Indexes in MySQL are one of the key factors in optimizing database performance. When designing and implementing the data structure of indexes, MySQL chose B+ Tree As the main storage structure. This choice performs well in terms of query performance, disk I/O efficiency, etc. However, B+ tree is not the only index implementation solution. Common alternative data structures include Hash Table,Binary Tree,B-Tree and Jump tableThis article will analyze the characteristics of these data structures in detail, compare their advantages and disadvantages, and focus on explaining why MySQL ultimately chose the B+ tree.

1. The relationship between database index and data structure

In the database,indexIt is a data structure used to quickly find data, which can significantly improve data query performance. In large-scale data query scenarios, choosing a suitable data structure directly determines the efficiency of the index. An ideal index structure should have the following characteristics:

• Find efficient: Can quickly find specific data.
• Compact storage: Store more data in as little disk space as possible.
• Low maintenance cost：Data structures can remain efficient when inserted and deleted.
• Support range query：Database queries include not only exact queries, but also often range queries, such as BETWEEN operate.

To achieve these goals, the database system evaluated a variety of data structures and ultimately chose the B+ tree. Next, we will introduce the characteristics, advantages and disadvantages of these data structures and their applicable scenarios one by one.

2. Characteristics and applications of common data structures

1. Hash Table

Features

• accomplish:passHash functionsMap data to different storage locations, with one-to-one correspondence between keys and values.
• Query time complexity: Under ideal circumstances, the query time complexity is O(1), which is extremely efficient.
• Range queries are not supported: Hash tables can only be used for exact queries and cannot effectively support range queries.

Pros and Cons

• advantage: Fast query and insertion speed, especially suitable for equal value queries.
• shortcoming: Hash tables do not support sequential access, so range queries are not possible.
• Applicable scenarios: Equivalence query, such as user ID query; for frequent insertion and deletion operations, hash tables have better performance.

Summarize

In database index design, hash tables are suitable for scenarios that require efficient and accurate searches, but due to the lack of support for sequential and range queries, MySQL did not choose hash tables as the primary index structure.

2. Binary Tree

Features

• accomplish：Each node has at most two child nodes, namely the left child node and the right child node.
• Query time complexity: Ideally it is O(log n), but it degenerates to O(n) in unbalanced cases.
• A balancing act: For unbalanced binary trees, additional balancing operations are required (such as red-black trees and AVL trees).

Pros and Cons

• advantage: Supports range query, and the tree structure supports sequential access.
• shortcoming：The insertion and deletion operations are more complex and the maintenance cost is higher; when the data scale is large, the binary tree level is deeper, resulting in an increase in disk I/O operations.
• Applicable scenarios: Suitable for small-scale data sets with relatively simple structures.

Summarize

When the amount of data is large, the binary tree will degenerate into a linked list, which reduces the query efficiency and is not easy to implement in disk storage. For large database indexes, it is not suitable as an index data structure.

3. B-Tree

Features

• accomplish：Multi-branch balanced tree, non-leaf nodes can store data. Each node contains multiple key values to maintain a balanced structure.
• Query time complexity: O(log n).
• Support sequential storage: The nodes of the B-tree store data in sequence and support range queries.

Pros and Cons

• advantage：The balanced multi-branch structure has a shallow hierarchy and fast search speed, which is suitable for the management of large-scale data.
• shortcoming: Since data is stored on all nodes, query operations will cause a lot of disk I/O.
• Applicable scenarios: B-tree is suitable for scenarios that require sequential search and range search, and is more commonly used in file system indexes.

Summarize

Although B-tree is suitable for range query, it stores data on each node, increasing the number of disk I/Os and reducing query efficiency. This is why B-tree is not the preferred choice in database index design.

4. Skip List

Features

• accomplish: On the basis of the ordered linked list, multiple layers of indexes are added. Each layer of index can span multiple nodes to speed up the query.
• Query time complexity: O(log n).
• Support range query: The data in the jump table is ordered, supporting efficient range queries.

Pros and Cons

• advantage: It has a simple structure and supports fast search, insertion, and deletion; it can perform range queries efficiently.
• shortcoming: Multi-layer indexes of skip table nodes will take up more space; suitable for data management in memory, and average performance on disk.
• Applicable scenarios: Suitable for memory-based KV databases, such as Redis.

Summarize

Skip tables are suitable for index structures in memory databases, but they are not selected as the main index structure in MySQL due to their low space efficiency and poor disk performance.

5. B+ Tree

Features

• accomplish：B+ tree is a variant of B tree, all data is stored only inLeaf Node, non-leaf nodes only store key values as indexes. Leaf nodes are connected through linked lists to support sequential access.
• Query time complexity: O(log n).
• Supports sequential and range queries: The leaf nodes of the B+ tree are arranged in sequence, which facilitates sequential scanning and range queries.

Pros and Cons

• advantage：The hierarchy is shallow, the data is stored in the leaf nodes, and the non-leaf nodes are only used for indexing, which reduces disk I/O operations. The leaf nodes of the linked list structure support efficient range queries.
• shortcoming: The structure is relatively complex and the maintenance cost is relatively high.
• Applicable scenarios: Suitable for disk storage management of large-scale data, especially database indexes.

Summarize

The B+ tree structure is suitable for disk storage, has high query efficiency, supports range queries, and takes both space and performance into consideration. It is the best choice for MySQL index structure.

3. Why does MySQL choose B+ tree as the index structure?

Through the analysis of the above data structure, we can summarize the unique advantages of B+ tree in database index design.

1. High disk I/O efficiency

The amount of data in a database is usually very large, and it is not practical to load it completely into memory, so the index structure needs to interact with the disk frequently. The structure of the B+ tree allows data to be stored inLeaf Node, non-leaf nodes are only used as indexes, taking up less space, and can load more nodes in memory, reducing the number of disk I/Os. In addition, the multi-branch structure of the B+ tree makes the hierarchy shallow, greatly improving the search efficiency.

2. Support range query

The leaf nodes of the B+ tree are connected by linked lists and can be directly scanned sequentially. This feature is very suitable forRange QueryDemand, such as BETWEEN Operations can quickly traverse the data set that meets the conditions. In contrast, hash tables and skip lists cannot efficiently support range queries and are therefore not suitable for database indexes.

3. Good cache hit rate

B+ tree nodes are stored in layers, and non-leaf nodes only store key values, not data. This design allows MySQL to load more index nodes into memory, improve cache hit rate, and further speed up search. Moreover, the multi-layer structure of the B+ tree is suitable for loading indexes into caches at different levels under large data volumes, reducing the frequency of disk reads.

4. Facilitates sequential and batch insertion

B+ tree supports sequential insertion and batch insertion operations.

And because of the stable structure, the insertion operation does not need to frequently perform complex balance adjustment operations due to imbalance like the binary tree, and the maintenance cost is low. The storage characteristics of the MySQL index enable it to effectively maintain the stability of the structure during frequent data writing and deletion.

4. Conclusion

MySQL chooses B+ tree as the index structure based on comprehensive considerations of index efficiency, data volume adaptability, disk I/O efficiency, and range query requirements. The multi-layer storage and ordered linked list structure of B+ tree give it significant performance advantages in large data volume queries, making it an ideal choice for supporting relational database indexes.

Understanding the data structure design principles of MySQL indexes can help developers better utilize indexes in practical applications and improve query performance. At the same time, choosing appropriate query and sorting schemes and using indexes reasonably can further optimize database query efficiency.

5. Reference Links

1. MySQL official documentation
2. Data Structure and Algorithm Overview - B+ Tree
3. MySQL B+ tree index principle
4. Relationship between hash tables and indexes