2. Introducing Oracle Regular Expressions
Session id: 40105
Introducing Oracle Regular Expressions
Jonathan Gennick, O'Reilly & Associates
Peter Linsley, Oracle Corporation

3. What are Regular Expressions?
A language, or syntax, you can use to describe patterns in text
Example: [0-9]{3}-[0-9]{4}
That which you can describe, you can find and manipulate
Unix ed, grep, perl, and now everywhere!

4. Why Describe Patterns? Humans have long worked with patterns:
Postal and addresses
URLs
Phone numbers
Often it’s not the data that’s important, but the pattern:
Bioinformatics
Validate format of URLs and addresses
Correct formatting of phone numbers
Would be nice to have a more specific statement regarding bioinformatics.

5. Pre-Oracle Database 10g Find parks with acreage in their descriptions:
SELECT *
FROM park
WHERE description LIKE '%acre%';
Finds '217-acre' and '27 acres', but also ‘few acres’, ‘more acres than all other parks’, 'the location of a massacre', etc.

6. Pre-Oracle Database 10g cont.
Pattern matching with LIKE
Limited to only two operators: % and _
OWA_PATTERN
No support for alternation, ASCII only, relatively poor performance
Non-native solutions
External Procedures
Difficult to deploy, maintain, and support
Client based solutions
Pull all that data down across the network

7. Oracle Database 10g Four regular expression functions
REGEXP_LIKE does pattern match?
REGEXP_INSTR where does it match?
REGEXP_SUBSTR what does it match?
REGEXP_REPLACE replace what matched.
POSIX Extended Regular Expressions
UNIX Regular Expressions
Backreference support added
Longest match not supported

8. REGEXP_LIKE Determine whether a pattern exists in a string
Revisiting the acreage problem:
SELECT *
FROM park
WHERE REGEXP_LIKE(description,
'[0-9]+(-| )acre');
Finds '217-acre' and '27 acres'
REJECTS ‘few acres’, ‘more acres than all other parks’, 'the location of a massacre', etc.

9. Useful for Constraints
Filter allowable data with check constraint
Only allow alphabetical characters:
CREATE TABLE t1 (c1 VARCHAR2(20), CHECK (REGEXP_LIKE(c1, '^[[:alpha:]]+$')));
INSERT INTO t1 VALUES ('newuser');
 1 row created.
INSERT INTO t1 VALUES ('newuser1');
 ORA-02290: check constraint violated

10. Metacharacters Operator Description . match any character
a? match 'a' zero or one time
a* match 'a' zero or more times
a+ match 'a' one or more times
a|b match either 'a' or 'b'
a{m,n} match 'a' between m and n times
[abc] match either 'a' or 'b' or 'c'
(abc) match group 'abc'
\n match nth group
[:cc:] match character class
[.ce.] match collation element
[=ec=] match equivalence class

11. REGEXP_INSTR Find out where a match occurs:
SELECT REGEXP_INSTR(description,
'[0-9]+(-| )acre')
FROM park;
REGEXP_INSTR(DESCRIPTION,'[0-9]+… 6 20 …

12. REGEXP_SUBSTR Determine what text matched:
SELECT REGEXP_SUBSTR(description,
'[0-9]+(-| )acre')
FROM park;
REGEXP_SUBSTR(DESCRIPT
217-acre
27 acre …

13. REGEXP_SUBSTR Cont To extract just the acreage value:
SELECT REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')
FROM park;
REGEXP_SUBSTR(REGEXP
217 27

14. REGEXP_REPLACE Convert acres to hectares: UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

15. REGEXP_REPLACE Cont. Convert acres to hectares: UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

16. REGEXP_REPLACE Cont. This 217-acre park is wonderful. UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

17. REGEXP_REPLACE Cont. This 217-acre park is wonderful. 217-acre
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

18. REGEXP_REPLACE Cont. This 217-acre park is wonderful. 217-acre 217
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

19. REGEXP_REPLACE Cont. This 217-acre park is wonderful. 217-acre 217
217 * =
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

20. REGEXP_REPLACE Cont. This 217-acre park is wonderful. 217-acre 217
217 * =
\2hectare
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

21. REGEXP_REPLACE Cont. 1 2 This 217-acre park is wonderful. 217-acre 217
217 * =
\2hectare
hectare 1 2
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

22. REGEXP_REPLACE Cont. This 217-acre park is wonderful. 217-acre 217
217 * =
\2hectare
hectare
This hectare park is wonderful.
UPDATE park
SET description = REGEXP_REPLACE(
description,'([0-9]+)(-| )acre',
TO_CHAR( * TO_NUMBER(
REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+')))
|| '\2' || 'hectare');

23. Oracle Regular Expressions
D E M O N S T R A T I O N
Oracle Regular Expressions

24. Performance Pattern matching can be complex
Need to compile to state machine
Lex and parse
Examine all possible branches until match found
Compiled once per statement
Can be faster than LIKE for complex scenarios
Usually faster than PL/SQL equivalent
ZIP code checking 5 times faster

25. Performance Cont. Some poorly-performing expressions:
'a{2}' will be slower than 'aa'
'.*b' on input that doesn't contain a 'b' can also be quite time-consuming
Mastering Regular Expressions
By Jeffrey Friedl
Chapter 6, Crafting an Efficient Expression

26. Using with Indexes Use function-based indexes:
CREATE INDEX acre_ind
ON park (REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+'));
To support regular expression queries:
SELECT * FROM park
WHERE REGEXP_SUBSTR(REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+') = 217;

27. Using with Views Hide the complexity from users:
CREATE VIEW park_acreage as
SELECT park_name,
REGEXP_SUBSTR(
description,
'[0-9]+(-| )acre'),
'[0-9]+') acreage
FROM park;

28. Using with PL/SQL REGEXP_LIKE acts as a Boolean function in PL/SQL:
IF REGEXP_LIKE(description,
'[0-9]+(-| )acre') THEN
acres := REGEXP_SUBSTR(
REGEXP_SUBSTR(description,
'[0-9]+(-| )acre'),'[0-9]+');
...
All other functions act identically in PL/SQL and SQL.

29. Longest Match vs Greediness
Greediness = each element matches as much as possible. For example:
SELECT REGEXP_SUBSTR(
'In the beginning','.+[[:space:]]')
FROM dual;
 In the

30. Longest Match vs Greediness
Longest match = find the variations resulting in the greatest number of matching characters:
SELECT REGEXP_SUBSTR('bbb','b|bb') FROM dual;
 b
SELECT REGEXP_SUBSTR('bbb','bb|b') FROM dual;
 bb

31. Optional Parameters
All but REGEXP_LIKE take optional parameters for starting position and occurrence:
REGEXP_INSTR (source, pattern, start, occurrence, match)
REGEXP_SUBSTR (source, pattern, start, occurrence, match)
REGEXP_REPLACE(source, pattern, replace, start, occurrence,
match)
For example:
REGEXP_SUBSTR('description','[^[:space:]]+',1,10)

32. Match Parameter All functions take an optional match parameter:
Is matching case sensitive?
Does period (.) match newlines?
Is the source string one line or many?
The match parameter comes last

33. Case-sensitivity Case-insensitive search: SELECT * FROM park
WHERE REGEXP_LIKE(
description,
'[0-9]+(-| )acre',
'i');

34. Newline matching INSERT INTO park VALUES ('Park 6',
'640' || CHR(10) || 'ACRE');
SELECT *
FROM park
WHERE REGEXP_LIKE(
description,
'[0-9]+.acre',
'in');

35. Yes! String anchors INSERT INTO employee (surname)
VALUES ('Ellison' || CHR(10) ||
'Gennick');
SELECT * FROM
EMPLOYEE
WHERE REGEXP_LIKE(
surname,'^Ellison');
Yes!

36. No! String anchors INSERT INTO employee (surname)
VALUES ('Ellison' || CHR(10) ||
'Gennick')
SELECT * FROM
EMPLOYEE
WHERE REGEXP_LIKE(
surname,'^Gennick');
No!

37. Yes! String anchors INSERT INTO employee (surname)
VALUES ('Ellison' || CHR(10) ||
'Gennick')
SELECT * FROM
EMPLOYEE
WHERE REGEXP_LIKE(
surname,'^Gennick','m');
Yes!

38. Locale Support Full Locale Support
All character sets
All languages
Case and accent insensitive searching
Linguistic range
Character classes
Collation elements
Equivalence classes

39. Character Sets and Languages
For example, you can search for Ukrainian names beginning with Ґ and ending with к:
SELECT *
FROM employee
WHERE REGEXP_LIKE(
surname,
'^Ґ[[:alpha:]]*к$','n');

40. Case- and Accent-Insensitive Searching
Respect for NLS settings:
ALTER SESSION
SET NLS_SORT = GENERIC_BASELETTER;
With this sort, case won't matter and an expression such as:
REGEXP_INSTR(x,'resume')
will find "resume", "résumé", "Résume", etc.

41. Linguistic Range Ranges respect NLS_SORT settings: a,b,c…z [a-z]
NLS_SORT=GERMAN
[a-z]
a,A,b,B,c,C…z,Z
NLS_SORT=GERMAN_CI

42. Character Classes
Character classes such as [:alpha:] and [:digit:] encompass more than just Latin characters.
For example, [:digit:] matches:
Latin 0 through 9
Arabic-Indic٠through ٩
And more

43. Collation Elements ALTER SESSION SET NLS_SORT=XSPANISH;
SELECT REGEXP_SUBSTR(
'El caballo, Chico come la tortilla.',
'[[:alpha:]]*[ch][[:alpha:]]*',
1,1,'i')
FROM dual;
caballo

44. Collation Elements ALTER SESSION SET NLS_SORT=XSPANISH;
SELECT REGEXP_SUBSTR(
'El caballo, Chico come la tortilla.',
'[[:alpha:]]*[[.ch.]][[:alpha:]]*',
1,1,'i')
FROM dual;
Chico

45. Equivalence Classes Ignore case and accents without changing NLS_SORT:
REGEXP_INSTR(x,'r[[=e=]]sum[[=e=]]')
Finds 'resume', 'résumé', and 'rEsumE'

46. Conclusion
String searching and manipulation is at the heart of a great many applications
Oracle Regular Expressions provide versatile string manipulation in the database instead of externalized in middle tier logic
They are Locale sensitive and support character large objects
Available in both SQL and PL/SQL

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Session #40202 Oracle HTML DB
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Jonathan Gennick
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