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B-trees were originally designed to work on

were originally designed to work on direct access secondary storage (Magnetic
disk). In a computer system the memory capacity is consists of two parts: primary
memory (uses memory chips) and secondary storage (based on magnetic disk). When
dealing with large datasets, it is often impossible to maintain a whole dataset
in the primary memory. Instead, a small part of the dataset is maintained in
the primary memory and the remaining data is read from a secondary storage 1.

is a data structure used for sorting huge datasets for fast retrieval from a
disk. A typical B-tree has a single-digit height, even with millions of entries
2. Which means
that few disk accesses are needed to find the place where data is stored in the
tree. At most two accesses are required to search for any node. “From a
practical point of view, B-trees, therefore, guarantee an access time of less
than 10 ms even for extremely large datasets.” —Dr. Rudolf Bayer,
inventor of the B-tree

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1.    Description


save time by using nodes with many branches (children). Unlike binary tree, in
which each node has only two children. When a node has many children a record
can be found easily by passing through fewer nodes than if there are two
children per node. See the example in Figure 1.


The example
in Figure 1 shows that the red node in a binary tree has depth of three while
the corresponding node in the B-tree has depth of one. Clearly, B-trees locates
desired records faster, assuming that all system parameters are identical. The
difference in depth between binary tree and B-tree is greater in a practical
database than in the example in Figure 1. Real world B-trees height depends on
the number of records in the database.  

tradeoff is that the decision process at each node in B-tree is more
complicated compared with a binary tree. A complex program is required to
execute B-tree’s operations, but this program runs fast because it is stored in

1.1   Definition


B-tree of order m (the maximum number of children for each node) is a
self-balancing search tree which satisfies the following properties:

·       Every node has at most m children.

·       A non-leaf node with k children contains k-1 keys.

·       The root has at least two children if it is not a leaf node.

·       Every non-leaf node (except root) has at least m?2 children.

·       All leaves appear in the same level.

Figure 2: A B-tree of order


2 shows a B-tree of order four. Each node contains up to three keys, and
internal nodes have up to four children 3.  





The height
of an n-key b-tree T of height h with a minimum degree t greater than or equal
to 2,  

                     , For n  

worst case height is O(log n). Since the branches of a B-tree can be large
compared to other balanced trees, the base of the algorithm tends to be large. But
the number of visited nodes during a search tends to be smaller than required
by other trees. Although this is does not affect the worst case height, B-tree
tends to has smaller heights than other trees 4.

Basic operations
on B-trees



The root of a B-tree is in the main memory (it is
not required to perform Disk-Read operation on the root).

Any node passed as a parameter must have had a Disk-Read
operation performed on them.

Top down algorithm, starts at the root of the tree.

1.2.1      Search


a B-tree is similar to searching a binary tree but instead of making two way branching
decision at a node, must make multi-way branching decision depending on the
number of a node’s children. To search for a node in a B-tree, a linear search
must be performed on the values of a node. After finding the value which is greater than or equal to the desired
value, the child pointer to the immediate left of that value is followed. If
all values are less than the desired value, the rightmost child pointer is
followed. The search is terminated as soon as the desired node is found 5.

Inputs:    is a pointer
to the root node of a sub-tree.

               is a key to be searched in the sub-tree.

   is a child pointer 1.

o   B-Tree-Search pseudo-code


1     i ? 1

2     while i ? nx and k > keyix

3              do i ? i + 1

4     if i ? nx and k == keyix

5              then return (x, i)

6      if leafx

7              then return NIL

8     else Disk-Read(cix)

return B-Tree-Search(cix, k)

Lines 1-3 : Using a linear
search procedure, find the smallest i
that k ? keyix, or else they set i to nx
+ 1

Lines 4-5 : Check if the key
is discovered, then return it.

Lines 6-9 : Either end the
search unsuccessfully if x is
a leaf or recurs to search the appropriate sub-tree of x, after performing necessary Disk-Read operation on that child 6.

o  B-Tree-Search Running

The number of disk pages accessed by B-Tree-Search
is ?(h) = (logt n), where h is the height of
the B-tree and n is the number of keys in the B-tree. is O(logt n). The while loop time in lines
2-3 for each node is O(t). The running time is O(th)
= O(t logt n) 6.

o  B-Tree-Search Examples ( Consider the B-tree in Figure 2 page )

Example 1 : Search for key = 7.

By applying

Starts at
the root (17), is key = 17? Is key > 17? No, go to left sub-tree.

Next node (4,9), is key = 4? Is key > 4? Yes, move to the next key.

Is key = 9? Is key > 9? No, go to sub-tree left of 9.

Next node (7,8), is key = 7? Yes, found.

Example 2 : Search for key = 11.

By applying B-Tree-Search:

Starts at the root (17), is key = 17? Is key
> 17? No, go to left sub-tree.

Next node (4,9), is key = 4? Is key > 4? Yes, move to the next key.

Is key = 9? Is key > 9? Yes, go to the right sub-tree (there is no
more keys in the node to check).

Next node (12), is key = 12? Is key >12? No, the left sub-tree is a
leaf. Which means that 11 is not in the tree.

1.2.2      Create


B-Tree-Create operation builds a B-tree by first creating a new empty root node and
then call B-Tree-Insert to add new keys. B-Tree-Create and B-Tree-Insert
operations are both using Allocate-Node which allocates
one disk page to be used as a new node in O(1) time. When a node is created by Allocate-Node it does not require Disk-Read,
because there is no useful information stored on the disk for that node yet.

o   B-Tree-Create pseudo-code


x ? Allocate-Node()

leafx ? TRUE

nx ? 0


5     rootT ?

o  B-Tree-Create Running
Time is O(1).

1.2.3      Split a node


a node becomes full it is necessary to perform split operation. B-Tree-Split-Child moves the median key of node x up into its parent y (it has to be non-full node), where x is
the ith child of y. All keys in x right
of the median key are moved to the new node. The keys left of the median key
remain in the original node x.
The new node becomes the right child of the median key that was
moved to its parent y. The original node x
becomes the left child of the median key that was moved to its
parent y 5.

Inputs:    is a non-full
internal node.

               is an index.

the ith child of x and is the node being split.
Node y originally has 2t – 1 children but is
reduced to t – 1 children by this operation.

 is the new child of x.

o   B-Tree-Split-Child pseudo-code

i, y)

z ? Allocate-Node()

leafz ? leafy

nz ? t – 1

for j ? 1 to t – 1

keyjz ? keyj+ty

if not leafy

for j ? 1 to t

cjz ? cj+ty

ny ? t – 1

for j ? nx + 1 downto i + 1

cj+1x ? cjx

ci+1 ? z

for j ? nx downto i

keyj+1x ? keyjx

keyix ? keyty

nx ? nx + 1



19     Disk-Write(x)

Lines 1-8 : Create a new node z and give it the larger t –
1 keys and corresponding t children of y.

Line 9 : Adjusts the key count for y.

Lines 10-16 : Insert z as a child of x. To separate y from z,
move the median key from y up to x, and adjust the
key count for x.

Lines 17-19 : write out all modified disk page 6.

o  B-Tree-Split-Child Running
Time is ?(t).


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