECEDING The window starts at the first row of the partition.
Only available for start points.
UNBOUNDED FOLLOWING The window ends at the last row of the partition. Only
available for end points.
CURRENT ROW The window starts or ends at the current row
value_expr PRECEDING A physical or logical offset before the current row.
When used with RANGE, can also be an interval literal
value_expr FOLLOWING As above, but an offset after the current row
RANGE BETWEEN start_point AND end_point
ROWS BETWEEN start_point AND end_point

9393
• The windows are limited to the current
partition
• Generally, the default window is the entire
work set unless said otherwise
Window Limitations

9494
Shortcuts
• Useful shortcuts for the windowing clause:
ROWS UNBOUNDED PRECEDING ROWS BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW
ROWS 10 PRECEDING ROWS BETWEEN 10 PRECEDING AND
CURRENT ROW
ROWS CURRENT ROW ROWS BETWEEN CURRENT ROW AND
CURRENT ROW

9595
• Cumulative aggregation
• Sliding average over proceeding and/or
following rows
• Using the RANGE parameter to filter
aggregation records
Windowing Clause Useful Usages

Top-N and Paging Queries
In Oracle 12c
96

97
Top-N Queries
• A Top-N query is used to retrieve the top or
bottom N rows from an ordered set
• Combining two Top-N queries gives you the
ability to page through an ordered set
• Oracle 12c has introduced the row limiting
clause to simplify Top-N queries

9898
• This is ANSI syntax
• The default offset is 0
• Null values in offset, rowcount or percent
will return no rows
Top-N in 12c
[ OFFSET offset { ROW | ROWS } ]
[ FETCH { FIRST | NEXT } [ { rowcount | percent PERCENT } ]
{ ROW | ROWS } { ONLY | WITH TIES } ]

9999
Top-N Examples
SELECT last_name, salary
FROM hr.employees
ORDER BY salary
FETCH FIRST 4 ROWS ONLY;
FROM hr.employees
ORDER BY salary
FETCH FIRST 4 ROWS WITH TIES;
FROM hr.employees
ORDER BY salary DESC
FETCH FIRST 10 PERCENT ROWS ONLY;

100100
• Before 12c we had to use the rownum pseudo
column to filter out rows
• That will require sorting the entire rowset
Paging Before 12c
SELECT val
FROM (SELECT val, rownum AS rnum
FROM (SELECT val
FROM rownum_order_test
ORDER BY val)
WHERE rownum <= 10)
WHERE rnum >= 5;

101101
• After 12c we have a syntax improvement for
paging using the Top-N queries
• This will use ROW_NUMBER and RANK in the
background – there is no real optimization
improvements
Paging in Oracle 12c
SELECT val
FROM rownum_order_test
ORDER BY val
OFFSET 4 ROWS FETCH NEXT 5 ROWS ONLY;

102102
• More information and examples could be
found on my blog:
http://www.realdbamagic.com/he/12c-top-n-query/
More Examples

103103
• Analytic functions has positive impact on
performance for the most part
• Using analytic functions can reduce the number
of table scans and reduce IO consumption
• The query might use more CPU and/or memory
but it will usually run faster than the same result
without analytic functions
• Top-N queries might struggle with cardinality
evaluation when using the “With Ties” option
Analytic Functions and Performance

105
• You can use hierarchical queries to retrieve data based
on a natural hierarchical relationship between rows in
a table.
• A relational database does not store records in a
hierarchical way; therefore, a hierarchical query is
possible only when a relationship exists between rows
in a table.
• However, where a hierarchical relationship exists
between the rows of a single table, a process called
“tree walking” enables the hierarchy to be constructed.
• A hierarchical query is a method of reporting, with the
branches of a tree in a specific order.
Using Hierarchical Queries

106106
• Getting all employees that report directly or
indirectly to a manager
• Managing documents and folders
• Managing privileges
• Aggregating levels on the same row
Business Challenges

Using Hierarchical Queries: Example
Sample Data from the EMPLOYEES Table
• In the EMPLOYEES table in the HR schema,
Kochhar, De Haan, and Hartstein report to the
MANAGER_ID 100, which is King’s EMPLOYEE_ID.
…

108
Natural Tree Structure
De Haan
HunoldWhalen
Kochhar
Higgins
Mourgos Zlotkey
Rajs Davies Matos
Gietz Ernst Lorentz
Hartstein
Fay
Abel Taylor Grant
Vargas
MANAGER_ID = 100
(Child)
EMPLOYEE_ID = 100
(Parent)
. . . . . .
. . .
. . .
. . .
King

109
condition:
Hierarchical Queries: Syntax
expr comparison_operator expr
SELECT [LEVEL], column, expr...
FROM table
[WHERE condition(s)]
[START WITH condition(s)]
[CONNECT BY PRIOR condition(s)] ;

110
– Use the START WITH clause to specify the starting
point, that is, the row or rows to be used as the root
of the tree:
• Specifies the condition that must be met
• Accepts any condition that is valid in a WHERE clause
– For example, using the HR.EMPLOYEES table, start
with the employee whose last name is Kochhar.
Walking the Tree:
Specifying the Starting Point
. . .
START WITH last_name = 'Kochhar'
START WITH column1 = value

111
• The direction of the query is determined by the
CONNECT BY PRIOR column placement.
• The PRIOR operator refers to the parent row.
• For example, you can walk the tree from top
down using the EMPLOYEES table as follows:
Walking the Tree:
Specifying the Direction of the Query
CONNECT BY PRIOR column1 = column2
. . .
CONNECT BY PRIOR employee_id = manager_id
. . .
Parent key Child key

112
Hierarchical Query Example:
Using the CONNECT BY Clause
SELECT employee_id, last_name, manager_id
FROM hr.employees
CONNECT BY PRIOR employee_id = manager_id;
. . .

113
Specifying the Direction of the Query:
From the Top Down
SELECT last_name||' reports to '||
PRIOR last_name "Walk Top Down"
FROM hr.employees
START WITH last_name = 'King'
CONNECT BY PRIOR employee_id = manager_id ;
. . .

114
Specifying the Direction of the Query:
From the Bottom Up
SELECT employee_id, last_name, job_id, manager_id
FROM hr.employees
START WITH employee_id = 101
CONNECT BY PRIOR manager_id = employee_id ;

115
Using the LEVEL Pseudocolumn
Level 1
root/
parent
Level 3
parent/
child/leaf
Level 4
leaf
De Haan
King
HunoldWhalen
Kochhar
Higgins
Mourgos Zlotkey
Rajs Davies Matos
Gietz Ernst Lorentz
Hartstein
Fay
Abel Taylor Grant
Vargas
Level 2
parent/
child

116
Using the LEVEL Pseudocolumn: Example
SELECT employee_id, last_name, manager_id, LEVEL
FROM hr.employees
START WITH employee_id = 100
ORDER siblings BY last_name;
. . .

117
• Create a report displaying company
management levels beginning with the highest
level and indenting each of the following levels.
Formatting Hierarchical Reports
Using LEVEL and LPAD
SELECT LPAD(last_name, LENGTH(last_name)+(LEVEL*2)-
2,'_') AS org_chart
FROM hr.employees
START WITH first_name = 'Steven' AND last_name = 'King'
ORDER SIBLINGS BY last_name;

118
– Use the WHERE clause to eliminate a node.
– Use the CONNECT BY clause to eliminate a
branch.
Pruning Nodes and Branches
Kochhar
Higgins
Gietz
Whalen
Kochhar
HigginsWhalen
Gietz
. . .
WHERE last_name != 'Higgins'
. . .
AND last_name != 'Higgins'
1 2

119
Pruning Branches Example 1:
Eliminating a Node
SELECT department_id, employee_id,last_name, job_id,
salary
FROM hr.employees
WHERE last_name != 'Higgins'
START WITH manager_id IS NULL
CONNECT BY PRIOR employee_id = manager_id;
. . .
. . .
. . .

120
Pruning Branches Example 2:
Eliminating a Branch
SELECT department_id, employee_id,last_name, job_id,
salary
FROM hr.employees
START WITH manager_id IS NULL
AND last_name != 'Higgins';
. . .

121121
• Join happens before connect by
• Where is happening after connect by
• Regular order by will rearrange the
returning rows
• Sibling order by will rearrange the
returning rows for each level
Order of Precedence

122122
Other Connect By Functions
• CONNECT_BY_ISCYCLE
• CONNECT_BY_ISLEAF
• CONNECT_BY_ROOT
• SYS_CONNECT_BY_PATH

123123
• ANSI SQL:2008 (Oracle 11g) introduced a new
way to run hierarchical queries: Recursive
Subquery Factoring (RSF)
• Using the with clause, making queries easier
to write
New Syntax: Recursive Subquery Factoring

124124
Recursive Subquery Factoring Example
with mytree(id, parent_id, "level")
as
(
select id, parent_id, 1 as "level"
from temp_v
where id = 1
union all
select temp_v.id, temp_v.parent_id,
mytree."level" + 1
from temp_v, mytree
where temp_v.parent_id = mytree.id
)
Select * from mytree;
Stop Condition
Actual
Recursion

125125
• Recursion and Hierarchies might have bad
impact on performance
• Watch out for mega-trees – it has CPU and
memory impact
• Using recursion might lead for multiple IO
reads of the same blocks
Warning: Recursion
might be Bad for Performance

Pattern Matching in
Oracle 12c
126

127127
• Identify and group rows with consecutive values
• Consecutive in this regards – row after row
• Uses regular expression like syntax to find
patterns
What is Pattern Matching

128128
• Finding sequences of events in security
applications
• Locating dropped calls in a CDR listing
• Financial price behaviors (V-shape, W-shape
U-shape, etc.)
• Fraud detection and sensor data analysis
Common Business Challenges

129129
MATCH_RECOGNIZE Syntax
SELECT
FROM [row pattern input table]
MATCH_RECOGNIZE
( [ PARTITION BY <cols> ]
[ ORDER BY <cols> ]
[ MEASURES <cols> ]
[ ONE ROW PER MATCH | ALL ROWS PER MATCH ]
[ SKIP_TO_option]
PATTERN ( <row pattern> )
DEFINE <definition list>
)

130130
• PARTITION BY divides the data in to logical groups
• ORDER BY orders the data in each logical group
• MEASURES define the data measures of the pattern
• ONE/ALL ROW PER MATCH defines what to do with the
pattern – return one row or all rows
• PATTERN says what the pattern actually is
• DEFINE gives us the condition that must be met for a
row to map to the pattern variables
Basix Syntax Legend

131131
• Find Simple V-Shape with 1 row output per
match
MATCH_RECOGNIZE Example
SELECT *
FROM Ticker MATCH_RECOGNIZE (
PARTITION BY symbol
ORDER BY tstamp
MEASURES STRT.tstamp AS start_tstamp,
LAST(DOWN.tstamp) AS bottom_tstamp,
LAST(UP.tstamp) AS end_tstamp
ONE ROW PER MATCH
AFTER MATCH SKIP TO LAST UP
PATTERN (STRT DOWN+ UP+)
DEFINE
DOWN AS DOWN.price < PREV(DOWN.price),
UP AS UP.price > PREV(UP.price)
) MR
ORDER BY MR.symbol, MR.start_tstamp;

133133
• Our goal: find uninterrupted sequences in a
book
• This can be useful for detecting missing
records or sequential behavior
Pages in a Book Example

134
Building Our Query
1. Define input
2. Pattern Matching
3. Order input
4. Process pattern
5. using defined conditions
6. Output: rows per match
7. Output: columns per row
8. Where to go after match?
SELECT *
FROM book_pages
MATCH_RECOGNIZE (
ORDER BY page
PATTERN (A B*)
DEFINE B AS page = PREV(page)+1
ONE ROW PER MATCH
MEASURES
A.page firstpage,
LAST(page) lastpage,
COUNT(*) cnt
AFTER MATCH SKIP PAST LAST ROW
);
SELECT *
FROM book_pages
MATCH_RECOGNIZE (
ORDER BY page
MEASURES
A.page firstpage,
LAST(page) lastpage,
COUNT(*) cnt
ONE ROW PER MATCH
AFTER MATCH SKIP PAST LAST ROW
PATTERN (A B*)
DEFINE B AS page = PREV(page)+1
);

135135135135
And The Output…
FIRSTPAGE LASTPAGE CNT
---------- ---------- ----------
1 3 3
5 7 3
10 15 6
42 42 1

136136136136
• Concatenation: No operator between elements.
• Quantifiers:
– * 0 or more matches.
– + 1 or more matches
– ? 0 or 1 match.
– {n} Exactly n matches.
– {n,} n or more matches.
– {n, m} Between n and m (inclusive) matches.
– {, m} Between 0 an m (inclusive) matches.
• Alternation: |
• Grouping: ()
Supported Regular Expression Patterns

137137
• CLASSIFIER(): Which pattern variable applies to which row
• MATCH_NUMBER(): Which rows are members of which match
• PREV(): Access to a column/expression in a previous row
• NEXT(): Access to a column/expression in the next row
• LAST(): Last value within the pattern match
• FIRST(): First value within the pattern match
• COUNT(), AVG(), MAX(), MIN(), SUM()
Functions

138138
• Find suspicious transfers – a large transfer after 3
small ones
Example: All Rows Per Match
SELECT userid, match_id, pattern_variable, time, amount
FROM (SELECT * FROM event_log
WHERE event = 'transfer')
MATCH_RECOGNIZE
(
PARTITION BY userid ORDER BY time
MEASURES
MATCH_NUMBER() match_id,
CLASSIFIER() pattern_variable
ALL ROWS PER MATCH
PATTERN ( x{3,} y)
DEFINE
x AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30),
y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10)
);

139139
• MATCH_ID shows current match sequence
• PATTERN_VARIABLE show which variable was
applied
• USERID is the partition key
The Output
USERID MATCH_ID PATTERN_VA TIME AMOUNT
-------- ---------- ---------- --------- ----------
john 1 X 06-JAN-12 1000
john 1 X 15-JAN-12 1500
john 1 X 20-JAN-12 1500
john 1 X 23-JAN-12 1000
john 1 Y 26-JAN-12 1000000

140140
• Same as before – show one row per match
Example: One Row Per Match
SELECT userid, first_trx, last_trx, amount
FROM (SELECT * FROM event_log WHERE event = 'transfer')
MATCH_RECOGNIZE
(
PARTITION BY userid ORDER BY time
MEASURES
FIRST(x.time) first_trx,
y.time last_trx,
y.amount amount
ONE ROW PER MATCH
PATTERN ( x{3,} y )
DEFINE
x AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30),
y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10)
);

141141
• USERID is the partition key
• FIRST_TRX is a calculated measure
• AMOUNT and LAST_TRX are measures
The Output
USERID FIRST_TRX LAST_TRX AMOUNT
-------- --------- --------- ----------
john 06-JAN-12 26-JAN-12 1000000

142142
• Test all cases: pattern matching can be very tricky
• Don’t forget to test your data with no matches
• There is no LISTAGG and no DISTINCT when
using match recognition
• Pattern variables cannot be used as bind
variables
Few Last Tips

144144
• XML stand for Extensible Markup Language
• Defines a set of rules for encoding documents
in a format which is both human-readable and
machine-readable
• Data is unstructured and can be transferred
easily to other system
What is XML

145145
• Root
• Element
• Attribute
• Forest
• XML Fragment
• XML Document
XML Terminology

146146
What Does XML Look Like?
<?xml version="1.0"?>
<ROWSET>
<ROW>
<USERNAME>SYS</USERNAME>
<USER_ID>0</USER_ID>
<CREATED>28-JAN-08</CREATED>
</ROW>
<ROW>
<USERNAME>SYSTEM</USERNAME>
<USER_ID>5</USER_ID>
<CREATED>28-JAN-08</CREATED>
</ROW>
</ROWSET>

147147
• Concatenating strings – building the XML
manually. This is highly not recommended
• Using DBMS_XMLGEN
• Using ANSI SQL:2003 XML functions
Generating XML From Oracle

148148
• The DBMS_XMLGEN package converts the
results of a SQL query to a canonical XML
format
• The package takes an arbitrary SQL query as
input, converts it to XML format, and returns
the result as a CLOB
• Using the DBMS_XMLGEN we can create
contexts and use it to build XML documents
• Old package – exists since Oracle 9i
Using DBMS_XMLGEN

149149
Example of Using DBMS_XMLGEN
select dbms_xmlgen.getxml(q'{
select column_name, data_type
from all_tab_columns
where table_name = 'EMPLOYEES' and owner = 'HR'}')
from dual
/
<ROWSET>
<ROW>
<COLUMN_NAME>EMPLOYEE_ID</COLUMN_NAME>
<DATA_TYPE>NUMBER</DATA_TYPE>
</ROW>
<ROW>
<COLUMN_NAME>FIRST_NAME</COLUMN_NAME>
<DATA_TYPE>VARCHAR2</DATA_TYPE>
</ROW>
[...]
</ROWSET>

150150
• DBMS_XMLGEN is an old package (9.0 and 9i)
• Any context change requires complex PL/SQL
• There are improved ways to use XML in
queries
• Use DBMS_XMLGEN for the “quick and dirty”
solution only
Why Not Use DBMS_XMLGEN

151151
• Introduced in ANSI SQL:2003 – Oracle 9iR2
and 10gR2
• Standard functions that can be integrated into
queries
• Removes the need for PL/SQL code to create
XML documents
Standard XML Functions

152152
XMLELEMENT The basic unit for turning column data into
XML fragments
XMLATTRIBUTES Converts column data into attributes of the
parent element
XMLFOREST Allows us to process multiple columns at once
XMLAGG Aggregate separate Fragments into a single
fragment
XMLROOT Allows us to place an XML tag at the start of
our XML document
XML Functions

153153
XMLELEMENT
SELECT XMLELEMENT("name", e.last_name) AS employee
FROM employees e
WHERE e.employee_id = 202;
EMPLOYEE
------------------------------
<name>Fay</name>

154154
XMLELEMENT (2)
SELECT XMLELEMENT("employee",
XMLELEMENT("works_number", e.employee_id),
XMLELEMENT("name", e.last_name)
) AS employee
FROM employees e
EMPLOYEE
----------------------------------------------------------
<employee><works_number>202</works_number><name>Fay</name>
</employee>

155155
XMLATTRIBUTES
XMLATTRIBUTES(
e.employee_id AS "works_number",
e.last_name AS "name")
) AS employee
FROM employees e
EMPLOYEE
----------------------------------------------------------
<employee works_number="202" name="Fay"></employee>

156156
XMLFOREST
XMLFOREST(
e.last_name AS "name",
e.phone_number AS "phone_number")
) AS employee
FROM employees e
EMPLOYEE
----------------------------------------------------------
<phone_number>603.123.6666</phone_number></employee>

157157
XMLFOREST Problem
XMLFOREST(
) AS employee
FROM employees e
WHERE e.employee_id in (202, 203);
EMPLOYEE
----------------------------------------------------------
<employee><works_number>203</works_number><name>Mavris</name>
2 row selected.

158158
XMLAGG
SELECT XMLAGG(
XMLELEMENT("employee",
XMLFOREST(
)) AS employee
FROM employees e
EMPLOYEE
----------------------------------------------------------
<phone_number>603.123.6666</phone_number></employee><employee>
<works_number>203</works_number><name>Mavris</name>
1 row selected.

159159
• Creating a well formed XML document
XMLROOT
SELECT XMLROOT (
XMLELEMENT("employees",
XMLAGG(
XMLELEMENT("employee",
XMLFOREST(
))), VERSION '1.0') AS employee
FROM employees e

160160
• Well formed, version bound, beatified XML:
XMLROOT
EMPLOYEE
------------------------------------------
<employees>
<employee>
<works_number>202</works_number>
<name>Fay</name>
<phone_number>603.123.6666</phone_number>
</employee>
<employee>
<works_number>203</works_number>
<name>Mavris</name>
<phone_number>515.123.7777</phone_number>
</employee>
</employees>

161161
• Using the XQuery language we can create, read
and manipulate XML documents
• Two main functions: XMLQuery and XMLTable
• XQuery is about sequences - XQuery is a
general sequence-manipulation language
• Each sequence can contain numbers, strings,
Booleans, dates, or other XML fragments
Using XQuery

162
Creating XML Document using XQuery
SELECT warehouse_name,
EXTRACTVALUE(warehouse_spec, '/Warehouse/Area'),
XMLQuery(
'for $i in /Warehouse
where $i/Area > 50000
return <Details>
<Docks num="{$i/Docks}"/>
<Rail>
{
if ($i/RailAccess = "Y") then "true" else
"false"
}
</Rail>
</Details>' PASSING warehouse_spec RETURNING CONTENT)
"Big_warehouses"
FROM warehouses;

163
Creating XML Document using XQuery
WAREHOUSE_ID Area Big_warehouses
------------ --------- --------------------------------------------------------
1 25000
2 50000
3 85700 <Details><Docks></Docks><Rail>false</Rail></Details>
4 103000 <Details><Docks num="3"></Docks><Rail>true</Rail></Details>
. . .

164164
Example: Using XMLTable to Read XML
SELECT lines.lineitem, lines.description, lines.partid,
lines.unitprice, lines.quantity
FROM purchaseorder,
XMLTable('for $i in /PurchaseOrder/LineItems/LineItem
where $i/@ItemNumber >= 8
and $i/Part/@UnitPrice > 50
and $i/Part/@Quantity > 2
return $i'
PASSING OBJECT_VALUE
COLUMNS lineitem NUMBER PATH '@ItemNumber',
description VARCHAR2(30) PATH 'Description',
partid NUMBER PATH 'Part/@Id',
unitprice NUMBER PATH 'Part/@UnitPrice',
quantity NUMBER PATH 'Part/@Quantity')
lines;

166166
• Javascript Object Notation
• Converts database tables to a readable
document – just like XML but simplier
• Very common in NoSQL and Big Data solutions
What is JSON
{"FirstName" : "Zohar",
"LastName" : "Elkayam",
"Age" : 36,
"Connection" :
[
{"Type" : “Email", "Value" : "zohar@DBAces.com"},
{"Type" : “Twitter", "Value" : “@realmgic"},
{"Type" : "Site", "Value" : "www.realdbamagic.com"},
]}

167167
• Ability to store data without requiring a Schema
– Store semi-structured data in its native (aggregated)
form
• Ability to query data without knowledge of
Schema
• Ability to index data with knowledge of Schema
JSON Benefits

168168
• Oracle supports JSON since version 12.1.0.2
• JSON documents stored in the database using
existing data types: VARCHAR2, CLOB or BLOB
• External JSON data sources accessible through
external tables including HDFS
• Data accessible via REST API
Oracle JSON Support

169169
• Simple well understood model
• CRUD operations are mapped to HTTP Verbs
– Create / Update : PUT / POST
– Retrieve : GET
– Delete : DELETE
– QBE, Bulk Update, Utilitiy functions : POST
• Stateless
REST based API for JSON documents

170170
• Similar role to XPATH in XML
• Syntactically similar to Java Script (. and [ ])
• Compatible with Java Script
JSON Path Expression

171171
• There are few common JSON Operators:
Common JSON SQL Functions
JSON_EXISTS Checks if a value exists in the JSON
JSON_VALUE Retrieve a scalar value from JSON
JSON_QUERY Query a string from JSON Document
JSON_TABLE Query data from JSON Document (like
XMLTable)

172172
• Extract JSON fragment from JSON document
JSON_QUERY
select count(*)
from J_PURCHASEORDER
where JSON_EXISTS(
PO_DOCUMENT, '$.ShippingInstructions.Address.state‘)
/

173173
• Generate rows from a JSON Array
• Pivot properties / key values into columns
• Use Nested Path clause to process multi-level
collections with a single JSON_TABLE operator.
Using JSON_TABLE

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• 1 Row of output for each row in table
Example: JSON_TABLE
select M.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT,
'$'
columns
PO_NUMBER NUMBER(10) path '$.PONumber',
REFERENCE VARCHAR2(30 CHAR) path '$.Reference',
REQUESTOR VARCHAR2(32 CHAR) path '$.Requestor',
USERID VARCHAR2(10 CHAR) path '$.User',
COSTCENTER VARCHAR2(16) path '$.CostCenter'
) M
where PO_NUMBER > 1600 and PO_Number < 1605
/

175175
• 1 row output for each member of LineItems array ``
Example: JSON_TABLE (2)
select D.*
from J_PURCHASEORDER p,
JSON_TABLE(
p.PO_DOCUMENT,
'$'
columns(
PO_NUMBER NUMBER(10) path '$.PONumber',
NESTED PATH '$.LineItems[*]'
columns(
ITEMNO NUMBER(16) path '$.ItemNumber',
UPCCODE VARCHAR2(14 CHAR) path '$.Part.UPCCode‘ ))
) D
where PO_NUMBER = 1600 or PO_NUMBER = 1601
/

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• Known Query Patterns : JSON Path expression
– Functional indexes using JSON_VALUE and,
JSON_EXISTS
– Materialized View using JSON_TABLE()
• Ad-hoc Query Strategy
– Based on Oracle’s full text index (Oracle Text)
– Support ad-hoc path, value and keyword query
search using JSON Path expressions
JSON Indexing

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• Regular expression (regexp) is a sequence of
characters that define a search pattern
• Commonly used for smart “Search and
Replace” of patterns and for input validations
of text
• Widely introduced in Oracle 10g (and it even
existed even before that)
Regular Expression

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Common REGEXP
Functions and Operators
REGEXP_LIKE Perform regular expression matching
REGEXP_REPLACE Extends the functionality of the REPLACE
function by using patterns
REGEXP_SUBSTR Extends the functionality of the SUBSTR
REGEXP_COUNT Count the number of matches of the
pattern in a given string
REGEXP_INSTR Extends the functionality of the INSTR

180180180180
• Concatenation: No operator between elements.
• Quantifiers:
– . Matches any character in the database character set
– * 0 or more matches
– + 1 or more matches
– ? 0 or 1 match
– {n} Exactly n matches
– {n,} n or more matches
– {n, m} Between n and m (inclusive) matches
– {, m} Between 0 an m (inclusive) matches
• Alternation: [|]
• Grouping: ()

181181
Value Description
^
Matches the beginning of a string. If used with
a match_parameter of 'm', it matches the start of a line
anywhere within expression.
$
Matches the end of a string. If used with
a match_parameter of 'm', it matches the end of a line
anywhere withinexpression.
W Matches a nonword character.
s Matches a whitespace character.
S matches a non-whitespace character.
A
Matches the beginning of a string or matches at the end of
a string before a newline character.
Z Matches at the end of a string.

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Character Class Description
[:alnum:] Alphanumeric characters
[:alpha:] Alphabetic characters
[:blank:] Blank Space Characters
[:cntrl:] Control characters (nonprinting)
[:digit:] Numeric digits
[:graph:] Any [:punct:], [:upper:], [:lower:], and [:digit:] chars
[:lower:] Lowercase alphabetic characters
[:print:] Printable characters
[:punct:] Punctuation characters
[:space:]
Space characters (nonprinting), such as carriage return,
newline, vertical tab, and form feed
[:upper:] Uppercase alphabetic characters
[:xdigit:] Hexidecimal characters
Character Classes

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• Regular expressions might be slow when used
on large amount of data
• Writing regular expression can be very tricky –
make sure your pattern is correct
• Oracle REGEXP syntax is not standard, regular
expression might not work or partially work
causing wrong results
• There can only be up to 9 placeholders in a
given quantifier
Pitfalls

SQLcl Introduction
The Next Generation of SQL*Plus?
185

186186
• Introduced in Oracle 5 (1985)
• Looks very simple but has tight integration with
other Oracle infrastructure and tools
• Very good for reporting, scripting, and
automation
• Replaced old CLI tool called …
UFI (“User Friendly Interface”)
SQL*Plus

187187
• Nothing really wrong with SQL*Plus – it is being
updated constantly but it is missing a lot of
functionality
• SQL*Plus forces us to use GUI tools to complete
some basic tasks
• Easy to understand, a bit hard to use
• Not easy for new users or developers
What’s Wrong With SQL*Plus?

188188
• SQLcl is a new command line interface (CLI) for SQL
users and DBAs
• It is part of the SQL Developer suite – developed by the
same team: Oracle Database Development Tools Team
• Can do most of what SQL*Plus does and much more
• Minimal installation, minimal requirements
Introducing SQLcl

189189
• It’s still in the early adopter version (current
version: 4.2.0.15.295.1605 RC, October 23,
2015)
• Uses Java, one version for Windows, Linux and
OS X
• Planned to be shipped out with Oracle
Database 12cR2 (“within the next 12 month”)
Introducing SQLcl (cont.)

190190
• Early Adopter version/RC
• QA still logging bugs from SQL*Plus regression tests
• Adding support for existing SQL*Plus commands and
syntax
• Adding new commands
• But can it do...?
– Yes
– Not yet
– No
Current Status

191191
• Download from: SQL Developer Download
page
• Unzip the file
• Run it
Installing

193193
• Use the tab key to complete commands
• Can be used to list tables, views or other queriable
objects
• Can be used to replace the * with actual column names
• Use the arrow keys to move around the command
• Use CTRL+W and CTRL+S to jump to the beginning/end
of commands
Object Completion and Easy Edit

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• 100 command history buffer
• Commands are persistent between sessions (watch out for
security!)
• Use UP and DOWN arrow keys to access old commands
• Usage:
history
history usage
History script
history full
History clear [session?]
• Load from history into command buffer:
history <number>
Command History

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• Describe lists the column of the tables just like SQL*Plus
• Information shows column names, default values, indexes
and constraints.
• In 12c database information shows table statistics and In
memory status
• Works for table, views, sequences, and code objects
• Info+ shows additional information regarding column
statistics and column histograms
Describe, Information and info+

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• Outputting query results becomes easier with the
“set sqlformat” command (also available in SQL
Developer)
• We can create a query in the “regular” way and
then switch between the different output styles:
– ANSIConsole
– Fixed column size output
– XML or JSON output
– HTML output generates a built in search field and a responsive html output for
the result only
Generating Pretty Output

197197
• We can generate loader ready output (with “|” as a
delimiter)
• We can generate insert commands
• We can easily generate CSV output
• Usage:
set sqlformat {
csv,html,xml,json,ansiconsole,insert,
loader,fixed,default}
Generating Other Useful Outputs

198198
• Loads a comma separated value (csv) file into
a table
• The first row of the file must be a header row
and the file must be encoded UTF8
• The load is processed with 50 rows per batch
• Usage:
LOAD [schema.]table_name[@db_link] file_name
Load Data From CSV File

199199
• There is a lot in SQL than meets the eye
• Wise use of analytic queries can be good for
readability and performance
• Recursive queries are good replacement for the
old connect by prior but a little dangerous
• Oracle 12c features are really cool!
• Look out for SQLcl: it’s cool and it’s going places!
Summary

200200
• The Model clause
• Using the WITH clause
• Adding PL/SQL to our SQL (Oracle 12c)
• Hints and other tuning considerations
• The SQL reference book is 1906 pages long.
We didn’t talk about most of it…
What Did We Not Talk About?

Q&A
Any Questions? Now will be the time!
201

202202
Zohar Elkayam
twitter: @realmgic
Zohar@Brillix.co.il
www.ilDBA.co.il
www.realdbamagic.com

Oracle Database Advanced Querying

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