1. CPSC-608 Database Systems Fall 2009 Instructor: Jianer Chen Office: HRBB 309B Phone: 845-4259 Email: chen@cs.tamu.edu 1 Notes #9

2. Linear hashing Another dynamic hashing scheme Two ideas: (a) Use i low order bits of hash 01110101 grows b i (b) File grows linearly 2

3. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 3

4. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 4

5. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0100 m = 01 (max used block) Future growth buckets If h(k)[i ]  m, then look at bucket h(k)[i ] Rule 5

6. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 01 11 00 10 m = 01 (max used block) Future growth buckets If h(k)[i ]  m, then look at bucket h(k)[i ] else, look at bucket h(k)[i ] - 2 i -1 Rule 6

7. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 0101 can have overflow chains! insert 0101 If h(k)[i ]  m, then look at bucket h(k)[i ] else, look at bucket h(k)[i ] - 2 i -1 Rule 7

8. Note In textbook, n is used instead of m n=m+1 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets n=10 8

9. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 10 9

10. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 10 1010 10

11. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 10 1010 0101 insert 0101 11

12. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 10 1010 0101 insert 0101 11 12

13. Example b=4 bits, i =2, 2 keys/bucket 00 01 1011 0101 1111 0000 1010 m = 01 (max used block) Future growth buckets 10 1010 0101 insert 0101 11 1111 0101 13

14. Example Continued: How to grow beyond this? 00 01 1011 111110100101 0000 m = 11 (max used block) i = 2... 14

15. Example Continued: How to grow beyond this? 00 01 1011 111110100101 0000 m = 11 (max used block) i = 2 0000 100 101 110 111 15

16. Example Continued: How to grow beyond this? 00 01 1011 111110100101 0000 m = 11 (max used block) i = 2 0000 100 101 110 111... 100 16

17. Example Continued: How to grow beyond this? 00 01 1011 111110100101 0000 m = 11 (max used block) i = 2 0000 100 101 110 111 3... 100 17

18. Example Continued: How to grow beyond this? 00 01 1011 111110100101 0000 m = 11 (max used block) i = 2 0000 100 101 110 111 3... 100 101 0101 18

19. If U > threshold then increase m (and maybe i )  When do we expand file? Keep track of: # used slots total # of slots = U 19

20. Linear Hashing Can handle growing files - with less wasted space - with no full reorganizations No indirection like extensible hashing Summary + + Can still have overflow chains - 20

21. Example: BAD CASE Very full Very emptyNeed to move m here… Would waste space… 21

22. Hashing - How it works - Dynamic hashing - Extensible - Linear Summary 22

23. Next: Indexing vs. hashing Index definition in SQL Multiple key access 23

24. Hashing good for probes given key e.g., SELECT … FROM R WHERE R.A = 5 Indexing vs. Hashing 24

25. INDEXING (including B-trees) good for range searches : e.g., SELECT FROM R WHERE R.A > 5 Indexing vs. Hashing 25

26. Index definition in SQL Create index name on rel (attr) Drop INDEX name 26

27. CANNOT SPECIFY TYPE OF INDEX (e.g. B-tree, hashing, …) OR PARAMETERS (e.g. load factor, size of hash, …) … at least not in SQL … Note 27