Presentation made at #UKOUG_Tech17, #DOAG2016 and #OUGN17. Download to see animations!

1. Meet Your Match
Advanced row pattern matching (12c)
Stew Ashton
UKOUG Tech 17
Can you read the following line? If not, please move closer.
It's much better when you can read the code ;)

2. Advanced usage, not all the syntax
• Reminder of the basics
• Exercises
• Bin fitting
• Positive and negative sequencing
• Hierarchical summaries
– Thanks, Kim Berg Hansen
• Alternatives to inequality joining
– Thanks, Jonathan Lewis
2

3. Reminder: the Basics
• To illustrate: table with PAGE column
– Group consecutive pages together
3
PAGE
1
2
3
5
FIRSTPAGE LASTPAGE CNT
1 3 3
5 5 1

4. Pattern and Matching Rows
• PATTERN
– Uninterrupted series of input rows
– Described as list of conditions (≅ “regular expressions”)
PATTERN (A B*)
"A" : 1 row, "B*" : 0 or more rows, as many as possible
• DEFINE (at least one) row condition
[A undefined = TRUE]
B AS page = PREV(page)+1
• Each series that matches the pattern is a “match”
– "A" and "B" identify the rows that meet their conditions
– There can be unmatched rows between series
4

5. Input, Processing, Output
1. Define input
2. Order input
3. Process pattern
4. using defined conditions
5. Output: rows per match
6. Output: columns per row
7. Go where after match?
5
SELECT *
FROM t
MATCH_RECOGNIZE (
ORDER BY page
MEASURES
A.page as firstpage,
LAST(page) as lastpage,
COUNT(*) cnt
ONE ROW PER MATCH
AFTER MATCH SKIP PAST LAST ROW
PATTERN (A B*)
DEFINE B AS page = PREV(page)+1
);

6. Which row do we mean?
pg id
DEFINE ALL ROWS PER MATCH ONE ROW PER MATCH
first Current last first Current last
Final
last
first Current last
Final
last
1 A 1 1 1 1 1 1 3
2 B 1 2 2 1 2 2 3
3 B 1 3 3 1 3 3 3 1 3 3 3
5 B? 1 5 5
6
Column name by itself = « current » row
• DEFINE: row being evaluated ; ALL ROWS: each row ; ONE ROW: last row

7. Exercise: what output from this input?
7
CUST_ID TX_DATE DESCR
C001 2016-01-01 Inquiry
C001 2016-01-01 Inquiry
C001 2016-01-10 Sales
C001 2016-01-21 Repeat Inquiry
C001 2016-02-10 Repeat Inquiry
C001 2016-05-01 Sales
C001 2016-05-06 Sales
C001 2016-06-10 Inquiry 1
C001 2016-09-01 Inquiry 2
C002 2016-02-01 Inquiry 1
C002 2016-02-25 Inquiry 2
C003 2016-02-01 Inquiry 2
C003 2016-02-10 Sales
C003 2016-02-10 Sales
C003 2016-03-10 Inquiry 2
C004 2016-04-15 Sales
select * from t match_recognize(
all rows per match
pattern (a*)
define a as 1=1
);

8. Add sequence number, starting over after 40 days
8
CUST_ID TX_DATE DESCR
C001 2016-01-01 Inquiry
C001 2016-01-01 Inquiry
C001 2016-01-10 Sales
C001 2016-01-21 Repeat Inquiry
C001 2016-02-10 Repeat Inquiry
C001 2016-05-01 Sales
C001 2016-05-06 Sales
C001 2016-06-10 Inquiry 1
C001 2016-09-01 Inquiry 2
C002 2016-02-01 Inquiry 1
C002 2016-02-25 Inquiry 2
C003 2016-02-01 Inquiry 2
C003 2016-02-10 Sales
C003 2016-02-10 Sales
C003 2016-03-10 Inquiry 2
C004 2016-04-15 Sales
select * from t match_recognize(
all rows per match
pattern (a*)
define a as 1=1
);

9. Add sequence number, starting over after 40 days
9
CUST_ID TX_DATE DESCR
C001 2016-01-01 Inquiry
C001 2016-01-01 Inquiry
C001 2016-01-10 Sales
C001 2016-01-21 Repeat Inquiry
C001 2016-02-10 Repeat Inquiry
C001 2016-05-01 Sales
C001 2016-05-06 Sales
C001 2016-06-10 Inquiry 1
C001 2016-09-01 Inquiry 2
C002 2016-02-01 Inquiry 1
C002 2016-02-25 Inquiry 2
C003 2016-02-01 Inquiry 2
C003 2016-02-10 Sales
C003 2016-02-10 Sales
C003 2016-03-10 Inquiry 2
C004 2016-04-15 Sales
select * from t match_recognize(
all rows per match
pattern (a*)
define
a as 1=1
);

10. Add sequence number, starting over after 40 days
10
CUST_ID TX_DATE DESCR
C001 2016-01-01 Inquiry
C001 2016-01-01 Inquiry
C001 2016-01-10 Sales
C001 2016-01-21 Repeat Inquiry
C001 2016-02-10 Repeat Inquiry
C001 2016-05-01 Sales
C001 2016-05-06 Sales
C001 2016-06-10 Inquiry 1
C001 2016-09-01 Inquiry 2
C002 2016-02-01 Inquiry 1
C002 2016-02-25 Inquiry 2
C003 2016-02-01 Inquiry 2
C003 2016-02-10 Sales
C003 2016-02-10 Sales
C003 2016-03-10 Inquiry 2
C004 2016-04-15 Sales
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
all rows per match
pattern (a*)
define
a as
);
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
all rows per match
pattern (a*)
define
a as tx_date <= first(tx_date) + 40
);
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) as seq
all rows per match
pattern (a*)
define
a as tx_date <= first(tx_date) + 40
);

11. Add sequence number, starting over after 40 days
11
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 1
C001 2016-01-01 Inquiry 2
C001 2016-01-10 Sales 3
C001 2016-01-21 Repeat Inquiry 4
C001 2016-02-10 Repeat Inquiry 5
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 1
C002 2016-02-01 Inquiry 1 1
C002 2016-02-25 Inquiry 2 2
C003 2016-02-01 Inquiry 2 1
C003 2016-02-10 Sales 2
C003 2016-02-10 Sales 3
C003 2016-03-10 Inquiry 2 4
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) as seq
all rows per match
pattern (a*)
define
a as tx_date <= first(tx_date) + 40
);

12. Sequence starts from First Sale, Inquiry outside 40 days = 0
12
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 1
C001 2016-01-01 Inquiry 2
C001 2016-01-10 Sales 3
C001 2016-01-21 Repeat Inquiry 4
C001 2016-02-10 Repeat Inquiry 5
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 1
C002 2016-02-01 Inquiry 1 1
C002 2016-02-25 Inquiry 2 2
C003 2016-02-01 Inquiry 2 1
C003 2016-02-10 Sales 2
C003 2016-02-10 Sales 3
C003 2016-03-10 Inquiry 2 4
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) as seq
all rows per match
pattern (a*)
define
a as tx_date <= first(tx_date) + 40
);

13. Sequence starts from Sale, Inquiry outside 40 days = 0
13
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 1
C001 2016-01-01 Inquiry 2
C001 2016-01-10 Sales 3
C001 2016-01-21 Repeat Inquiry 4
C001 2016-02-10 Repeat Inquiry 5
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 1
C002 2016-02-01 Inquiry 1 1
C002 2016-02-25 Inquiry 2 2
C003 2016-02-01 Inquiry 2 1
C003 2016-02-10 Sales 2
C003 2016-02-10 Sales 3
C003 2016-03-10 Inquiry 2 4
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) as seq
all rows per match
pattern (a *)
define
a as tx_date <= first(tx_date) + 40
);

14. Sequence starts from Sale, Inquiry outside 40 days = 0
14
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 1
C001 2016-01-01 Inquiry 2
C001 2016-01-10 Sales 3
C001 2016-01-21 Repeat Inquiry 4
C001 2016-02-10 Repeat Inquiry 5
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 1
C002 2016-02-01 Inquiry 1 1
C002 2016-02-25 Inquiry 2 2
C003 2016-02-01 Inquiry 2 1
C003 2016-02-10 Sales 2
C003 2016-02-10 Sales 3
C003 2016-03-10 Inquiry 2 4
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define
more_tx as tx_date <= + 40
);
- count(inq.*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

15. Sequence starts from Sale, Inquiry outside 40 days = 0
15
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 0
C001 2016-01-01 Inquiry 0
C001 2016-01-10 Sales 1
C001 2016-01-21 Repeat Inquiry 2
C001 2016-02-10 Repeat Inquiry 3
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 0
C002 2016-02-01 Inquiry 1 0
C002 2016-02-25 Inquiry 2 0
C003 2016-02-01 Inquiry 2 0
C003 2016-02-10 Sales 1
C003 2016-02-10 Sales 2
C003 2016-03-10 Inquiry 2 3
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) - count(inq.*) as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

16. Negative sequence for Inquiries within 10 days prior to Sale
16
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 0
C001 2016-01-01 Inquiry 0
C001 2016-01-10 Sales 1
C001 2016-01-21 Repeat Inquiry 2
C001 2016-02-10 Repeat Inquiry 3
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 0
C002 2016-02-01 Inquiry 1 0
C002 2016-02-25 Inquiry 2 0
C003 2016-02-01 Inquiry 2 0
C003 2016-02-10 Sales 1
C003 2016-02-10 Sales 2
C003 2016-03-10 Inquiry 2 3
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures count(*) - count(inq.*) as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

17. Negative sequence for Inquiries within 10 days prior to Sale
17
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 0
C001 2016-01-01 Inquiry 0
C001 2016-01-10 Sales 1
C001 2016-01-21 Repeat Inquiry 2
C001 2016-02-10 Repeat Inquiry 3
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 0
C002 2016-02-01 Inquiry 1 0
C002 2016-02-25 Inquiry 2 0
C003 2016-02-01 Inquiry 2 0
C003 2016-02-10 Sales 1
C003 2016-02-10 Sales 2
C003 2016-03-10 Inquiry 2 3
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures
count(*) - count(inq.*)
as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

18. Negative sequence for Inquiries within 10 days prior to Sale
18
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry 0
C001 2016-01-01 Inquiry 0
C001 2016-01-10 Sales 1
C001 2016-01-21 Repeat Inquiry 2
C001 2016-02-10 Repeat Inquiry 3
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 0
C002 2016-02-01 Inquiry 1 0
C002 2016-02-25 Inquiry 2 0
C003 2016-02-01 Inquiry 2 0
C003 2016-02-10 Sales 1
C003 2016-02-10 Sales 2
C003 2016-03-10 Inquiry 2 3
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures case when classifier() = 'INQ'
and tx_date >=
final first(sale1.tx_date) - 10
then
count(inq.*) - final count(inq.*) - 1
else
count(*) - count(inq.*)
end as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

19. Negative sequence for Inquiries within 10 days prior to Sale
19
CUST_ID TX_DATE DESCR SEQ
C001 2016-01-01 Inquiry -2
C001 2016-01-01 Inquiry -1
C001 2016-01-10 Sales 1
C001 2016-01-21 Repeat Inquiry 2
C001 2016-02-10 Repeat Inquiry 3
C001 2016-05-01 Sales 1
C001 2016-05-06 Sales 2
C001 2016-06-10 Inquiry 1 3
C001 2016-09-01 Inquiry 2 0
C002 2016-02-01 Inquiry 1 0
C002 2016-02-25 Inquiry 2 0
C003 2016-02-01 Inquiry 2 -1
C003 2016-02-10 Sales 1
C003 2016-02-10 Sales 2
C003 2016-03-10 Inquiry 2 3
C004 2016-04-15 Sales 1
select * from t match_recognize(
partition by cust_id
order by tx_date, descr
measures case when classifier() = 'INQ'
and tx_date >=
final first(sale1.tx_date) - 10
then
count(inq.*) - final count(inq.*) - 1
else
count(*) - count(inq.*)
end as seq
all rows per match
pattern (inq* sale1{0,1} more_tx*)
define inq as descr != 'Sales',
sale1 as descr = 'Sales',
more_tx as tx_date <= sale1.tx_date + 40
);

20. Hierarchical Summary: get salaries of mgr + subordinates
20
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno;
LVL ENAME SAL
1 KING 5000
2 JONES 2975
3 SCOTT 3000
4 ADAMS 1100
3 FORD 3000
4 SMITH 800
2 BLAKE 2850
3 ALLEN 1600
3 WARD 1250
3 MARTIN 1250
3 TURNER 1500
3 JAMES 950
2 CLARK 2450
3 MILLER 1300
>2

21. Hierarchical Summary: get salaries of mgr + subordinates
21
select * from (
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno
)
match_recognize(
measures a.lvl lvl, a.ename ename,
a.sal sal, sum(sal) as sum_sal
pattern(a b*)
define b as lvl > a.lvl
);
LVL ENAME SAL
1 KING 5000
2 JONES 2975
3 SCOTT 3000
4 ADAMS 1100
3 FORD 3000
4 SMITH 800
2 BLAKE 2850
3 ALLEN 1600
3 WARD 1250
3 MARTIN 1250
3 TURNER 1500
3 JAMES 950
2 CLARK 2450
3 MILLER 1300

22. Hierarchical Summary: get salaries of mgr + subordinates
22
LVL ENAME SAL SUM_SAL
1 KING 5000 29025
select * from (
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno
)
match_recognize(
measures a.lvl lvl, a.ename ename,
a.sal sal, sum(sal) as sum_sal
pattern(a b*)
define b as lvl > a.lvl
);

23. Hierarchical Summary: get salaries of mgr + subordinates
23
LVL ENAME SAL SUM_SAL
1 KING 5000 29025
select * from (
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno
)
match_recognize(
measures a.lvl lvl, a.ename ename,
a.sal sal, sum(sal) as sum_sal
after match skip past last row
pattern(a b*)
define b as lvl > a.lvl
);

24. Hierarchical Summary: get salaries of mgr + subordinates
24
LVL ENAME SAL SUM_SAL
1 KING 5000 29025
select * from (
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno
)
match_recognize(
measures a.lvl lvl, a.ename ename,
a.sal sal, sum(sal) as sum_sal
after match skip to next row
pattern(a b*)
define b as lvl > a.lvl
);

25. Hierarchical Summary: get salaries of mgr + subordinates
25
LVL ENAME SAL SUM_SAL
1 KING 5000 29025
2 JONES 2975 10875
3 SCOTT 3000 4100
4 ADAMS 1100 1100
3 FORD 3000 3800
4 SMITH 800 800
2 BLAKE 2850 9400
3 ALLEN 1600 1600
3 WARD 1250 1250
3 MARTIN 1250 1250
3 TURNER 1500 1500
3 JAMES 950 950
2 CLARK 2450 3750
3 MILLER 1300 1300
select * from (
select level lvl, ename, sal
from scott.emp
start with mgr is null
connect by mgr = prior empno
)
match_recognize(
measures a.lvl lvl, a.ename ename,
a.sal sal, sum(sal) as sum_sal
after match skip to next row
pattern(a b*)
define b as lvl > a.lvl
);
http://www.kibeha.dk/2015/07/row-pattern-matching-nested-within.html

26. Inequality joins
26
>create table t1(id, jd, v) cache
as
select level, level + .1, level
from dual
connect by level <= 20000;
• Equality
• Band Join: compare T1.ID to T2.ID + a constant
• Range Join: T1.ID within a range T2 (ID to JD)
• Overlap Join: T1 range (ID to JD) overlaps T2 range
>create table t2 cache
as
select * from t1;

27. Equality
27
select t1.id id1, t2.id id2, t1.v v1, t2.v v2
from t1, t2
where t1.id = t2.id
Elapsed: .O1 seconds

28. Band Join
28
select t1.id id1, t2.id id2, t1.v v1, t2.v v2
from t1, t2
where t1.id between t2.id and t2.id + .1
Elapsed: .O4 seconds
• New implementation in 12.2
• Before 12.2, about the same time as range join =>

29. Range Join
29
select t1.id id1, t2.id id2, t1.v v1, t2.v v2
from t1, t2
where t1.id between t2.id and t2.jd
Elapsed: 30 seconds
(Equality: .01
Band: .04)

30. Range Join Execution Plan
30
-------------------------------------------------------
| Id | Operation | Name | Starts | A-Rows |
-------------------------------------------------------
| 0 | SELECT STATEMENT | | 1 | 20000 |
| 1 | MERGE JOIN | | 1 | 20000 |
| 2 | SORT JOIN | | 1 | 20000 |
| 3 | TABLE ACCESS FULL | T1 | 1 | 20000 |
|* 4 | FILTER | | 20000 | 20000 |
|* 5 | SORT JOIN | | 20000 | 200M|
| 6 | TABLE ACCESS FULL| T2 | 1 | 20000 |
-------------------------------------------------------

31. 31
T1 T2
ID ID JD
1 1 1.1
1 2 2.1
1 3 3.1
2 1 1.1
2 2 2.1
2 3 3.1
3 1 1.1
3 2 2.1
3 3 3.1
All possible combinations

32. 32
T1 T2
ID ID JD
1 1 1.1
1 2 2.1
1 3 3.1
2 1 1.1
2 2 2.1
2 3 3.1
3 1 1.1
3 2 2.1
3 3 3.1
5 - access("T1"."ID">="T2"."ID")

33. 33
T1 T2
ID ID JD
1 1 1.1
1 2 2.1
1 3 3.1
2 1 1.1
2 2 2.1
2 3 3.1
3 1 1.1
3 2 2.1
3 3 3.1
4 - filter("T1"."ID"<="T2"."JD")
5 - access("T1"."ID">="T2"."ID")

34. Sort all and Match?
34
T2 T1 T2 T1 T2 T1 T2
1 1 1
2 2.1 2 2
3 3.1 3 3
4 4.1 4
5 5.1
Sort by ID, T2 first. (order shown from left to right)
T2 range diff. is now 1.1, so will match 2 T1 rows

35. Sort all and Match?
35
T2
1 1
2 2.1
3
4
5
Start
Look for following T1 rows with ID <= 2.1
Due to sort, their IDs must be >= T2.ID

36. Sort all and Match?
36
T2 T1
1 1 1
2 2.1
3
4
5
Start Join
T1.ID < 2.1
Due to sort, T1.ID is automatically >= T2.ID

37. Sort all and Match?
37
T2 T1 T2
1 1 1
2 2.1 2
3 3.1
4
5
Start Join match
T2.ID < 2.1
So match, but do not output

38. Sort all and Match?
38
T2 T1 T2 T1
1 1 1
2 2.1 2 2
3 3.1
4
5
Start Join match Join
T1.ID < 2.1
So match and output

39. Sort all and Match?
39
T2 T1 T2 T1 T2
1 1 1
2 2.1 2 2
3 3.1 3
4 4.1
5
Start Join match Join X
Match ended
Skip to next

40. Sort all and Match?
40
T2 T1 T2 T1 T2 T1 T2
1 1 1
2 2.1 2 2
3 3.1 3 3
4 4.1 4
5 5.1
Start Join match Join X

41. (Almost) All Rows per Match
• PATTERN ( A {- B A -} B)
– The parts of the pattern enclosed
between {- and -} are excluded from the output.
– Here only two rows per match will be returned
– More granular than using a WHERE clause
• Alternation: | means OR
– "Alternatives are preferred in the order they are specified."
PATTERN ( A | B ) =
If A condition is true then A, else if B condition is true then B
41

42. Range Match
42
select ID ID1, ID2, JD2
from (
select t2.*, 1 is_t2 from t2
union all
select t1.*, null from t1
)
match_recognize(
order by id, is_t2
measures t2.id id2, t2.jd jd2
all rows per match
after match skip to next row
pattern({-T2-} ( T1 | {-T2-} )* T1)
define T2 as is_t2 = 1 and id < first(t2.jd),
T1 as is_t2 is null and id < first(t2.jd)
);
Elapsed: .12 secs
Equality: .01
Band: .04
Range join: 30.00

43. Overlap Join
43
select t1.id id1, t2.id id2, t1.v v1, t2.v v2
from t1, t2
where (t2.id <= t1.id and t1.id < t2.jd)
or (t1.id <= t2.id and t2.id < t1.jd)
Elapsed: 50 seconds

44. Overlap Join Execution Plan
44
| 0 | SELECT STATEMENT | | 1 | | 1 |
| 1 | SORT AGGREGATE | | 1 | 1 | 1 |
| 2 | VIEW | VW...| 1 | 175M| 20000 |
| 3 | UNION-ALL | | 1 | | 20000 |
| 4 | MERGE JOIN | | 1 | 100M| 20000 |
| 5 | SORT JOIN | | 1 | 20000 | 20000 |
| 6 | TABLE ACCESS FULL | T2 | 1 | 20000 | 20000 |
|* 7 | FILTER | | 20000 | | 20000 |
|* 8 | SORT JOIN | | 20000 | 20000 | 200M|
| 9 | TABLE ACCESS FULL| T1 | 1 | 20000 | 20000 |
| 10 | MERGE JOIN | | 1 | 75M| 0 |
| 11 | SORT JOIN | | 1 | 20000 | 20000 |
| 12 | TABLE ACCESS FULL | T2 | 1 | 20000 | 20000 |
|* 13 | FILTER | | 20000 | | 0 |
|* 14 | SORT JOIN | | 20000 | 20000 | 200M|
| 15 | TABLE ACCESS FULL| T1 | 1 | 20000 | 20000 |

45. Overlap Match
45
select * from (
select t1.*, 1 table_num from t1
union all
select t2.*, 2 from t2
)
match_recognize(
order by id, jd
all rows per match
after match skip to next row
pattern({-ta-} ( tb | {-x-} )* tb)
define tb as table_num != ta.table_num
and id < first(jd),
x as table_num = ta.table_num
and id < first(jd)
);
Elapsed: .12 secs
Equality: .01
Band: .04
Range match: .12
No need to wait for 18c

46. 46
Child'
s play

47. Solving Problems with pattern matching
• Clear knowledge of input & requirement
– Beware of assumptions
• Identify typical problems and solutions
– Consecutive sequences
– Ad hoc grouping
– Bin fitting
– Ranges
• Visualize the data processing flow
– Output from other rows is not available, input is.
47

48. Meet Your Match
Advanced row pattern matching (12c)
Stew Ashton
UKOUG Tech 17
https://stewashton.wordpress.com/
Twitter: @stewashton

49. Anchors
• Anchors
– ^ matches the position before the first row in the
partition.
– $ matches the position after the last row in the
partition
PATTERN(^ A $) = partition must have 1 row
49

50. JOIN alternative: CDC compare
50
PKVAL
1Same value
2Delete this
3Old value
PKVAL
1Same value
3New value
4Insert this
T1 T2 select pk, op, val, oldrid from (
select pk, val, rowid rid from t1
union all
select pk, val, null from t2
)
match_recognize(
partition by pk order by rid
measures classifier() op,
first(rid) oldrid
all rows per match
pattern(^ D $ | ^ I $ | (^ O U $) )
define D as rid is not null,
U as decode(O.val, val, 0, 1) = 1
);
PK OP VAL OLDRID
2D Delete this AAAkdlAAH…MAAB
3O Old value AAAkdlAAH…MAAC
3U New value AAAkdlAAH…MAAC
4I Insert this