1. Context Tailoring the DBMS –To support particular applications Beyond alphanumerical data Beyond retrieve + process –To support particular hardware New storage devices –To incorporate novel techniques New join implementations

2. Extensibility Language extensions –Abstract data types (ADT) –User defined functions (UDF) Data management extensions –New access methods –New storage methods Query processing extensions –New join methods –New optimization techniques

3. Starburst Contributions Revisited internal data structures –Query graph model –Query execution plan: low-level operators and stars Mechanisms for extensibility –Rules for query rewrite and plan optimization

4. Predator Contributions Enhanced abstract data types –Encapsulation principle applied to storage, optimization and evaluation –Type centric DBMS design

5. Outline Introduction Starburst –Language extensions –Data management extensions –Query processing extensions Predator –E-ADT processing Summary

6. Starburst - Language Extensions User defined functions (1) –Scalar functions In:one or more field values from a single tuple Out:a single value –Aggregate functions In:one or more field values from several tuples Out:a single value

7. Starburst - Language Extensions User defined functions (2) –Set predicate functions In:a simple predicate and a subquery (defines the range for the predicate) Out:a boolean value –Table functions In:one or several table expressions as well as field values Out:a relation

8. Starburst – Language Extensions Abstract data types –Considered useful for: Type checking Structuring of users’data –Add-on to the system design

9. Starburst – Data Management Extensions Uniform record structure: –Header + offset directory + data area –Advantages: Support for nested records Treatment of null values and variable length fields –Inconvenients: Overhead per record due to the offset directory Core system services –Logging, recovery manager, predicate evaluator, event queues, lock manager, interface to OS services, debugging, tracing, error reporting.

10. Starburst – Data Management Extensions Storage methods [associated to a relation] –Run-time methods for accessing relations: scan, fetch, insert, update, delete, destroy –Implementation: the run-time methods are registered in vector lists –Compile-time cost estimates Attachments [associated to a relation] –Access methods, integrity constraints and trigger extensions

11. Starburst – Data Management Extensions Advantages –New storage methods and attachments can be added without modifying existing code Limitations –Attachments only called after storage methods –Order in which attachments are called in fixed order

12. Starburst – Query Processing Extensions Internal representation of queries –Query graph model Beyond parse trees for the low-level plan operators Used for query rewrite –Query execution plan Operator based representation Strategy alternative rules (stars) to represent execution plan Used for query plan generation

13. Query Graph Model Boxes Stored relations Derived relations Vertices Setformers iterators: produce tuples for a derived relation Quantifiers iterators: restrict tuples for a derived relation Edges Range edges connecting a vertex and a box: access to a stored or a derived relation Qualifier edges connecting one or more vertices: conjunction of predicates

14. Query Rewrite Objectives: –Equivalent representation for alternative phrasings of a query –Only the DBMS can rewrite queries involving views Example rules: –Views may be merged –Redundant joins may be eliminated –Selections may be pushed down

15. Query Rewrite Rules A rule transforms a QGM into another QGM Condition / action: IF THEN rules Rule engine –Forward chaining –Various control strategies for rule application Search strategy –Top down (depth first / breadth first)/ bottom up

16. How to Choose Between Alternative Rules? Cost based decision Problem: cost estimates are only known at the query execution plan level Approach: several alternatives are kept in the QGM – CHOOSE operation

17. Query Execution Plan Execution plan represented using production rules: –Terminals: low-level plan operators In:0 or more streams of tuples Out:0 or more streams of tuples Each stream of tuples is tagged with properties –Relational: schema information –Operational: order, location –Estimated: –Non terminals: STAR Name Alternative definitions in terms of low-level plan operators or other STARs

18. Query Execution Plan A query execution plan is a tree of low- level plan operators STAR production rules are used for generating query execution plans –General purpose STAR evaluator –Search strategy to choose next STAR to apply –Vector list of stars

19. Starburst Contributions Revisited internal data structures –Query graph model –Query execution plan: low-level operators and STARs Mechanisms for extensibility –Rules for query rewrite and plan optimization

20. Outline Introduction Starburst –Language extensions –Data management extensions –Query processing extensions Predator –E-ADT processing Summary

21. Basic Techniques for ADTs Vector List of ADTs Each ADT implements: –Common internal interface for access to ADT values –Functions for storage and indexed retrieval Methods associated to ADT –ADT methods can be composed –DBMS understands minimal semantics about each method “Black box” ADT Approach

22. Motivation for E-ADTs Basic observation: –ADT Methods can be expensive! Need to identify optimizations on ADT methods Need to define a framework for applying these optimizations systematically

23. Possible Optimizations Algorithmic: –Using different algorithms for each method depending on data characteristics Transformational: –Changing the order of methods Constraint: –Pushing physical constraints through a method Pipelining: –Avoiding materialization of intermediate results

24. Architectural Framework Each E-ADT supports some of the following enhancements: –Optimization: transforms a method expression into a query execution plan expression –Evaluation: routines to execute the query execution plan expression –Catalog management: routines to store schema information and maintain statistics –Storage management: physical representation of values of its type

25. E-ADT Rewrite Rules Some of the optimizations for ADT methods can be applied on a logical representation of queries using rewrite rules

26. Predator Contributions Enhanced abstract data types –Encapsulation principle applied to storage, optimization and evaluation –Type centric DBMS design