1. Extendible Indexing Dina Said

2. Motivation for extendible indexing
Searching in trees is O(log(n))
We want searching to be fixed O(1)
Static indexing ...Problem?
Dynamic indexing
Extendible indexing

3. Excerise 11.1
1) What can you say about the last entry that was inserted into the index?

4. Excerise 11.1
2) What can you say about the last entry that was inserted into the index if you know that there have been no deletions from this index so far?

5. Excerise 11.1
3) Suppose you are told that there have been no deletions from this index so far. What can you say about the last entry whose insertion into the index caused a split?

6. Excerise 11.1
4) Show the index after inserting an entry with hash value 68.
Note: To convert decemial to binary

7. Excerise 11.1

8. **Reward** Excerise 11.1
5) Show the index after inserting entries with hash values 17 and 69 into the original tree.

9. Excerise 11.1

10. Excerise 11.1
6) Show the index after deleting the entry with hash value 21 into the original tree. (Assume that the full deletion algorithm is used.)

11. Excerise 11.1

12. Excerise 11.1
7) Show the index after deleting the entry with hash value 10 into the original tree. Is a merge triggered by this deletion? If not, explain why. (Assume that the full deletion algorithm is used.)

13. Excerise 11.1

14. Extendable Hashing vs. Other Schemes
Benefits of extendable hashing:
Hash performance does not degrade with growth of file
Minimal space overhead
Disadvantages of extendable hashing
Extra level of indirection to find desired record
Bucket address table may itself become very big (larger than memory)
Need a tree structure to locate desired record in the structure!
Changing size of bucket address table is an expensive operation
Linear hashing is an alternative mechanism which avoids these disadvantages at the possible cost of more bucket overflows