2. New SQL Capabilities in Oracle Database 10 g Geoff Lee, Principal Product Manager Peter Linsley, PMTS Oracle Corporation Session id: 40088

3. Agenda  Overview  New Features  Demo  Summary  Q&A

4. Oracle8 Evolution of Oracle SQL Engine Oracle8iOracle9iOracle9i R2 SQL /XML Integration (XMLType, URIType, etc.) OLAP Datetime Types ANSI Joins CASE Expressions XML DB SQL/XML W3C Standards Data Mining Grid Web Services Regular Expressions Native Numbers BLAST Expression Filter Objects Extension Data Cartridges SQL/Java Integration interMedia Types Data and Processing complexity

5. Oracle SQL Engine Architecture Optimizer Query Engine Index Engine Type Manager Extensibility TextText Text Spatial Utilities Tools ImageImage Image TextSpatial Image Data Cartridges SpatIalSpatIal

6. Oracle8 Evolution of Oracle Objects Oracle8iOracle9iOracle9i R2 UDT/REF/ Collections/LOBs Object Tables Object Views Instead-Of Trigger Performance JavaVM/JDBC/JPub OO4O Type Inheritance Multi-Level Collections Type Evolution Long to LOB API Anytype/XMLType/ URIType/SQLJ Object Type Parallel Piplelined Table Function OCCI Type Synonym User-Defined Constructor Collection/LOB Enhancements

7. Agenda  Overview  New Features – Regular Expressions – Native Numbers – LOB Enhancements – Collection Enhancements  Demo  Summary  Q&A

8. Introduction to Pattern Matching  Pattern matching and manipulation – Match 'abc', match email address, etc  LIKE often not expressive enough  Non-native and client solutions have drawbacks

9. Introduction to Regular Expressions  Origins in mathematics  First computerized in UNIX – From ed, grep, perl, cgi, web, to everywhere  Multitude of applications – Validation in HTML FORMS – Bioinformatics – Server configuration – Datamining

10. Oracle Regular Expressions  Native support to the database – Interfaces in SQL and PL/SQL – Based on POSIX standard – Sync with GNU, PERL, Java, XQuery, etc.  Patterns describing data become a property of the data  Powerful string manipulation within the database

11. Enhanced Data Flow Validate and Format Filter and Format

12. Key Features  POSIX Extended Regular Expressions  Interfaces – REGEXP_LIKEdoes pattern match? – REGEXP_SUBSTRwhat does it match? – REGEXP_INSTRwhere does it match? – REGEXP_REPLACEreplace what matched.  Match options  Locale Support  LOB Support

13. . 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 Metacharacters Operator Description

14. REGEXP_LIKE  Determine whether pattern exists  Find variations on the name 'Jon Stevens' – John or Jon – Steven or Stevens or Stephen or Stephens SELECT c1 FROM t1 WHERE REGEXP_LIKE(c1, ‘Joh?n Ste(ph|v)ens?’);  Jon Stevens  John Stephens  John Stevens

15. REGEXP_SUBSTR  Determine what text matched  Extract matching variation SELECT REGEXP_SUBSTR( ‘I am the real Jon Stevens’, ‘Joh?n Ste(ph|v)ens?’) FROM dual;  Jon Stevens

16. REGEXP_INSTR  Find out where the match occurred  Find the matching word SELECT REGEXP_INSTR( ‘I am the real Jon Stevens’, ‘Joh?n Ste(ph|v)ens?’) FROM dual;  15

17. REGEXP_REPLACE  Replace matching text  Supports back references in replacement text  Fix alternative spellings SELECT REGEXP_REPLACE ( ‘I am the real John Stephens.’, ‘Joh?n Ste(ph|v)ens?’, ‘Jon Stevens’) FROM t1;  I am the real Jon Stevens.

18. Using with DDL  Filter allowable data with check constraint – ZIP code column is VARCHAR2(5) but can hold any 5 characters.  Query subset and format with views – format phone number to (xxx) xxx-xxxx  Create functional based index

19. Using with PL/SQL  Very powerful string manipulation abilities src := REGEXP_REPLACE (src, ‘ ’);  Can enhance on existing abilities – Support PERL shorthand – Extract nth subexpression  Replace hundreds of lines of code – String manipulation functions can be simplified

20. Performance Considerations  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  Write fast performing expressions.

21. Agenda  Overview  New Features – Regular Expressions – Native Numbers – LOB Enhancements – Collection Enhancements  Demo  Summary  Q&A

22. Native Floating-Point Data Types  Two new numeric data types BINARY_FLOAT, BINARY_DOUBLE – IEEE 754 Standard for binary floating point arithmetic – Part of numerous other standards (e.g, Java, XML Schema) and hardware platforms – Prevalent in Business Intelligence, Life Sciences, Engineering/Scientific Computation, etc.

23. Native Floating-Point Data Types vs. Number Data Type  More efficient than the NUMBER type – Hardware arithmetic/math are 5 – 10 times faster – Take up less space in memory/disk (5/9 vs. 1 – 22 Bytes each) – BINARY_DOUBLE has wider value range (e308 vs. e125) – No type convesion (use a byte-order neutral storage format)

24. Native Floating-Point Functions  New type conversion functions – TO_BINARY_FLOAT, TO_BINARY_DOUBLE – TO_NUMBER  SQL function support – Numberic functions (sin, cos, etc.) – Aggregate functions (sum, avg, stddev, etc.) – Analytic functions (sum, avg, stddev, etc.)  Seamless support in SQL, PL/SQL, Java, XML Schema registration, ODP.NET and OCI/OCCI

25. Native Floating Point Constraints create table floating_point_table1 ( fltNnull binary_float constraint flt_null not null, dblNnull binary_double constraint dbl_null not null, fltUnq binary_float constraint flt_unq unique, dblUnq binary_double constraint dbl_unq unique, fltChk binary_float constraint flt_chk check ( fltChk is not NaN ), dblChk binary_double constraint dbl_chk check ( dblChk is not infinite), fltPrm binary_float constraint flt_prm primary key); * NaN (Not a Number) – e.g. 0/0, infinity/infinity

26. Native Floating Point Constraints create table floating_point_table2 ( dblPrm binary_double constraint dbl_prm primary key, fltFrn binary_float constraint flt_frn references floating_point_table1(fltPrm) on delete cascade);

27. Agenda  Overview  New Features – Regular Expressions – Native Numbers – LOB Enhancements – Collection Enhancements  Demo  Summary  Q&A

28. LOB Enhancements  LOBs are prevalent in storing unstructured data (text, AVI, genomic/proteomic sequences, etc.)  Implicit charset conversion Between CLOB and NCLOB

29. LOB Performance Improvements  5x performance gain for accessing inline (< 4KB) LOBs  Temporary LOBs uses reference counting to provide orders of magnitude performance gain – Reference on Read – Copy on Write

30. Ultra-Sized LOBs  Terabyte (8 – 128 TB) sized Lobs – DB_BLOCK_SIZE (2 – 32 KB) x (4GB –1) – New DBMS_LOB.GET_STORAGE_LIMIT function – OCI, JDBC, and DBMS_LOB now supports LOBs greater than 4GB  OCILobRead2(), OCIWriteAppend2(), and OCILobWrite2() functions  Same APIs for JDBC and DBMS_LOB

31. Data Interface for Persistent LOBs  Client DML (insert/update) can bind char buffers (up to 4GB) to LOB values without the locator – OCI, JDBC, and ODP.NET support – Up to 32KB with PL/SQL  No server round-trip to get the locator before insert/update

32. Implicit Conversion Between CLOB and NCLOB CREATE TABLE my_table (nclob_col NCLOB); DECLARE clob_var CLOB; nclob_var NCLOB; BEGIN clob_var := 'clob data'; -- initialize the CLOB value; -- Bind a CLOB into an NCLOB column INSERT INTO my_table VALUES (clob_var); SELECT nclob_col INTO clob_var FROM my_table; -- Define an NCLOB column as a CLOB var END; /

33. Datetime Enhancements  The best implementation of ANSI SQL timestamp with (local) time zone – Expanded region time zone names support  Default: $ORACLE_HOME/oracore/zoneinfo /timezone.dat (146 KB)  Larger set: ORACLE_HOME/oracore/zoneinfo /timezlrg.dat (380 KB) – ORA_TZFILE specifies the file used by the database – Loaded into SGA during instance startup – Valid region names are listed in v$timezone_names

34. Agenda  Overview  New Features – Regular Expressions – Native Numbers – LOB Enhancements – Collection Enhancements  Demo  Summary  Q&A

35. Collection Types Enhancements  User-defined types, collection types, and Ref type simplify complex- structured data modeling  Collections types are used for mapping containment relationships in real world applications – For example, a shopping cart contains a number of items

36. VARRAY Enhancements  Type evolution of VARRAYs CREATE TYPE email_list_typ AS OBJECT ( section_no NUMBER, emails email_list_arr); / CREATE TYPE email_varray_typ AS VARRAY(5) OF email_list_typ; / ALTER TYPE email_varray_typ MODIFY LIMIT 100 CASCADE;  Support VARRAY columns in temporary tables – Provides application development flexibility

37. Nested Table Enhancements  Use a different tablespace for a Nested Table’s storage table – Tables with Nested Table columns can distribute I/O load among tablespaces CREATE TABLE purchase_orders ( order_items_column order_items_typ ) NESTED TABLE order_items_column STORE AS order_items_column_nt (TABLESPACE users);

38. ANSI SQL Multiset Operations for Nested Tables  A Nested Table stores unordered elements – A set contains only unique elements – A multiset can contain duplicate elements  Full range of Multiset operations on Nested Tables

39. Multiset Operations for Nested Tables  Cardinality, Collect, Multiset Except, Multiset Intersection, Multiset Union, Powermultiset, Powermultiset_by_Cardinality, and Set operations – Powerful tools to find frequent item sets (market basket analysis)

40. Multiset Operation Example create type categories as table of int; / create table carts (c1 int, c2 categories) nested table c2 store as tb1_c2; insert into carts values(1, categories(1,2,3,4)); insert into carts values(2, categories(2,4,6)); select t1.c2 multiset intersect t2.c2 from carts t1, carts t2 where t1.c1=1 and t2.c1=2; categories(2,4) <- frequent set

41. Comparisons of Nested Tables  Equal and Not Equal, In, Submultiset, Member Of, Empty, and Is [Not] A Set SELECT * FROM customer c WHERE item_typ(2) MEMBER OF c.freq_set;

42. Agenda  Overview  New Features  Demo  Summary  Q&A

43. D E M O N S T R A T I O N SQL for Data Grids

44. Aggregated Information Web Service JDBC /JXQI SOAP XQuery Engine Aggregated Information EIS Oracle SQL Engine HTTP/SOAP J2EE TM CA Streams Heterogeneous Services

45. XQuery FLWOR Expressions  A FLWOR (flower) expression binds some expressions, applies a predicate, and constructs a new result. FOR var IN expr LET var := expr WHERE expr RETURN expr FOR and LET clauses generate a list of tuples of bound expressions WHERE clause applies a predicate, eliminating some of the tuples RETURN clause is executed for each surviving tuple, generating a list of outputs ORDER BY expr

46. Agenda  Overview  New Features  Demo  Summary  Q&A

47. Summary  Oracle Database 10 g SQL engine – Data Grid information aggregation from diverse and distributed data sources – Seamless integration with Java, XML, and essential data processing capabilities – A comprehensive set of high-performance and standards-based (SQL-2003, W3C, J2SE, POSIX, etc.) features – Simplified application development, deployment, and management – RAS, manageability, and security

48. Next Steps….  Relevant web sites to visit for more information – http://otn.oracle.com/products/database/ application_development/ – OTN SQL/PL-SQL discussion forum – http://otn.oracle.com/tech/xml/xmldb/htdocs /xquery.html

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50. A Q & Q U E S T I O N S A N S W E R S