Row Pattern Matching in SQL:2016

Row Pattern Matching
@MarkusWinand
Image: 72hoursamericanpower.com

Row Pattern Matching Availability
1999
2001
2003
2005
2007
2009
2011
2013
2015
MariaDB
MySQL
PostgreSQL
SQLite
DB2 LUW
12cR1 Oracle
SQL Server

Grouping Consecutive Events
Time
30 minutes
Example: Logﬁle

Grouping Consecutive Events
Example: Logﬁle
Time
30 minutes
Session 1 Session 2
Session 3
Session 4
Example problems:
‣ Count sessions
‣ Average session duration
Two approaches:
‣ Start-of-group tagging
‣ Row pattern matching

SELECT COUNT(grp_start) groups
FROM (SELECT CASE WHEN ts > LAG( ts, 1, DATE'1900-01-01' )
OVER( ORDER BY ts )
+ INTERVAL '30' minute
THEN 1
END grp_start
FROM log
) T
Consecutive Events: Counting Start-of-group tagging
Time
30 minutes

SELECT COUNT(grp_start) groups
FROM (SELECT CASE WHEN ts > LAG( ts, 1, DATE'1900-01-01' )
OVER( ORDER BY ts )
THEN 1
END grp_start
FROM log
) T
Consecutive Events: Counting Start-of-group tagging
Time
30 minutes
count the 
non-NULL 
values

SELECT COUNT(*) sessions
FROM log
MATCH_RECOGNIZE(
ORDER BY ts
PATTERN ( new )
DEFINE new AS ts > COALESCE( PREV(ts)
, DATE '1900-01-01'
)
) t
Consecutive Events: Counting Row Pattern Matching
Time
30 minutes

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
PATTERN ( new )
, DATE '1900-01-01'
)
) t
Time
30 minutes
row pattern
variable

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
PATTERN ( new )
, DATE '1900-01-01'
)
) t
Time
30 minutes
match 
only “new”
rows

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
PATTERN ( new )
, DATE '1900-01-01'
)
) t
Time
30 minutes
count 
rows

, AVG(duration) avg_duration
FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
LAST(ts) - FIRST(ts) AS duration
ONE ROW PER MATCH
PATTERN ( new cont* )
DEFINE cont AS ts < PREV(ts)
) t
Row Pattern MatchingConsecutive Events: Statistics
Time
30 minutes
define 
continuation
Oracle doesn’t support avg on intervals — query doesn’t work as shown

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
ONE ROW PER MATCH
) t
Time
30 minutes
undefined 
pattern variable:
matches any row

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
ONE ROW PER MATCH
) t
Time
30 minutes
any number 
of “cont” 
rows

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
ONE ROW PER MATCH
) t
Time
30 minutes
Very much 
like GROUP BY

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
ONE ROW PER MATCH
) t
Time
30 minutes
Very much 
like SELECT

FROM log
MATCH_RECOGNIZE(
ORDER BY ts
MEASURES
ONE ROW PER MATCH
) t
Time
30 minutes

Consecutive Events: Statistics Start-of-group tagging
Time
30 minutes
Now, let’s try using window functions

SELECT count(*) sessions, avg(duration) avg_duration
FROM (SELECT MAX(ts) - MIN(ts) duration
FROM (SELECT ts, COUNT(grp_start) OVER(ORDER BY ts) session_no
FROM (SELECT ts, CASE WHEN ts >= LAG( ts, 1, DATE’1900-01-1' )
OVER( ORDER BY ts )
THEN 1
END grp_start
FROM log
) tagged
) numbered
GROUP BY session_no
) grouped
Time
30 minutes
Start-of-group
tags

OVER( ORDER BY ts )
THEN 1
END grp_start
FROM log
) tagged
) numbered
GROUP BY session_no
) grouped
Time
30 minutes
number
sessions
2222 2 33 3 44 42 3 4
1

OVER( ORDER BY ts )
THEN 1
END grp_start
FROM log
) tagged
) numbered
GROUP BY session_no
) grouped
Time
30 minutes 2222 2 33 3 44 42 3 4
1

OVER( ORDER BY ts )
THEN 1
END grp_start
FROM log
) tagged
) numbered
GROUP BY session_no
) grouped
Time
30 minutes
4 Levels:
2 with window functions
2 for grouping
 
What about performance?
2222 2 33 3 44 42 3 4
1

Tolerating Gaps
Example: Comments (new vs. read)

Tolerating Gaps
Example: Comments (new vs. read)
Show comments which…
‣ …are new or
‣ …between two new ones 
(show the comment instead of a “load more” button)
Two approaches:
‣ Start-of-group tagging
‣ Row pattern matching
Comments
new commentread comment

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
PATTERN ( new+ (read new+)* )
DEFINE
new AS (marker = 'X')
) T
ORDER BY id
Tolerating Gaps Row Pattern Matching
Comments
Start with one 
or more NEW 
comment(s)

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments
Start with one 
or more NEW 
comment(s)
Doesn’t match

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments
Start with one 
or more NEW 
comment(s)
Two rows
match “new+”

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments
Match exactly 
one row (any)

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments
Repeat group 
any number 
of times

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
Comments
First match

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
ORDER BY id
CommentsSecond 
match

Tolerating Gaps (also ﬁrst/last) Row Pattern Matching
Comments
What about
this?

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
PATTERN ( (^read)? new+ (read new+)* (read$)? )
DEFINE
) T
ORDER BY thread_id, id
Comments
What about
this?

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
Comments
Match 
“read” at the very 
beginning

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
Comments
Optionally match 
“read” at the very 
beginning

SELECT id, marker
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
ALL ROWS PER MATCH
DEFINE
) T
Comments

Tolerating Gaps lead & lag
Comments
Now, let’s try using window functions

SELECT t.*
FROM (SELECT msg.*
, LAG ( marker, 1, 'X' ) OVER( ORDER BY id ) prev_marker
, LEAD( marker, 1, 'X' ) OVER( ORDER BY id ) next_marker
FROM msg
) t
WHERE marker = 'X'
OR (prev_marker = 'X' and next_marker = ‘X')
ORDER BY id
Tolerating Gaps lead & lag
Comments

I don't care what anything was designed to do,
I care about what it can do.
—Apollo 13, Universal Pictures

Tolerating Gaps (with grouped gaps) Row Pattern Matching
Comments

Comments
Load 2 more Load 9 more

Comments
Tell me how many rows you skipped in between

SELECT id, marker, gap_length
FROM msg
MATCH_RECOGNIZE (
ORDER BY id
MEASURES
FINAL COUNT(more.*) AS gap_length
ALL ROWS PER MATCH
PATTERN ( (^read)? new+ (read new+)* (read$)? | more {- more* -} )
DEFINE new AS (marker = 'X'),
more AS (marker != 'X')
) T
ORDER BY id
Comments

FROM msg
MATCH_RECOGNIZE (
ORDER BY id
MEASURES
ALL ROWS PER MATCH
) T
ORDER BY id
Comments
Alternative
Match, but
don’t return

FROM msg
MATCH_RECOGNIZE (
ORDER BY id
MEASURES
ALL ROWS PER MATCH
) T
ORDER BY id
Comments
Consider 
all rows

FROM msg
MATCH_RECOGNIZE (
ORDER BY id
MEASURES
ALL ROWS PER MATCH
) T
ORDER BY id
Comments
Only rows
matched to the
pattern variable
“more”

SELECT id, marker
, CASE WHEN marker != 'X' AND gap_length > 2
THEN gap_length
END gap_length
FROM (SELECT t2.*, COUNT( CASE WHEN marker != 'X' THEN 1 END )
OVER( PARTITION BY new_counter ) gap_length
FROM (SELECT msg.*, COUNT( CASE WHEN marker = 'X' THEN 1 END )
OVER( ORDER BY id ) new_counter
, LAG ( marker, 1, 'X' )
OVER( ORDER BY id ) prev_marker
FROM msg
) t2
) t3
WHERE marker = 'X' OR gap_length = 1 OR prev_marker= 'X'
ORDER BY id
Start-of-group taggingTolerating Gaps (with grouped gaps)
Comments

Top-N Per Group
Example: List 3 most recent comments per topic

Top-N Per Group
Time
Topic 3
Topic 2
Topic 1

Top-N Per Group
Three approaches:
‣ lateral sub-query (requires speciﬁc indexing)
‣ Row pattern matching (requires 12c)
‣ row_number() window function
Time
Topic 3
Topic 2
Topic 1

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
RUNNING COUNT(*) AS rn
ALL ROWS PER MATCH
PATTERN ( ^a{1,3} )
DEFINE
a AS 1=1
)
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group
per “topic”
processing

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
ALL ROWS PER MATCH
PATTERN ( ^a{1,3} )
DEFINE
a AS 1=1
)
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group
Consider rows
up till current
row

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
ALL ROWS PER MATCH
PATTERN ( ^a{1,3} )
DEFINE
a AS 1=1
)
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group
1, 2, or 3 times

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
ALL ROWS PER MATCH
PATTERN ( ^a{1,3} )
DEFINE
a AS 1=1
)
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group
DEFINE is
non-optional:
Use dummy

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
ALL ROWS PER MATCH
PATTERN ( ^a{1,3} )
DEFINE
a AS 1=1
)
SELECT *
FROM (
SELECT t.*
, ROW_NUMBER()
OVER (PARTITION BY topic
ORDER BY val) rn
FROM t
) t
WHERE rn <= 3
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group

SELECT *
FROM t
MATCH_RECOGNIZE(
PARTITION BY topic
ORDER BY val
MEASURES
ALL ROWS PER MATCH
PATTERN ( ^a+ )
DEFINE
a AS count(*) <= 3
)
SELECT *
FROM (
SELECT t.*
, ROW_NUMBER()
OVER (PARTITION BY topic
ORDER BY val) rn
FROM t
) t
WHERE rn <= 3
Time
Topic 3
Topic 2
Topic 1
Top-N Per Group
Always
RUNNING
semantic

Time Intervals (non-overlapping)
Example: Bookings are stored as [begin; end[ intervals

Example: Bookings are stored as [begin; end[ intervals
Two problems:
‣ Find free time-slots
‣ Close free time-slots
Time
Busy Busy Busy

Time
SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
AFTER MATCH SKIP TO b
PATTERN ( a b )
DEFINE
b AS a.end < begin
)

Time
SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
a b

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
a b
Time

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
a b
Time
Default is to 
continue AFTER 
last matched row

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
Time

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
Time
a b

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
a b
Time

SELECT *
FROM reservations
MATCH_RECOGNIZE(
ORDER BY begin
MEASURES
a.end AS begin,
b.begin AS end
ONE ROW PER MATCH
PATTERN ( a b )
DEFINE
b AS a.end < begin
)
Time
SELECT *
FROM (SELECT end begin
, LEAD(begin)
OVER(ORDER BY begin) end
FROM reservations
)
WHERE begin < end

Time
Time Intervals (close gaps)

SELECT b begin, e end, type
FROM reservations MATCH_RECOGNIZE(
ORDER BY begin
MEASURES CASE WHEN free.begin IS NULL THEN busy.begin
ELSE busy.end
END AS b
, COALESCE(free.begin, busy.end) AS e
, CLASSIFIER() as type
ALL ROWS PER MATCH
AFTER MATCH SKIP TO NEXT ROW
PATTERN ( busy free? )
DEFINE free AS begin > PREV(end)
)
Time
Always match 
one row. Second only 
if there is a gap
Busy Free

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Time
Busy Free

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Time
Busy Free
If it is not a 
“free” row, pass 
row through

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Row Pattern MatchingTime Intervals (close gaps)
Busy Free
Time

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Busy Free
Time
Free

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Busy
Time
Free

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Busy
Time
Busy FreeFree

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Busy Busy FreeFree
Time
Free

ORDER BY begin
ELSE busy.end
END AS b
ALL ROWS PER MATCH
)
Busy BusyFree
Time
Free Busy

SELECT begin, end, type
FROM (SELECT end begin
, LEAD(begin) OVER(ORDER BY begin) end
, 'FREE' type
FROM reservations
)
WHERE begin < end
UNION ALL
SELECT begin
, end
, 'BUSY' type
FROM reservations
Busy BusyFree
Time
Free Busy
Time Intervals (close gaps) Window function

Endless possibilitesRow Pattern Matching
GROUP BY 
➡ ONE ROW PER MATCH
OVER () 
➡ ALL ROWS PER MATCH, FINAL, RUNNING
HAVING, WHERE 
➡ PATTERN (unmatched, suppressed {- … -})
Mixing GROUP BY and OVER() 
➡ ALL ROWS PER MATCH + all-but-one rows suppressed
Data-driven match length  
➡ SUM, COUNT, … in DEFINE
Duplicating rows (to some extend) 
➡ ALL ROWS PER MATCH + AFTER MATCH SKIP TO …

What if I told you,
you can also
ﬁnd patterns?

Not/Barely covered in this presentationRow Pattern Matching
‣ Reluctant (non-greedy) matching
‣ SHOW/OMIT EMPTY MATCHES 
WITH UNMATCHED ROWS
‣ SUBSET (deﬁne pattern-vars for use in MEASURES, 
DEFINE and AFTER MATCH SKIP TO)
‣ PREV, NEXT, FIRST, LAST (with some nesting!)
‣MATCH_NUMBER()

Obstacles and Issues (as of 12r1)Row Pattern Matching
‣ JDBC !!!!! 
Tokens ?, {, } have special meaning in JDBC. 
You have to escape them using { ... } 
https://docs.oracle.com/database/121/JJDBC/apxref.htm#CHECHCJH
‣ ORA-62513: Quantiﬁed subpatterns that can have empty matches are not yet supported 
 
PATTERN ( x (a* b*)+ y )
‣ ORA-62512: This aggregate is not yet supported in MATCH_RECOGNIZE clause. 
(only COUNT, SUM, AVG, MIN, and MAX)

About @MarkusWinand
‣Training for Developers
‣ SQL Performance (Indexing)
‣ Modern SQL
‣ On-Site or Online
‣SQL Tuning
‣ Index-Redesign
‣ Query Improvements
‣ On-Site or Online
http://winand.at/

About @MarkusWinand
€0,-
€10-30
sql-performance-explained.com

About @MarkusWinand
@ModernSQL
http://modern-sql.com

Row Pattern Matching in SQL:2016

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (12)

Similaire à Row Pattern Matching in SQL:2016

Similaire à Row Pattern Matching in SQL:2016 (15)

Plus de Markus Winand

Plus de Markus Winand (9)

Dernier

Dernier (20)

Row Pattern Matching in SQL:2016