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Start with the data and rules, not the screens
List the entities the application needs to represent—such as customers, products, orders, and categories—and distinguish them from attributes and relationships. A user-interface screen is not automatically a table: one screen may display data from several tables, while a single entity may appear on multiple screens.
For each entity, write down its facts and rules before choosing columns. Ask which values are required, which must be unique, which states are allowed, and which relationships must exist. A product may belong to a category; an order may belong to a customer; an order may contain several products.
Sketch one-to-many relationships directly and represent many-to-many relationships with a linking table. For example, if an order can contain many products and a product can appear on many orders, use an order_items table to represent each order-product association. Avoid packing multiple independently repeating values into one field, such as a comma-separated list of product IDs: those values are difficult to validate, join, update, and index as separate facts.
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Choose a primary key that identifies the row
Every table should have a clear row identity. A primary key enforces uniqueness and entity integrity; its columns cannot be null. In SQL Server, a primary key creates a unique index, and PostgreSQL 18 likewise creates a unique B-tree index for a primary key. See Microsoft Learn’s SQL Server key-constraint documentation and PostgreSQL 18’s constraints documentation.
Choose an identifier that remains dependable as the entity’s descriptive attributes change. A customer’s email address, for instance, may be unique today but change later; using it as the primary key makes that change ripple into every reference. A generated identifier is often simpler for an entity, while a genuinely stable natural identifier can also be appropriate when the domain supplies one. The right choice depends on the domain and on how other tables will reference the row.
When a composite key fits
A composite primary key uses more than one column when the combination, rather than either value alone, identifies a row. In a linking table, the pair (order_id, product_id) may identify one order-product association if each product can appear only once per order. If the same product can occur on multiple distinct lines, that pair is not unique; the schema needs a line identifier or another column that distinguishes those lines. Tables that reference a composite key must carry and match all of its key columns, so consider that complexity before choosing one.
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Represent relationships with foreign keys
A foreign key makes the database reject a reference to a row that does not exist in the referenced table. PostgreSQL describes the rule this way: “A foreign key constraint specifies that the values in a column (or a group of columns) must match the values appearing in some row of another table.” PostgreSQL 18 documentation covers the constraint and its behavior.
For example, an order’s customer_id can reference a customer’s primary key. That keeps an order from pointing to a nonexistent customer even if data is inserted or changed outside the application code. Foreign keys also document the relationship for future developers and database tools.
Decide what deletion or key changes mean
Choose the foreign-key action to reflect the business rule. Restricting or rejecting deletion protects a referenced row while dependent records exist. Cascading deletion removes dependent rows automatically, which may be appropriate for tightly owned data but can destroy more than intended if used casually. Other supported actions may be suitable when the relationship should be cleared or adjusted. SQL Server documents foreign-key cascade actions and their role in preserving valid references in its primary and foreign key constraints guidance. Confirm the available actions and exact semantics in the chosen engine.
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Normalize related facts to prevent avoidable duplication
Normalization is a way to organize facts so that each is stored in an appropriate place and changes do not require correcting repeated copies. Suppose each product row repeats a category name and a category description. Renaming the category then requires updating every product; missed rows leave contradictory data. Instead, store category details once in a categories table and let products refer to it by key. Microsoft’s Database design basics explains normalization and illustrates separating category details from products.
Normalization is not a goal detached from the application. Start with clear, non-duplicated facts and then assess the actual query patterns. Any deliberate denormalization should have a concrete reason and a plan for keeping duplicated values consistent; do not denormalize merely because a generic rule says it will be faster.
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Use types, nullability, and constraints to express the domain
Columns should represent the kind of value the application actually stores. A timestamp, money amount, phone number, and status are not interchangeable strings or numbers: their formats, valid operations, and rules differ. Select types supported by the target database, decide whether each value is required, and define allowed values or ranges where the domain is known.
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- NOT NULL: Use it when a value is required for every row, rather than relying on application screens to always supply it.
- UNIQUE: Enforce a business rule such as a unique account identifier at the database level when that rule applies.
- CHECK: Restrict values to a valid range or set when the engine’s check-constraint behavior supports the intended rule.
- DEFAULT: Supply a database default only when it accurately represents the domain rule; do not use a default to conceal missing or unknown information.
- PRIMARY KEY and FOREIGN KEY: Use them for row identity and required relationships, respectively.
Constraint syntax and behavior vary by product and version. For example, the MySQL 8.4 CREATE TABLE reference documents MySQL’s table and constraint definitions, while PostgreSQL 18’s constraint documentation describes PostgreSQL rules. Consult the documentation for the database version that will run the schema, not just a generic SQL example.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Add indexes for the workload, not by habit
A primary key commonly has a supporting unique index created by the database. A foreign key, however, does not guarantee a corresponding index in every engine. SQL Server’s documentation explicitly says it does not automatically create an index for a foreign key; it notes that an index is often useful when those columns are used for joins or checks. Do not assume identical behavior in PostgreSQL or MySQL—check the selected engine’s documentation.
Consider additional indexes for columns used frequently in important filters, joins, ordering, or uniqueness rules. The useful index depends on the query: a multi-column index may help a recurring combination of predicates, while an index on a rarely queried column may add cost without meaningful benefit. Indexes consume storage and can add work to inserts, updates, and deletes, so indexing every column is not a sound default.
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Use representative queries and their execution plans to guide decisions. Microsoft’s SQL Server Index Architecture and Design Guide discusses index structures and design considerations. It is guidance for SQL Server, not a universal index recipe or a performance guarantee for other engines.
Validate the design with the target database
DDL details and behavior are engine- and version-specific. Build and test the schema on the database product that will run it; an example that works on PostgreSQL 18 may need different syntax or choices for MySQL 8.4 or SQL Server.
- Create the tables, keys, constraints, and indexes in a test database using the target engine and version.
- Insert representative valid rows, including the normal one-to-many and many-to-many cases the application needs.
- Try invalid cases deliberately: duplicate a value that must be unique, omit a required value, use an out-of-range status, or reference a missing parent row. Confirm the intended constraint rejects each case.
- Test updates and deletes against the chosen foreign-key actions, including cases where dependent rows exist.
- Run important application queries with representative data and inspect their execution plans before deciding whether additional indexes are warranted.
There is no universal benchmark threshold or one-size-fits-all index list established by these design principles. The appropriate trade-offs depend on the actual data, queries, write workload, and database engine.
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