How to Store a List in a DB Column

sql

database-normalization

data-relationships

database-performance

byNikita Barsukov·Oct 12, 2024

Here's the bottom line: normalize your database, establish separate tables for list items, and forge a one-to-many relationship. Storing user's phone numbers can quickly illustrate this concept:

-- For all the 'John Doe's out there
CREATE TABLE users (
    user_id INT PRIMARY KEY,
    username VARCHAR(255) NOT NULL
);

-- 'Cause you can never have too many pizzas delivered
CREATE TABLE phone_numbers (
    phone_id INT PRIMARY KEY,
    user_id INT,
    number VARCHAR(255),
    FOREIGN KEY (user_id) REFERENCES users(user_id)
);

-- Let us dance to the tune of phone numbers and pizzas
INSERT INTO users (user_id, username) VALUES (1, 'johndoe');
INSERT INTO phone_numbers (phone_id, user_id, number) VALUES 
(1, 1, '123-4567'), (2, 1, '234-5678');

This prevents data duplication and upholds data integrity, unlike clumsy single-column, comma-separated mess.

Dissecting Data Relationships

Disruption of data into digestible sections is fulfilling database normalization fundamentals, especially when dealing with objects in one-to-many or many-to-many relationships. Say, for an object FOO having a list of Fruit, take apart the data without crossing the normalization rules.

Got your data analyst hat on? Let's roll:

-- FOO table, because Foo Fighters rock!
CREATE TABLE FOO (
    foo_id INT PRIMARY KEY,
    /* Imagine this table as your playlist! */
);

-- Because an apple a day keeps the bugs away!
CREATE TABLE Fruits (
    fruit_id INT PRIMARY KEY,
    name VARCHAR(255) NOT NULL
);

-- 'Cause Jonny Appleseed knew all about relationships!
CREATE TABLE FOO_Fruits (
    foo_id INT,
    fruit_id INT,
    PRIMARY KEY (foo_id, fruit_id),
    FOREIGN KEY (foo_id) REFERENCES FOO(foo_id),
    FOREIGN KEY (fruit_id) REFERENCES Fruits(fruit_id)
);

For every FOO object jamming in your playlist, you should add rows to FOO_Fruits, each being a sweet relationship to appropriate Fruits. This harmony ensures scalability, maintainability, and referential integrity, forming the rhythm of high performance in the long haul. Say no to serialization of the list into a single column of binary or textual data. Neither your code nor your mixtape should ever sound boring!

Sculpting the Schema

Crafting a flexible database schema is the need of the hour. Your database should look like a carbon copy of the FOO object, including its three integers and variable-length list of Fruit. Through the magic of a junction table, the database isn't afraid of a Fruit list, no matter its length.

Now, who doesn't love a fruit salad? Think of the need to lookup individual fruits or ensuring that the fruit salad serves up a consistent order. Storing a serialized list as a single nvarchar(max) or text column can feel like peeling onions, with each layer revealing more complexity and hindered performance as data size grows.

Don't let tears cloud your judgment! Even considering serialization should only come in when your use case promises to pay off performance trade-offs. Remember, each language has a secret weapon to handle data escapes and queries on serialized data. Use it wisely!

Advanced Angles

Let's dive deeper to uncover more tidbits:

Data Integrity: Don't lazy around! Be diligent with primary and foreign key constraints to maintain data integrity.
Database Performance: The "single-column-all-data" approach is a big no! Choose a solution that ages like fine wine.
Flexibility: Your data structure is like the weather; it's bound to change. Choose normalization for sunny data handling.
Data Redundancy: A normalized schema stops at "No entry" for duplicate data while saying "Yes" to storage conservation.