Basic data structures such as red-black tree, B Tree, and B+Tree in Go language

With the advent of the big data era, data processing and storage have become inevitable problems in the computer field. In this regard, the optimization of data structures and algorithms becomes particularly important. This article will introduce several basic data structures commonly used in Go language-red-black tree, B Tree, and B Tree.

Red-black tree

The red-black tree is a self-balancing binary search tree. Its characteristic is that it uses two nodes with colors of black and red as the tree structure. The arrangement of black nodes and red nodes must meet the five properties of red-black trees:

1. Each node All have a color, either red or black.
2. The root node is black.
3. Each leaf node (NULL node) is black.
4. If a node is red, its child nodes must be black.
5. All paths from a node to all descendant nodes of that node contain the same number of black nodes.

The time complexity of inserting, deleting and searching elements in a red-black tree is O(log n), so the red-black tree is one of the most widely used basic data structures. In the Go language, you can use the tree in the container library to implement a red-black tree.

B Tree

B Tree is a multi-way balanced search tree and a self-balancing tree structure that can automatically maintain the balance of the tree. B Tree stores multiple information in a node, and each node stores a key value and a link to the root node of its subtree. B Tree has the following characteristics:

1. Each node can store multiple elements, not just one element.
2. The number of branches of all nodes is equal.
3. All leaf nodes are on one level.
4. Except for the root node, each node has at least M/2 children and at most M children.
5. Each node divides the range into M blocks through keys. Each block stores a pointer to a child, and elements are stored in the first M-1 blocks.
6. All leaf nodes are on the same level.

B Tree can reduce the number of disk accesses and improve data retrieval efficiency through multiple elements in the node, and is widely used in actual use.

B Tree

B Tree is a variant of B Tree, which mainly optimizes the number of disk I/O reads and writes of B Tree. It differs from B Tree in that the intermediate nodes of B Tree only store keys, not values, and all values ​​are stored in leaf nodes. Leaf nodes remain connected and in key order, making range-based queries easy to implement. B Tree has the following characteristics:

1. The elements stored in all nodes only exist in leaf nodes.
2. All leaf nodes are on the same layer.
3. Each node can store more elements.
4. Intermediate nodes only store keys, no values.
5. The elements in all leaf nodes maintain storage order, and each leaf node remains connected through a pointer chain.
6. The elements in all leaf nodes are adjacent and have close values.

Since the B Tree intermediate nodes only store keys, not values, the number of disk accesses can be reduced, and the intermediate nodes can be directly skipped when accessing the disk, which improves data retrieval efficiency.

By introducing several commonly used basic data structures such as red-black tree, B Tree, B Tree, etc., programmers in Go language can better understand and use various data structures in actual development, and improve the program operating efficiency.

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