1. Object-relational database systems Yong Yao CS632 April 17, 2001

2. Content Introduction Michael Stonebraker: Inclusion of New Types in Relational Data Base Systems. Michael Stonebraker, Greg Kemnitz: The Postgres Next Generation Database Management System. PREDATOR System Design Document: A detailed description of internal design decisions and implementation details.

3. Introduction The relational model Dominant mainstream approach Work well on business data processing But things have changed drastically, nontraditional problems.

4. Taxonomy of DBMS Application Yes No Use SQL data Simple Complex complexity

5. Simple data Yes No Use SQL data Simple Complex complexity Relational DBMS File System

6. OODBMS Yes No Use SQL data Simple Complex complexity Relational DBMS File SystemOODBMS A programming language with a type system Persistent object

7. ORDBMS Yes No Use SQL data Simple Complex complexity Relational DBMS ORDBMS File SystemOODBMS

8. Features Base type extension Inheritance Complex object

9. Inclusion of New Types in Relational Database Systems Michael Stonebraker EECS Dept. University of California, Berkeley

10. Example Represent a set of boxes in the DBMS and a simple query- find all the boxes that overlap the unit square(0,1,0,1). RDBMS: Create box(id=i4, x1=f8, x2=f8,y1=f8,y2=f8) select * from box where !(box.x2 =1 or box,y2 =1) Too hard to understand Too complex to optimize Too many clauses to check

11. Example (cont’d) Create box(id=i4,desc=box) select * from box where box.desc!!”0,1,0,1” Box: new data type “!!”: overlap operator with two operands of data type box, return a boolean

12. Operators for Boxes

13. Motivation Needs of business data processing applications Geographic Information System New Types: Point, Line, Polygon; New operators: distance, intersection ; New access methods: R-trees, KDB trees;

14. Conditions 1. The definition of user-defined data types 2. The definition of new operators for these data types 3. The implementation of new access methods for data types 4. Optimized query processing for commands containing new data types and operators

15. Definition of New Types Follow a registration process define type-name length = value, input = file-name, output = file-name Occupy a fixed amount of space Conversion routines

16. Definition of New Operators

17. Safety loophole Problem: an ADT routine which has an error can overwrite DBMS data structures Unclear whether such errors are due to bugs in the user routines or in the DBMS Solutions: Run in separate address space Build a language processor Provide two environments

18. New access methods Users can add new access methods to support user-defined data types Goal: extensibility Interface: Conditions for the operators Information on the data types of operators Define set of operators

19. Example: B-tree Conditions for operators:

20. Information on the data types of operators (B-tree) “<=“ is required Type: specific type, fixed, variable, fix-var, type1, type2

21. New set of operators (B-tree) F1=(value - low-key) / (high-key – low-key) F2=(high-key – value) / (high-key – low-key)

22. Implementing New Access Methods A collection of procedure calls open (relation-name) close (descriptor) get-next(descriptor, OPR, value, tuple-id) insert (descriptor,tuple) delete(descriptor, tuple-id) replace(descriptor, tuple-id, new-tuple) build(descriptor,keyname,OPR )

23. Hard problem - work with transaction management log pages – simple, but may suffer from performance penalty log events Event-oriented interface REDO(T) UNDO(T) LOG(event-type, event-data)

24. Access path selection Four pieces of information for optimization Stups: estimate the expected number of records …where rel-name.field-name OPR value A second selectivity factor S: …where relname-1.field-1 OPR relname-2.field-2 Feasibility of merge-sort Feasibility of hash-join Define operator token= AE, …… Stups=1 S= min(N1.N2). merge-sort with AL, hash-join

25. Generate query processing plan relname-1.field-1 OPR relname-2.field-2 Merge sort Iterative substitution Hash Join Relname.field-name OPR value any access method with field-name as a key Secondary index Sequential search

26. Summary How to extend an abstract data type How to define new access method Integrate new access method with transaction management Generation of optimized query processing plan

27. The POSTGRES Next-Generation Database Management System Michael Stonebraker Greg Kemnitz

28. Three kinds of services for DBMS Traditional data management Simple data type Object management Complex data types, e.g. bitmaps, icons, text… Knowledge management Store and enforce a collection of rules

29. Example- newspaper layout store and manipulate text and graphics, Bill customers for advertisement. Data: customer information Object: text, pictures and icons Rules: control newspaper layout

30. POSTGRES Data Model-Design criteria Traditional relational DBMSs data model: a collection of named relations Orientation toward database access from a query language Interact with database by query language- POSTQUEL User defined functions

31. Design criteria Orientation toward multilingual access Two selections One language tightly coupled to the system Multilingual POSTGRES is programming language neutral

32. Design criteria Small number of concepts As few as possible Four constructs: Class Inheritance Type Function

33. Data Model - Class Class(constructed type or relation) : a named collection of instances of objects. Instances(record or tuple) : has the same collection of attributes create EMP(name=string, salary= float, age=int)

34. Inherit data elements from other classes create EMP(name=string, salary= float, age=int) create SALESMAN(quota=float) inherits EMP Multiple inheritance – only create objects without ambiguity Data model - Inheritance SALESMAN EMP

35. Three kinds of classes Real class: instances are stored in the database Derived class :view Version: store differential relative to its base class Initially has all instances of the base class Freely updated to diverge from the base class Updates to the version do not affect the base class Updates to the base class are reflected in the version Supported by the rule system

36. Version Example create version my-EMP from EMP Two classes generated: EMP-MINUS(deleted-OID) EMP-PLUS(all-fields-in EMP, replaced-OID) Retrieve: retrieve EMP-PLUS instead Insert: All new instances for EMP or my-EMP will be added into EMP-PLUS; Delete: move records from EMP-PLUS to EMP-MINUS

37. Data model - Types Base types: Int, float, string construct new base types. Arrays of base types: Composite types: Construct complex objects: attributes contain other instances as part or all of their value

38. Composite types Contains zero or more instances of the same class create EMP (…, manager=EMP, coworkers=EMP) Set: value is a collection of instances from all classes create EMP(…, hobbies=set) {softball, skiing, skating…}

39. Data Model - Functions C functions Arbitrary C procedures Can not be optimized by the POSTGRES Argument: base types or composite types Inherited down the class hierarchy overpaid(EMP) overpaid(SALESMAN)

40. Functions Operators Utilize index Functions with one or two operands {ALT, ALE, AE, AGT, AGE} Allow new access methods POSTQUEL functions Any collection of commands in the POSTQUEL Define function high-pay returns EMP as Retrieve (EMP.all) Where EMP.salary>50000

41. POSTQUEL Set-oriented query language Support nested queries Transitive closure Support for inheritance Support for time travel Allow a user to run historical query retrieve (EMP.salary) from EMP [T] where EMP.name=“Sam” Maintain two different physical collections of records Vacuum cleaner: a daemon moves records

42. The Rules System Requirements: referential integrity View management Triggers Integrity constraints Protection Version control Design a general purpose rules system

43. Syntax of rules ON event (TO) object WHEREon new EMP.salary where POSTQUEL-qualificationEMP.name=“Fred” THEN DO [instead]then do replace POSTQUEL-command(s)E (salary=new.salary) from E in EMP where E.name=“Joe” Event: retrieve, replace, delete, new … Object: name of a class or class.column POSTQUEL-qualification: normal qualification POSTQUEL-commands: a set of POSTQUEL commands

44. Forward and Backward chaining Rules specify additional actions, and these actions may activate other rules on new EMP.salary whereon retrieve to EMP.salary where EMP.name=“Fred”EMP.name=“Joe” then do replacethen do instead retrieve E(salary=new.salary)(EMP.salary) from E in EMPwhere EMP.name=“Fred” where E.name=“Joe”

45. Implementation of Rules Record level processing Rules system is called when individual records are accessed Place a marker on the record Query rewrite module Perform poorly for a large number of small-scope rules Desirable when there are a small number of larger-scope rules

46. When rules are activated Immediate-same transaction Immediate-different transaction Deferred-same transaction Deferred-different transaction Currently only implements the first option

47. Storage System ‘no-overwrite’ storage manager Old record remains in the database whenever an update occurs Instantaneous crash recovery Support time travel stable main memory required

48. Performance Better than UCB-INGRES

49. But Compare to the Cattell system, loses by about a factor of two.

50. Comments POSTGRES allows an application designer to trade off performance for data independence Imports only specific user functions into its address space

51. PREDATOR DESIGN AND INPLEMENTATION PARVEEN SESHADRI

52. System overview A client-server database system Goal: Extensibility - adding the ability to process new kinds of data Basic components Extensible table of Enhanced Abstract Data Type (E-ADTs) Extensible table of query processing engines(QPEs)

53. Server Architecture

54. Enhanced Data Types Standard ADT Specify the storage format for values Specify methods that can be invoked on values Specify how some methods can be matched using indexes Motivation: methods can be very expensive Data.image.sharpen().clip()

55. Improvement Data.image.sharpen().clip() It is unnecessary for Sharpen to compress and write its result to disk -> Pass result directly in memory to Clip Rewrite the expression -> Data.image.clip().sharpen() No need to retrieve the entire image

56. E-ADT Expose the semantics of its methods Query optimizations are performed using these semantics Find more at : The Case for Enhanced Abstract Data Types. VLDB 1997

57. Thank You