The document discusses different database indexing strategies and their performance for query processing. It compares the performance of naive file scanning, sorted file scanning, clustered and unclustered B+ tree indexes, and hash indexes. It also analyzes the performance of processing queries with conjunctions and disjunctions using different indexes.

1. Selection
(R)
value
R.attr op


3.  Tuple Length
◦ Reserves
 40 bytes
◦ Sailors
 50 bytes
 # of Tuples
◦ Reserves
 100,000
◦ Sailors
 40,000
 Page Size
◦ 4k
 Size on Disk
◦ Reserves ???
◦ Sailors ???

4.  Naive Approach (Heap File)
◦ 1000 pages
 Sorted File (on rname)
◦ ???
 Clustered Index
◦ ???
 Unclustered Index
◦ ???
(R)
value
R.attr op


5.  M:= Size of R on Disk (in pages)
 X:= # of tuples satisfying “R.attr op value”
 Y:= # of tuples per page
 No Index, Unsorted Data
◦ File Scan
 O(M)
 No Index, Sorted Data
◦ Sorted-file Scan
 Scan following a Binary Search
 O(log2 M) + O(X/Y)
(R)
value
R.attr op


6.  B+ Tree Index (Clustered)
◦ Alternative 1 (Data Entry == Data Record)
 O(1) + O(X/Y)
◦ Alternative 2,3
 O(1) + O(X/Z) + O(X/Y)
 Z:= # of Data Entries per Page
 Recall Z is much larger than Y
(R)
value
R.attr op


7.  B+ Tree Index (Unclustered)
◦ Alternative 1 (Data Entry == Data Record)
 ???
◦ Alternative 2,3
 O(1) + O(X/Z) + O(X)
 X can be much Larger than M
 Smart Approach
 Sort Data Entries on the basis of page-
id or their rids
 Any Gain???
(R)
value
R.attr op


8.  Hash Index (Only for Equality Selection)
◦ X is typically small
◦ X == 1 implies ???
(R)
value
R.attr op


9.  R.rnarne='Joe' AND R.bid=r
(R)
r
R.bid
Joe'
'
R.rname 




10.  CNF
◦ Collection of Conjuncts connected through Ʌ
Operator
 Conjunct
◦ One or more terms connected through V
Operator
◦ If a conjunct contains V then it is called
Disjunctive or containing Disjunction
◦ Primary Conjunct if an Index is available

16.  File Scan
 Single Index
◦ Selectivity

17.  Multiple Indexes

18.  File Scan

19.  Qualifying Tuples

20.  Union