2. 2
Indexed Sequential Files
• Provide a choice between two alternative
views of a file:
1. Indexed: the file can be seen as a set of
records that is indexed by key; or
2. Sequential: the file can be accessed
sequentially (physically contiguous
records), returning records in order by key.
3. 3
Example of applications
• Student record system in a university:
– Indexed view: access to individual records
– Sequential view: batch processing when posting
grades
• Credit card system:
– Indexed view: interactive check of accounts
– Sequential view: batch processing of payments
4. 4
The initial idea
• Maintain a sequence set:
– Group the records into blocks in a sorted way.
– Maintain the order in the blocks as records are
added or deleted through splitting,
concatenation, and redistribution.
• Construct a simple, single level index for
these blocks.
– Choose to build an index that contain the key
for the last record in each block.
5. 5
Maintaining a Sequence Set
• Sorting and re-organizing after insertions and
deletions is out of question. We organize the
sequence set in the following way:
– Records are grouped in blocks.
– Blocks should be at least half full.
– Link fields are used to point to the preceding block and
the following block (similar to doubly linked lists)
– Changes (insertion/deletion) are localized into blocks
by performing:
• Block splitting when insertion causes overflow
• Block merging or redistribution when deletion causes
underflow.
8. 8
Add an Index set
Key Block
BERNE 1
CAGE 2
DUTTON 3
EVANS 4
FOLK 5
GADDIS 6
9. 9
Tree indexes
• This simple scheme is nice if the index fits in
memory.
• If index doesn’t fit in memory:
– Divide the index structure into blocks,
– Organize these blocks similarly building a tree
structure.
• Tree indexes:
– B Trees
– B+ Trees
– Simple prefix B+ Trees
– …
12. 12
B Trees
• B-tree is one of the most important data structures
in computer science.
• What does B stand for? (Not binary!)
• B-tree is a multiway search tree.
• Several versions of B-trees have been proposed,
but only B+ Trees has been used with large files.
• A B+tree is a B-tree in which data records are in
leaf nodes, and faster sequential access is possible.
13. 13
Formal definition of B+ Tree Properties
• Properties of a B+ Tree of order v :
– All internal nodes (except root) has at least v keys
and at most 2v keys .
– The root has at least 2 children unless it’s a leaf..
– All leaves are on the same level.
– An internal node with k keys has k+1 children
14. 14
B+ tree: Internal/root node
structure
P0 K1 P1 K2 ……………… Pn-1 Kn Pn
Requirements:
K1 < K2 < … < Kn
For any search key value K in the subtree pointed by Pi,
If Pi = P0, we require K < K1
If Pi = Pn, Kn K
If Pi = P1, …, Pn-1, Ki < K Ki+1
Each Pi is a pointer to a child node; each Ki is a search key value
# of search key values = n, # of pointers = n+1
15. 15
Pointer L points to the left neighbor; R points to
the right neighbor
K1 < K2 < … < Kn
v n 2v (v is the order of this B+ tree)
We will use Ki* for the pair <Ki, ri> and omit L and
R for simplicity
B+ tree: leaf node structure
L K1 r1 K2 ……………… Kn rn
R
16. 16
Example: B+ tree with order of 1
• Each node must hold at least 1 entry, and at most
2 entries
10* 15* 20* 27* 33* 37* 40* 46* 51* 55* 63* 97*
20 33 51 63
40
Root
17. 17
Example: Search in a B+ tree order 2
• Search: how to find the records with a given search key value?
– Begin at root, and use key comparisons to go to leaf
• Examples: search for 5*, 16*, all data entries >= 24* ...
– The last one is a range search, we need to do the sequential scan, starting from
the first leaf containing a value >= 24.
Root
17 24 30
2* 3* 5* 7* 14* 15* 19* 20* 22* 24* 27* 29* 33* 34* 38* 39*
13
18. 18
B+ Trees in Practice
• Typical order: 100. Typical fill-factor: 67%.
– average fanout = 133 (i.e, # of pointers in internal
node)
• Can often hold top levels in buffer pool:
– Level 1 = 1 page = 8 Kbytes
– Level 2 = 133 pages = 1 Mbyte
– Level 3 = 17,689 pages = 133 MBytes
• Suppose there are 1,000,000,000 data entries.
– H = log133(1000000000/132) < 4
– The cost is 5 pages read
19. 19
How to Insert a Data Entry into a
B+ Tree?
• Let’s look at several examples first.
20. 20
Inserting 16*, 8* into Example B+ tree
Root
17 24 30
13
2* 3* 5* 7* 8*
2* 5* 7*
3*
17 24 30
13
8*
You overflow
One new child (leaf node)
generated; must add one more
pointer to its parent, thus one more
key value as well.
14* 15* 16*
21. 21
Inserting 8* (cont.)
• Copy up the
middle value
(leaf split)
2* 3* 5* 7* 8*
5
Entry to be inserted in parent node.
(Note that 5 is
continues to appear in the leaf.)
s copied up and
13 17 24 30
You overflow!
5 13 17 24 30
22. 22
(Note that 17 is pushed up and only
appears once in the index. Contrast
Entry to be inserted in parent node.
this with a leaf split.)
5 24 30
17
13
Insertion into B+ tree (cont.)
5 13 17 24 30
• Understand
difference
between copy-up
and push-up
• Observe how
minimum
occupancy is
guaranteed in
both leaf and
index pg splits.
We split this node, redistribute entries evenly,
and push up middle key.
23. 23
Example B+ Tree After Inserting 8*
Notice that root was split, leading to increase in height.
2* 3*
Root
17
24 30
14* 15* 19* 20* 22* 24* 27* 29* 33* 34* 38* 39*
13
5
7*
5* 8*
24. 24
Inserting a Data Entry into a B+ Tree:
Summary
• 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)
• Redistribute entries evenly, put middle key in L2
• copy up middle key.
• Insert index entry pointing to L2 into parent of L.
• This can happen recursively
– 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.
26. 26
Delete 19* and 20*
2* 3*
Root
17
24 30
14* 16* 19* 20* 22* 24* 27* 29* 33* 34* 38* 39*
13
5
7*
5* 8*
22*
27* 29*
22* 24*
Have we still forgot something?
27. 27
Deleting 19* and 20* (cont.)
• Notice how 27 is copied up.
• But can we move it up?
• Now we want to delete 24
• Underflow again! But can we redistribute this time?
2* 3*
Root
17
30
14* 16* 33* 34* 38* 39*
13
5
7*
5* 8* 22* 24*
27
27* 29*
28. 28
Deleting 24*
• Observe the two leaf
nodes are merged, and
27 is discarded from
their parent, but …
• Observe `pull down’ of
index entry (below).
30
22* 27* 29* 33* 34* 38* 39*
2* 3* 7* 14* 16* 22* 27* 29* 33* 34* 38* 39*
5* 8*
30
13
5 17
New root
29. 29
Deleting a Data Entry from a B+ Tree:
Summary
• 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.
30. 30
Example of Non-leaf Re-distribution
• Tree is shown below during deletion of 24*. (What
could be a possible initial tree?)
• In contrast to previous example, can re-distribute entry
from left child of root to right child.
Root
13
5 17 20
22
30
14* 16* 17* 18* 20* 33* 34* 38* 39*
22* 27* 29*
21*
7*
5* 8*
3*
2*
31. 31
After Re-distribution
• Intuitively, entries are re-distributed by `pushing
through’ the splitting entry in the parent node.
• It suffices to re-distribute index entry with key 20;
we’ve re-distributed 17 as well for illustration.
14* 16* 33* 34* 38* 39*
22* 27* 29*
17* 18* 20* 21*
7*
5* 8*
2* 3*
Root
13
5
17
30
20 22
32. 32
Terminology
• Bucket Factor: the number of records which can
fit in a leaf node.
• Fan-out : the average number of children of an
internal node.
• A B+tree index can be used either as a primary
index or a secondary index.
– Primary index: determines the way the records are
actually stored (also called a sparse index)
– Secondary index: the records in the file are not
grouped in buckets according to keys of secondary
indexes (also called a dense index)
33. 33
Summary
• Tree-structured indexes are ideal for range-
searches, also good for equality searches.
• B+ tree is a dynamic structure.
– Inserts/deletes leave tree height-balanced; High fanout (F)
means depth rarely more than 3 or 4.
– Almost always better than maintaining a sorted file.
– Typically, 67% occupancy on average.
– If data entries are data records, splits can change rids!
• Most widely used index in database management
systems because of its versatility. One of the most
optimized components of a DBMS.
