1. B-Trees Chapter 9

2. Limitations of binary search Though faster than sequential search, binary search still requires an unacceptable number of accesses for data files with more than 1000 records Resorting the index after each record is inserted is not practical if the index cannot be kept in memory

3. 9.3 Binary search tree index Tree structure includes pointers to left and right index nodes in addition to a key (and data record pointer) Each left node defines a subtree with smaller keys, each right node with larger Pointers make sorting the index unnecessary. Why?

4. Binary tree balance problem Building the tree from the root by inserting randomly ordered incoming records results in paths to some leaves that are much longer than others Performance is unacceptably poor for keys on remote paths Keeping the tree balanced is non-trivial

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6. A Y W X H IM Balanced AVL tree

7. 9.3.2 Paged binary trees multiple binary nodes are located on the same page (sector) on secondary storage each disk seek returns several nodes in a search path, reducing search complexity from log 2 N to log k+1 N random insertions cause imbalance which cannot be easily fixed because keys must be shifted to different pages throughout the tree

8. Multi-record index number of records in a data file exceeds the maximum number of keys allowed in a single record index index must still be maintained in sorted order (across multiple records) to allow binary search

9. Searching multi-record index total number of keys (data records) is N each index record holds k keys for binary search, first look at the index record in the middle of the index file compare search key to smallest and largest keys in current index record

10. record 1 keys 1 : k record 2 keys k+1 : 2k record N/2k keys N/2k + 1 : N/2 record N/k + 1 keys N - N mod k : N Starting record for binary search Multi-record index file

11. 9.4 Multilevel indexing Level-1 index is a multi-record index for the entire data file Each higher level index below the root is a multi-record index to the index below it Root level index is a single record Though multilevel index is entry sequenced in that the records at each level need not be ordered, record insertion is still a problem

12. 9.5 B-trees insertion problem of simple multilevel index is solved by (1) using partially filled index records (2) splitting records when they fill up, instead of shifting keys to the next record when an index node is split, the largest key in the new node is promoted to the next higher index level at worst, insertion causes one node at each level to split

13. D C T S Initial node contains keys C, D, S, and T. C D S T A D T A A D C Figure 9.14 Growth of a B-tree Insertion of A causes node to split. A new root node is created and the largest key in each leaf node placed in the root. Key A can now be inserted in the correct leaf node.

14. 9.7 B-Tree implementation Class BTreeNode (supports index record) –subclass of SimpleIndex class –template class allows different types of keys –uses same Search method as SimpleIndex Class BTree (supports B-tree index file) –uses RecordFile object to access index file –FindLeaf method sets an array of pointers, Nodes, to define a search path

15. 9.9-10 Formal definition The order of a B-tree (m) is the maximum number of descendents for each node. Every node except the root and leaves must have at least  m / 2  descendents. The root must have at least 2 descendents unless it is a leaf (i.e., the only node). All leaves are on the same level. The leaf level is a complete index.

16. Implications of formal definition Path length is the same for all searches, and is equal to the tree depth, since only the leaf nodes point to data records. The worst case depth can be computed for a B-tree with a given order and number of keys (see § 9.11 in the text)

17. Deletion maintaining balance requires that each index node hold no more than m keys and no fewer than  m / 2  keys when insertion causes overflow (more than m keys) in a node, it is split what happens when deletion results in “underflow” (fewer than  m / 2  keys)?

18. Situations arising from deletion (Figure 9.21) a) Victim node has more than  m / 2  keys, and key to be deleted is not the largest key. b) Victim node has more than  m / 2  keys, and key to be deleted is the largest key. c) Victim node has exactly  m / 2  keys.

19. Merging and Redistribution Needed for situation c), when deletion leaves fewer than  m / 2  keys. Two options: –merge with a sibling that has  m / 2  or  m / 2  + 1 keys –move at least 1 key from a sibling that has at least  m / 2  + 1 keys

20. Questions What is the minimum and maximum number of siblings a node can have? Is it possible that there are no siblings available with which to merge or redistribute after a deletion? Is it possible to have a choice of either merging with or redistributing from the same sibling? Is it ever possible to merge two nodes without first deleting at least one key?

21. B*tree and Redistribution Redistribution may be used optionally to improve storage utilization B*tree uses redistribution during insertion to maintain each node 2/3 full (rather than 1/2, as results from simply splitting) Notes on B*trees by Jan Jannink: http://www.cise.ufl.edu/~jhammer/classes/b_star.html http://www.cise.ufl.edu/~jhammer/classes/b_star.html

22. 9.15 Page buffering Keep a page buffer, or collection of index pages in memory. Whenever an index page is needed, first look for it in the page buffer. If it’s there, you save seeking for it on the disk. If a needed index page is not in the buffer, load it into the buffer from the disk

23. Page replacement schemes If a needed index page is not in the buffer, but the buffer is full, a page must be replaced. LRU replacement scheme is based on the assumption of temporal locality. Page height scheme favors pages on higher levels. Why?