對於後端開發人員來說，存取資料庫至關重要。
核心使用者資料通常安全地儲存在 MySQL 或 Oracle 等資料庫中。
日常任務經常涉及建立資料庫和表格來滿足業務需求，但建立表格的頻率要高得多。
本文將重點討論表創建，因為忽略關鍵細節可能會導致部署後維護成本高昂的問題。
順便說一句，糟糕的資料庫設計實踐也會導致您的 API 在高並發時響應緩慢。下圖是使用 EchoAPI 工具對 API 進行效能測試的結果。
今天，我們來討論在資料庫中建立表格的 18 個技巧。
本文中提到的許多細節都源自於我自己在工作中遇到的經驗和挑戰，希望對您有幫助。
建立表格、欄位和索引時，給它們一個好名字非常重要。
名稱是表格、欄位和索引的門面，給人留下第一印象。
好的名字簡潔、具有自我描述性，讓溝通和維護更容易。
糟糕的名字會含糊不清、令人困惑，導致混亂和沮喪。
不好的例子：
像 abc、abc_name、name、user_name_123456789 這樣的欄位名稱會讓你感到困惑。
好例子：
欄位名稱為 user_name。
溫馨提醒：名字也不宜太長，最好控制在 30 個字元以內。
名字最好使用小寫字母，因為這樣比較容易視覺上閱讀。
不好的例子：
像 PRODUCT_NAME、PRODUCT_name 這樣的欄位名稱並不直觀。大小寫混合閱讀起來不太舒服。
好例子：
欄位名稱作為product_name看起來比較舒服。
為了更好地理解，名稱通常可能包含多個單字。
多個單字之間應該使用什麼分隔符號？
不好的例子：
不建議使用諸如productname、productName、product name 或product@name 之類的欄位名稱。
好例子：
欄位名稱為product_name。
強烈建議在單字之間使用底線 _。
對於表名稱，建議使用有意義、簡潔的名稱以及業務前綴。
訂單相關的表，在表名前加上order_，如order_pay、order_pay_detail。
對於與產品相關的表，前綴為product_，例如product_spu、product_sku。
這種做法有助於快速將與同一業務相關的表格分類在一起。
另外，如果非訂單業務可能需要建立名為pay的表，可以輕鬆區分為finance_pay，避免名稱衝突。
欄位名稱具有最大的靈活性，但很容易導致混亂。
例如，在一個表格中使用標誌來表示狀態，而在另一個表格中使用狀態可能會造成不一致。
建議標準化代表狀態的狀態。
當一個表格使用另一個表格的主鍵時，在欄位名稱結尾追加_id或_sys_no，例如product_spu_id或product_spu_sys_no。
另外，標準化建立時間為create_time，修改時間為update_time，刪除狀態固定為delete_status。
其他公共欄位也應該在不同的表之間保持統一的命名約定，以提高清晰度。
資料庫中有多種類型的索引，包括主鍵、常規索引、唯一索引和複合索引。
表通常有一個主鍵，通常命名為 id 或 sys_no。
常規索引和複合索引可以使用 ix_ 前綴，例如 ix_product_status。
唯一索引可以使用ux_前綴，例如ux_product_code。
設計表格時，選擇欄位類型有足夠的自由度。
時間格式欄位可以是日期、日期時間或時間戳記等
字元資料型別包括varchar、char、text等
數字型別包括 int、bigint、smallint 和tinyint。
選擇合適的欄位類型至關重要。
Overestimating types (e.g., using bigint for a field that will only store values between 1 and 10) wastes space; tinyint would suffice.
Conversely, underestimating (e.g., using int for an 18-digit ID) will lead to data storage failures.
Here are some principles for choosing field types:
After defining field names and selecting appropriate field types, the focus should shift to field lengths, like varchar(20) or bigint(20).
What does varchar indicate in terms of length—bytes or characters?
The answer: In MySQL, varchar and char represent character length, while most other types represent byte length.
For example, bigint(4) specifies the display length, not the storage length, which remains 8 bytes.
If the zerofill property is set, numbers less than 4 bytes will be padded, but even if filled, the underlying data storage remains at 8 bytes.
When designing a table, it’s crucial to limit the number of fields.
I’ve seen tables with dozens or even hundreds of fields, leading to large data volumes and low query efficiency.
If this situation arises, consider splitting large tables into smaller ones while retaining common primary keys.
As a rule of thumb, keep the number of fields per table below 20.
Create a primary key when setting up a table.
Primary keys inherently come with primary key indexes, making queries more efficient, as they don’t require additional lookups.
In a single database, primary keys can use AUTO_INCREMENT for automatic growth.
For distributed databases, particularly in sharded architectures, it’s best to use external algorithms (like Snowflake) to ensure globally unique IDs.
Moreover, keep primary keys independent of business values to reduce coupling and facilitate future expansions.
However, for one-to-one relationships, such as user tables and user extension tables, it’s acceptable to directly use the primary key from the user table.
Before MySQL 8, the default storage engine was MyISAM; from MySQL 8 onward, it’s now InnoDB.
Historically, there was much debate about which storage engine to choose.
MyISAM separates index and data storage, enhancing query performance but lacks support for transactions and foreign keys.
InnoDB, while slightly slower in queries, supports transactions and foreign keys, making it more robust.
It was previously advised to use MyISAM for read-heavy and InnoDB for write-heavy scenarios.
However, optimizations in MySQL have reduced performance differences, so using the default InnoDB storage engine in MySQL 8 and later is recommended without any additional modifications.
When creating fields, decide whether they can be NULL.
Defining fields as NOT NULL whenever possible is advisable.
Why?
In InnoDB, storing NULL values requires extra space, and they can also lead to index failures.
NULL values can only be queried using IS NULL or IS NOT NULL, as using = always returns false.
Thus, define fields as NOT NULL wherever feasible.
However, when a field is directly defined as NOT NULL, and a value is forgotten during input, it will prevent data insertion.
This can be an acceptable situation when new fields are added and scripts run before deploying new code, leading to errors without default values.
For newly added NOT NULL fields, setting a default value is crucial:
ALTER TABLE product_sku ADD COLUMN brand_id INT(10) NOT NULL DEFAULT 0;
Foreign keys in MySQL serve to ensure data consistency and integrity.
For instance:
CREATE TABLE class ( id INT(10) PRIMARY KEY AUTO_INCREMENT, cname VARCHAR(15) );
This creates a class table.
Then, a student table can be constructed that references it:
CREATE TABLE student( id INT(10) PRIMARY KEY AUTO_INCREMENT, name VARCHAR(15) NOT NULL, gender VARCHAR(10) NOT NULL, cid INT, FOREIGN KEY (cid) REFERENCES class(id) );
Here, cid in the student table references id in the class table.
Attempting to delete a record in student without removing the corresponding cid record in class will raise a foreign key constraint error:
a foreign key constraint fails.
Thus, consistency and integrity are preserved.
Note that foreign keys are only usable with the InnoDB storage engine.
If only two tables are linked, it might be manageable, but with several tables, deleting a parent record requires synchronously deleting many child records, which can impact performance.
Thus, for internet systems, it is generally advised to avoid using foreign keys to prioritize performance over absolute data consistency.
In addition to foreign keys, stored procedures and triggers are also discouraged due to their performance impact.
When creating tables, beyond specifying primary keys, it’s essential to create additional indexes.
For example:
CREATE TABLE product_sku( id INT(10) PRIMARY KEY AUTO_INCREMENT, spu_id INT(10) NOT NULL, brand_id INT(10) NOT NULL, name VARCHAR(15) NOT NULL );
This table includes spu_id (from the product group) and brand_id (from the brand table).
In situations that save IDs from other tables, a regular index can be added:
CREATE TABLE product_sku ( id INT(10) PRIMARY KEY AUTO_INCREMENT, spu_id INT(10) NOT NULL, brand_id INT(10) NOT NULL, name VARCHAR(15) NOT NULL, KEY `ix_spu_id` (`spu_id`) USING BTREE, KEY `ix_brand_id` (`brand_id`) USING BTREE );
Such indexes significantly enhance query efficiency.
However, do not create too many indexes as they can hinder data insertion efficiency due to additional storage requirements.
A single table should ideally have no more than five indexes.
If the number of indexes exceeds five during table creation, consider dropping some regular indexes in favor of composite indexes.
Also, when creating composite indexes, always apply the leftmost matching rule to ensure the indexes are effective.
For fields with high duplication rates (like status), avoid creating separate regular indexes. MySQL may skip the index and choose a full table scan instead if it’s more efficient.
I’ll address index inefficiency issues in a separate article later, so let’s hold off on that for now.
The range of types available for time fields in MySQL is fairly extensive: date, datetime, timestamp, and varchar.
Using varchar might be for API consistency where time data is represented as a string.
However, querying data by time ranges can be inefficient with varchar since it cannot utilize indexes.
Date is intended only for dates (e.g., 2020-08-20), while datetime and timestamp are suited for complete date and time.
There are subtle differences between them.
Timestamp: uses 4 bytes and spans from 1970-01-01 00:00:01 UTC to 2038-01-19 03:14:07. It’s also timezone-sensitive.
Datetime: occupies 8 bytes with a range from 1000-01-01 00:00:00 to 9999-12-31 23:59:59, independent of time zones.
Using datetime to save date and time is preferable for its wider range.
As a reminder, when setting default values for time fields, avoid using 0000-00-00 00:00:00, which can cause errors during queries.
MySQL provides several types for floating-point numbers: float, double, decimal, etc.
Given that float and double may lose precision, it’s recommended to use decimal for monetary values.
Typically, floating numbers are defined as decimal(m,n), where n represents the number of decimal places, and m is the total length of both integer and decimal portions.
For example, decimal(10,2) allows for 8 digits before the decimal point and 2 digits after it.
During table structure design, you may encounter fields needing to store variable data values.
For example, in an asynchronous Excel export feature, a field in the async task table may need to save user-selected query conditions, which can vary per user.
Traditional database fields don’t handle this well.
Using MySQL’s json type enables structured data storage in JSON format for easy saving and querying.
MySQL also supports querying JSON data by field names or values.
Unique indexes are frequently used in practice.
You can apply unique indexes to individual fields, like an organization’s code, or create composite unique indexes for multiple fields, like category numbers, units, specifications, etc.
Unique indexes on individual fields are straightforward, but for composite unique indexes, if any field is NULL, the uniqueness constraint may fail.
Another common issue is having unique indexes while still producing duplicate data.
Due to its complexity, I’ll elaborate on unique index issues in a later article.
When creating unique indexes, ensure that none of the involved fields contain NULL values to maintain their uniqueness.
MySQL supports various character sets, including latin1, utf-8, utf8mb4, etc.
Here’s a table summarizing MySQL character sets:
| Character Set | Description | Encoding Size | Notes |
|---|---|---|---|
| latin1 | Encounters encoding issues; rarely used in real projects | 1 byte | Limited support for international characters |
| utf-8 | Efficient in storage but cannot store emoji | 3 bytes | Suitable for most text but lacks emoji support |
| utf8mb4 | Supports all Unicode characters, including emoji | 4 bytes | Recommended for modern applications |
It’s advisable to set the character set to utf8mb4 during table creation to avoid potential issues.
When creating tables in MySQL, the COLLATE parameter can be configured.
For example:
CREATE TABLE `order` ( `id` BIGINT NOT NULL AUTO_INCREMENT, `code` VARCHAR(20) COLLATE utf8mb4_bin NOT NULL, `name` VARCHAR(30) COLLATE utf8mb4_bin NOT NULL, PRIMARY KEY (`id`), UNIQUE KEY `un_code` (`code`), KEY `un_code_name` (`code`,`name`) USING BTREE, KEY `idx_name` (`name`) ) ENGINE=InnoDB AUTO_INCREMENT=5 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_bin;
The collation determines how character sorting and comparison are conducted.
Character collation depends on the character set, which for utf8mb4 would also start with utf8mb4_. Common types include utf8mb4_general_ci and utf8mb4_bin.
The utf8mb4_general_ci collation is case-insensitive for alphabetical characters, while utf8mb4_bin is case-sensitive.
This distinction is important. For example, if the order table contains a record with the name YOYO and you query it using lowercase yoyo under utf8mb4_general_ci, it retrieves the record. Under utf8mb4_bin, it will not.
Choose collation based on the actual business needs to avoid confusion.
Special attention is warranted for fields that consume substantial storage space, such as comments.
A user comment field might require limits, like a maximum of 500 characters.
Defining large fields as text can waste storage, thus it’s often better to use varchar for better efficiency.
For much larger data types, like contracts that can take up several MB, it may be unreasonable to store directly in MySQL.
Instead, such data could be stored in MongoDB, with the MySQL business table retaining the MongoDB ID.
To enhance performance and query speed, some fields can be redundantly stored.
For example, an order table typically contains a userId to identify users.
However, many order query pages also need to display the user ID along with the user’s name.
If both tables are small, a join is feasible, but for large datasets, it can degrade performance.
In that case, creating a redundant userName field in the order table can resolve performance issues.
While this adjustment allows direct querying from the order table without joins, it requires additional storage and may lead to inconsistency if user names change.
Therefore, carefully evaluate if the redundant fields strategy fits your particular business scenario.
When designing tables, ensure to add clear comments for tables and associated fields.
For example:
CREATE TABLE `sys_dept` ( `id` BIGINT NOT NULL AUTO_INCREMENT COMMENT 'ID', `name` VARCHAR(30) NOT NULL COMMENT 'Name', `pid` BIGINT NOT NULL COMMENT 'Parent Department', `valid_status` TINYINT(1) NOT NULL DEFAULT 1 COMMENT 'Valid Status: 1=Valid, 0=Invalid', `create_user_id` BIGINT NOT NULL COMMENT 'Creator ID', `create_user_name` VARCHAR(30) NOT NULL COMMENT 'Creator Name', `create_time` DATETIME(3) DEFAULT NULL COMMENT 'Creation Date', `update_user_id` BIGINT DEFAULT NULL COMMENT 'Updater ID', `update_user_name` VARCHAR(30) DEFAULT NULL COMMENT 'Updater Name', `update_time` DATETIME(3) DEFAULT NULL COMMENT 'Update Time', `is_del` TINYINT(1) DEFAULT '0' COMMENT 'Is Deleted: 1=Deleted, 0=Not Deleted', PRIMARY KEY (`id`) USING BTREE, KEY `index_pid` (`pid`) USING BTREE ) ENGINE=InnoDB AUTO_INCREMENT=1 DEFAULT CHARSET=utf8mb4 COMMENT='Department';
Detailed comments clarify the purpose of tables and fields.
Particularly for fields representing statuses (like valid_status), it immediately conveys the intent behind the data, such as indicating valid versus invalid.
Avoid situations where numerous status fields exist without comments, leading to confusion about what values like 1, 2, or 3 signify.
Initially, one might remember, but after a year of operation, it’s easy to forget, potentially leading to significant pitfalls.
Thus, when designing tables, meticulous commenting and regular updates of these comments are essential.
That wraps up the technical section of this article,If you have a different opinion, let me know?.
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