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What data structure does mysql index generally use?

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2019-06-05 14:46:134844browse

MyISAM is the default storage engine for versions before MySQL 5.5. After 5.5, InnoDB begins to become the default storage engine for MySQL.

MyISAM uses B-Tree to implement primary key index, unique index and non-primary key index.

The non-primary key index in InnoDB uses the B-Tree data structure, while the primary key index uses the B-Tree.

What data structure does mysql index generally use?

B-Tree

B-tree (multi-way search tree, not binary) is a common data structure. Using the B-tree structure can significantly reduce the intermediate process experienced when locating records, thereby speeding up access. According to translation, B is usually considered to be the abbreviation of Balance. This data structure is generally used for database indexing and has high overall efficiency.

Performance(Recommended learning: MySQL video tutorial)

B-tree has the following characteristics:

1. Keywords The collection is distributed throughout the tree;

2. Any keyword appears and only appears in one node;

3. The search may end at a non-leaf node;

4. Its search performance is equivalent to a binary search in the complete set of keywords;

5. Automatic hierarchical control;

B Tree

Different storage engines may use different data structures for storage. InnoDB uses B Tree;

So what is B Tree?
B Tree is a variant of B-Tree required by the file system. The difference between an m-order B-tree and an m-order B-tree is:

B and B- (that is, B) are because the keywords on each node are different. One more, one less.

For B-tree, its node structure is the same as B-tree. The difference is the keyword of each node and the number of child nodes it can have. For example, in an m-order B-tree, each node can have at most m child nodes. Non-root nodes have at least [m/2] child nodes, and the number of keywords is one more than B-tree, which is [m/2]~m.

The differences between these two data structures for processing indexes:

1. The same key value will not appear multiple times in the B-tree, and it may appear in leaf nodes or non-leaf nodes. The keys of the B-tree will definitely appear in leaf nodes, and may also appear repeatedly in non-leaf nodes to maintain the balance of the B-tree.

2. Because the B-tree key position is uncertain and only appears once in the entire tree structure, although it can save storage space, it significantly increases the complexity of insertion and deletion operations. B-trees are a better compromise in comparison.

3. The query efficiency of B-tree is related to the position of the key in the tree. The maximum time complexity is the same as that of B-tree (when it is at the leaf node), and the minimum time complexity is 1 (when it is at the root node). The time complexity of B-tree is fixed for a certain built tree.

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