Transcript B+-tree and Hashing

Advanced Data Structures
NTUA Spring 2007
B+-trees and External
memory Hashing
Model of Computation
Data stored on disk(s)
 Minimum transfer unit:
a page(or block) = b
bytes or B records
 N records -> N/B = n
pages
 I/O complexity: in
number of pages

CPU
Memory
Disk
I/O complexity
An ideal index has space O(N/B), update
overhead O(1) or O(logB(N/B)) and search
complexity O(a/B) or O(logB(N/B) + a/B)
where a is the number of records in the
answer
 But, sometimes CPU performance is also
important… minimize cache misses ->
don’t waste CPU cycles

B+-tree
http://en.wikipedia.org/wiki/B-tree
Records must be ordered over an attribute,
SSN, Name, etc.
 Queries: exact match and range queries
over the indexed attribute: “find the name
of the student with ID=087-34-7892” or
“find all students with gpa between 3.00 and
3.5”

B+-tree:properties



Insert/delete at log F (N/B) cost; keep tree heightbalanced. (F = fanout)
Minimum 50% occupancy (except for root). Each
node contains d <= m <= 2d entries.
Two types of nodes: index (non-leaf) nodes and
(leaf) data nodes; each node is stored in 1 page
(disk based method)
[BM72] Rudolf Bayer and McCreight, E. M. Organization and
Maintenance of Large Ordered Indexes. Acta Informatica 1, 173189, 1972
180
200
150
156
179
120
130
100
101
110
30
35
3
5
11
120
150
180
30
100
Example
Root
to keys
< 57
to keys
57 k<81
95
81
57
Index node
to keys
81k<95
to keys
95
To record
with key 85
To record
with key 81
To record
with key 57
95
81
57
Data node
From non-leaf node
to next leaf
in sequence
B+tree 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”
(3) Number of pointers/keys for B+tree
Max Max Min
ptrs keys ptrs
Non-leaf
(non-root)
Leaf
(non-root)
Root
Min
keys
n
n-1
n/2
n/2- 1
n
n-1
(n-1)/2
(n-1)/2
n
n-1
2
1
Insert into B+tree
(a) simple case
space available in leaf
(b) leaf overflow
(c) non-leaf overflow
(d) new root

30
31
32
3
5
11
30
100
(a) Insert key = 32
n=4
(a) Insert key = 7
30
30
31
3
57
11
3
5
7
100
n=4
180
200
160
179
150
156
179
180
120
150
180
160
100
(c) Insert key = 160
n=4
(d) New root, insert 45
40
45
40
30
32
40
20
25
10
12
1
2
3
10
20
30
30
new root
n=4
Insertion


Find correct leaf L.
Put data entry onto L.


If L has enough space, done!
Else, must split L (into L and a new node L2)



This can happen recursively


Redistribute entries evenly, copy up middle key.
Insert index entry pointing to L2 into parent of L.
To split index node, redistribute entries evenly, but push
up middle key. (Contrast with leaf splits.)
Splits “grow” tree; root split increases height.

Tree growth: gets wider or one level taller at top.
Deletion from B+tree
(a) Simple case - no example
(b) Coalesce with neighbor (sibling)
(c) Re-distribute keys
(d) Cases (b) or (c) at non-leaf
(a) Simple case
n=5
40
50
10
40
100
Delete 30
10
20
30

(b) Coalesce with sibling
n=5
40
50
10
40
100
Delete 50
10
20
30
40

(c) Redistribute keys
n=5
35
40
50
10
40 35
100
Delete 50
10
20
30
35

(d) Non-leaf coalesce
n=5
Delete 37
25

40
45
30
37
30
40
25
26
30
20
22
10
14
1
3
10
20
25
40
new root
Deletion


Start at root, find leaf L where entry belongs.
Remove the entry.




If L is at least half-full, done!
If L has only d-1 entries,
 Try to re-distribute, borrowing from sibling
(adjacent node with same parent as L).
 If re-distribution fails, merge L and sibling.
If merge occurred, must delete entry (pointing to L
or sibling) from parent of L.
Merge could propagate to root, decreasing height.
Complexity

Optimal method for 1-d range queries:
Tree height: logd(N/d)
Space: O(N/d)
Updates: O(logd(N/d))
Query:O(logd(N/d) + a/d)
d = B/2
180
200
120
150
180
30
100
Root
150
156
179
120
130
100
101
110
30
35
3
5
11
Example
Range[32, 160]
Other issues

Internal node architecture [Lomet01]:

Reduce the overhead of tree traversal.


Prefix compression: In index nodes store only the prefix
that differentiate consecutive sub-trees. Fanout is
increased.
Cache sensitive B+-tree


Place keys in a way that reduces the cache faults during
the binary search in each node.
Eliminate pointers so a cache line contains more keys for
comparison.
References
[BM72] Rudolf Bayer and McCreight, E. M. Organization and Maintenance
of Large Ordered Indexes. Acta Informatica 1, 173-189, 1972
[L01] David B. Lomet: The Evolution of Effective B-tree: Page Organization and
Techniques: A Personal Account. SIGMOD Record 30(3): 64-69 (2001)
http://www.acm.org/sigmod/record/issues/0109/a1-lomet.pdf
[B-Y95] Ricardo A. Baeza-Yates: Fringe Analysis Revisited. ACM Comput. Surv.
27(1): 111-119 (1995)
Selection Queries
B+-tree is perfect, but....
to answer a selection query (ssn=10) needs to traverse a full path.
In practice, 3-4 block accesses (depending on the height of the tree,
buffering)
Any better approach?
Yes!
Hashing
 static hashing
 dynamic hashing
Hashing
Hash-based indexes are best for equality
selections. Cannot support range searches.
 Static and dynamic hashing techniques
exist; trade-offs similar to ISAM vs. B+
trees.

Static Hashing


# primary pages fixed, allocated sequentially, never deallocated; overflow pages if needed.
h(k) MOD N= bucket to which data entry with key k
belongs. (N = # of buckets)
h(key) mod N
key
0
1
h
N-1
Primary bucket pages
Overflow pages
Static Hashing (Contd.)



Buckets contain data entries.
Hash fn works on search key field of record r. Use
its value MOD N to distribute values over range 0
... N-1.
 h(key) = (a * key + b) usually works well.
 a and b are constants… more later.
Long overflow chains can develop and degrade
performance.
 Extensible and Linear Hashing: Dynamic
techniques to fix this problem.
Extensible Hashing


Situation: Bucket (primary page) becomes full.
Why not re-organize file by doubling # of buckets?
 Reading and writing all pages is expensive!
Idea: Use directory of pointers to buckets, double
# of buckets by doubling the directory, splitting
just the bucket that overflowed!
 Directory much smaller than file, so doubling it
is much cheaper. Only one page of data entries
is split. No overflow page!
 Trick lies in how hash function is adjusted!
Example
• Directory is array of size 4.
• Bucket for record r has entry with index = `global depth’ least significant
bits of h(r);
– If h(r) = 5 = binary 101, it is in bucket pointed to by 01.
– If h(r) = 7 = binary 111, it is in bucket pointed to by 11.
LOCAL DEPTH
2
4* 12* 32* 16*
Bucket A
GLOBAL DEPTH
2
1
00
1*
01
10
11
2
DIRECTORY
10*
5* 7*
13*
Bucket B
Bucket C
we denote r by h(r).
Handling Inserts

Find bucket where record belongs.

If there’s room, put it there.

Else, if bucket is full, split it:

increment local depth of original page

allocate new page with new local depth

re-distribute records from original page.

add entry for the new page to the
directory
Example: Insert 21, then 19, 15
LOCAL DEPTH
2
4* 12* 32* 16*
Bucket A
GLOBAL DEPTH



21 = 10101
19 = 10011
15 = 01111
2
2
1
00
1*
01
10
11
2
DIRECTORY
5* 21*
7* 13*
Bucket C
10*
2
7*
Bucket B
19* 15*
DATA PAGES
Bucket D
Insert h(r)=20 (Causes Doubling)

LOCAL DEPTH
GLOBAL DEPTH
2
00
3
20 = 10100
2
Bucket A
16*
4* 12*32*
32*16*
11
3
32* 16*
GLOBAL DEPTH
2
1* 5* 21*13* Bucket B
01
10
LOCAL DEPTH
3
000
2
1* 5* 21* 13* Bucket B
001
2
10*
Bucket C
Bucket D
011
10*
101
2
110
15* 7* 19*
Bucket D
111
3
4* 12* 20*
2
100
2
15* 7* 19*
010
Bucket A2
(`split image'
of Bucket A)
3
4* 12* 20*
Bucket A2
(`split image'
of Bucket A)
Points to Note


20 = binary 10100. Last 2 bits (00) tell us r belongs
in either A or A2. Last 3 bits needed to tell which.
 Global depth of directory: Max # of bits needed
to tell which bucket an entry belongs to.
 Local depth of a bucket: # of bits used to
determine if an entry belongs to this bucket.
When does bucket split cause directory doubling?
 Before insert, local depth of bucket = global depth.
Insert causes local depth to become > global
depth; directory is doubled by copying it over and
`fixing’ pointer to split image page.
Linear Hashing
This is another dynamic hashing scheme,
alternative to Extensible Hashing.
 Motivation: Ext. Hashing uses a directory
that grows by doubling… Can we do
better? (smoother growth)
 LH: split buckets from left to right,
regardless of which one overflowed
(simple, but it works!!)

Linear Hashing (Contd.)

Directory avoided in LH by using overflow pages. (chaining
approach)




Splitting proceeds in `rounds’. Round ends when all NR initial
(for round R) buckets are split.
Current round number is Level.
Search: To find bucket for data entry r, find hLevel(r):
 If hLevel(r) in range `Next to NR’ , r belongs here.
 Else, r could belong to bucket hLevel(r) or bucket hLevel(r) +
NR; must apply hLevel+1(r) to find out.
Family of hash functions:
h0, h1, h2, h3, ….
hi+1 (k) = hi(k)
or
hi+1 (k) = hi(k) + 2i-1N0
Linear Hashing: Example
Initially: h(x) = x mod N (N=4 here)
Assume 3 records/bucket
Insert 17 = 17 mod 4
1
Bucket id
0
1
2
3
13
4
8 5
9
6
7
11
Linear Hashing: Example
Initially: h(x) = x mod N (N=4 here)
Assume 3 records/bucket
Insert 17 = 17 mod 4
1Overflow for Bucket 1
Bucket id
0
1
2
3
13
4
8
5
9
6
7
11
Split bucket 0, anyway!!
Linear Hashing: Example
To split bucket 0, use another function h1(x):
h0(x) = x mod N , h1(x) = x mod (2*N)
Split pointer
17
0
1
2
3
13
4
8 5
9
6
7
11
Linear Hashing: Example
To split bucket 0, use another function h1(x):
h0(x) = x mod N , h1(x) = x mod (2*N)
Split pointer
17
Bucket id
0
1
2
3
4
13
8
5
9
6
7
11
4
Linear Hashing: Example
To split bucket 0, use another function h1(x):
h0(x) = x mod N , h1(x) = x mod (2*N)
Bucket id
0
8
1
5
2
13
9
17
6
3
7
11
4
4
Linear Hashing: Example
h0(x) = x mod N , h1(x) = x mod (2*N)
Insert 15 and 3
Bucket id
0
8
1
5
13
17
2
9
6
3
7
4
11
4
Linear Hashing: Example
h0(x) = x mod N , h1(x) = x mod (2*N)
Bucket id
0
1
2
17
8
9
3
4
5
15
6
7 11
3
4
13 5
Linear Hashing: Search
h0(x) = x mod N (for the un-split buckets)
h1(x) = x mod (2*N) (for the split ones)
Bucket id
0
8
1
17
9
2
6
3
15
7 11
3
4
4
5
13 5
Linear Hashing: Search
Algorithm for Search:
Search(k)
1
b = h0(k)
2
if b < split-pointer then
3
b = h1(k)
4
read bucket b and search there
References
[Litwin80] Witold Litwin: Linear Hashing: A New Tool for File and Table
Addressing. VLDB 1980: 212-223
http://www.cs.bu.edu/faculty/gkollios/ada01/Papers/linear-hashing.PDF
[B-YS-P98] Ricardo A. Baeza-Yates, Hector Soza-Pollman: Analysis of
Linear Hashing Revisited. Nord. J. Comput. 5(1): (1998)