This presentation is part of the Oracle OpenWorld 2016 session: EOUC Database ACES Share Their Favorite Database Things: Part II. In this session (UGF-2632) ACE Directors share their favorite database features in our now traditional quick-fire sessions (of 5 minutes per speaker).

1. SQL Pattern Recognition
(boldly go beyond analytical)
from 12.1
Lucas Jellema speaks at conferences and user group events, writes blog articles
(on the AMIS Technology Blog) and has published two books with Oracle
Press (on Oracle SOA Suite). His interests range from client side UI and
JavaScript through integration middleware to Database development and
platform design. Creative usages of SQL and PL/SQL are among his favorite
pastimes. In his day time job, Lucas is CTO and architecture consultant at
AMIS in The Netherlands and is affiliated with the Dutch Oracle User Group
(OGh).
Lucas Jellema
@lucasjellema
Oracle ACE Director

2. Patterns
• Special, significant sequences of data

3. Pattern Matching
• Discover special patterns in [potentially pretty
big] data sets
• Crucial requirement: records have to be
ordered in some way
– Frequently by time [of observation]
– Could be by the location along some axis

4. Presenting: Modern Art
• “Who’s afraid of red, yellow and blue”
– Barnett Newman, Stedelijk Museum, Amsterdam

5. Find art in the car park
• Find if we have cars parked according to the
red-yellow-blue pattern of the painting
Parking
Space
Car
color

6. Analytical Functions
• Solution with Lag or Lead
• With LEAD it is easy to compare a row with its successor(s)
– As long as the pattern is fixed, LEAD will suffice
with look_ahead_cars as
( SELECT c.* -- for each car, find next and next after that
, lead(car_color,1) over (order by parking_space) next_color
, lead(car_color,2) over (order by parking_space) sec_nxt_color
FROM parked_cars c
)
select parking_space
from look_ahead_cars
where car_color ='red' –- for each red car
and next_color ='yellow' -- check if next is yellow
and sec_nxt_color='blue' –- and the one after that is blue

7. Match Recognize
• New operator in 12c: MATCH_RECOGNIZE
– Specifically provided for pattern matching
– Pretty fast and very versatile

8. Match Recognize
• New operator in 12c: MATCH_RECOGNIZE
– Specifically provided for pattern matching
SELECT * -- produces columns from parked_cars and from match_recognize
FROM parked_cars -- record set to find pattern in
MATCH_RECOGNIZE
(
ORDER BY parking_space -- specify ordering of records for pattern
MEASURES RED.parking_space AS red_space -- results to be produced
, MATCH_NUMBER() AS match_num -- umptieth match
ALL ROWS PER MATCH –- all records in pattern or only the first
PATTERN (RED YELLOW BLUE) –- the pattern to locate
DEFINE –- row conditions to be used in pattern
RED AS RED.car_color ='red', –- match on row with red car
YELLOW AS YELLOW.car_color ='yellow', –- match on yellow car
BLUE AS BLUE.car_color ='blue‘–- match on blue car
) MR
ORDER
BY MR.red_space
, MR.parking_space

9. Match Recognize
• New operator in 12c: MATCH_RECOGNIZE
– Specifically provided for pattern matching
SELECT * -- produces columns from parked_cars and from match_recognize
FROM parked_cars -- record set to find pattern in
MATCH_RECOGNIZE
(
ORDER BY parking_space -- specify ordering of records for pattern
MEASURES RED.parking_space AS red_space -- results to be produced
, MATCH_NUMBER() AS match_num -- umptieth match
ALL ROWS PER MATCH –- all records in pattern or only the first
PATTERN (RED YELLOW BLUE) –- the pattern to locate
DEFINE –- row conditions to be used in pattern
RED AS RED.car_color = 'red', –- identify row with red car
YELLOW AS YELLOW.car_color = 'yellow', –- locate row with yellow car
BLUE AS BLUE.car_color = 'blue' –- record with blue car
) MR
ORDER
BY MR.red_space
, MR.parking_space

10. Up the ante – a little
• Suppose we also want to find blocks of cars
– For example: red-red-red-yellow-yellow-blue-blue
• And we accept white cars interspersed
between the colored ones
– So red-red-white-yellow-white-yellow-blue also
satisfies the pattern
• Lag/Lead solution quickly becomes unwieldy

11. Extending the pattern
match_recognize solution
SELECT * -- produces columns from parked_cars and from match_recognize
FROM parked_cars -- record set to find pattern in
MATCH_RECOGNIZE
(
ORDER BY parking_space -- specify ordering of records for pattern
MEASURES RED.parking_space AS red_space -- results to be produced
, MATCH_NUMBER() AS match_num -- umptieth match
ALL ROWS PER MATCH –- all records in pattern or only the first
PATTERN (RED+ WHITE* YELLOW+ WHITE* BLUE+)
DEFINE –- row conditions to be used in pattern
RED AS RED.car_color ='red', –- match on row with red car
YELLOW AS YELLOW.car_color ='yellow', –- match on yellow car
BLUE AS BLUE.car_color ='blue'–- match on blue car
WHITE AS WHITE.car_color ='white'–- match on white car
) MR
ORDER
BY MR.red_space
, MR.parking_space

12. Extending the pattern
match_recognize solution
SELECT * -- produces columns from parked_cars and from match_recognize
FROM parked_cars -- record set to find pattern in
MATCH_RECOGNIZE
(
ORDER BY parking_space -- specify ordering of records for pattern
MEASURES RED.parking_space AS red_space -- results to be produced
, MATCH_NUMBER() AS match_num -- umptieth match
ALL ROWS PER MATCH –- all records in pattern or only the first
PATTERN (RED+ WHITE* YELLOW+ WHITE* BLUE+)
DEFINE –- row conditions to be used in pattern
RED AS RED.car_color ='red', –- match on row with red car
YELLOW AS YELLOW.car_color ='yellow', –- match on yellow car
BLUE AS BLUE.car_color ='blue'–- match on blue car
WHITE AS WHITE.car_color ='white'–- match on white car
) MR
ORDER
BY MR.red_space
, MR.parking_space

13. Use a regular expression to
describe sought after pattern
• Supported operators for the pattern clause include:
– * for 0 or more iterations
– + for 1 or more iterations
– ? for 0 or 1 iterations
– { n } for exactly n iterations (n > 0)
– { n, } for n or more iterations (n >= 0)
– { n, m } for between n and m (inclusive) iterations (0 <= n <= m, 0 < m)
– { , m } for between 0 and m (inclusive) iterations (m > 0)
– reluctant qualifiers - *?, +?, ??, {n}?, {n,}?, { n, m }?, {,m}?
– | for alternation (OR)
– grouping using () parentheses
– exclusion using {- and -}
– empty pattern using ()
– ^ and $ for start and end of a partition
PATTERN (RED+ WHITE* YELLOW+ WHITE* BLUE+)

14. Elements of Match_Recognize
• FIRST, LAST, NEXT, PREV
• MATCH_NUMBER()
• CLASSIFIER()
• COUNT, SUM, AVG, MAX, MIN
• FINAL or RUNNING
• PER MATCH
– ALL ROWS or ONE ROW
• AFTER MATCH
– SKIP TO LAST, TO NEXT, FIRST, PAST LAST ROW

15. Did we ever hire three employees
in a row in the same job?
SELECT *
FROM EMP
MATCH_RECOGNIZE
(
ORDER BY hiredate
MEASURES SAME_JOB.hiredate AS hireday
, MATCH_NUMBER() AS match_num
ALL ROWS PER MATCH
PATTERN (SAME_JOB{3})
DEFINE
SAME_JOB AS SAME_JOB.job = FIRST(SAME_JOB.job)
) MR

16. Did we ever hire three employees
in a row in the same job?
SELECT *
FROM EMP
MATCH_RECOGNIZE
(
ORDER BY hiredate
MEASURES SAME_JOB.hiredate AS hireday
, MATCH_NUMBER() AS match_num
ALL ROWS PER MATCH
PATTERN (SAME_JOB{3})
DEFINE
SAME_JOB AS SAME_JOB.job = FIRST(SAME_JOB.job)
) MR

17. Conclusion
• Cool stuff
• Very fast
• Nifty tool for the SQL toolbox
• Useful for analysis of database activity
• Takes us beyond Analytical Functions for
advanced record interdependencies