MySQL and PostgreSQL: How to optimize table structures and indexes?

MySQL and PostgreSQL: How to optimize table structures and indexes?

Introduction:
In database design and application development, optimizing table structures and indexes is an important step to improve database performance and response speed. MySQL and PostgreSQL are two common relational database management systems. This article will introduce how to optimize table structures and indexes, using actual code examples in the two databases to illustrate.

1. Optimize table structure

1. Normalized data:
   Normalization is the core principle of database design. By decomposing data into multiple related tables, data can be avoided to the greatest extent. Redundant and inconsistent. For example, decomposing a table containing order and product information into an order table and a product table can improve the efficiency of data updates and queries.
2. Choose the appropriate data type:
   The choice of data type directly affects the storage space and query performance of the database. The appropriate data type should be selected based on actual needs to save storage space and improve query efficiency. For example, when storing date and time type data, you can use a date type (DATE) or a timestamp type (TIMESTAMP) instead of a string type (VARCHAR).
3. Avoid using too many NULL values:
   NULL values ​​require additional storage space and increase complexity when querying. When designing the table structure, you should try to avoid setting certain columns to allow NULL values ​​unless you really need to store NULL values.

2. Optimize indexes

1. Choose the appropriate index type:
   Both MySQL and PostgreSQL support multiple index types, such as B-tree index, hash index and Full text index. Choosing the appropriate index type can improve query efficiency based on the characteristics of the query. Generally speaking, B-tree indexes are suitable for range queries, hash indexes are suitable for equivalent queries, and full-text indexes are suitable for full-text searches.
2. Try to avoid too many indexes:
   The number of indexes will affect the performance of insert and update operations. If there are too many indexes in a table, data storage space and maintenance costs will increase. When designing indexes, you should select the necessary indexes based on actual needs and try to avoid too many redundant indexes.
3. Selection of clustered index:
   Clustered index is a special type of index that can store data on the leaf nodes of the index to improve query efficiency. In MySQL, you can set the primary key to a clustered index when creating a table; in PostgreSQL, you can use the CLUSTER command to create a clustered index on an existing table.

The following is a code example for table structure and index optimization in MySQL and PostgreSQL:

MySQL example:

-- 创建订单表
CREATE TABLE orders (
  order_id INT PRIMARY KEY,
  customer_id INT,
  order_date DATE,
  total_amount DECIMAL(10,2)
);

-- 创建产品表
CREATE TABLE products (
  product_id INT PRIMARY KEY,
  product_name VARCHAR(100),
  unit_price DECIMAL(10,2)
);

-- 创建订单产品表
CREATE TABLE order_products (
  order_id INT,
  product_id INT,
  quantity INT,
  PRIMARY KEY (order_id, product_id),
  FOREIGN KEY (order_id) REFERENCES orders(order_id),
  FOREIGN KEY (product_id) REFERENCES products(product_id)
);

-- 创建订单日期索引
CREATE INDEX idx_order_date ON orders(order_date);

-- 创建产品名称索引
CREATE INDEX idx_product_name ON products(product_name);

PostgreSQL example:

-- 创建订单表
CREATE TABLE orders (
  order_id SERIAL PRIMARY KEY,
  customer_id INT,
  order_date DATE,
  total_amount DECIMAL(10,2)
);

-- 创建产品表
CREATE TABLE products (
  product_id SERIAL PRIMARY KEY,
  product_name VARCHAR(100),
  unit_price DECIMAL(10,2)
);

-- 创建订单产品表
CREATE TABLE order_products (
  order_id INT,
  product_id INT,
  quantity INT,
  PRIMARY KEY (order_id, product_id),
  FOREIGN KEY (order_id) REFERENCES orders(order_id),
  FOREIGN KEY (product_id) REFERENCES products(product_id)
);

-- 创建订单日期索引
CREATE INDEX idx_order_date ON orders(order_date);

-- 创建产品名称索引
CREATE INDEX idx_product_name ON products(product_name);

-- 创建聚簇索引
CLUSTER orders USING idx_order_date;

Conclusion:
By optimizing the table structure and indexes, the performance and response speed of the database can be significantly improved. When designing the table structure, you should follow the principles of normalization, choose appropriate data types, and avoid excessive use of NULL values. When designing indexes, you should choose the appropriate index type, avoid too many indexes, and choose clustered indexes based on your needs. Use the code in the example to optimize table structures and indexes in MySQL and PostgreSQL.

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