MySQL database and Go language: How to perform efficient indexing?

As the amount of data continues to grow, database performance issues have increasingly become an urgent problem for small and large businesses. In order to manage and query data more efficiently, index technology is widely used in databases. How to create efficient indexes in MySQL database and Go language? The following will introduce it to you in detail.

1. Index establishment in MySQL database

MySQL is one of the most popular relational databases at present. Indexing is a key technology in the MySQL database for quickly locating and querying data. In MySQL, there are three common index types: B-Tree index, Hash index and full-text index. Among them, B-Tree index and Hash index have higher performance and are more widely used.

1. B-Tree index

B-Tree index is the most commonly used index type and can be applied to most data types in MySQL, such as integers and strings. , date, etc. The B-Tree index indexes data through the B-Tree structure. The data is stored on the leaf nodes according to the sorting rules, so that the data that needs to be queried can be found through fast binary search.

When establishing a B-Tree index on a table, you need to pay attention to the following points:

(1) Do not create an index on a column that is too large. You can choose to create an index on a column with a smaller character length. This can prevent the index file from becoming too large and reducing query performance.

(2) When using joint indexes, you need to pay attention to the sorting rules.

(3) For columns that are queried frequently, you can consider establishing a covering index, so as to avoid reading data pages from the disk.

2. Hash index

Hash index is an index type suitable for equivalent queries. It calculates the corresponding index value by hashing the query keywords. , and then find the corresponding data. The query speed of the Hash index is very fast, but when the amount of data increases, the hash conflict will become more serious, affecting the query efficiency.

When establishing a Hash index on a table, you need to pay attention to the following points:

(1) Hash indexes do not support range queries and can only perform equivalent queries.

(2) Hash index is usually used for high-speed data access, such as cache tables, etc.

(3) The storage space of Hash index is smaller than that of B-Tree index, which is suitable for scenarios with frequent reading and writing.

2. Index creation in Go language

Go language is an efficient, safe, and simple programming language. It is one of the preferred languages ​​for developing high-concurrency and high-performance applications. In Go language, you can use data types such as Map and Slice for data indexing. The following is how to use Map:

1. Map

Map is a reference type. Similar to a dictionary in Python. The key-value pairs in Map are unordered. The process of obtaining values ​​through keys is called "lookup". You can use map[key] to check. The following is an example of using Map for index lookup:

// 定义一个Map
age := make(map[string]int)

// 将键值对存入Map
age["Tom"] = 18
age["Jack"] = 20
age["Mary"] = 19

// 通过键查阅值
fmt.Println(age["Tom"]) // 输出 18

2. Slice

Slice is a dynamic array, which is similar to an array, but the length can change dynamically. In Go language, you can use Slice for data indexing. The query process of Slice is actually to traverse Slice to find the target data. The following is an example of using Slice for index query:

// 定义一个Slice
age := []int{18, 20, 19}

// 通过索引查阅值
fmt.Println(age[0]) // 输出 18

3. How to create efficient indexes

In practical applications, in order to To improve query efficiency, the table is generally indexed. However, the more indexes you create, the better. Too many indexes will take up a lot of disk space and reduce database performance. Therefore, how to perform efficient indexing is very important.

In the MySQL database, you can create efficient indexes through the following methods:

(1) For frequently used query columns, it is recommended to create indexes.

(2) Combine indexes in a fixed way on the access fields.

(3) It is recommended to create indexes for fields with relatively high cardinality and combined fields of query conditions at the same time.

(4) The joint index needs to standardize the sorting rules.

In the Go language, efficient indexing can be achieved through the following methods:

(1) For data that requires frequent queries, it is recommended to use Map for indexing.

(2) For data that needs to be added, deleted, modified, and checked, it is recommended to use Slice for indexing.

(3) When using data types, you should pay attention to selecting the data type suitable for the scenario.

To sum up, indexing is an important technology for efficient database query and management of data. Whether in MySQL database or Go language, we need to pay attention to the efficiency and rationality of index establishment in order to make the database more efficient. Work.

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