Presentation made several times from OOW14 through ILOUG18 - with improvements as time goes on...

1. “Row Pattern Matching” with
Database 12c MATCH_RECOGNIZE
Beating the Best Pre-12c Solutions
Stew Ashton (stewashton.wordpress.com)
OUGN17
Can you read the following line? If not, please move closer.
It's much better when you can read the code ;)

2. Agenda
• Who am I?
• Pre-12c solutions compared to row pattern
matching with MATCH_RECOGNIZE
– For all sizes of data
– Thinking in patterns
– Syntax only as needed
• Watch out for “catastrophic backtracking”
• Other things to keep in mind (time permitting)
2

3. Who am I?
• 1967: first FORTRAN program
• 1981-2015: software engineer / architect
• 2005-present: focus on Oracle DB development
– Contribute to asktom & OTN SQL forum
– Presented at OOW, UKOUG tech, DOAG
– 2015 DEVVY awards finalist.
– Advocate of data-centric application architecture
3

4. 4 pre-12c Solutions
• Analytics
1) Tabibitosan / Fixed Difference
2) "Start of Group"
• Model
3) Bin fitting (fixed size)
4) Bin fitting (fixed number)
4

5. 1) “Fixed Difference” / Tabibitosan
PAGE
1
2
3
5
6
7
10
11
12
42
5
-1
-2
-3
-4
= 0
= 0
= 0
= 1
• Identify and group rows with
consecutive values
• My presentation: print best liked slides
• Math: subtract known consecutives (1, 2,…)
– Consecutive means A = B - 1
– If A = B - 1 (consecutive) then A - 1 = B – 2
Else (not consecutive) A - 1 <> B - 2
– Consecutive becomes equality,
non-consecutive becomes inequality

6. 1) Pre-12c
select min(page) firstpage,
max(page) lastpage,
count(*) cnt
FROM (
SELECT page,
page –
Row_Number() over(order by page)
as grp_id
FROM t
)
GROUP BY grp_id;
PAGE [RN] GRP_ID
1 - 1 0
2 - 2 0
3 - 3 0
5 - 4 1
6 - 5 1
7 - 6 1
10 - 7 3
11 - 8 3
12 - 9 3
42 -10 32 6
FIRSTPAGE LASTPAGE CNT
1 3 3
5 7 3
10 12 3
42 42 1
PAGE GRP_ID
1 0
2 0
3 0
5 1
6 1
7 1
10 3
11 3
12 3
42 32

7. 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 each condition
– There can be unmatched rows between series
7

8. 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?
8
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
);

9. 1) Run_Stats comparison
9
For one million rows:
“Latches” are serialization devices: fewer means more scalable
Stat Pre 12c Match_R Pct
Latches 5047 4836 96%
Elapsed Time 1.1164 1.0528 94%
CPU used by this session 1.09 1.04 95%

10. 1) Execution Plans
10
Operation Used-Mem
SELECT STATEMENT
HASH GROUP BY 49M (0)
VIEW
WINDOW SORT 20M (0)
TABLE ACCESS FULL
Operation Used-Mem
SELECT STATEMENT
VIEW
MATCH RECOGNIZE SORT DETERMINISTIC FINITE AUTOMATON 20M (0)
TABLE ACCESS FULL

11. 2) “Start of Group”
• Identify group boundaries, often using LAG()
• 3 steps instead of 2:
1. For each row: if start of group, assign 1
Else assign 0
2. Running total of 1s and 0s produces a group
identifier
3. Group by the group identifier
11

12. 2) Requirement
GROUP_NAME START_TS END_TS
X 2014-01-01 00:00 2014-02-01 00:00
X 2014-03-01 00:00 2014-04-01 00:00
X 2014-04-01 00:00 2014-05-01 00:00
X 2014-06-01 00:00 2014-06-01 01:00
X 2014-06-01 01:00 2014-06-01 02:00
X 2014-06-01 02:00 2014-06-01 03:00
Y 2014-06-01 03:00 2014-06-01 04:00
Y 2014-06-01 04:00 2014-06-01 05:00
Y 2014-07-03 08:00 2014-09-29 17:00
12
Merge contiguous date ranges in same group

13. 1
2
2
3
3
3
1
1
2
X 01-01 00:00 02-01 00:00 1
X 03-01 00:00 04-01 00:00 1
X 04-01 00:00 05-01 00:00 0
X 06-01 00:00 06-01 01:00 1
X 06-01 01:00 06-01 02:00 0
X 06-01 02:00 06-01 03:00 0
Y 06-01 03:00 06-01 04:00 1
Y 06-01 04:00 06-01 05:00 0
Y 07-03 08:00 09-29 17:00 1
X 01-01 00:00 02-01 00:00
X 03-01 00:00 05-01 00:00
X 06-01 00:00 06-01 03:00
Y 06-01 03:00 06-01 05:00
Y 07-03 08:00 09-29 17:00
13
with grp_starts as (
select a.*,
case when start_ts =
lag(end_ts) over(
partition by group_name
order by start_ts
)
then 0 else 1 end grp_start
from t a
), grps as (
select b.*,
sum(grp_start) over(
partition by group_name
order by start_ts
) grp_id
from grp_starts b)
select group_name,
min(start_ts) start_ts,
max(end_ts) end_ts
from grps
group by group_name, grp_id;

14. 2) Match_Recognize
14
SELECT * FROM t
MATCH_RECOGNIZE(
PARTITION BY group_name
ORDER BY start_ts
MEASURES
A.start_ts start_ts,
end_ts end_ts
PATTERN(A B*)
DEFINE B AS start_ts = prev(end_ts)
);
New this time:
• Added PARTITION BY
• ONE ROW PER MATCH
and
SKIP PAST LAST ROW
are the defaults
One solution replaces two methods: simple!

15. 2) Run_Stats comparison
15
For 500,000 rows:
Stat Pre 12c Match_R Pct
Latches 8108 7133 88%
Elapsed Time 1.7551 0.9765 56%
CPU used by this session 1.73 0.96 55%

16. Operation Used-Mem
SELECT STATEMENT
HASH GROUP BY 22M (0)
VIEW
WINDOW BUFFER 32M (0)
VIEW
WINDOW SORT 27M (0)
TABLE ACCESS FULL
Operation Used-Mem
SELECT STATEMENT
VIEW
MATCH RECOGNIZE SORT DETERMINISTIC FINITE AUTOMATON 27M (0)
TABLE ACCESS FULL
2) Execution Plans
16

17. 2) Matching within a group
17
SELECT * FROM (
SELECT * from t
WHERE group_name = 'X'
)
MATCH_RECOGNIZE
…
);
Filter before MATCH_RECOGNIZE to avoid extra work

18. 2) Predicate pushing
18
Select * from <view> where group_name = 'X'
Operation Name A-Rows Buffers
SELECT STATEMENT 3 4
VIEW 3 4
MATCH RECOGNIZE SORT DETERMINISTIC
FINITE AUTO
3 4
TABLE ACCESS BY INDEX ROWID
BATCHED
T 6 4
INDEX RANGE SCAN TI 6 3

19. 3) “Bin fitting”: fixed size
• Requirement
– Order by study_site
– Put in “bins” with size =
65,000 max
STUDY_SITE CNT STUDY_SITE CNT
1001 3407 1026 137
1002 4323 1028 6005
1004 1623 1029 76
1008 1991 1031 4599
1011 885 1032 1989
1012 11597 1034 3427
1014 1989 1036 879
1015 5282 1038 6485
1017 2841 1039 3
1018 5183 1040 1105
1020 6176 1041 6460
1022 2784 1042 968
1023 25865 1044 471
1024 3734 1045 3360
FIRST_SITE LAST_SITE SUM_CNT
1001 1022 48081
1023 1044 62203
1045 1045 3360
19

20. SELECT s first_site, MAX(e) last_site, MAX(sm) sum_cnt FROM (
SELECT s, e, cnt, sm FROM t
MODEL
MEASURES (study_site s, study_site e, cnt, cnt sm)
RULES (
sm[ > 1] =
CASE WHEN sm[cv() - 1] + cnt[cv()] > 65000 OR cnt[cv()] > 65000
THEN cnt[cv()]
ELSE sm[cv() - 1] + cnt[cv()]
END,
s[ > 1] =
CASE WHEN sm[cv() - 1] + cnt[cv()] > 65000 OR cnt[cv()] > 65000
THEN s[cv()]
ELSE s[cv() - 1]
END
)
)
GROUP BY s;
• DIMENSION with row_number
orders data and processing
• rn can be used like a subscript
• cv() means current row
• cv()-1 means previous row
DIMENSION BY (row_number() over(order by study_site) rn)
rn
[cv() – 1] [cv()] [cv()]
[cv()]
[cv() – 1] [cv()]
rn
[cv() - 1] [cv()] [cv()]
[cv()]
[cv() – 1]
20

21. SELECT * FROM t
MATCH_RECOGNIZE (
ORDER BY study_site
MEASURES
FIRST(study_site) first_site,
LAST(study_site) last_site,
SUM(cnt) sum_cnt
PATTERN (A+)
DEFINE A AS SUM(cnt) <= 65000
);
New this time:
• PATTERN
(A+) replaces (A B*)
means 1 or more rows
• Why? In previous
examples I used PREV(),
which returns NULL on
the first row.
One solution replaces 3 methods: simpler!
21

22. 3) Run_Stats comparison
For 150000 rows:
Stat Pre 12c Match_R Pct
Latches 3501 1312 37%
Elapsed Time 2.4036 0.1028 4%
CPU used by this session 2.39 0 0%
22

23. Id Operation Used-Mem
0 SELECT STATEMENT
1 HASH GROUP BY 1481K (0)
2 VIEW
3 SQL MODEL ORDERED 17M (0)
4 WINDOW SORT 4330K (0)
5 TABLE ACCESS FULL
Id Operation Used-Mem
0 SELECT STATEMENT
1 VIEW
2 MATCH RECOGNIZE SORT DETERMINISTIC FINITE AUTO 4330K(0)
3 TABLE ACCESS FULL
3) Execution Plans
23

24. Name Val Val BIN1 BIN2 BIN3
1 1 10 10
2 2 9 10 9
3 3 8 10 9 8
4 4 7 10 9 15
5 5 6 10 15 15
6 6 5 15 15 15
7 7 4 19 15 15
8 8 3 19 18 15
9 9 2 19 18 17
10 10 1 19 18 18
4) “Bin fitting”: fixed number
24
• Requirement
– Distribute values in 3
parts as equally as
possible
• “Best fit decreasing”
– Sort values in
decreasing order
– Put each value in least
full “bin”

25. 4) Brilliant pre 12c solution
SELECT bin, Max (bin_value) bin_value
FROM (
SELECT * FROM items
MODEL
DIMENSION BY
(Row_Number() OVER
(ORDER BY item_value DESC) rn)
MEASURES (
item_name,
item_value,
Row_Number() OVER
(ORDER BY item_value DESC) bin,
item_value bin_value,
Row_Number() OVER
(ORDER BY item_value DESC) rn_m,
0 min_bin,
Count(*) OVER () - 3 - 1 n_iters
)
RULES ITERATE(100000)
UNTIL (ITERATION_NUMBER >= n_iters[1]) (
min_bin[1] = Min(rn_m) KEEP (DENSE_RANK
FIRST ORDER BY bin_value)[rn<= 3],
bin[ITERATION_NUMBER + 3 + 1] =
min_bin[1],
bin_value[min_bin[1]] =
bin_value[CV()] +
Nvl(item_value[ITERATION_NUMBER+4], 0))
)
WHERE item_name IS NOT NULL
group by bin;
25

26. SELECT * from items
MATCH_RECOGNIZE (
ORDER BY item_value desc
MEASURES
sum(bin1.item_value) bin1,
sum(bin2.item_value) bin2,
sum(bin3.item_value) bin3
PATTERN ((bin1|bin2|bin3)+)
DEFINE
bin1 AS count(bin1.*) = 1
OR sum(bin1.item_value)-bin1.item_value
<= least(
sum(bin2.item_value),
sum(bin3.item_value)
),
bin2 AS count(bin2.*) = 1
OR sum(bin2.item_value)-bin2.item_value
<= sum(bin3.item_value)
);
• ()+ = 1 or more of whatever
is inside
• '|' = alternatives,
“preferred in the order
specified”
• Bin1 condition:
• No rows here yet,
• Or this bin least full
• Bin2 condition
• No rows here yet, or
• This bin less full than 3
PATTERN ((bin1|bin2|bin3)+)
bin1 AS count(bin1.*) = 1
OR sum(bin1.item_value)-bin1.item_value
<= least(
sum(bin2.item_value),
sum(bin3.item_value)
),
bin2 AS count(bin2.*) = 1
OR sum(bin2.item_value)-bin2.item_value
<= sum(bin3.item_value)
26

27. 4) Run_Stats comparison
27
For 20,000 rows:
Stat Pre 12c Match_R Pct
Latches 99165 794 .80%
Elapsed Time 58.3251 0.028 .05%
CPU used by this session 56.75 0 .00%

28. Backtracking
• What happens when there is no match???
• “Greedy” quantifiers * + {2,}
– are not that greedy
– Take all the rows they can, BUT
give rows back if necessary – one at a time
• Regular expression engines will test all possible
combinations to find a match
28

29. 29
123456789
A
AB
ABBB
ABBBB
ABBBBB
ABBBBBB
ABBBBBBB
ABBBBBBBC
ABBBBBBC
ABBBBBC
ABBBBC
ABBBC
ABBC
ABC
AC
Backtracking in action:
pattern(a b* c)
1. Find A
2. Find all the Bs you can
3. At the end, look for a C
4. No C? Backtrack through the Bs
5. Still no C? No Match!

30. Repeating conditions
select 'match' from (
select level n from dual
connect by level <= 100
)
match_recognize(
pattern(a b* c)
define b as n > prev(n)
, c as n = 0
);
Runs in 0.006 secs
select 'match' from (
select level n from dual
connect by level <= 100
)
match_recognize(
pattern(a b* b* b* c)
define b as n > prev(n)
, c as n = 0
);
Runs in 2.934 secs
30

31. SELECT * FROM Ticker
MATCH_RECOGNIZE (
PARTITION BY symbol
ORDER BY tstamp
MEASURES FIRST(tstamp) AS start_tstamp,
LAST(tstamp) AS end_tstamp
AFTER MATCH SKIP TO LAST UP
PATTERN (STRT DOWN+ UP+ DOWN+ UP+)
DEFINE DOWN AS price < PREV(price),
UP AS price > PREV(price),
STRT AS price >= nvl(PREV(PRICE),0)
);
Runs in 0.014 seconds
Imprecise Conditions
CREATE TABLE Ticker (
SYMBOL VARCHAR2(10),
tstamp DATE,
price NUMBER
);
insert into ticker
select 'ACME',
sysdate + level/24/60/60,
10000-level
from dual
connect by level <= 5000;
SELECT * FROM Ticker
MATCH_RECOGNIZE (
PARTITION BY symbol
ORDER BY tstamp
MEASURES FIRST(tstamp) AS start_tstamp,
LAST(tstamp) AS end_tstamp
AFTER MATCH SKIP TO LAST UP
PATTERN (STRT DOWN+ UP+ DOWN+ UP+)
DEFINE DOWN AS price < PREV(price),
UP AS price > PREV(price)
);
Runs in 13 seconds
INMEMORY: 7 seconds 31

32. Keep in Mind
• Backtracking
– Precise conditions
– Test data with no matches
• To debug:
Measures classifier() cl,
match_number() mn
All rows per match with
unmatched rows
• No DISTINCT, no LISTAGG
• MEASURES columns must
have aliases
• “Reluctant quantifier” = ?
= JDBC bind variable
(Fix in 12.2 JDBC driver !)
• “Pattern variables” are
identifiers, not bind
variables
32

33. Questions?
The really good stuff is tomorrow!
"Meet your Match", 10:30, Bundestag 2
33
1) consecutive
values
2) based on
starting point
3) Bin fitting:
fixed size
4) Bin fitting:
fixed number
Pre 12c Analytic function,
Group by
2 analytics,
Group by
MODEL clause
using “subscript”
Procedural
MODEL clause
Match_
Recognize
C = PREV(C)+1 C1=PREV(C2) SUM(C1) <= X 3 conditions
Elapsed 94% 56% 4% 0.05%

34. Which row do we mean?
Expression DEFINE
MEASURES
ALL ROWS… ONE ROW…
FIRST(start_ts) First row of match
start_ts current row last row of match
LAST(end_ts) current row last row of match
FINAL
LAST(end_ts)
ORA-62509 last row of match
B.start_ts most recent B row last B row
PREV(), NEXT() Physical offset from referenced row
COUNT(*) from first to current row all rows in match
COUNT(B.*) B rows including current row all B rows
34

35. Output Row “shape”
Per Match PARTITION BY ORDER BY MEASURES Other input
ONE ROW X Omitted X omitted
ALL ROWS X X X X
35
ORA-00918, anyone?