1. ORDB Implementation Discussion

2. From RDB to ORDB Issues to address when adding OO extensions to DBMS system

3. Layout of Data Deal with large data types : ADTs/blobs – special-purpose file space for such data, with special access methods Large fields in one tuple : –One single tuple may not even fit on one disk page –Must break into sub-tuples and link via disk pointers Flexible layout : –constructed types may have flexible sized sets,, e.g., one attribute can be a set of strings. –Need to provide meta-data inside each type concerning layout of fields within the tuple –Insertion/deletion will cause problems when contiguous layout of ‘tuples’ is assumed

4. Layout of Data More layout design choices (clustering on disk): –Lay out complex object nested and clustered on disk (if nested and not pointer based) –Where to store objects that are referenced (shared) by possibly several other and different structures –Many design options for objects that are in a type hierarchy with inheritance –Constructed types such as arrays require novel methods, like array chunking into (4x4) subarrays for non-continuous access

5. Objects/OIDs OID generation : uniqueness across time and system Object reference handling : –must avoid dangling references –semantics for object manipulation for shared objects

6. ADTs – Type representation : size/storage – Type access : import/export – Type manipulation : special methods to serve as filter predicates and join predicates – Special-purpose index structures : efficiency

7. ADTs Mechanism to add index support along with ADT: –External storage of index file outside DBMS –Provide “access method interface” a la: Open(), close(), search(x), retrieve-next() Plus, statistics on external index –Or, generic ‘template’ index structure Generalized Search Tree (GiST) – user-extensible Concurrency/recovery provided

8. Query Processing Query Parsing : –Type checking for methods –Subtyping/Overriding Query Rewriting: –May translate path expressions into join operators –Deal with collection hierarchies (UNION?) –Indices or extraction out of collection hierarchy

9. Query Optimization Core –New algebra operators must be designed : such as nest, unnest, array-ops, values/objects, etc. –Query optimizer must integrate them into optimization process : New Rewrite rules New Costing New Heuristics

10. Query Optimization Revisited –Existing algebra operators revisited : SELECT –Where clause expressions can be expensive –So SELECT pushdown may be bad heuristic

11. Selection Condition Rewriting EXAMPLE: (tuple.attribute < 50) –Only CPU time (on the fly) (tuple.location OVERLAPS lake-object) –Possibly complex CPU-heavy computations –May Involve both IO and CPU costs State-of-art: – consider reduction factor only Now, we must consider both factors: –Cost factor : dramatic variations –Reduction factor: unrelated to cost factor

12. Operator Ordering op1 op2

13. Ordering of SELECT Operators –Cost factor : dramatic variations –Reduction factor: orthogonal to cost factor –We want: maximal reduction and minimal cost –Rank ( operator ) = (reduction) * ( 1/cost ) –Order operators by increasing ‘rank’ –High rank (good) -> low in cost, and large reduction –Low rank (bad) -> high in cost, and small reduction

14. Access Methods ( on what ?) Indexes that are ADT specific Indexes on navigation path Indexes on methods, not just on columns Indexes over collection hierarchies (trade-offs) Indexes for new WHERE clause expressions not just =, ; but also “overlaps”

15. Registering New Index (to Optimizer) What WHERE conditions it supports Estimated cost for “matching tuple” –Given by index designer (user?) –Monitor statistics; even construct test plans Estimation of reduction factors/join factors: Register auxiliary function to estimate factor Provide simple defaults Estimation of method costs (~IO/CPU)

16. Methods Dynamic linking of methods (outside DB) Overwriting methods for type hierarchy Use of “methods” with implied semantics Incorporation of methods into query process : termination? “untrusted” methods : methods corrupt server or modify DB content (side effects) Handling of “untrusted” methods : – restrict language; interpret vs compile, separate address space as DB server

17. Query Optimization with Methods Estimation of “costs” of method predicates Optimization of Method execution: –Similar idea as handling correlated nested subqueries; must recognize repetition and rewrite physical plan. –Provide some level of precomputation and reuse Optimization of Method execution: –1. If called on same input, cache that one result –2. If on full column, presort column first (groupby) –3. Or, precompute results of methods for each possible value in domain; and put in hash-table : fct (val ); Look up in hash-table during query processing or even join with it, instead of recomputing : val  fct (val)

18. Query Processing User-defined aggregate functions: –E.g., “second largest” or “second yellowest” Distributive aggregates: incremental computation Provide: –Initialize(): set up state space –Iterate(): per tuple update the state –Terminate(): compute final result based on state; and cleanup state For example : “second largest” –Initialize(): 2 fields –Iterate(): per tuple compare numbers –Terminate(): remove 2 fields

19. Following Disk Pointers? Complex object structures with object pointers may exist (~ disk pointers) Navigate complex object into memory for a long-running transaction like in CAD design What to do about “pointers” between subobjects or related objects ? –Swizzle = replace OIDs dereferences by in-memory pointers, and unswizzle back at end. –Issues : In-memory table of OIDs and their state; indicate in each object pointer via a bit. –Different policies for swizzling: on access, attached to object brought in, etc.

20. Models of Persistence Different models of persistence for OODB implementations: Parallel type systems: –E.g., int and dbint –User must make decision at object creation time –Allow for user control by “casting” types Persistence by container management: –Objects must be placed into “persistent containers” such as relations in order to stay around –Eg., Insert o into Collection MyBooks; –Could be rather dynamic control without casting Persistence by reachability : –Use global variable names to objects and structures –Objects being referenced by other objects that are reachable by application, they by transitivity are also persistent. –need garbage collection

21. Summary A lot of work to get there: From physical database design/layout issues up to logical query optimizer extensions ORDB: reuses existing implementation base and incrementally adds new features on (but relation is first-class citizen)