Oracle Database Advanced Querying

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Oracle Database Advanced Querying

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Zohar Elkayam CTO, Brillix

[email protected]

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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We are committed to provide the highest quality of services delivered by our dedicated team of highly trained and experienced industry’s top data experts. We offer:

Complete integrated end-to-end solutions based on best-of-breed innovations in database, security and big data technologies

On-site professional customized trainings led by our team of Oracle ACEs and Oracle Certified Professionals

Comprehensive security solutions and services for leading database platforms and business applications, leveraging a world-class team of security experts

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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יכולות –Oracle SQL"הספר •

מדריך לשולף , מתקדמות

2011-פורסם ב" המהיר

הראשון SQL-זה ספר ה•

והיחיד שנכתב בעברית

מתחילתו ועד סופו

הספר נכתב על ידי עמיאל •דיוויס ועבר עריכה טכנית שלי

יכולות מתקדמות–Oracle SQLאודות

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The REAL Agenda

הפסקה10:30-10:45

י הכנסארוחת צהריים לכל משתתפ12:30-13:30

הפסקה מתוקה15:00-15:15

הולכים הביתה16:30

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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.employeesWHERE job_id LIKE 'SA%';

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SELECT department_id, job_id, SUM(salary), COUNT(employee_id)

FROM hr.employeesGROUP BY department_id, job_idOrder by department_id;

The GROUP BY Clause

SELECT [column,] group_function(column)FROM table[WHERE condition][GROUP BY group_by_expression][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][GROUP BY group_by_expression][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 BYclause

• 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: ExampleSELECT department_id, job_id, SUM(salary)FROM hr.employeesWHERE department_id < 60GROUP BY ROLLUP(department_id, job_id);

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2

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Total by DEPARTMENT_IDand JOB_ID

Total by DEPARTMENT_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 SELECTstatement

Using the CUBE Operator

SELECT [column,] group_function(column)...FROM table[WHERE condition][GROUP BY [CUBE] group_by_expression][HAVING having_expression][ORDER BY column];

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3

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Grand total

Total by JOB_ID

Total by DEPARTMENT_IDand JOB_ID

Total by DEPARTMENT_ID

SELECT department_id, job_id, SUM(salary)FROM hr.employeesWHERE department_id < 60GROUP 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][HAVING having_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.employeesWHERE department_id < 50GROUP BY ROLLUP(department_id, job_id);

Working with the GROUPING Function: Example

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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 ExampleSELECT department_id DEPTID, job_id JOB,

SUM(salary),GROUPING_ID(department_id,job_id) GRP_ID

FROM hr.employeesWHERE department_id < 40GROUP BY CUBE(department_id, job_id);

DEPTID JOB SUM(SALARY) GRP_ID---------- ---------- ----------- ----------

48300 3

MK_MAN 13000 2MK_REP 6000 2PU_MAN 11000 2AD_ASST 4400 2PU_CLERK 13900 2

10 4400 110 AD_ASST 4400 020 19000 120 MK_MAN 13000 020 MK_REP 6000 030 24900 130 PU_MAN 11000 030 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 ExampleSELECT department_id DEPTID, job_id JOB,

SUM(salary),GROUP_ID() UNIQ_GRP_ID

FROM hr.employeesWHERE department_id < 40GROUP BY department_id, CUBE(department_id, job_id);

DEPTID JOB SUM(SALARY) UNIQ_GRP_ID---------- ---------- ----------- -----------

10 AD_ASST 4400 020 MK_MAN 13000 020 MK_REP 6000 030 PU_MAN 11000 030 PU_CLERK 13900 010 AD_ASST 4400 120 MK_MAN 13000 120 MK_REP 6000 130 PU_MAN 11000 130 PU_CLERK 13900 110 4400 020 19000 030 24900 010 4400 120 19000 130 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 SETSclause are computed and the results of individual groupings are combined with a UNION ALLoperation.

• 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.employeesGROUP BY GROUPING SETS

((department_id,job_id), (job_id,manager_id));

GROUPING SETS: Example

. . .

. . .

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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

…

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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.employeesGROUP BY department_id,

ROLLUP(job_id),CUBE(manager_id);

Concatenated Groupings: Example

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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 QUARTERColumn: Conceptual Example

30,000

40,000

60,000

30,000

40,000

20,000

AMOUNT_SOLD

2,500Q1IUSAKids Jeans

2,000Q2CJapanKids Jeans

2,000Q3SUSAShorts

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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 = 'Q2' then amount_sold else null end) Q2,sum(case when quarter = 'Q3' then amount_sold else null end) Q3,sum(case when quarter = 'Q4' then amount_sold else null end) Q4from sales

group by product;

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Pivot Clause Syntaxtable_reference PIVOT [ XML ]

( aggregate_function ( expr ) [[AS] alias ][, aggregate_function ( expr ) [[AS] alias ] ]...

pivot_for_clausepivot_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 ASSELECTprod_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, productsWHERE sales.time_id = times.time_id AND

sales.prod_id = products.prod_id ANDsales.cust_id = customers.cust_id ANDcustomers.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 DataSELECT product, country, channel, quarter, quantity_soldFROM sales_view;

PRODUCT COUNTRY CHANNEL QUARTER QUANTITY_SOLD------------ ------------ ---------- -------- -------------Y Box Italy 4 01 21Y Box Italy 4 02 17Y Box Italy 4 03 20. . .Y Box Japan 2 01 35Y Box Japan 2 02 39Y Box Japan 2 03 36Y Box Japan 2 04 46Y Box Japan 3 01 65. . .Bounce Italy 2 01 34Bounce 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_soldFROM 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

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• 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 salesUNION ALLSELECT product, '02' AS quarter, Q2_value FROM salesUNION ALLSELECT product, '03' AS quarter, Q3_value FROM salesUNION ALLSELECT product, '04' AS quarter, Q4_value FROM sales);

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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, UNPIVOTcannot 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_clauseunpivot_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 ASSELECT *FROM

(SELECT product, quarter, quantity_soldFROM 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 pivotedtableUNPIVOT (quantity_sold For Quarter IN (Q1, Q2, Q3, Q4))ORDER BY product DESC, quarter;

. . .

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• More information and examples could be found on my Blog:

http://www.realdbamagic.com/he/pivot-a-table/

More Examples…

Analytic FunctionsLet’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

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• 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;

Ranking Functions

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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_idORDER BY salary DESC) "Rank"

FROM employees WHERE department_id = 60ORDER 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 BYclause.

• The group expressions in the PARTITION BYsubclause 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: ExampleSELECT channel_desc, calendar_month_desc, TO_CHAR(SUM(amount_sold),'9,999,999,999') SALES$, RANK() OVER (PARTITION BY channel_descORDER BY SUM(amount_sold) DESC) AS RANK_BY_CHANNELFROM sales, products, customers, times, channelsWHERE sales.prod_id = products.prod_idAND sales.cust_id = customers.cust_idAND sales.time_id = times.time_id AND sales.channel_Id = channels.channel_idAND 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

SELECT department_id, last_name, salary,RANK() OVER (PARTITION BY department_idORDER BY salary DESC) "Rank",

DENSE_RANK() over (partition by department_idORDER BY salary DESC) "Drank"

FROM employees WHERE department_id = 60ORDER BY department_id, last_name, salary DESC, "Rank" DESC;

DENSE_RANK ( ) OVER ([query_partition_clause] order_by_clause)

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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, countriesWHERE 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)

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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.employeesORDER BY department_id, pr, salary;

. . .

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• 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 )

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• ROWNUM is a pseudo column, ROW_NUMBERis 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

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• 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)

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• 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

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• 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)

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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 salesWHERE time_id >= TO_DATE('10-OCT-2000') AND

time_id <= TO_DATE('14-OCT-2000')GROUP BY time_id;

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• 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 FunctionLISTAGG(measure_expr [, 'delimiter'])

WITHIN GROUP (order_by_clause) [OVER query_partition_clause]

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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";

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• 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 Functionsaggregate_function KEEP

(DENSE_RANK FIRST ORDER BYexpr [ DESC | ASC ][ NULLS { FIRST | LAST } ][, expr [ DESC | ASC ] [ NULLS { FIRST | LAST } ]]...

)[ OVER query_partition_clause ]

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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;

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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;

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• 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)

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Using FIRST_VALUE Analytic FunctionExample

SELECT department_id, last_name, salary,

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;

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• Returns the last value in an ordered set of values.

Using LAST_VALUE Analytic Function

LAST_VALUE (expr [ IGNORE NULLS ]) OVER (analytic_clause)

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• 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)

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Using NTH_VALUE Analytic FunctionExample

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;

Window Functions

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• 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

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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

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• The windows are limited to the current partition

• Generally, the default window is the entire work set unless said otherwise

Window Limitations

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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

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• 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 QueriesIn Oracle 12c

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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

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• This is ANSI syntax

• The default offset is 0

• Null values in offset, rowcount or percentwill 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, salaryFROM hr.employeesORDER BY salaryFETCH FIRST 4 ROWS ONLY;

SELECT last_name, salaryFROM hr.employeesORDER BY salaryFETCH FIRST 4 ROWS WITH TIES;

SELECT last_name, salaryFROM hr.employeesORDER BY salary DESCFETCH FIRST 10 PERCENT ROWS ONLY;

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• 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 valFROM (SELECT val, rownum AS rnum

FROM (SELECT valFROM rownum_order_testORDER BY val)

WHERE rownum <= 10)WHERE rnum >= 5;

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• 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 valFROM rownum_order_testORDER BY valOFFSET 4 ROWS FETCH NEXT 5 ROWS ONLY;

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• More information and examples could be found on my blog:

http://www.realdbamagic.com/he/12c-top-n-query/

More Examples

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• 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

Hierarchical Queries

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• 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

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• 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: ExampleSample 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.

…

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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

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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)] ;

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– 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

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• 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

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Hierarchical Query Example: Using the CONNECT BY Clause

SELECT employee_id, last_name, manager_idFROM hr.employeesCONNECT BY PRIOR employee_id = manager_id;

. . .

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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 ;

. . .

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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 ;

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Using the LEVEL Pseudocolumn

Level 1root/

parent

Level 3parent/

child/leaf

Level 4leaf

De Haan

King

HunoldWhalen

Kochhar

Higgins

Mourgos Zlotkey

Rajs Davies Matos

Gietz Ernst Lorentz

Hartstein

Fay

Abel Taylor Grant

Vargas

Level 2parent/child

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Using the LEVEL Pseudocolumn: ExampleSELECT employee_id, last_name, manager_id, LEVELFROM hr.employeesSTART WITH employee_id = 100CONNECT BY PRIOR employee_id = manager_idORDER siblings BY last_name;

. . .

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• 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_chartFROM hr.employeesSTART WITH first_name = 'Steven' AND last_name = 'King' CONNECT BY PRIOR employee_id = manager_idORDER SIBLINGS BY last_name;

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– 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'

. . .CONNECT BY PRIOR employee_id = manager_idAND last_name != 'Higgins'

1 2

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Pruning Branches Example 1:Eliminating a Node

SELECT department_id, employee_id,last_name, job_id, salary

FROM hr.employeesWHERE last_name != 'Higgins'START WITH manager_id IS NULLCONNECT BY PRIOR employee_id = manager_id;

. . .

. . .

. . .

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Pruning Branches Example 2:Eliminating a Branch

SELECT department_id, employee_id,last_name, job_id, salaryFROM hr.employeesSTART WITH manager_id IS NULLCONNECT BY PRIOR employee_id = manager_idAND last_name != 'Higgins';

. . .

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• 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

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Other Connect By Functions

• CONNECT_BY_ISCYCLE

• CONNECT_BY_ISLEAF

• CONNECT_BY_ROOT

• SYS_CONNECT_BY_PATH

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• 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

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Recursive Subquery Factoring Example

with mytree(id, parent_id, "level")as (

select id, parent_id, 1 as "level"from temp_vwhere id = 1

union allselect temp_v.id, temp_v.parent_id,

mytree."level" + 1from temp_v, mytreewhere temp_v.parent_id = mytree.id

)Select * from mytree;

Stop Condition

Actual Recursion

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• 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 inOracle 12c

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• 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

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• 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

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MATCH_RECOGNIZE Syntax

SELECTFROM [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>)

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• 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

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• Find Simple V-Shape with 1 row output per match

MATCH_RECOGNIZE Example

SELECT *FROM Ticker MATCH_RECOGNIZE (

PARTITION BY symbolORDER BY tstampMEASURES STRT.tstamp AS start_tstamp,

LAST(DOWN.tstamp) AS bottom_tstamp,LAST(UP.tstamp) AS end_tstamp

ONE ROW PER MATCHAFTER MATCH SKIP TO LAST UPPATTERN (STRT DOWN+ UP+)DEFINE

DOWN AS DOWN.price < PREV(DOWN.price),UP AS UP.price > PREV(UP.price)

) MRORDER BY MR.symbol, MR.start_tstamp;

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What Will Be Matched?

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• Our goal: find uninterrupted sequences in a book

• This can be useful for detecting missing records or sequential behavior

Pages in a Book Example

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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

);

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And The Output…

FIRSTPAGE LASTPAGE CNT---------- ---------- ----------

1 3 35 7 310 15 642 42 1

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• 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

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• 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

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• Find suspicious transfers – a large transfer after 3 small ones

Example: All Rows Per Match

SELECT userid, match_id, pattern_variable, time, amountFROM (SELECT * FROM event_logWHERE event = 'transfer')MATCH_RECOGNIZE(PARTITION BY userid ORDER BY timeMEASURESMATCH_NUMBER() match_id,CLASSIFIER() pattern_variableALL ROWS PER MATCHPATTERN ( x{3,} y)DEFINEx AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30),y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10));

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• 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 1000john 1 X 15-JAN-12 1500john 1 X 20-JAN-12 1500john 1 X 23-JAN-12 1000john 1 Y 26-JAN-12 1000000

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• Same as before – show one row per match

Example: One Row Per Match

SELECT userid, first_trx, last_trx, amountFROM (SELECT * FROM event_log WHERE event = 'transfer')MATCH_RECOGNIZE(PARTITION BY userid ORDER BY timeMEASURESFIRST(x.time) first_trx,y.time last_trx,y.amount amountONE ROW PER MATCHPATTERN ( x{3,} y )DEFINEx AS (amount < 2000 AND LAST(x.time) -FIRST(x.time) < 30),y AS (amount >= 1000000 AND y.time-LAST(x.time) < 10));

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• 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

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• 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

Using XML with SQL

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• 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

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• Root

• Element

• Attribute

• Forest

• XML Fragment

• XML Document

XML Terminology

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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>

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• Concatenating strings – building the XML manually. This is highly not recommended

• Using DBMS_XMLGEN

• Using ANSI SQL:2003 XML functions

Generating XML From Oracle

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• 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

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Example of Using DBMS_XMLGENselect dbms_xmlgen.getxml(q'{select column_name, data_typefrom all_tab_columnswhere table_name = 'EMPLOYEES' and owner = 'HR'}')from dual/

<?xml version="1.0"?><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>

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• 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

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• 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

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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 employeeFROM employees eWHERE e.employee_id = 202;

EMPLOYEE------------------------------<name>Fay</name>

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XMLELEMENT (2)

SELECT XMLELEMENT("employee",XMLELEMENT("works_number", e.employee_id),XMLELEMENT("name", e.last_name)

) AS employeeFROM employees eWHERE e.employee_id = 202;

EMPLOYEE----------------------------------------------------------<employee><works_number>202</works_number><name>Fay</name></employee>

155155

XMLATTRIBUTES

SELECT XMLELEMENT("employee",XMLATTRIBUTES(e.employee_id AS "works_number",e.last_name AS "name")

) AS employeeFROM employees eWHERE e.employee_id = 202;

EMPLOYEE----------------------------------------------------------<employee works_number="202" name="Fay"></employee>

156156

XMLFOREST

SELECT XMLELEMENT("employee",XMLFOREST(e.employee_id AS "works_number",e.last_name AS "name",e.phone_number AS "phone_number")

) AS employeeFROM employees eWHERE e.employee_id = 202;

EMPLOYEE----------------------------------------------------------<employee><works_number>202</works_number><name>Fay</name><phone_number>603.123.6666</phone_number></employee>

157157

XMLFOREST Problem

SELECT XMLELEMENT("employee",XMLFOREST(e.employee_id AS "works_number",e.last_name AS "name",e.phone_number AS "phone_number")

) AS employeeFROM employees eWHERE e.employee_id in (202, 203);

EMPLOYEE----------------------------------------------------------<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>

2 row selected.

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XMLAGG

SELECT XMLAGG(XMLELEMENT("employee",

XMLFOREST(e.employee_id AS "works_number",e.last_name AS "name",e.phone_number AS "phone_number")

)) AS employeeFROM employees eWHERE e.employee_id in (202, 203);

EMPLOYEE----------------------------------------------------------<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>

1 row selected.

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• Creating a well formed XML document

XMLROOT

SELECT XMLROOT (XMLELEMENT("employees",XMLAGG(XMLELEMENT("employee",

XMLFOREST(e.employee_id AS "works_number",e.last_name AS "name",e.phone_number AS "phone_number")

))), VERSION '1.0') AS employeeFROM employees eWHERE e.employee_id in (202, 203);

160160

• Well formed, version bound, beatified XML:

XMLROOT

EMPLOYEE------------------------------------------<?xml version="1.0"?><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>

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• 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 /Warehousewhere $i/Area > 50000return <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 250002 500003 85700 <Details><Docks></Docks><Rail>false</Rail></Details>4 103000 <Details><Docks num="3"></Docks><Rail>true</Rail></Details>

. . .

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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 >= 8and $i/Part/@UnitPrice > 50and $i/Part/@Quantity > 2

return $i'PASSING OBJECT_VALUECOLUMNS 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;

Oracle 12c JSON Support

165

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• 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" : "[email protected]"},{"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

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• 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‘)

/

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• 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

174174

• 1 Row of output for each row in table

Example: JSON_TABLE

select M.*from J_PURCHASEORDER p,

JSON_TABLE(p.PO_DOCUMENT,'$'columnsPO_NUMBER NUMBER(10) path '$.PONumber',REFERENCE VARCHAR2(30 CHAR) path '$.Reference',REQUESTOR VARCHAR2(32 CHAR) path '$.Requestor',USERID VARCHAR2(10 CHAR) path '$.User',COSTCENTER VARCHAR2(16) path '$.CostCenter'

) Mwhere PO_NUMBER > 1600 and PO_Number < 1605/

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• 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‘ ))

) Dwhere 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

Regular Expression

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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 REPLACEfunction by using patterns

REGEXP_SUBSTR Extends the functionality of the SUBSTRfunction by using patterns

REGEXP_COUNT Count the number of matches of the pattern in a given string

REGEXP_INSTR Extends the functionality of the INSTRfunction by using patterns

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: ()

Supported Regular Expression Patterns

181181

Supported Regular Expression Patterns

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.

\AMatches 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.

182182

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

Regular Expression Demo

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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?

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• 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

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• 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?

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• 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

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• 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.)

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• 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

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• Download from: SQL Developer Download page

• Unzip the file

• Run it

Installing

What Can It Do?

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• Use the tab key to complete commands

• Can be used to list tables, views or other queriableobjects

• 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:

historyhistory usageHistory scripthistory fullHistory 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

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• 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

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• 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

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• 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

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• 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&AAny Questions? Now will be the time!

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Zohar Elkayamtwitter: @[email protected]

www.ilDBA.co.ilwww.realdbamagic.com

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