CS 245: Database System Principles Notes 4: Indexing

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CS 245 Database System PrinciplesNotes 4
Indexing

• Hector Garcia-Molina

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Chapter 4

• Indexing Hashing
• value

record
?
value
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Topics

• Conventional indexes
• B-trees
• Hashing schemes

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Sequential File
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Sequential File
Dense Index
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Sequential File
Sparse Index
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Sequential File
Sparse 2nd level
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• Comment
• FILE,INDEX may be contiguous
• or not (blocks chained)

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Question

• Can we build a dense, 2nd level index for a dense
  index?

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Notes on pointers

• (1) Block pointer (sparse index) can be smaller
  than record pointer
• BP
• RP

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Notes on pointers

• (2) If file is contiguous, then we can omit
• pointers (i.e., compute them)

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K1
K2
K3
K4
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Sparse vs. Dense Tradeoff

• Sparse Less index space per record can
  keep more of index in memory
• Dense Can tell if any record exists
  without accessing file
• (Later
• sparse better for insertions
• dense needed for secondary indexes)

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Terms

• Index sequential file
• Search key ( ? primary key)
• Primary index (on Sequencing field)
• Secondary index
• Dense index (all Search Key values in)
• Sparse index
• Multi-level index

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Next

• Duplicate keys
• Deletion/Insertion
• Secondary indexes

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Duplicate keys
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Dense index, one way to implement?
Duplicate keys
10
10
10
10
10
10
20
20
20
20
30
30
30
30
30
30
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Duplicate keys
Dense index, better way?
10
20
30
40
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Duplicate keys
Sparse index, one way?
10
10
20
30
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Duplicate keys
Sparse index, another way?

• place first new key from block

10
20
30
30
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Duplicate values, primary index
Summary

• Index may point to first instance of each value
  only
• File
• Index

a
a
a
. .
b
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Deletion from sparse index
10
30
50
70
90

110
130
150
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Deletion from sparse index

• delete record 40

10
30
50
70
90

110
130
150
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Deletion from sparse index

• delete record 30

10
30
50
70
90

110
130
150
25
Deletion from sparse index

• delete records 30 40

10
30
50
70
90

110
130
150
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Deletion from dense index
10
20
30
40
50

60
70
80
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Deletion from dense index

• delete record 30

10
20
30
30
40
40
50

60
70
80
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Insertion, sparse index case

10
30
40
60
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Insertion, sparse index case

• insert record 34

10
30
40
60
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Insertion, sparse index case

• insert record 15

10
30
40
60

• Illustrated Immediate reorganization
• Variation
• insert new block (chained file)
• update index

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Insertion, sparse index case

• insert record 25

10
30
40
60
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Insertion, dense index case

• Similar
• Often more expensive . . .

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Secondary indexes
Sequence field
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Secondary indexes
Sequence field

• Sparse index

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Secondary indexes
Sequence field

• Dense index

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With secondary indexes

• Lowest level is dense
• Other levels are sparse

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Duplicate values secondary indexes
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Duplicate values secondary indexes
one option...

• Problem
• excess overhead!
• disk space
• search time

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Duplicate values secondary indexes
another option...
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Problem variable size records in index!
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Duplicate values secondary indexes
?
?
Another idea (suggested in class)Chain records
with same key?
?
?

• Problems
• Need to add fields to records
• Need to follow chain to know records

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Duplicate values secondary indexes
buckets
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Why bucket idea is useful

• Indexes Records
• Name primary EMP (name,dept,floor,...)
• Dept secondary
• Floor secondary

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Query Get employees in (Toy Dept) (2nd
floor)
? Intersect toy bucket and 2nd Floor
bucket to get set of matching EMPs
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This idea used in text information retrieval

• Documents

...the cat is fat ...
...was raining cats and dogs...
...Fido the dog ...
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IR QUERIES

• Find articles with cat and dog
• Find articles with cat or dog
• Find articles with cat and not dog

• Find articles with cat in title
• Find articles with cat and dog within 5
  words

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Common technique more info in inverted
list
position
location
type
d1

• cat

Title
5
Author
10
Abstract
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d2
d3
dog
Title
100
Title
12
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Posting an entry in inverted list. Represents
occurrence of term in article

• Size of a list 1 Rare words or
• (in postings) miss-spellings
• 106 Common words

Size of a posting 10-15 bits (compressed)
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IR DISCUSSION

• Stop words
• Truncation
• Thesaurus
• Full text vs. Abstracts
• Vector model

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Vector space model

• w1 w2 w3 w4 w5 w6 w7
• DOC lt1 0 0 1 1 0 0 gt
• Query lt0 0 1 1 0 0 0 gt

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• Tricks to weigh scores normalize
• e.g. Match on common word not as useful as
  match on rare words...

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• How to process V.S. Queries?
• w1 w2 w3 w4 w5 w6
• Q lt 0 0 0 1 1 0 gt

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• Try Stanford Libraries
• Try Google, Yahoo, ...

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Summary so far

• Conventional index
• Basic Ideas sparse, dense, multi-level
• Duplicate Keys
• Deletion/Insertion
• Secondary indexes
• Buckets of Postings List

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Conventional indexes

• Advantage
• - Simple
• - Index is sequential file
• good for scans

Disadvantage - Inserts expensive, and/or -
Lose sequentiality balance
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• Example Index (sequential)
• continuous
• free space

10
20
30
40
50
60
70
80
90
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Outline

• Conventional indexes
• B-Trees ? NEXT
• Hashing schemes

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• NEXT Another type of index
• Give up on sequentiality of index
• Try to get balance

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BTree Example n3

• Root

100
120 150 180
30
3 5 11
120 130
180 200
100 101 110
150 156 179
30 35
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Sample non-leaf
57 81 95

• to keys to keys to keys to keys
• lt 57 57? klt81 81?klt95 ?95

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Sample leaf node

• From non-leaf node
• to next leaf
• in sequence

57 81 95
To record with key 57 To record with key
81 To record with key 85
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In textbooks notation n3

• Leaf
• Non-leaf

30 35
30
35
30
30
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• Size of nodes n1 pointers
• n keys

(fixed)
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Dont want nodes to be too empty

• Use at least
• Non-leaf ?(n1)/2? pointers
• Leaf ?(n1)/2? pointers to data

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n3

• Full node min. node
• Non-leaf
• Leaf

120 150 180
30
3 5 11
30 35
counts even if null
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Btree rules tree of order n

• (1) All leaves at same lowest level (balanced
  tree)
• (2) Pointers in leaves point to records except
  for sequence pointer

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• (3) Number of pointers/keys for Btree

Max Max Min Min ptrs keys
ptrs?data keys
Non-leaf (non-root)
n1
n
?(n1)/2?
?(n1)/2?- 1
Leaf (non-root)
n1
n
?(n1)/2?
?(n1)/2?
Root
n1
n
1
1
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Insert into Btree

• (a) simple case
• space available in leaf
• (b) leaf overflow
• (c) non-leaf overflow
• (d) new root

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• (a) Insert key 32

n3
100
30
3 5 11
30 31
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• (a) Insert key 7

n3
100
30
3 5 11
30 31
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• (c) Insert key 160

n3
100
120 150 180
180 200
150 156 179
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• (d) New root, insert 45

n3
10 20 30
1 2 3
10 12
20 25
30 32 40
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Deletion from Btree

• (a) Simple case - no example
• (b) Coalesce with neighbor (sibling)
• (c) Re-distribute keys
• (d) Cases (b) or (c) at non-leaf

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• (b) Coalesce with sibling
• Delete 50

n4
10 40 100
10 20 30
40 50
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• (c) Redistribute keys
• Delete 50

n4
10 40 100
10 20 30 35
40 50
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• (d) Non-leaf coalese
• Delete 37

n4
25
10 20
30 40
25 26
30 37
40 45
20 22
10 14
1 3
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Btree deletions in practice

• Often, coalescing is not implemented
• Too hard and not worth it!

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Comparison B-trees vs. static indexed
sequential file

• Ref 1 Held Stonebraker
• B-Trees Re-examined
• CACM, Feb. 1978

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• Ref 1 claims
• - Concurrency control harder in B-Trees
• - B-tree consumes more space
• For their comparison
• block 512 bytes
• key pointer 4 bytes
• 4 data records per block

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Example 1 block static index
1 data block

• 127 keys
• (1271)4 512 Bytes
• -gt pointers in index implicit! up to 127
• blocks

k1
k2
k3
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Example 1 block B-tree
k1
1 data block
63 keys 63x(44)8 512 Bytes -gt
pointers needed in B-tree up to 63 blocks
because index is blocks not contiguous
k2
...
k63
-
next
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Size comparison Ref. 1

• Static Index B-tree
• data data
• blocks height blocks height
• 2 -gt 127 2 2 -gt 63 2
• 128 -gt 16,129 3 64 -gt 3968 3
• 16,130 -gt 2,048,383 4 3969 -gt 250,047
  4
• 250,048 -gt 15,752,961 5

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Ref. 1 analysis claims

• For an 8,000 block file, after 32,000 inserts
• after 16,000 lookups
• ? Static index saves enough accesses to allow
  for reorganization

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Ref 2 M. Stonebraker, Retrospective on a
database system, TODS, June 1980
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• DBA does not know when to reorganize
• DBA does not know how full to load pages of new
  index

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• Buffering
• B-tree has fixed buffer requirements
• Static index must read several
  overflow blocks to be efficient (large
  variable size buffers needed for this)

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• Speaking of buffering
• Is LRU a good policy for Btree buffers?

? Of course not! ? Should try to keep root in
memory at all times (and perhaps some nodes
from second level)
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Interesting problem

• For Btree, how large should n be?

n is number of keys / node
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Sample assumptions

• (1) Time to read node from disk is (STn) msec.

(2) Once block in memory, use binary search to
locate key (a b LOG2 n) msec. For some
constants a,b Assume a ltlt S
(3) Assume Btree is full, i.e., nodes to
examine is LOGn N where N records
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?Can get f(n) time to find a record

• f(n)
• nopt n

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? FIND nopt by f(n) 0

• Answer is nopt few hundred
• (see homework for details)

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Variation on Btree B-tree (no )

• Idea
• Avoid duplicate keys
• Have record pointers in non-leaf nodes

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• to record to record to record
• with K1 with K2 with K3
• to keys to keys to keys to
  keys
• lt K1 K1ltxltK2 K2ltxltk3 gtk3

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B-tree example n2
65 125

145 165
85 105
25 45
10 20
30 40
110 120
90 100
70 80
170 180
50 60
130 140
150 160
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Note on inserts

• Say we insert record with key 25

10 20 30
n3
leaf
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So, for B-trees

• MAX MIN
• Tree Rec Keys Tree Rec Keys
• Ptrs Ptrs Ptrs Ptrs
• Non-leaf
• non-root n1 n n ?(n1)/2? ?(n1)/2?-1
  ?(n1)/2?-1
• Leaf
• non-root 1 n n 1 ?(n1)/2? ?(n1)/2?
• Root
• non-leaf n1 n n 2 1 1
• Root
• Leaf 1 n n 1 1 1

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Tradeoffs

• ? B-trees have faster lookup than Btrees
• ? in B-tree, non-leaf leaf different sizes
• ? in B-tree, deletion more complicated

? Btrees preferred!
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But note

• If blocks are fixed size (due to disk and
  buffering restrictions)
• Then lookup for Btree is actually better!!

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• Example
• - Pointers 4 bytes
• - Keys 4 bytes
• - Blocks 100 bytes (just example)
• - Look at full 2 level tree

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B-tree

• Root has 8 keys 8 record pointers 9 son
  pointers
• 8x4 8x4 9x4 100 bytes

Each of 9 sons 12 rec. pointers (12 keys)
12x(44) 4 100 bytes
2-level B-tree, Max records 12x9 8 116
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Btree

• Root has 12 keys 13 son pointers
• 12x4 13x4 100 bytes

Each of 13 sons 12 rec. ptrs (12 keys)
12x(4 4) 4 100 bytes
2-level Btree, Max records 13x12 156
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So...
8 records

• ooooooooooooo ooooooooo
• 156 records 108 records
• Total 116

B
B

• Conclusion
• For fixed block size,
• B tree is better because it is bushier

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Outline/summary

• Conventional Indexes
• Sparse vs. dense
• Primary vs. secondary
• B trees
• Btrees vs. B-trees
• Btrees vs. indexed sequential
• Hashing schemes --gt Next