CS 245: Database System Principles

1
CS 245 Database System Principles
Notes 5 Hashing and More

• Hector Garcia-Molina

2
Hashing

• key ? h(key)

ltkeygt
Buckets (typically 1 disk block)
. . .
3

• Two alternatives

. . .
records
(1) key ? h(key)
. . .
4
Two alternatives
record
(2) key ? h(key)
key 1
Index

• Alt (2) for secondary search key

5
Example hash function

• Key x1 x2 xn n byte character string
• Have b buckets
• h add x1 x2 .. xn
• compute sum modulo b

6

• ? This may not be best function
• ? Read Knuth Vol. 3 if you really need to
  select a good function.

Good hash ? Expected number of
function keys/bucket is the same for all
buckets
7
Within a bucket

• Do we keep keys sorted?

• Yes, if CPU time critical
• Inserts/Deletes not too frequent

8
Next example to illustrate inserts,
overflows, deletes

• h(K)

9
EXAMPLE 2 records/bucket
0 1 2 3

• INSERT
• h(a) 1
• h(b) 2
• h(c) 1
• h(d) 0

h(e) 1
10
EXAMPLE deletion
Deleteef
0 1 2 3
a
b
d
c
c
e
f
g
11
Rule of thumb

• Try to keep space utilization
• between 50 and 80
• Utilization keys used
• total keys that fit

12
How do we cope with growth?

• Overflows and reorganizations
• Dynamic hashing

13
Extensible hashing two ideas

• (a) Use i of b bits output by hash function
• b
• h(K) ?
• use i ? grows over time.

00110101
14

• (b) Use directory
• h(K)i to bucket

. . .
. . .
15
Example h(k) is 4 bits 2 keys/bucket
1

• i

0001
1
1001
1100
Insert 1010
16
Example continued
i
2
00 01 10 11
1
0001
0111
1001
1010
Insert 0111 0000
1100
17
Example continued
2
0000
0001
i
2
00 01 10 11
2
0111
Insert 1001
18
Extensible hashing deletion

• No merging of blocks
• Merge blocks and cut directory if possible
• (Reverse insert procedure)

19
Deletion example

• Run thru insert example in reverse!

20
Extensible hashing
Summary

• Can handle growing files
• - with less wasted space
• - with no full reorganizations

21
Linear hashing

• Another dynamic hashing scheme

22
Example b4 bits, i 2, 2 keys/bucket

• insert 0101

Future growth buckets
0101
0000
1111
1010

• 00 01 10 11

m 01 (max used block)
23
Example b4 bits, i 2, 2 keys/bucket
Future growth buckets
0101
0000
1111
1010

• 00 01 10 11

m 01 (max used block)
24
Example Continued How to grow beyond this?
i 2
1111
1010
0101
0000
0101

• 00 01 10 11

. . .
m 11 (max used block)
25
? When do we expand file?

• Keep track of used slots
• total of slots

U

• If U gt threshold then increase m
• (and maybe i )

26
Linear Hashing
Summary

• Can handle growing files
• - with less wasted space
• - with no full reorganizations
• No indirection like extensible hashing

27
Example BAD CASE

• Very full
• Very empty Need to move
• m here
• Would waste
• space...

28
Summary

• Hashing
• - How it works
• - Dynamic hashing
• - Extensible
• - Linear

29
Next

• Indexing vs Hashing
• Index definition in SQL
• Multiple key access

30
Indexing vs Hashing

• Hashing good for probes given key
• e.g., SELECT
• FROM R
• WHERE R.A 5

31
Indexing vs Hashing

• INDEXING (Including B Trees) good for
• Range Searches
• e.g., SELECT
• FROM R
• WHERE R.A gt 5

32
Index definition in SQL

• Create index name on rel (attr)
• Create unique index name on rel (attr)

defines candidate key

• Drop INDEX name

33

• CANNOT SPECIFY TYPE OF INDEX
• (e.g. B-tree, Hashing, )
• OR PARAMETERS
• (e.g. Load Factor, Size of Hash,...)
• ... at least in SQL...

Note
34

• ATTRIBUTE LIST ? MULTIKEY INDEX
• (next)
• e.g., CREATE INDEX foo ON R(A,B,C)

Note
35

Multi-key Index

• Motivation Find records where
• DEPT Toy AND SAL gt 50k

36
Strategy I

• Use one index, say Dept.
• Get all Dept Toy records and
  check their salary

I1
37
Strategy II

• Use 2 Indexes Manipulate Pointers
• Toy Sal
• gt 50k

38
Strategy III

• Multiple Key Index
• One idea

I2
I3
I1
39
Example
10k
15k

• Example
• Record
• Dept
• Index
• Salary
• Index

17k
21k
NameJoe DEPTSales SAL15k
12k
15k
15k
19k
40
For which queries is this index good?

• Find RECs Dept Sales SAL20k
• Find RECs Dept Sales SAL gt 20k
• Find RECs Dept Sales
• Find RECs SAL 20k

41
Interesting application

• Geographic Data
• DATA
• ltX1,Y1, Attributesgt
• ltX2,Y2, Attributesgt

y
x
. . .
42
Queries

• What city is at ltXi,Yigt?
• What is within 5 miles from ltXi,Yigt?
• Which is closest point to ltXi,Yigt?

43
Example

44
Queries

• Find points with Yi gt 20
• Find points with Xi lt 5
• Find points close to i lt12,38gt
• Find points close to b lt7,24gt

45

• Many types of geographic index structures have
  been suggested
• Quad Trees
• R Trees

46
Two more types of multi key indexes

• Grid
• Partitioned hash

47
Grid Index

• Key 2
• X1 X2 Xn
• V1
• V2
• Key 1
• Vn

To records with key1V3, key2X2
48
CLAIM

• Can quickly find records with
• key 1 Vi ? Key 2 Xj
• key 1 Vi
• key 2 Xj

• And also ranges.
• E.g., key 1 ? Vi ? key 2 lt Xj

49

• ? But there is a catch with Grid Indexes!

• How is Grid Index stored on disk?

• Problem
• Need regularity so we can compute position of
  ltVi,Xjgt entry

50
Solution Use Indirection

• Buckets
• V1
• V2
• V3 Grid only
• V4 contains
• pointers to
• buckets
• Buckets

X1 X2 X3
-- -- --
-- -- --
-- -- --
-- -- --
-- -- --
51
With indirection

• Grid can be regular without wasting space
• We do have price of indirection

52
Can also index grid on value ranges

• Salary Grid

0-20K
1
20K-50K
2
50K-
3
8
Linear Scale
1
2
3
Toy
Sales
Personnel
53
Grid files

• Good for multiple-key search
• Space, management overhead (nothing is
  free)
• Need partitioning ranges that evenly split keys

-
-
54
Partitioned hash function
010110 1110010

• Idea
• Key1 Key2

h1
h2
55
EX

• h1(toy) 0 000
• h1(sales) 1 001
• h1(art) 1 010
• . 011
• .
• h2(10k) 01 100
• h2(20k) 11 101
• h2(30k) 01 110
• h2(40k) 00 111
• .
• .
• ltFred,toy,10kgt,ltJoe,sales,10kgt
• ltSally,art,30kgt

Insert
56

• h1(toy) 0 000
• h1(sales) 1 001
• h1(art) 1 010
• . 011
• .
• h2(10k) 01 100
• h2(20k) 11 101
• h2(30k) 01 110
• h2(40k) 00 111
• .
• .
• Find Emp. with Dept. Sales ? Sal40k

ltFredgt
ltJoegtltJangt
ltMarygt
ltSallygt
ltTomgtltBillgt
ltAndygt
57

• h1(toy) 0 000
• h1(sales) 1 001
• h1(art) 1 010
• . 011
• .
• h2(10k) 01 100
• h2(20k) 11 101
• h2(30k) 01 110
• h2(40k) 00 111
• .
• .
• Find Emp. with Sal30k

ltFredgt
ltJoegtltJangt
ltMarygt
ltSallygt
ltTomgtltBillgt
ltAndygt
58

• h1(toy) 0 000
• h1(sales) 1 001
• h1(art) 1 010
• . 011
• .
• h2(10k) 01 100
• h2(20k) 11 101
• h2(30k) 01 110
• h2(40k) 00 111
• .
• .
• Find Emp. with Dept. Sales

ltFredgt
ltJoegtltJangt
ltMarygt
ltSallygt
ltTomgtltBillgt
ltAndygt
59
Summary

• Post hashing discussion
• - Indexing vs. Hashing
• - SQL Index Definition
• - Multiple Key Access
• - Multi Key Index
• Variations Grid, Geo Data
• - Partitioned Hash

60
Reading Chapter 5

• Skim the following sections
• 5.3.6, 5.3.7, 5.3.8
• 5.4.2, 5.4.3, 5.4.4
• Read the rest

61
The BIG picture.

• Chapters 2 3 Storage, records, blocks...
• Chapter 4 5 Access Mechanisms - Indexes
• - B trees
• - Hashing
• - Multi key
• Chapter 6 7 Query Processing

NEXT