1. Generalized Search Trees J.M Hellerstein, J.F. Naughton and A. Pfeffer, “Generalized Search Trees for Database Systems,” Proc. 21 st Int’l Conf. On VLDB, Sep. 1995 Presented By Ihab Ilyas

2. Topics Motivation. Database Search Trees. Generalized Search Tree. Properties. Methods. Applications.

3. Motivation New applications (Multimedia, CAD tools, document libraries…etc.) New Data types Extending search trees to maximum flexibility

4. Specialized Search Trees Example: Spatial Search Trees ( R-Trees) Problem: New Applications implies new tree structure from scratch Search Trees For Extensible Data Types Example: Extending B+ to index any ordinal data Problem: Extending data but not the set of queries supported. Before GiST

5. GiST A third direction for extending search trees Extensible both in data types supported and in the queries applied on this data. Allows new data types to be indexed in a manner that supports the queries natural to the data type.

6. GiST (Cont.) Unifies previously disparate structures for currently common data types. Examples: B+ and R trees can be implemented as extensions to GiST. Single code base for indexing multiple dissimilar applications.

7. Database Search Trees Canonical rough picture of database search tree Leaf nodes (Linked List) Internal Nodes Key1 Key2 ….

8. Search Trees (cont.) Search Key: A search key may be arbitrary predicate that holds for each datum below the key. Search Tree: A hierarchy of categorizations, in which each categorization holds for all data stored under it in the hierarchy.

9. Generalized Search Tree Definition: A GiST is a balanced multi-way tree of variable fan-out between kM and M Where k is the fill factor. With the exception of the root node that can have fan-out from 2 to M.

10. GiST (Cont.) Leaf nodes: (p,ptr) p: Predicate used as a search key. ptr: the identifier of some tuple of the database. Non-leaf nodes: (p,ptr) p: Predicate used as a search key. ptr: Pointer to another tree node.

11. Properties Every node contain between kM and M unless it is the root. For each index entry (p,ptr) in a leaf node, p holds for the tuple For each index entry (p,ptr) in a non-leaf node, p is true when instantiated with the values of any tuple reachable from ptr. All leaves appear on the same level.

12. Note on Properties …. (p,ptr) ….. …. (p’,ptr’) ….. …. (p1,ptr1) …..…. (p2,ptr2) p holds for p1,p2 p’ holds for p1,p2 p’  p Not Required The ability of orthogonal classification.. Recall R-Tree

13. GiST Methods Key Methods: the methods the user can specify to configure the GiST. The methods encapsulate the structure and behavior of the object class used for keys in the tree. Tree Methods: Provided by the GiST, and may invoke the required key methods.

14. Key Methods Consistent(E,q): False if p^q guaranteed unsatisfiable, true otherwise. Union(P): returns predicate r that holds for all predicates in P Compress(E): returns (p’,ptr). Decompress(E): returns (r,ptr) where p  r. This a lossy compression as we do not require p  r E is an entry of the form (p,ptr), q is a query, P a set of entries

15. Key Methods (Cont.) Penalty(E1,E2): returns domain specific penalty for inserting E2 into the subtree rooted at E1. Typically the penalty metric is representation of the increase of size from E1.p to Union(E1,E2). PickSplit(P): M+1 entries, splits P into two sets of entries P1,P2, each of the size kM. The choice of the minimum fill factor is controlled here.

16. Tree Methods Search: Controlled by the Consistent Method. Insert: Controlled by the Penalty and PickSplit. Delete: Controlled by the Consistent

17. Example New (q,ptr) Penalty = mPenalty = n m < n Penalty =iPenalty = j j < i Full.. Then split according to PickSplit (p,ptr) R (q,ptr) (p,ptr) New (q,ptr)

18. Applications GiST Over Z (B+ Trees) GiST Over Polygons in R 2 (R Trees)

19. B+ Trees Using GiST p here is on the form Contains([x p,y p ),v) Consistent(E,q) returns true if If q= Contains([x q,y q ),v): (x p x q ) If q= Equal (x q,v): x p  x q <y p Union(P) returns [Min(x1,x2,…,xn),MAX(y1,y2,….,yn)).

20. B+ Trees Using GiST (Cont.) Penalty(E,F) If E is the leftmost pointer on its node, returns MAX(y2-y1,0) If E is the rightmost pointer on its node, returns MAX(x1-x2,0) Otherwise, returns MAX(y2-y1,0)+MAX(x1-x2,0) PickSplit(P) let the first entries in order to go to the left node and the remaining in the right node.

21. B+ Trees Using GiST (Cont.) Compress(E) if E is the leftmost key on a non-leaf node return 0 bytes otherwise, returns E.p.x Decompress(E) if E is the leftmost key on a non-leaf node let x= -  otherwise let x=E.p.x If E is the rightmost key on a non-leaf node let y= . If E is other entry in a non-leaf node, let y = the value stored in the next key. Otherwise, let y = x+1

22. R - Trees Using GiST The key here is in the form (x ul,y ul,x lr,y lr ) Query predicates are: Contains ((x ul1,y ul1,x lr1,y lr1 ), (x ul2,y ul2,x lr2,y lr2 )) Returns true if (x ul1  x ul2 ) ^( y ul1  y ul2 ) ^ ( x lr1  x lr2 ) ^ ( y lr1  y lr2 ) Overlaps ((x ul1,y ul1,x lr1,y lr1 ), (x ul2,y ul2,x lr2,y lr2 )) Returns true if (x ul1  x lr2 ) ^( y ul1  y lr2 ) ^ ( x ul2  x lr1 ) ^ ( y lr1  y ul2 ) Equal ((x ul1,y ul1,x lr1,y lr1 ), (x ul2,y ul2,x lr2,y lr2 )) Returns true if (x ul1 = x ul2 ) ^( y ul1 = y ul2 ) ^ ( x lr1 = x lr2 ) ^ ( y lr1 = y lr2 )

23. R – Trees Using GiST(Cont.) Consistent(E,q) p contains (x ul1,y ul1,x lr1,y lr1 ), and q is either Contains, Overlap or Equal (x ul2,y ul2,x lr2,y lr2 ) Returns true if Overlaps ((x ul1,y ul1,x lr1,y lr1 ), (x ul2,y ul2,x lr2,y lr2 )) Union(P) returns coordinates of the maximum bounding rectangles of all rectangles in P.

24. R – Trees Using GiST (Cont.) Penalty(E,F) Compute q= Union(E,F) and return area(q) – area(E.p) PickSplit(P) Variety of algorithms are provided to best split the entries in a over-full node.

25. R – Trees Using GiST (Cont.) Compress(E) Form the bounding rectangle of E.p Decompress(E) The identity function