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B+ Tree

B+ tree - Wikipedia

B+ 樹背後的想法是內部節點可以有在預定範圍內的可變數目的子節點

因此，B+ 樹不需要像其他自平衡二叉搜尋樹那樣經常的重新平衡

m is order

For each node, m childs, m - 1 keys

Three kind of node: root node, internal node, leaf node

For children / subtree / pointer

Node maximum m children

Node minimum ceil(m / 2) children

For keys / elements

Node maximum m - 1 keys

Node minimum of ceil(m / 2) - 1 keys (except root node)

For example, m is 4:

3 key and 4 children pointer

|    | k0 |    | k1 |    | k2 |    |
| p0 |    | p1 |    | p2 |    | p3 |

所有 leaf node 鏈結成一個單鏈表

All key in leaf node

Left / right biasing​

Databases: left biasing and right biasing in B+ tree insertion

插入並且分裂時左邊 key 比較多還是右邊 key 比較多 (ceil vs floor in left right)

Compare element:

  • Left biasing use <= and >

  • Right biasing use < and >=

Index when split

  • Left biasing use left side max as index

  • Right biasing use right side min as index

Wiki use left biasing, but most of the example in internet use right biasing (also in school teaching)

Result of left / right biasing with same insert order can be different (even can be different in depth of tree)

Insertion 插入 (right biasing)​

5.29 B+ Tree Insertion | B+ Tree Creation example | Data Structure Tutorials - YouTube

節點已滿

  • Left have floor((m + 1) / 2), right have ceil((m + 1) / 2)
  • 取出右邊最小 element 作為 index 插入到 parent
  • if 滿 is in non-leaf node, index as parent, use successor replace

Example​

For m = 4 B+ tree

Insert 1, 3, 5, 7, 9, 2, 4, 6, 8, 10

Insert 7

It is [1, 3, 5, 7], medium between 3 and 5, by default it is right biasing, use 5 as index

Insert 9, 2

Insert 4

It is [1, 2, 3, 4], medium between 2 and 3, use 3 as index

Insert 6

It is [5, 6, 7, 9], medium between 6 and 7, use 7 as index

Insert 8

Insert 10

It is [7, 8, 9, 10], medium between 8 and 9, use 9 as index

In parent, It is [3, 5, 7, 9], medium between 5 and 7, use 7 as index (move as parent)

Be care in non-leaf node, the index will move upper, and the replace with successor

Deletion 刪除​

5.30 B+ Tree Deletion| with example |Data structure & Algorithm Tutorials - YouTube

Deletion from a B+ Tree

Half full mean ceil(m / 2) - 1

(1) 刪除 node 後仍然多於 ceil(m / 2) - 1 keys -> it is ok, just delete

(2) 刪除 node 後 less then ceil(m / 2) - 1 keys

  • Try and check 從 sibling node 兄弟節點 borrowing 借用, use successor as index
  • If can't (after 借用 sibling node will less than half full), merge (delete index, use smallest as index)

(3) 刪除 node 是 index

  • Use successor (replace by next node)

Check parent layer one by one until ok

Example​

Delete 9, 7, 8 in following B+ Tree

Online sketcher

9
3,5,7/11
1,2/3,4/5,6/7,8/9,10/11,12

Delete 9

[10] is less than half full, need borrow or merge

Sibling node [11, 12] will less than half full if borrowing

Merge sibling tree, delete index 11, then remaining is [10, 11, 12], index is 10

In [3, 5, 7] and [10], left side can borrow

7 go to parent, right side use 10 as index

Delete 7

[8] is less than half full

Sibling node is [10, 11, 12], can borrowing

Move 10 to left side, it is [8, 10]

Use successor 11 as index

Check parent, replace 7 with successor 8

Delete 8

It is [10], right side cannot borrowing, need merge

Merge, [10, 11, 12], delete index 11, use 10 as index

In [3, 5] and [10], left side cannot borrowing, need merge

Merge, [3, 5, 10], delete index 8

Visualization Tool / Simulator​

B-Sketcher (also for B+ Tree)

B+ Tree Visualization