Submit Search
Upload
Latin America Tour 2019 - pattern matching
•
1 like
•
162 views
Connor McDonald
Follow
Introduction to the 12c MATCH_RECOGNIZE function for matching patterns with SQL
Read less
Read more
Technology
Report
Share
Report
Share
1 of 206
Download now
Download to read offline
Recommended
Latin America tour 2019 - Flashback
Latin America tour 2019 - Flashback
Connor McDonald
Pattern Matching with SQL - APEX World Rotterdam 2019
Pattern Matching with SQL - APEX World Rotterdam 2019
Connor McDonald
OpenWorld 2018 - 20 years of hints and tips
OpenWorld 2018 - 20 years of hints and tips
Connor McDonald
Kscope19 - Flashback: Good for Developers as well as DBAs
Kscope19 - Flashback: Good for Developers as well as DBAs
Connor McDonald
OpenWorld 2018 - Common Application Developer Disasters
OpenWorld 2018 - Common Application Developer Disasters
Connor McDonald
APEX Connect 2019 - SQL Tuning 101
APEX Connect 2019 - SQL Tuning 101
Connor McDonald
OpenWorld 2018 - Pagination
OpenWorld 2018 - Pagination
Connor McDonald
Latin America Tour 2019 - 10 great sql features
Latin America Tour 2019 - 10 great sql features
Connor McDonald
Recommended
Latin America tour 2019 - Flashback
Latin America tour 2019 - Flashback
Connor McDonald
Pattern Matching with SQL - APEX World Rotterdam 2019
Pattern Matching with SQL - APEX World Rotterdam 2019
Connor McDonald
OpenWorld 2018 - 20 years of hints and tips
OpenWorld 2018 - 20 years of hints and tips
Connor McDonald
Kscope19 - Flashback: Good for Developers as well as DBAs
Kscope19 - Flashback: Good for Developers as well as DBAs
Connor McDonald
OpenWorld 2018 - Common Application Developer Disasters
OpenWorld 2018 - Common Application Developer Disasters
Connor McDonald
APEX Connect 2019 - SQL Tuning 101
APEX Connect 2019 - SQL Tuning 101
Connor McDonald
OpenWorld 2018 - Pagination
OpenWorld 2018 - Pagination
Connor McDonald
Latin America Tour 2019 - 10 great sql features
Latin America Tour 2019 - 10 great sql features
Connor McDonald
Latin America Tour 2019 - slow data and sql processing
Latin America Tour 2019 - slow data and sql processing
Connor McDonald
Flashback features in Oracle - UKOUG 2017
Flashback features in Oracle - UKOUG 2017
Connor McDonald
APEX Connect 2019 - array/bulk processing in PLSQL
APEX Connect 2019 - array/bulk processing in PLSQL
Connor McDonald
Cool SQL Features
Cool SQL Features
Connor McDonald
Sangam 2019 - The Latest Features
Sangam 2019 - The Latest Features
Connor McDonald
KScope19 - SQL Features
KScope19 - SQL Features
Connor McDonald
Latin America Tour 2019 - 18c and 19c featues
Latin America Tour 2019 - 18c and 19c featues
Connor McDonald
ANSI vs Oracle language
ANSI vs Oracle language
Connor McDonald
Perth APAC Groundbreakers tour - SQL Techniques
Perth APAC Groundbreakers tour - SQL Techniques
Connor McDonald
UKOUG 2019 - SQL features
UKOUG 2019 - SQL features
Connor McDonald
Sangam 19 - PLSQL still the coolest
Sangam 19 - PLSQL still the coolest
Connor McDonald
Sangam 19 - Analytic SQL
Sangam 19 - Analytic SQL
Connor McDonald
Sangam 19 - Successful Applications on Autonomous
Sangam 19 - Successful Applications on Autonomous
Connor McDonald
UKOUG - 25 years of hints and tips
UKOUG - 25 years of hints and tips
Connor McDonald
OOW19 - Flashback, not just for DBAs
OOW19 - Flashback, not just for DBAs
Connor McDonald
Flashback ITOUG
Flashback ITOUG
Connor McDonald
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
Connor McDonald
OOW19 - Ten Amazing SQL features
OOW19 - Ten Amazing SQL features
Connor McDonald
SQL techniques for faster applications
SQL techniques for faster applications
Connor McDonald
Oracle Database 12c Application Development
Oracle Database 12c Application Development
Saurabh K. Gupta
Wellington APAC Groundbreakers tour - SQL Pattern Matching
Wellington APAC Groundbreakers tour - SQL Pattern Matching
Connor McDonald
18c and 19c features for DBAs
18c and 19c features for DBAs
Connor McDonald
More Related Content
What's hot
Latin America Tour 2019 - slow data and sql processing
Latin America Tour 2019 - slow data and sql processing
Connor McDonald
Flashback features in Oracle - UKOUG 2017
Flashback features in Oracle - UKOUG 2017
Connor McDonald
APEX Connect 2019 - array/bulk processing in PLSQL
APEX Connect 2019 - array/bulk processing in PLSQL
Connor McDonald
Cool SQL Features
Cool SQL Features
Connor McDonald
Sangam 2019 - The Latest Features
Sangam 2019 - The Latest Features
Connor McDonald
KScope19 - SQL Features
KScope19 - SQL Features
Connor McDonald
Latin America Tour 2019 - 18c and 19c featues
Latin America Tour 2019 - 18c and 19c featues
Connor McDonald
ANSI vs Oracle language
ANSI vs Oracle language
Connor McDonald
Perth APAC Groundbreakers tour - SQL Techniques
Perth APAC Groundbreakers tour - SQL Techniques
Connor McDonald
UKOUG 2019 - SQL features
UKOUG 2019 - SQL features
Connor McDonald
Sangam 19 - PLSQL still the coolest
Sangam 19 - PLSQL still the coolest
Connor McDonald
Sangam 19 - Analytic SQL
Sangam 19 - Analytic SQL
Connor McDonald
Sangam 19 - Successful Applications on Autonomous
Sangam 19 - Successful Applications on Autonomous
Connor McDonald
UKOUG - 25 years of hints and tips
UKOUG - 25 years of hints and tips
Connor McDonald
OOW19 - Flashback, not just for DBAs
OOW19 - Flashback, not just for DBAs
Connor McDonald
Flashback ITOUG
Flashback ITOUG
Connor McDonald
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
Connor McDonald
OOW19 - Ten Amazing SQL features
OOW19 - Ten Amazing SQL features
Connor McDonald
SQL techniques for faster applications
SQL techniques for faster applications
Connor McDonald
Oracle Database 12c Application Development
Oracle Database 12c Application Development
Saurabh K. Gupta
What's hot
(20)
Latin America Tour 2019 - slow data and sql processing
Latin America Tour 2019 - slow data and sql processing
Flashback features in Oracle - UKOUG 2017
Flashback features in Oracle - UKOUG 2017
APEX Connect 2019 - array/bulk processing in PLSQL
APEX Connect 2019 - array/bulk processing in PLSQL
Cool SQL Features
Cool SQL Features
Sangam 2019 - The Latest Features
Sangam 2019 - The Latest Features
KScope19 - SQL Features
KScope19 - SQL Features
Latin America Tour 2019 - 18c and 19c featues
Latin America Tour 2019 - 18c and 19c featues
ANSI vs Oracle language
ANSI vs Oracle language
Perth APAC Groundbreakers tour - SQL Techniques
Perth APAC Groundbreakers tour - SQL Techniques
UKOUG 2019 - SQL features
UKOUG 2019 - SQL features
Sangam 19 - PLSQL still the coolest
Sangam 19 - PLSQL still the coolest
Sangam 19 - Analytic SQL
Sangam 19 - Analytic SQL
Sangam 19 - Successful Applications on Autonomous
Sangam 19 - Successful Applications on Autonomous
UKOUG - 25 years of hints and tips
UKOUG - 25 years of hints and tips
OOW19 - Flashback, not just for DBAs
OOW19 - Flashback, not just for DBAs
Flashback ITOUG
Flashback ITOUG
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
Wellington APAC Groundbreakers tour - Upgrading to the 12c Optimizer
OOW19 - Ten Amazing SQL features
OOW19 - Ten Amazing SQL features
SQL techniques for faster applications
SQL techniques for faster applications
Oracle Database 12c Application Development
Oracle Database 12c Application Development
Similar to Latin America Tour 2019 - pattern matching
Wellington APAC Groundbreakers tour - SQL Pattern Matching
Wellington APAC Groundbreakers tour - SQL Pattern Matching
Connor McDonald
18c and 19c features for DBAs
18c and 19c features for DBAs
Connor McDonald
Melbourne Groundbreakers Tour - Upgrading without risk
Melbourne Groundbreakers Tour - Upgrading without risk
Connor McDonald
Sangam 18 - The New Optimizer in Oracle 12c
Sangam 18 - The New Optimizer in Oracle 12c
Connor McDonald
OpenWorld 2018 - SQL Tuning in 20 mins
OpenWorld 2018 - SQL Tuning in 20 mins
Connor McDonald
ITOUG 2019 - 18c, 19c features
ITOUG 2019 - 18c, 19c features
Connor McDonald
Top 10 SQL Performance tips & tricks for Java Developers
Top 10 SQL Performance tips & tricks for Java Developers
gvenzl
Hyderabad Mar 2019 - Database 18c / 19c
Hyderabad Mar 2019 - Database 18c / 19c
Connor McDonald
Analytic functions in Oracle SQL - BIWA 2017
Analytic functions in Oracle SQL - BIWA 2017
Connor McDonald
Perth APAC Groundbreakers tour - 18c features
Perth APAC Groundbreakers tour - 18c features
Connor McDonald
Melbourne Groundbreakers Tour - Hints and Tips
Melbourne Groundbreakers Tour - Hints and Tips
Connor McDonald
OG Yatra - Flashback, not just for developers
OG Yatra - Flashback, not just for developers
Connor McDonald
OG Yatra - upgrading to the new 12c+ optimizer
OG Yatra - upgrading to the new 12c+ optimizer
Connor McDonald
Redo logfile addition in oracle rac 12c
Redo logfile addition in oracle rac 12c
Debasish Nayak
Five more things about Oracle SQL and PLSQL
Five more things about Oracle SQL and PLSQL
Connor McDonald
A close encounter_with_real_world_and_odd_perf_issues
A close encounter_with_real_world_and_odd_perf_issues
Riyaj Shamsudeen
Flashback features in Oracle - UKOUG 2017
Flashback features in Oracle - UKOUG 2017
Connor McDonald
ILOUG 2019 - SQL features for Developers
ILOUG 2019 - SQL features for Developers
Connor McDonald
ILOUG 2019 - Flashback, the forgotten feature
ILOUG 2019 - Flashback, the forgotten feature
Connor McDonald
Advanced SQL - Quebec 2014
Advanced SQL - Quebec 2014
Connor McDonald
Similar to Latin America Tour 2019 - pattern matching
(20)
Wellington APAC Groundbreakers tour - SQL Pattern Matching
Wellington APAC Groundbreakers tour - SQL Pattern Matching
18c and 19c features for DBAs
18c and 19c features for DBAs
Melbourne Groundbreakers Tour - Upgrading without risk
Melbourne Groundbreakers Tour - Upgrading without risk
Sangam 18 - The New Optimizer in Oracle 12c
Sangam 18 - The New Optimizer in Oracle 12c
OpenWorld 2018 - SQL Tuning in 20 mins
OpenWorld 2018 - SQL Tuning in 20 mins
ITOUG 2019 - 18c, 19c features
ITOUG 2019 - 18c, 19c features
Top 10 SQL Performance tips & tricks for Java Developers
Top 10 SQL Performance tips & tricks for Java Developers
Hyderabad Mar 2019 - Database 18c / 19c
Hyderabad Mar 2019 - Database 18c / 19c
Analytic functions in Oracle SQL - BIWA 2017
Analytic functions in Oracle SQL - BIWA 2017
Perth APAC Groundbreakers tour - 18c features
Perth APAC Groundbreakers tour - 18c features
Melbourne Groundbreakers Tour - Hints and Tips
Melbourne Groundbreakers Tour - Hints and Tips
OG Yatra - Flashback, not just for developers
OG Yatra - Flashback, not just for developers
OG Yatra - upgrading to the new 12c+ optimizer
OG Yatra - upgrading to the new 12c+ optimizer
Redo logfile addition in oracle rac 12c
Redo logfile addition in oracle rac 12c
Five more things about Oracle SQL and PLSQL
Five more things about Oracle SQL and PLSQL
A close encounter_with_real_world_and_odd_perf_issues
A close encounter_with_real_world_and_odd_perf_issues
Flashback features in Oracle - UKOUG 2017
Flashback features in Oracle - UKOUG 2017
ILOUG 2019 - SQL features for Developers
ILOUG 2019 - SQL features for Developers
ILOUG 2019 - Flashback, the forgotten feature
ILOUG 2019 - Flashback, the forgotten feature
Advanced SQL - Quebec 2014
Advanced SQL - Quebec 2014
More from Connor McDonald
APEX tour 2019 - successful development with autonomous
APEX tour 2019 - successful development with autonomous
Connor McDonald
APAC Groundbreakers 2019 - Perth/Melbourne
APAC Groundbreakers 2019 - Perth/Melbourne
Connor McDonald
OOW19 - Read consistency
OOW19 - Read consistency
Connor McDonald
OOW19 - Slower and less secure applications
OOW19 - Slower and less secure applications
Connor McDonald
OOW19 - Killing database sessions
OOW19 - Killing database sessions
Connor McDonald
OG Yatra - 25 years of hints and tips
OG Yatra - 25 years of hints and tips
Connor McDonald
Kscope19 - Understanding the basics of SQL processing
Kscope19 - Understanding the basics of SQL processing
Connor McDonald
APEX Connect 2019 - successful application development
APEX Connect 2019 - successful application development
Connor McDonald
More from Connor McDonald
(8)
APEX tour 2019 - successful development with autonomous
APEX tour 2019 - successful development with autonomous
APAC Groundbreakers 2019 - Perth/Melbourne
APAC Groundbreakers 2019 - Perth/Melbourne
OOW19 - Read consistency
OOW19 - Read consistency
OOW19 - Slower and less secure applications
OOW19 - Slower and less secure applications
OOW19 - Killing database sessions
OOW19 - Killing database sessions
OG Yatra - 25 years of hints and tips
OG Yatra - 25 years of hints and tips
Kscope19 - Understanding the basics of SQL processing
Kscope19 - Understanding the basics of SQL processing
APEX Connect 2019 - successful application development
APEX Connect 2019 - successful application development
Recently uploaded
Architecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Orbitshub
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Orbitshub
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Angeliki Cooney
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Edi Saputra
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
The Digital Insurer
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Zilliz
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
UiPathCommunity
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Jeffrey Haguewood
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
The Digital Insurer
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
Rustici Software
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Khushali Kathiriya
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
rafiqahmad00786416
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Juan lago vázquez
Recently uploaded
(20)
Architecting Cloud Native Applications
Architecting Cloud Native Applications
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
Repurposing LNG terminals for Hydrogen Ammonia: Feasibility and Cost Saving
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
AXA XL - Insurer Innovation Award Americas 2024
AXA XL - Insurer Innovation Award Americas 2024
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Latin America Tour 2019 - pattern matching
1.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. |Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | bienvenido
2.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. |Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | Lo siento, no hablo español :-(
3.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. |Copyright © 2018, Oracle and/or its affiliates. All rights reserved. | But I will do my best to help Pero haré todo lo posible para ayudar
4.
Pattern Matching Connor McDonald Database
Advocate La coincidencia de patrones
5.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. Connor McDonald
6.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. 6
7.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. 7
8.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. | Me youtube bit.ly/youtube-connor blog bit.ly/blog-connor twitter bit.ly/twitter-connor 400+ posts mainly on database & development 250 technical videos, new uploads every week Más de 400 publicaciones principalmente en bases de datos y desarrollo 250 videos técnicos, nuevas cargas cada semana
9.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. etc... facebook bit.ly/facebook-connor linkedin bit.ly/linkedin-connor instagram bit.ly/instagram-connor slideshare bit.ly/slideshare-connor
10.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. 10https://asktom.oracle.com
11.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. https://asktom.oracle.com/officehours
12.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 12 SQL> lots and lots and lots of code :-) SQL> mucho mucho mucho código
13.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | classical problem 13 problema clásico
14.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno, ename 2 from emp 3 order by 1,2; DEPTNO ENAME ---------- ---------- 10 CLARK 10 KING 10 MILLER 20 ADAMS 20 FORD 20 JONES 20 SCOTT 20 SMITH 30 ALLEN 30 BLAKE 30 JAMES 30 MARTIN 30 TURNER 30 WARD 14
15.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | DEPTNO MEMBERS ---------- ------------------------------------- 10 CLARK,KING,MILLER 20 SMITH,JONES,SCOTT,ADAMS,FORD 30 ALLEN,WARD,MARTIN,BLAKE,TURNER,JAMES 15
16.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | how we used to do it 16 como solíamos hacerlo
17.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno , rtrim(ename,',') enames 2 from ( select deptno,ename,rn 3 from emp 4 model 5 partition by (deptno) 6 dimension by ( 7 row_number() over 8 (partition by deptno order by ename) rn 9 ) 10 measures (cast(ename as varchar2(40)) ename) 11 rules 12 ( ename[any] 13 order by rn desc = ename[cv()]||','||ename[cv()+1]) 14 ) 15 where rn = 1 16 order by deptno; DEPTNO ENAMES ---------- ---------------------------------------- 10 CLARK,KING,MILLER 20 ADAMS,FORD,JONES,SCOTT,SMITH 30 ALLEN,BLAKE,JAMES,MARTIN,TURNER,WARD 17
18.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno, 2 substr(max(sys_connect_by_path(ename, ',')), 2) members 3 from (select deptno, ename, 4 row_number () 5 over (partition by deptno order by empno) rn 6 from emp) 7 start with rn = 1 8 connect by prior rn = rn - 1 9 and prior deptno = deptno 10 group by deptno 11 / DEPTNO MEMBERS ---------- --------------------------------------------------------- 30 ALLEN,WARD,MARTIN,BLAKE,TURNER,JAMES 20 SMITH,JONES,SCOTT,ADAMS,FORD 10 CLARK,KING,MILLER 18
19.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno, 2 xmltransform 3 ( sys_xmlagg 4 ( sys_xmlgen(ename) 5 ), 6 xmltype 7 ( 8 '<?xml version="1.0"?><xsl:stylesheet version="1.0" 9 xmlns:xsl="http://www.w3.org/1999/XSL/Transform"> 10 <xsl:template match="/"> 11 <xsl:for-each select="/ROWSET/ENAME"> 12 <xsl:value-of select="text()"/>,</xsl:for-each> 13 </xsl:template> 14 </xsl:stylesheet>' 15 ) 16 ).getstringval() members 17 from emp 18 group by deptno; DEPTNO MEMBERS ---------- -------------------------------------------------------- 10 CLARK,MILLER,KING, 20 SMITH,FORD,ADAMS,SCOTT,JONES, 30 ALLEN,JAMES,TURNER,BLAKE,MARTIN,WARD, 19
20.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> create or replace type string_agg_type as object 2 ( 3 total varchar2(4000), 4 5 static function 6 ODCIAggregateInitialize(sctx IN OUT string_agg_type ) 7 return number, 8 9 member function 10 ODCIAggregateIterate(self IN OUT string_agg_type , 11 value IN varchar2 ) 12 return number, 13 14 member function 15 ODCIAggregateTerminate(self IN string_agg_type, 16 returnValue OUT varchar2, 17 flags IN number) 18 return number, 19 20 member function 21 ODCIAggregateMerge(self IN OUT string_agg_type, 22 ctx2 IN string_agg_type) 23 return number 24 ); 25 / 20
21.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 11g 21
22.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno, 2 listagg( ename, ',') 3 within group (order by empno) members 4 from emp 5 group by deptno; DEPTNO MEMBERS ---------- ----------------------------------------- 10 CLARK,KING,MILLER 20 SMITH,JONES,SCOTT,ADAMS,FORD 30 ALLEN,WARD,MARTIN,BLAKE,TURNER,JAMES 22
23.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | question solution 23 pregunta solución
24.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select deptno, 2 listagg( ename, ',') 3 within group (order by empno) members 4 from emp 5 group by deptno; 24
25.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | still challenges 25 todavía hay desafíos
26.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | example 26 ejemplo
27.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 27
28.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 28
29.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "I want to reward active customers… If their transaction volume grows by 20% in a day, or grows by 10% for 2 consecutive days, then show me their details" 29 "Quiero recompensar a los clientes activos ... Si su volumen de transacciones crece un 20% en un día, o crece un 10% durante 2 días consecutivos, entonces muéstrame sus detalles "
30.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from cust_summary 2 order by 1,2; CUSTOMER DTE TXN_CNT ------------------------------ --------- ---------- Gerald Jones 06-FEB-17 100 Gerald Jones 07-FEB-17 130 Gerald Jones 08-FEB-17 145 Gerald Jones 09-FEB-17 200 Gerald Jones 10-FEB-17 225 Gerald Jones 11-FEB-17 255 Gerald Jones 12-FEB-17 285 Gerald Jones 13-FEB-17 315 John Smith 01-FEB-17 100 John Smith 02-FEB-17 103 John Smith 03-FEB-17 116 John Smith 04-FEB-17 129 John Smith 05-FEB-17 142 Sue Brown 06-FEB-17 50 Sue Brown 07-FEB-17 53 Sue Brown 08-FEB-17 72 ... 30 20% in a day 10% each day over 2 days
31.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 31
32.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | analytics 32 analítica
33.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select row_number() OVER ( order by sal ) 2 from emp 3 ... 33 https://bit.ly/analytic_sql
34.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "I want to reward active customers… If their transaction volume grows by 20% in a day, or grows by 10% for 2 consecutive days, then show me the details" 34 En un día
35.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, dte, cumu_txns, next_txn, next_txn - cumu_txns daily_txn 2 from ( 3 select customer, dte, cumu_txns 4 , lead(cumu_txns) over ( 5 partition by customer order by dte 6 ) next_txn 7 from cust_summary 8 ); CUSTOMER DTE CUMU_TXNS NEXT_TXN DAILY_TXN ------------------------------ --------- ---------- ---------- ---------- Gerald Jones 06-FEB-17 100 130 30 Gerald Jones 07-FEB-17 130 145 15 Gerald Jones 08-FEB-17 145 200 55 Gerald Jones 09-FEB-17 200 225 25 Gerald Jones 10-FEB-17 225 255 30 35
36.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "I want to reward active customers… If their transaction volume grows by 20% in a day, or grows by 10% for 2 consecutive days, then show me the details" 36 crece un 20% en un día, o crece en un 10%
37.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, dte, cumu_txns, next_txn, next_txn - cumu_txns daily_txn 2 , case 3 when next_txn >= cumu_txns * 1.20 then 'FAST' 4 when next_txn >= cumu_txns * 1.10 then 'SLOW' 5 end growth_class 6 from ( 7 select customer, dte, cumu_txns 8 , lead(cumu_txns) over ( 9 partition by customer order by dte 10 ) next_txn 11 from cust_summary 12 ); CUSTOMER DTE CUMU_TXNS NEXT_TXN DAILY_TXN GROWTH_CLASS ------------------------------ --------- ---------- ---------- ---------- ------------ Gerald Jones 06-FEB-17 100 130 30 FAST Gerald Jones 07-FEB-17 130 145 15 SLOW Gerald Jones 08-FEB-17 145 200 55 FAST Gerald Jones 09-FEB-17 200 225 25 SLOW Gerald Jones 10-FEB-17 225 255 30 SLOW Gerald Jones 11-FEB-17 255 285 30 SLOW Gerald Jones 12-FEB-17 285 315 30 SLOW 37
38.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "I want to reward active customers… If their transaction volume grows by 20% in a day, or grows by 10% for 2 consecutive days, then show me the details" 38 2 días consecutivos
39.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, dte, cumu_txns, next_txn, daily_txn 2 , case 3 when growth_class is not null and 4 ( lag(growth_class) over ( 5 partition by customer order by dte 6 ) is null 7 or 8 lag(growth_class) over ( 9 partition by customer order by dte 10 ) != growth_class 11 ) 12 then dte 13 end growthstartdate 14 from ( 15 select customer, dte, cumu_txns, next_txn, next_txn - cumu_txns daily_txn 16 , case 17 when next_txn >= cumu_txns * 1.20 then 'FAST' 18 when next_txn >= cumu_txns * 1.10 then 'SLOW' 19 end growth_class 20 from ( 21 select customer, dte, cumu_txns 22 , lead(cumu_txns) over ( 23 partition by customer order by dte 24 ) next_txn 25 from cust_summary 26 ) ) 39
40.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, dte, cumu_txns, next_txn, daily_txn 2 , last_value(growthstartdate ignore nulls) over ( 3 partition by customer, growth_class order by dte 4 rows between unbounded preceding and current row 5 ) startdate 6 from ( 7 select customer, growth_class, dte, cumu_txns, next_txn, daily_txn 8 , case 9 when growth_class is not null and 10 ( lag(growth_class) over ( 11 partition by customer order by dte ) is null or 12 lag(growth_class) over ( 13 partition by customer order by dte 14 ) != growth_class ) 15 then dte 16 end growthstartdate 17 from ( 18 select customer, dte, cumu_txns, next_txn, next_txn - cumu_txns daily_txn 19 , case when next_txn >= cumu_txns * 1.20 then 'FAST' 20 when next_txn >= cumu_txns * 1.10 then 'SLOW' 21 end growth_class 22 from ( 23 select customer, dte, cumu_txns 24 , lead(cumu_txns) over (partition by customer order by dte 25 ) next_txn 26 from cust_summary 27 ) ) ) 28 where growth_class is not null 40
41.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate 2 , min(cumu_txns) keep (dense_rank first order by dte) start_txn 3 , max(dte) enddate 4 , max(next_txn) keep (dense_rank last order by dte) end_txn 5 , avg(daily_txn) avg_daily_txn 6 from ( 7 select customer, growth_class, dte, cumu_txns, next_txn, daily_txn 8 , last_value(growthstartdate ignore nulls) over ( 9 partition by customer, growth_class order by dte 10 rows between unbounded preceding and current row 11 ) startdate 12 from ( 13 select customer, growth_class, dte, cumu_txns, next_txn, daily_txn 14 , case when growth_class is not null and 15 ( lag(growth_class) over (partition by customer order by dte) is null or 16 lag(growth_class) over (partition by customer order by dte) != growth_class ) 17 then dte end growthstartdate 18 from ( 19 select customer, dte, cumu_txns, next_txn, next_txn - cumu_txns daily_txn 20 , case 21 when next_txn >= cumu_txns * 1.20 then 'FAST' 22 when next_txn >= cumu_txns * 1.10 then 'SLOW' 23 end growth_class 24 from ( 25 select customer, dte, cumu_txns 26 , lead(cumu_txns) over ( 27 partition by customer order by dte 28 ) next_txn 29 from cust_summary 30 ) ) ) 31 where growth_class is not null ) 32 group by customer, growth_class, startdate 33 having count(*) >= case growth_class when 'FAST' then 1 when 'SLOW' then 2 end 34 order by customer, startdate; 41
42.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | CUSTOMER GROWTH_CLASS STARTDATE START_TXN END_TXN AVG_DAILY_TXN ------------------- ---------------- --------- ---------- ---------- ------------- Gerald Jones FAST 06-FEB-17 100 130 30 Gerald Jones FAST 08-FEB-17 145 200 55 Gerald Jones SLOW 09-FEB-17 200 315 28.75 John Smith SLOW 02-FEB-17 103 160 14.25 John Smith FAST 07-FEB-17 165 210 45 Sue Brown FAST 07-FEB-17 53 97 22 42
43.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | easy! 43 ¡fácil!
44.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 44
45.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 45
46.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "W T F ?!?"
47.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | analytics are about computation 47 la analítica se trata cálculo
48.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | users interested in patterns 48 los usuarios están interesados en patrones
49.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | question solution 49
50.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | need a syntax ... 50 necesitamos una sintaxis
51.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | ... to describe patterns 51 para describir patrones
52.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte 6 measures 7 classifier() as growth_class 8 , first(dte) as startdate 9 , first(cumu_txns) as start_txn 10 , last(dte) as enddate 11 , next(cumu_txns) as end_txn 12 , (next(cumu_txns) - first(cumu_txns)) / count(*) as avg_daily_txn 13 one row per match after match skip past last row 14 pattern ( fast+ | slow{2,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 18 ) 19 order by customer, startdate; 52
53.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 53 CUSTOMER STARTDATE START_TXN END_TXN AVG_DAILY_TXN ------------------- --------- ---------- ---------- ------------- Gerald Jones 06-FEB-17 100 130 30 Gerald Jones 08-FEB-17 145 200 55 Gerald Jones 09-FEB-17 200 315 28.75 John Smith 02-FEB-17 103 160 14.25 John Smith 07-FEB-17 165 210 45 Sue Brown 07-FEB-17 53 97 22
54.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte 6 measures 7 classifier() as growth_class 8 , first(dte) as startdate 9 , first(cumu_txns) as start_txn 10 , last(dte) as enddate 11 , next(cumu_txns) as end_txn 12 , (next(cumu_txns) - first(cumu_txns)) / count(*) as avg_daily_txn 13 one row per match after match skip past last row 14 pattern ( fast+ | slow{2,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 18 ) 19 order by customer, startdate; 54
55.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 55 CUSTOMER GROWTH_CLASS STARTDATE START_TXN END_TXN AVG_DAILY_TXN ------------------- ---------------- --------- ---------- ---------- ------------- Gerald Jones FAST 06-FEB-17 100 130 30 Gerald Jones FAST 08-FEB-17 145 200 55 Gerald Jones SLOW 09-FEB-17 200 315 28.75 John Smith SLOW 02-FEB-17 103 160 14.25 John Smith FAST 07-FEB-17 165 210 45 Sue Brown FAST 07-FEB-17 53 97 22
56.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | done ! 56 hecho
57.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "W T F "
58.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "Hello World" 58
59.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t; X ---------- 1 2 3 5 6 10 11 16 17 9 19 21 30 59 find the odd numbers X ---------- 1 3 5 11 17 9 19 21 encuentra los números impares
60.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 60
61.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select x, 2 case when mod(x,2) = 1 then 'Odd' end odd 3 from t; X ODD ---------- --- 1 Odd 2 3 Odd 5 Odd 6 10 11 Odd 16 17 Odd 9 Odd 19 Odd 21 Odd 30 61
62.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select x, 2 case when mod(x,2) = 1 then 'Odd' end odd 3 from t 4 where mod(x,2) = 1; X ODD ---------- --- 1 Odd 3 Odd 5 Odd 11 Odd 17 Odd 9 Odd 19 Odd 21 Odd 62
63.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "pattern matching talk?" podemos hablar de patrones?
64.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from 3 ( select x, 4 case when mod(x,2) = 1 then 'Odd' end odd 5 from t 6 ) 7 where odd = 1; X ODD ---------- --- 1 Odd 3 Odd 5 Odd 11 Odd 17 Odd 9 Odd 19 Odd 21 Odd 64 define a variable pattern = rule using that variable patrón = regla usando esa variable definir una variable
65.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 all rows per match 5 pattern ( odd ) 6 define odd as mod(x,2) = 1 7 ); X ODD ---------- --- 1 Odd 3 Odd 5 Odd 11 Odd 17 Odd 9 Odd 19 Odd 21 Odd 65 define a variable pattern = rule using that variable
66.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "But.......... WHY?" "¿Pero por qué?"
67.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t; X ---------- 1 2 3 5 6 10 11 16 17 9 19 21 30 67 find consecutive odd numbers encontrar consecutivo números impares
68.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | consecutive = order 68
69.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t; X ---------- 1 2 3 5 6 10 11 16 17 9 19 21 30 69 find consecutive odd numbers order by x; X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 encontrar consecutivo números impares
70.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( odd odd ) 7 define odd as mod(x,2) = 1 8 ); X ---------- 3 5 17 19 70 X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 ?
71.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 71 found a match encontré una coincidencia X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 resume from next row reanudar desde el próximo registro
72.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 after match skip to next row 7 pattern ( odd odd ) 8 define odd as mod(x,2) = 1 9 ); X ---------- 3 5 17 19 21 72 X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30
73.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "Hello World" #2 73
74.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | my journey 74 mi viaje
75.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by x; X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 75 find contiguous numbers and show the range LO HI ---------- ---------- 1 3 5 6 9 11 16 17 19 19 21 21 30 30 encontrar números contiguos y mostrar el rango
76.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( contig* ) 7 define contig as x = prev(x) + 1 8 ); 76 regular expression style format formato de estilo de expresión regular
77.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "If you have a text parsing problem, you can use regular expressions… … now you have two problems" 77 "Si tiene un problema de análisis de texto, puedes usar expresiones regulares ... ahora tienes dos problemas
78.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( contig* ) 7 define contig as x = prev(x) + 1 8 ); X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 78
79.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 79
80.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( contig+ ) 7 define contig as x = prev(x) + 1 8 ); X ---------- 2 3 6 10 11 17 80 1 2 3 5 6 9 10 11 16 1719 21 30
81.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( starting_row contig* ) 7 define 8 contig as x = prev(x) + 1, 9 starting_row as 1=1 10 ); 81 "every row is the potentially the start of a contiguous sequence…" cada registro es potencialmente el comienzo de una secuencia contigua ... "
82.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( starting_row contig* ) 7 define 8 contig as x = prev(x) + 1, 9 starting_row as 1=1 10 ); 82 "... followed by zero or more contiguous values" cada registro es potencialmente el comienzo de una secuencia contigua ...
83.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( starting_row contig* ) 7 define 8 contig as x = prev(x) + 1, 9 starting_row as 1=1 10 ); 83
84.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by x 5 all rows per match 6 pattern ( starting_row contig* ) 7 define contig as x = prev(x) + 1 ); X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 84
85.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | measures 85 what we want to see lo que queremos ver
86.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 all rows per match 7 pattern ( starting_row contig* ) 8 define contig as x = prev(x) + 1 ); X LO HI ---------- ---------- ---------- 1 1 1 2 1 2 3 1 3 5 5 5 6 5 6 9 9 9 10 9 10 11 9 11 16 16 16 17 16 17 19 19 19 21 21 21 30 30 30 86 first/last row in the matched pattern primera / última fila en el patrón combinado
87.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "I just want THOSE rows... Solo quiero esos registros
88.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "... not ALL of the rows" no todos los registros
89.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 all rows per match 7 pattern ( starting_row contig* ) 8 define contig as x = prev(x) + 1 ); LO HI ---------- ---------- 1 3 5 6 9 11 16 17 19 19 21 21 30 30 89 6 one row per match
90.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 7 pattern ( starting_row contig* ) 8 define contig as x = prev(x) + 1 ); LO HI ---------- ---------- 1 3 5 6 9 11 16 17 19 19 21 21 30 30 90 one row per match = default 6 one row per match
91.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | how big is each range ? 91 ¿Qué tan grande es cada rango?
92.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 , count(*) range 7 pattern ( starting_row contig* ) 8 define contig as x = prev(x) + 1 ); LO HI RANGE ---------- ---------- ---------- 1 3 3 5 6 2 9 11 3 16 17 2 19 19 1 21 21 1 30 30 1 92
93.
Copyright © 2018,
Oracle and/or its affiliates. All rights reserved. "That's not a normal COUNT(*)" que COUNT(*) no es normal
94.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | keywords 94
95.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 95 include the column / expression incluir la columna / expresión
96.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 96 value from first row in match valor de la primera fila del partid
97.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 97 value from last row in match valor de la última registros del partido
98.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 98 value from previous referred row valor de la registros referida anterior
99.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 99 value from next referred row valor de la siguiente registros referida
100.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 100 count of rows in the match recuento de registros en el partido
101.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 101 last encountered row in 'contig' última registro encontrada en 'contig'
102.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define contig as x = prev(x) + 1 15 ); X LO HI PRV NXT RANGE CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 2 6 5 6 5 9 2 6 1 11 9 11 10 16 3 11 2 17 16 17 16 19 2 17 1 19 19 19 17 21 1 0 21 21 21 19 30 1 0 30 30 30 21 1 0 102 count of 'contig' rows conteo de registros 'contig'
103.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | there's more :-) 109 hay más
104.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 x x 5 , first(x) lo 6 , last(x) hi 7 , prev(x) prv 8 , next(x) nxt 9 , count(*) range 10 , final last(x) fin 11 , contig.x as contig_x 12 , count(contig.*) contig_count 13 pattern ( starting_row contig* ) 14 define 15 contig as x = prev(x) + 1 16 ); X LO HI PRV NXT RANGE FIN CONTIG_X CONTIG_COUNT ---------- ---------- ---------- ---------- ---------- ---------- ---------- ---------- ------------ 3 1 3 2 5 3 3 3 2 6 5 6 5 9 2 6 6 1 11 9 11 10 16 3 11 11 2 17 16 17 16 19 2 17 17 1 19 19 19 17 21 1 19 0 21 21 21 19 30 1 21 0 30 30 30 21 1 30 0 110 similar expressions in DEFINE section expresiones similares en la sección DEFINE
105.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures ... pattern ... 4 DEFINE 5 p1 as x = 1 6 , p2 as first(x) = 1 7 , p3 as last(x) = 1 8 , p4 as prev(x) = 1 9 , p5 as next(x) = 1 10 , p6 as count(*) = 1 11 , p7 as contig.x = 1 12 , p8 as count(contig.*) = 1 13 ); 111
106.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | review 112
107.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, enddate, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte 6 measures 7 classifier() as growth_class 8 , first(dte) as startdate 9 , first(cumu_txns) as start_txn 10 , last(dte) as enddate 11 , next(cumu_txns) as end_txn 12 , (next(cumu_txns) - first(cumu_txns)) / count(*) as avg_daily_txn 13 one row per match after match skip past last row 14 pattern ( fast+ | slow{3,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 18 ) 19 order by customer, startdate; 113 SQL> select customer, growth_class, startdate, start_txn, enddate, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte 6 measures 7 classifier() as growth_class 8 , first(dte) as startdate 9 , first(cumu_txns) as start_txn 10 , last(dte) as enddate 11 , next(cumu_txns) as end_txn 12 , (next(cumu_txns) - first(cumu_txns)) / count(*) as avg_daily_txn 13 one row per match after match skip past last row 14 pattern ( fast+ | slow{2,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 18 ) 19 order by customer, startdate;
108.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, enddate, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte ... 114
109.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, enddate, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte ... 115 logical subsets critical as per analytics según el análisis
110.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | patterns 116
111.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 117 SQL> select customer, growth_class, startdate, start_txn, enddate, ... 14 pattern ( fast+ | slow{3,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 regular expression syntax
112.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | * + ? {3} {3,} {3,6} {,2} ? 118 0 or more matches 1 or more matches 0 or 1 match exactly 3 matches 3 or more matches between 3 and 6 matches between 0 and 2 matches reluctance
113.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | a|b (a b){3}c permute a b c ^ $ {- a -} 119 a or b 3 times ( a then b ) then c abc,acb,bac,bca,cab,cba first row in pattern last row in pattern (^ p+ $) not a
114.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | reluctance 120 reluctancia
115.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "How long was someone's job?... i.e hired, worked for some time, then fired" 121 http://www.kibeha.dk/2015/07/row-pattern-matching-nested-within.html "¿Cuánto tiempo estuvo el trabajo de alguien? ... es decir Contratado, trabajado por algún tiempo, luego despedido "
116.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by 1; SEQ COL ---------- ---------- 1 Hired 2 Worked 3 Worked 4 Worked 5 Worked 6 Worked 7 Terminated 8 Hired 9 Worked 10 Worked 11 Worked 12 Worked 13 Worked 14 Terminated 15 Hired 16 Worked 17 Worked 18 Worked 19 Worked 20 Worked 21 Terminated 122
117.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by 1; SEQ COL ---------- ---------- 1 Hired 2 Worked 3 Worked 4 Worked 5 Worked 6 Worked 7 Terminated 8 Hired 9 Worked 10 Worked 11 Worked 12 Worked 13 Worked 14 Terminated 15 Hired 16 Worked 17 Worked 18 Worked 19 Worked 20 Worked 21 Terminated 123
118.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by seq 5 measures 6 first(col) as fcol, 7 col as col, 8 first(seq) p_start, 9 last(seq) p_end, 10 count(*) tot 11 one row per match 12 pattern ( hired worked* fired ) 13 define 14 hired as col = 'Hired', 15 fired as col = 'Terminated' 16 ); FCOL COL P_START P_END TOT ---------- ---------- ---------- ---------- ---------- Hired Terminated 1 21 21 124 "always true" esto siempre es cierto
119.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 125
120.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | greediness 126
121.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by 1; SEQ COL ---------- ---------- 1 Hired 2 Worked 3 Worked 4 Worked 5 Worked 6 Worked 7 Terminated 8 Hired 9 Worked 10 Worked 11 Worked 12 Worked 13 Worked 14 Terminated 15 Hired 16 Worked 17 Worked 18 Worked 19 Worked 20 Worked 21 Terminated 127 pattern ( hired worked* fired ) "Cool, I have my 'hired'" "Awesome, I found 'worked' (ie, anything)" "Consume as many rows as I can to see if I can find 'fired'" "Woo Hoo!" He encontrado mi 'Hired' He encontrado mi 'Worked' "Consume tantas registros como puedo ver si puedo encontrar 'Fired'
122.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by seq 5 measures 6 first(col) as fcol, 7 col as col, 8 first(seq) p_start, 9 last(seq) p_end, 10 count(*) tot 11 one row per match 12 pattern ( hired worked*? fired ) 13 define 14 hired as col = 'Hired', 15 fired as col = 'Terminated' ); FCOL COL P_START P_END TOT ---------- ---------- ---------- ---------- ---------- Hired Terminated 1 7 7 Hired Terminated 8 14 7 Hired Terminated 15 21 7 128 reluctantly search
123.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | be careful 129 ten cuidado
124.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 130
125.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by 1; SEQ COL ---------- ---------- 1 Hired 2 Worked 3 Worked 4 Worked 5 Worked 6 Worked 7 Terminated 8 Worked 9 Worked 10 Worked 11 Worked 12 Worked 13 Worked 14 Worked 15 Worked 16 Worked 17 Worked 18 Worked 19 Worked 20 Worked 21 Worked 131
126.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by seq 5 measures 6 first(col) as fcol, 7 col as col, 8 first(seq) p_start, ... ORA-4030: Out of process memory when ... 132
127.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by seq 5 measures 6 first(col) as fcol, 7 col as col, 8 first(seq) p_start, 9 last(seq) p_end, 10 count(*) tot 11 one row per match 12 pattern ( hired worked* fired ) 13 define 14 hired as col = 'Hired', 15 worked as col = 'Worked', 15 fired as col = 'Terminated' ); 133 worked+
128.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t order by 1; SEQ COL ---------- ---------- 1 Hired 2 Worked 3 Worked 4 Worked 5 Worked 6 Worked 7 Terminated 8 Hired 9 Worked 10 Worked 11 Worked 12 Worked 13 Worked 14 Terminated 15 Hired 16 Worked 17 Worked 18 Worked 19 Worked 20 Worked 21 Terminated 134 pattern ( hired worked+ fired ) "Cool, I have my 'hired'" "Awesome, I found 'worked'" "worked+ has finished, now look for fired'" He encontrado mi 'Hired' He encontrado mi 'Worked' worked+ ha terminado, ahora busca 'fired'
129.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | “imagination is more important than knowledge” - Albert Einstein 135 "La imaginación es más importante que el conocimiento "
130.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 136 examples ejemplos
131.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "count of my subordinates" 137 http://www.kibeha.dk/2015/07/row-pattern-matching-nested-within.html "cuenta de mis subordinados"
132.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | EMPNO ENAME REPORTS --------- -------------------- ---------- 7839 KING 13 7566 JONES 4 7788 SCOTT 1 7876 ADAMS 0 7902 FORD 1 7369 SMITH 0 7698 BLAKE 5 7499 ALLEN 0 7521 WARD 0 7654 MARTIN 0 7844 TURNER 0 7900 JAMES 0 7782 CLARK 1 7934 MILLER 0 138
133.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 139 conventional style
134.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select empno 2 , lpad(' ', (level-1)*2) || ename as ename 3 , ( select count(*) 4 from emp sub 5 start with sub.mgr = emp.empno 6 connect by sub.mgr = prior sub.empno 7 ) reports 8 from emp 9 start with mgr is null 10 connect by mgr = prior empno 11 order siblings by empno; EMPNO ENAME REPORTS ---------- -------------------- ---------- 7839 KING 13 7566 JONES 4 7788 SCOTT 1 7876 ADAMS 0 7902 FORD 1 ... 140 select count(*) from emp sub start with sub.mgr = emp.empno connect by sub.mgr = prior sub.empno select count(*) from emp sub start with sub.mgr = emp.empno connect by sub.mgr = prior sub.empno select count(*) from emp sub start with sub.mgr = emp.empno connect by sub.mgr = prior sub.empno select count(*) from emp sub start with sub.mgr = emp.empno connect by sub.mgr = prior sub.empno select count(*) from emp sub start with sub.mgr = emp.empno connect by sub.mgr = prior sub.empno
135.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 141 where's the pattern ? SQL> select ... 2 from cust_summary 3 match_recognize ( 4 order by ... 5 pattern ... ... ¿Dónde está el patrón?
136.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 142 ordering sequence
137.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 143 "starting level" "nivel inicial"
138.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 144 "starting level" "next level higher then starting level?" "siguiente nivel más alto entonces nivel inicial?
139.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 145 "starting level" "next level higher then starting level?" "siguiente nivel más alto entonces nivel inicial?
140.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 146 "starting level" "next level higher then starting level?" "siguiente nivel más alto entonces nivel inicial?
141.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 147 "Done! How many?" "¡Listo! ¿Cuántos?"
142.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> with raw_data as ( 2 select lvl, empno, ename, rownum as rn 3 from ( select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ) 8 ) ... 148 as before
143.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | ... 9 select empno 10 , lpad(' ', (lvl-1)*2) || ename as ename 11 , reports 12 from raw_data 13 match_recognize ( 14 order by rn 15 measures 16 starting_level.rn as rn 17 , starting_level.lvl as lvl 18 , starting_level.empno as empno 19 , starting_level.ename as ename 20 , count(higher_level.lvl) as reports 21 one row per match 22 after match skip to next row 23 pattern (starting_level higher_level*) 24 define higher_level as lvl > starting_level.lvl 25 ) 26 order by rn; 149
144.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> with raw_data as ( 2 select lvl, empno, ename, rownum as rn 3 from ( select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ) 8 ) 9 select empno 10 , lpad(' ', (lvl-1)*2) || ename as ename 11 , reports 12 from raw_data 13 match_recognize ( 14 order by rn 15 measures 16 starting_level.rn as rn 17 , starting_level.lvl as lvl 18 , starting_level.empno as empno 19 , starting_level.ename as ename 20 , count(higher_level.lvl) as reports 21 one row per match 22 pattern (starting_level higher_level*) 23 define higher_level as lvl > starting_level.lvl 24 ) 25 order by rn; EMPNO ENAME REPORTS ---------- -------------------- ---------- 7839 KING 13 150
145.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 151
146.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 152 recall recordar
147.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 153 found a match encontré una coincidencia X ---------- 1 2 3 5 6 9 10 11 16 17 19 21 30 resume from next row reanudar desde el próximo registro
148.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 154
149.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 155 "done!"
150.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | ... 9 select empno 10 , lpad(' ', (lvl-1)*2) || ename as ename 11 , reports 12 from raw_data 13 match_recognize ( 14 order by rn 15 measures 16 starting_level.rn as rn 17 , starting_level.lvl as lvl 18 , starting_level.empno as empno 19 , starting_level.ename as ename 20 , count(higher_level.lvl) as reports 21 one row per match 22 after match skip to next row 23 pattern (starting_level higher_level*) 24 define higher_level as lvl > starting_level.lvl 25 ) 26 order by rn; 156
151.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select lvl, empno, ename, rownum as rn 2 from ( 3 select level as lvl, empno, ename 4 from emp 5 start with mgr is null 6 connect by mgr = prior empno 7 order siblings by empno ); LVL EMPNO ENAME RN ---------- ---------- -------------------- ---------- 1 7839 KING 1 2 7566 JONES 2 3 7788 SCOTT 3 4 7876 ADAMS 4 3 7902 FORD 5 4 7369 SMITH 6 2 7698 BLAKE 7 3 7499 ALLEN 8 3 7521 WARD 9 3 7654 MARTIN 10 3 7844 TURNER 11 3 7900 JAMES 12 2 7782 CLARK 13 3 7934 MILLER 14 157
152.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> with raw_data as ( ... 13 match_recognize ( 14 order by rn 15 measures 16 starting_level.rn as rn 17 , starting_level.lvl as lvl 18 , starting_level.empno as empno 19 , starting_level.ename as ename 20 , count(higher_level.lvl) as reports 21 one row per match 22 after match skip to next row 23 pattern (starting_level higher_level*) 24 define higher_level as lvl > starting_level.lvl 25 ) 26 order by rn; EMPNO ENAME REPORTS ---------- -------------------- ---------- 7839 KING 13 7566 JONES 4 7788 SCOTT 1 7876 ADAMS 0 7902 FORD 1 7369 SMITH 0 7698 BLAKE 5 7499 ALLEN 0 ... 158
153.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 159 the power of a single SQL un solo SQL tiene mucho poder
154.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |160 select ename from emp where empno = :1 select sum(...) from emp, dept group by ... select min(..) keep ( dense_rank ) ... from emp ... select ... from emp match_recognize measures define ...
155.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 161 what if it doesn't work ? ¿Qué pasa si no funciona?
156.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 162 Requirement "Woo hoo!"
157.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 163 3GL ... we debug usamos depuración
158.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | procedure check_auto_no_new_questions is l_app_controls_rec ate_application_controls%rowtype; l_auto_no_new_limit number; l_new_never_read_cnt number; begin apex_debug.message('Checking metadata'); select * into l_app_controls_rec from ate_application_controls where application_code = 'NONEW'; if l_app_controls_rec.enabled = 'Y' then apex_debug.message('NONEW'); return; end if; apex_debug.message('l_new_never_read_cnt='||l_new_never_read_cnt ); apex_debug.message('l_auto_no_new_limit ='||l_new_never_read_cnt ); if l_new_never_read_cnt >= l_auto_no_new_limit then apex_debug.message('Turning off settings'); update ate_application_controls set enabled = 'Y' where application_code = 'NONEW'; ... 164
159.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select customer, growth_class, startdate, start_txn, enddate, 2 end_txn, avg_daily_txn 3 from cust_summary 4 match_recognize ( 5 partition by customer order by dte 6 measures 7 final last(dte) as termdate 8 , first(dte) as startdate 9 , first(cumu_txns) as start_txn 10 , last(dte) as enddate 11 , next(cumu_txns) as end_txn 12 , (next(cumu_txns) - first(cumu_txns)) / count(*) as avg_daily_txn 13 one row per match after match skip past last row 14 pattern ( fast+ | slow{3,} ) 15 define fast as next(cumu_txns) / cumu_txns >= 1.20 16 , slow as next(slow.cumu_txns) / slow.cumu_txns >= 1.10 and 17 next(slow.cumu_txns) / slow.cumu_txns < 1.20 18 ) 19 order by customer, startdate; 165
160.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 166
161.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | match_number() 167
162.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "Hello World" 168
163.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 , count(*) range 7 one row per match 8 pattern ( starting_row contig* ) 9 define contig as x = prev(x) + 1 ); LO HI RANGE ---------- ---------- ---------- 1 3 3 5 6 2 9 11 3 16 17 2 19 19 1 21 21 1 30 30 1 169
164.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 , count(*) range 7 , match_number() as mn 8 all rows per match 9 pattern ( starting_row contig* ) 10 define contig as x = prev(x) + 1 ); X LO HI RANGE MN ---------- ---------- ---------- ---------- ---------- 1 1 1 1 1 2 1 2 2 1 3 1 3 3 1 5 5 5 1 2 6 5 6 2 2 9 9 9 1 3 10 9 10 2 3 11 9 11 3 3 16 16 16 1 4 17 16 17 2 4 19 19 19 1 5 21 21 21 1 6 30 30 30 1 7 170
165.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | classifier() 171
166.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures 4 first(x) lo 5 , last(x) hi 6 , count(*) range 7 , match_number() as mn 8 , classifier() as cl 9 all rows per match 10 pattern ( starting_row contig* ) 11 define contig as x = prev(x) + 1 ); X LO HI RANGE MN CL ---------- ---------- ---------- ---------- ---------- ------------- 1 1 1 1 1 STARTING_ROW 2 1 2 2 1 CONTIG 3 1 3 3 1 CONTIG 5 5 5 1 2 STARTING_ROW 6 5 6 2 2 CONTIG 9 9 9 1 3 STARTING_ROW 10 9 10 2 3 CONTIG 11 9 11 3 3 CONTIG 16 16 16 1 4 STARTING_ROW 17 16 17 2 4 CONTIG 19 19 19 1 5 STARTING_ROW 21 21 21 1 6 STARTING_ROW 30 30 30 1 7 STARTING_ROW 172
167.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 173 more examples ejemplos
168.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | pattern aggregates as measures 174 patrones agregados como medidas
169.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "W patterns in sales transactions" 175 "Patrones W en transacciones de ventas"
170.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |176
171.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |177 slope1 = amt < prev(amt) slope2 = amt > prev(amt) slope3 = amt < prev(amt) slope4 = amt < prev(amt) pattern = ( slope1+ slope2+ slope3+ slope4+ )
172.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "average AMT in the matched W" 178 "AMT promedio en la W coincidente"
173.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |179 slope1 = amt < prev(amt) slope2 = amt > prev(amt) slope3 = amt < prev(amt) slope4 = amt < prev(amt) pattern = ( slope1+ slope2+ slope3+ slope4+ ) measures avg(amt) as w_average
174.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "average AMT in first downward slide" 180 "AMT promedio en la primera diapositiva descendente"
175.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |181 slope1 = amt < prev(amt) slope2 = amt > prev(amt) slope3 = amt < prev(amt) slope4 = amt < prev(amt) pattern = ( slope1+ slope2+ slope3+ slope4+ ) measures avg(slope1.amt) as slide_avg
176.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | pattern subsets 182 subconjuntos de patrones
177.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "average AMT in first V of the W" 183 "AMT promedio en la primera V de la W"
178.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |184 slope1 = amt < prev(amt) slope2 = amt > prev(amt) slope3 = amt < prev(amt) slope4 = amt < prev(amt) pattern = ( slope1+ slope2+ slope3+ slope4+ ) subset s1s2 = ( slope1,slope2) measures avg(s1s2.amt) as slide_avg
179.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | pattern aggregates as predicates 185 agregados de patrones como predicados
180.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "W patterns in sales transactions, capped at 7 days" 186 "Patrones de W en transacciones de ventas, limitado a los 7 días "
181.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. |187 slope1 = amt < prev(amt) slope2 = amt > prev(amt) slope3 = amt < prev(amt) slope4 = amt < prev(amt) and slope4.dte - first(slope1.dte) < 7 Apr 3 Apr 8
182.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 188
183.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "Divides a set of weights into 3 equi-sized buckets" 189 https://stewashton.wordpress.com/2014/06/06/bin-fitting-problems-with-sql/ "Divide un conjunto de pesas en 3 cubos de igual tamaño"
184.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t; KG ---------- 1 3 4 6 7 8 11 12 13 14 17 18 19 190 1 3 4 6 7 8 11 12 13 14 17 18 19
185.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | pattern ( (bin1|bin2|bin3)* ) define bin1 as count(bin1.*) = 1 or sum(bin1.kg) <= least(sum(bin2.kg), sum(bin3.kg) , bin2 as count(bin2.*) = 1 or sum(bin2.kg)-bin2.kg <= sum(bin3.kg) 191 I will want 3 bins (matching my as yet unknown rules) the bin is empty - bin1.kg or my bin has less than either of the other two bins ... so far use my bin if ...
186.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by kg desc 5 measures 6 classifier() bin#, 7 sum(bin1.kg) bin1, 8 sum(bin2.kg) bin2, 9 sum(bin3.kg) bin3 10 all rows per match 11 pattern ( 12 (bin1|bin2|bin3)* 13 ) 14 define 15 bin1 as count(bin1.*) = 1 or 16 sum(bin1.kg)-bin1.kg <= least(sum(bin2.kg), sum(bin3.kg)) 17 , bin2 as count(bin2.*) = 1 or 18 sum(bin2.kg)-bin2.kg <= sum(bin3.kg) 19 ); 192
187.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | KG BIN# BIN1 BIN2 BIN3 ---------- ------ ---------- ---------- ---------- 19 BIN1 19 18 BIN2 19 18 17 BIN3 19 18 17 14 BIN3 19 18 31 13 BIN2 19 31 31 12 BIN1 31 31 31 11 BIN1 42 31 31 8 BIN2 42 39 31 7 BIN3 42 39 38 6 BIN3 42 39 44 4 BIN2 42 43 44 3 BIN1 45 43 44 1 BIN2 45 44 44 193
188.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * 2 from t 3 match_recognize ( 4 order by kg desc 5 measures 6 sum(bin1.kg) bin1, 7 sum(bin2.kg) bin2, 8 sum(bin3.kg) bin3 9 pattern ( 10 (bin1|bin2|bin3)* 11 ) 12 define 13 bin1 as count(bin1.*) = 1 or 14 sum(bin1.kg)-bin1.kg <= least(sum(bin2.kg), sum(bin3.kg)) 15 , bin2 as count(bin2.*) = 1 or 16 sum(bin2.kg)-bin2.kg <= sum(bin3.kg) 17 ); BIN1 BIN2 BIN3 ---------- ---------- ---------- 45 44 44 194
189.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | aggregates as patterns 195 agregados como patrones
190.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "If customers reaches their shipping target, ship the products now. Otherwise ship their products after 30 days" 196 http://www.kibeha.dk/2015/07/row-pattern-matching-nested-within.html "Si los clientes alcanzan su objetivo de envío, envíe los productos ahora. De lo contrario, envíe sus productos después de 30 días "
191.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> desc CUST Name Null? Type ----------------------------- -------- ---------------- CUST_ID NUMBER(38) CUST_NAME VARCHAR2(30) MIN_SHIP_VALUE NUMBER(38) SQL> desc CUST_ORDERS Name Null? Type ----------------------------- -------- ---------------- CUST_ID NUMBER ORDER_NO NUMBER AMT NUMBER(38) SHIP_DATE DATE 197
192.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from cust; CUST_ID CUST_NAME MIN_SHIP_VALUE ---------- ------------------------------ -------------- 1 Cust A 350 2 Cust B 750 SQL> select * 2 from cust_orders co 3 order by cust_id, ship_date, order_no; CUST_ID ORDER_NO AMT SHIP_DATE ---------- ---------- ---------- --------- 1 11 100 31-JAN-17 1 13 10 10-FEB-17 1 12 250 11-FEB-17 1 21 1000 21-FEB-17 1 31 4000 31-MAR-17 2 41 175 31-JAN-17 2 51 100 10-FEB-17 2 42 500 11-FEB-17 2 52 1000 21-FEB-17 2 61 100 31-MAR-17 198
193.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from cust; CUST_ID CUST_NAME MIN_SHIP_VALUE ---------- ------------------------------ -------------- 1 Cust A 350 2 Cust B 750 SQL> select * 2 from cust_orders co 3 order by cust_id, ship_date, order_no; CUST_ID ORDER_NO AMT SHIP_DATE ---------- ---------- ---------- --------- 1 11 100 31-JAN-17 1 13 10 10-FEB-17 1 12 250 11-FEB-17 1 21 1000 21-FEB-17 1 31 4000 31-MAR-17 2 41 175 31-JAN-17 2 51 100 10-FEB-17 2 42 500 11-FEB-17 2 52 1000 21-FEB-17 2 61 100 31-MAR-17 199
194.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from cust; CUST_ID CUST_NAME MIN_SHIP_VALUE ---------- ------------------------------ -------------- 1 Cust A 350 2 Cust B 750 SQL> select * 2 from cust_orders co 3 order by cust_id, ship_date, order_no; CUST_ID ORDER_NO AMT SHIP_DATE ---------- ---------- ---------- --------- 1 11 100 31-JAN-17 1 13 10 10-FEB-17 1 12 250 11-FEB-17 1 21 1000 21-FEB-17 1 31 4000 31-MAR-17 2 41 175 31-JAN-17 2 51 100 10-FEB-17 2 42 500 11-FEB-17 2 52 1000 21-FEB-17 2 61 100 31-MAR-17 200
195.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | pattern 201 "any number under the shipping limit" UNDER_LIMIT* "then hit/exceed the shipping limit" OVER_LIMIT "some might never hit the shipping limit" {0,1} "cualquier número bajo el límite de envío" luego exceden el límite de envío "algunos pueden nunca alcanzar el límite de envío"
196.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | definitions 202 UNDER_LIMIT OVER_LIMIT "rolling" sum(amt) < cust.min_ship_limit "rolling" sum(amt) >= cust.min_ship_limit
197.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | recall 203
198.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from t 2 match_recognize ( order by x 3 measures ... pattern ... 4 DEFINE 5 p1 as x = 1 6 , p2 as first(x) = 1 7 , p3 as last(x) = 1 8 , p4 as prev(x) = 1 9 , p5 as next(x) = 1 10 , p6 as count(*) = 1 11 , p7 as final last(x) = 1 12 , p8 as contig.x = 1 13 , p9 as count(contig.*) = 1 14 ); 204 as per ALL ROWS PER MATCH - this row - first row of match pattern - this row - previous row from this row - next row from this row - count from first to this row - ILLEGAL - this 'contig' row - count from first to this contig row contar desde el primero hasta esta fila
199.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from ( 2 select co.*, c.min_ship_value 3 from cust c, cust_orders co 4 where c.cust_id = co.cust_id 5 ) 6 match_recognize ( 7 partition by cust_id 8 order by ship_date, order_no 9 measures 10 match_number() as mno, 11 classifier() as cls, 12 sum(amt) as tot 13 all rows per match 14 pattern ( under_limit* over_limit{0,1} ) 15 define 16 under_limit as sum(amt) < min_ship_value, 17 over_limit as sum(amt) >= min_ship_value ); CUST_ID SHIP_DATE ORDER_NO MNO CLS TOT AMT MIN_SHIP_VALUE ---------- --------- ---------- ----- --------------- ---------- ---------- -------------- 1 31-JAN-17 11 1 UNDER_LIMIT 100 100 350 1 10-FEB-17 13 1 UNDER_LIMIT 110 10 350 1 11-FEB-17 12 1 OVER_LIMIT 360 250 350 1 21-FEB-17 21 2 OVER_LIMIT 1000 1000 350 1 31-MAR-17 31 3 OVER_LIMIT 4000 4000 350 2 31-JAN-17 41 1 UNDER_LIMIT 175 175 750 2 10-FEB-17 51 1 UNDER_LIMIT 275 100 750 2 11-FEB-17 42 1 OVER_LIMIT 775 500 750 2 21-FEB-17 52 2 OVER_LIMIT 1000 1000 750 2 31-MAR-17 61 3 UNDER_LIMIT 100 100 750 205
200.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | "If customers hit their minimum shipping target, ship that batch now. Otherwise ship their products after 30 days" 206 De lo contrario, envíe sus productos después de 30 días
201.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | SQL> select * from ( 2 select co.*, c.min_ship_value 3 from cust c, cust_orders co 4 where c.cust_id = co.cust_id 5 ) 6 match_recognize ( 7 partition by cust_id 8 order by ship_date, order_no 9 measures 10 match_number() as mno, 11 classifier() as cls, 12 sum(amt) as tot, 13 nvl(final last(over_limit.ship_date), last(ship_date)+30) last_ship_date 14 all rows per match 15 pattern ( under_limit* over_limit{0,1} ) 16 define 17 under_limit as sum(amt) < min_ship_value, 18 over_limit as sum(amt) >= min_ship_value ); 207
202.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 208 wrap up
203.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 209
204.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 210 be patient se paciente
205.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | 211 match_number() classifier() ALL ROWS PER MATCH
206.
Copyright © 2016,
Oracle and/or its affiliates. All rights reserved. | Thank you! youtube bit.ly/youtube-connor blog bit.ly/blog-connor twitter bit.ly/twitter-connor Gracias!
Download now