Data quality inspection can be achieved through constraints, scripts, triggers and foreign keys. 1. Define key field integrity constraints, such as NOT NULL, UNIQUE, and CHECK to ensure the required, uniqueness and value range; 2. Establish a regular verification script to detect null values, illegal formats and logical errors, and execute them through timed tasks; 3. Use triggers to automatically intercept abnormal data before insertion or update to improve real-time verification capabilities; 4. Use foreign key constraints to ensure the consistency of multi-table associated data to prevent "orphan data".

MySQL data quality inspection and verification are actually not that complicated, but they are easily overlooked. Many people only care about whether the data can be stored and found out, but ignore whether the data itself is "clean" and "accurate". When I really encountered a problem, I found that some fields contained empty values, format errors, repeated records, and even logical contradictions. Therefore, establishing a basic data quality inspection mechanism is an important part of maintaining the healthy operation of the database.

Let’s talk about how to do it from a few practical perspectives.

1. Define the integrity constraints of the key fields

The first step in data quality is to ensure that the key fields are not empty or illegal. MySQL provides many built-in constraints, such as NOT NULL , UNIQUE , CHECK (in supported versions), which can be used to force fields to meet basic requirements.

• NOT NULL : Used for required fields, such as user ID, order number, etc.
• UNIQUE : Prevents repeated insertion of the same value, such as username, email address, etc.
• CHECK : Limit the value range of fields, for example, gender can only be male/female, age cannot be negative.

For example, if you have an order table, the order amount should be a positive number:

 CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    amount DECIMAL(10,2) CHECK (amount > 0)
);

This can avoid dirty data from being stored, which is much easier than cleaning afterwards.

2. Create regular verification scripts

Even if there are constraints, there is no guarantee that there will be no foolproof. Sometimes it is because the application layer bypasses the inspection, or the historical data itself has problems. At this time, you need to scan exception data regularly through SQL scripts.

You can write some simple query statements to check:

• Whether there is a null value:

   SELECT * FROM users WHERE email IS NULL;

• Is there an illegal format:

   SELECT * FROM users WHERE NOT REGEXP_LIKE(email, '^[A-Za-z0-9._% -] @[A-Za-z0-9.-] \\.[A-Za-z]{2,}$');

• Is there any logical error, such as the date of birth is greater than the current time:

   SELECT * FROM users WHERE birth_date > CURDATE();

It is recommended to make these scripts into timed tasks (such as using cron or scheduling tools), run once a week, and deal with problems in a timely manner when they are found.

3. Use triggers to automatically intercept exception data

In addition to manual checking, you can also use a trigger (Trigger) to perform automatic verification before inserting or updating data. For example, you want to check whether the email address is legal every time you insert a new user:

 DELIMITER //
CREATE TRIGGER before_user_insert
BEFORE INSERT ON users
FOR EACH ROW
BEGIN
  IF NEW.email NOT REGEXP '^[A-Za-z0-9._% -] @[A-Za-z0-9.-] \\.[A-Za-z]{2,}$' THEN
    SIGNAL SQLSTATE '45000' SET MESSAGE_TEXT = 'Invalid email format';
  END IF;
END//
DELIMITER ;

This will prevent content that does not comply with the specifications before the data enters the database, avoiding subsequent troubles.

However, it should be noted that triggers will increase write overhead and are not suitable for too complex logic. And if an error occurs, it will affect the entire insertion or update operation and needs to be used with caution.

4. Use foreign key constraints to ensure consistency of associated data

Data quality problems often occur between multiple tables. For example, an order refers to a non-existent user ID, which is a typical "orphan data".

MySQL supports foreign key constraints, which can automatically prevent this inconsistency:

 CREATE TABLE orders (
    order_id INT PRIMARY KEY,
    user_id INT,
    FOREIGN KEY (user_id) REFERENCES users(user_id)
);

In this way, if you try to insert a user_id that does not exist in the user table, it will be rejected.

But it should be noted that not all storage engines support foreign keys (MyISAM does not support them), and it is best to use InnoDB in a unified manner.

Basically that's it. Data quality control does not need to be complicated at the beginning. It starts with the field definition and then gradually improves it with scripts and triggers. The key is to develop habits and don’t wait until there is a problem with the data before thinking about remedies.

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