Hash-Based Indexes
Hash-Based Indexes
Credits: Ramakrishnan & Gehrke
What you will learn from this lecture
▪
Review of static hashing
▪
How to adjust hash structure
dynamically against inserts and
deletes?
–
Extendible hashing
–
Linear hashing
–
Relative strengths of B+trees and
Hashing: when to use what
Introduction
▪
Hash-based
indexes are best for
equality
selections
–
n
o
t
r
a
v
e
r
s
a
l
;
d
i
r
e
c
t
c
o
m
p
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t
a
t
i
o
n
o
f
w
h
e
r
e
k
e
y
s
h
o
u
l
d
b
e
–
c
a
n
n
o
t
s
u
p
p
o
r
t
r
a
n
g
e
s
e
a
r
c
h
e
s
▪
Static and dynamic hashing techniques exist;
trade-offs similar to ISAM vs. B+ trees, on a
certain level.
Direct Mapping Table
▪
#
primary/home buckets
fixed, allocated sequentially,
never de-allocated
▪
h
(
k
)
=
k
=
b
u
c
k
e
t
t
o
w
h
i
c
h
d
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n
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y
w
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t
h
k
e
y
k
b
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l
o
n
g
s
.
(
M
=
#
o
f
b
u
c
k
e
t
s
)
▪
#slots per bucket s, let s = 1 here
h(key) mod M
h
key
P
r
i
m
a
r
y
b
u
c
k
e
t
p
a
g
e
s
1
0
M-1
Direct Mapping Table Bucket
private class Bucket
{int nKeys = 0; // 0 or 1
Integer key = null;
V value = null;
Bucket next = null;
V find (Integer k)
{if (key != null && key.equals (k)) return value;
return null;
} // find
void add (Integer k, V v) { nKeys = 1; key = k; value = v; } void print () { out.println ("[ " + key + " . ]" ); }} // Bucket inner class
Direct Mapping Table
Hash Function
private int h (Object key) { return (int) key; }get Method
@SuppressWarnings("unchecked")public V get (Object key)
{var i = h (key);
var bh = hTable.get (i);
return bh.find ((Integer) key);
} // get
Static Hashing
▪
#
primary/home buckets
fixed, allocated sequentially,
never de-allocated;
overflow buckets
if needed.
▪
h
(
k
)
m
o
d
M
=
b
u
c
k
e
t
t
o
w
h
i
c
h
d
a
t
a
e
n
t
r
y
w
i
t
h
k
e
y
k
b
e
l
o
n
g
s
.
(
M
=
#
o
f
b
u
c
k
e
t
s
)
h(key) mod M
h
key
P
r
i
m
a
r
y
b
u
c
k
e
t
p
a
g
e
s
O
v
e
r
f
l
o
w
p
a
g
e
s
1
0
M-1
Static Hashing (Contd.)
▪
Buckets may contain
data entries
or
references
.
▪
Hash function works on
search key
field of record
r.
Must
distribute values over range 0 ... M-1.
–
h
(
k
e
y
)
=
(
k
e
y
m
o
d
M
)
u
s
u
a
l
l
y
w
o
r
k
s
w
e
l
l
f
o
r
p
r
i
m
e
M
.
–
l
o
t
s
k
n
o
w
n
a
b
o
u
t
h
o
w
t
o
t
u
n
e
h
.
▪
Long overflow chains
can develop and degrade
performance (when there are updates).
–
Extendible
and
Linear
Hashing
: two major dynamic techniques to fix
this problem.
In this lecture, you will delve into hash-based indexes, exploring their application in database management. Topics covered include static and dynamic hashing techniques, review of hashing structures, adjustments against inserts and deletes, extendible hashing, linear hashing, and the relative strengths of B+trees compared to hashing methods. Discover the primary characteristics and advantages of hash-based indexes for equality selections and learn about direct mapping tables and hash functions. Join the discussion on when to utilize hash-based indexes effectively within the context of database management.
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Presentation Transcript
Hash-Based Indexes Credits: Ramakrishnan & Gehrke Textbook to be published by Pearson Ed in early 2014 CSCIX370: Database Management http://www.funwebdev.com
What you will learn from this lecture Review of static hashing How to adjust hash structure dynamically against inserts and deletes? Extendible hashing Linear hashing Relative strengths of B+trees and Hashing: when to use what CSCIX370: Database Management
Introduction Hash-based indexes are best for equality selections no traversal; direct computation of where key should be cannot support range searches Static and dynamic hashing techniques exist; trade-offs similar to ISAM vs. B+ trees, on a certain level. CSCIX370: Database Management
Direct Mapping Table # primary/home buckets fixed, allocated sequentially, never de-allocated h(k) = k = bucket to which data entry with key k belongs. (M = # of buckets) #slots per bucket s, let s = 1 here 0 h(key) mod M 1 key h M-1 Primary bucket pages CSCIX370: Database Management
Direct Mapping Table Bucket private class Bucket { int nKeys = 0; // 0 or 1 Integer key = null; V value = null; Bucket next = null; V find (Integer k) { if (key != null && key.equals (k)) return value; return null; } // find void add (Integer k, V v) { nKeys = 1; key = k; value = v; } void print () { out.println ("[ " + key + " . ]" ); } } // Bucket inner class CSCIX370: Database Management
Direct Mapping Table Hash Function private int h (Object key) { return (int) key; } get Method @SuppressWarnings("unchecked") public V get (Object key) { var i = h (key); var bh = hTable.get (i); return bh.find ((Integer) key); } // get CSCIX370: Database Management
Static Hashing # primary/home buckets fixed, allocated sequentially, never de-allocated; overflow buckets if needed. h(k) mod M = bucket to which data entry with key k belongs. (M = # of buckets) 0 h(key) mod M 1 key h M-1 Primary bucket pages Overflow pages CSCIX370: Database Management
Static Hashing (Contd.) Buckets may contain data entries or references. Hash function works on search key field of record r. Must distribute values over range 0 ... M-1. h(key) = (key mod M) usually works well for prime M. lots known about how to tune h. Long overflow chains can develop and degrade performance (when there are updates). Extendible and Linear Hashing: two major dynamic techniques to fix this problem. CSCIX370: Database Management
