


Modeling Product Variants
You're trying to model product variants and had some concerns about using EAV (Entity-Attribute-Value). Here's an alternative design you could consider:
Normalized Design
The following design normalizes the data structure for product variants:
+---------------+ +-----------------+ | PRODUCTS |-----<p><strong>Primary, Unique, and Foreign Keys:</strong></p><pre class="brush:php;toolbar:false">PRODUCTS - PK: product_id - UK: product_name OPTIONS - PK: option_id - UK: option_name OPTION_VALUES - PK: option_id, value_id - UK: option_id, value_name - FK: option_id REFERENCES OPTIONS (option_id) PRODUCT_OPTIONS - PK: product_id, option_id - FK: product_id REFERENCES PRODUCTS (product_id) - FK: option_id REFERENCES OPTIONS (option_id) PRODUCT_VARIANTS - PK: product_id, variant_id - UK: sku_id - FK: product_id REFERENCES PRODUCTS (product_id) VARIANT_VALUES - PK: product_id, variant_id, option_id - FK: product_id, variant_id REFERENCES PRODUCT_VARIANTS (product_id, variant_id) - FK: product_id, option_id REFERENCES PRODUCT_OPTIONS (product_id, option_id) - FK: option_id, value_id REFERENCES OPTION_VALUES (option_id, value_Id)
How it Works
- PRODUCTS contains basic product information like name.
- OPTIONS lists the available options, such as Size or Color.
- OPTION_VALUES holds the specific values of options, such as Small or Red.
- PRODUCT_OPTIONS maps which options are associated with products.
- PRODUCT_VARIANTS stores the actual product variants along with their SKUs.
- VARIANT_VALUES links variants to their option values.
This design allows you to define the options and their values independently, making it flexible to add new options or values in the future.
Sample Data
Here's an example of how you could enter data in these tables based on the spreadsheet in your question:
PRODUCTS ======== product_id product_name ---------- ------------ 1 Widget 1 2 Widget 2 3 Widget 3 OPTIONS ======= option_id option_name --------- ----------- 1 Size SL 2 Color 3 Size SM 4 Class 5 Size ML OPTION_VALUES ============= option_id value_id value_name --------- -------- ------------ 1 1 Small (Size SL) 1 2 Large (Size SL) 2 1 White (Color) 2 2 Black (Color) 3 1 Small (Size SM) 3 2 Medium (Size SM) 4 1 Amateur (Class) 4 2 Professional (Class) 5 1 Medium (Size ML) 5 2 Large (Size ML) PRODUCT_OPTIONS =============== product_id option_id ---------- --------- 1 1 (Widget 1; Size SL) 1 2 (Widget 1; Color) 2 3 (Widget 2; Size SM) 3 4 (Widget 3; Class) 3 5 (Widget 4; Size ML) PRODUCT_VARIANTS ================ product_id variant_id sku_id ---------- ---------- ------ 1 1 W1SSCW (Widget 1) 1 2 W1SSCB (Widget 1) 1 3 W1SLCW (Widget 1) 1 4 W1SLCB (Widget 1) 2 1 W2SS (Widget 2) 2 2 W2SM (Widget 2) 3 1 W3CASM (Widget 3) 3 2 W3CASL (Widget 3) 3 3 W3CPSM (Widget 3) 3 4 W3CPSL (Widget 3) VARIANT_VALUES ============== product_id variant_id option_id value_id ---------- ---------- --------- -------- 1 1 1 1 (W1SSCW; Size SL; Small) 1 1 2 1 (W1SSCW; Color; White) 1 2 1 1 (W1SSCB; Size SL; Small) 1 2 2 2 (W1SSCB; Color; Black) 1 3 1 2 (W1SLCW; Size SL; Large) 1 3 2 1 (W1SLCW; Color; White) 1 4 1 2 (W1SLCB; Size SL; Large) 1 4 2 2 (W1SLCB; Color; Black) 2 1 3 1 (W2SS; Size SM; Small) 2 2 3 2 (W2SM; Size SM; Medium) 3 1 4 1 (W3CASM; Class; Amateur) 3 1 5 1 (W3CASM; Size ML; Medium) 3 2 4 1 (W3CASL; Class; Amateur) 3 2 5 2 (W3CASL; Size ML; Large) 3 3 4 2 (W3CPSM; Class; Professional) 3 3 5 1 (W3CPSM; Size ML; Medium) 3 4 4 2 (W3CPSL; Class; Professional) 3 4 5 2 (W3CPSL; Size ML; Large)
Advantages
- Provides greater flexibility and scalability.
- Simplifies querying by avoiding complex joins.
- Enforces data integrity through foreign keys.
Disadvantages
- Requires more tables compared to EAV.
- May involve more complex database design and maintenance.
Conclusion
This normalized design is a viable alternative to EAV for modeling product variants. It offers flexibility, scalability, and data integrity while also being relatively easy to query. However, the specific choice between EAV and normalization should be made based on the specific requirements and trade-offs of your application.
The above is the detailed content of How can a normalized database design effectively model product variants as an alternative to the Entity-Attribute-Value (EAV) model?. For more information, please follow other related articles on the PHP Chinese website!

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.

SQL commands in MySQL can be divided into categories such as DDL, DML, DQL, DCL, etc., and are used to create, modify, delete databases and tables, insert, update, delete data, and perform complex query operations. 1. Basic usage includes CREATETABLE creation table, INSERTINTO insert data, and SELECT query data. 2. Advanced usage involves JOIN for table joins, subqueries and GROUPBY for data aggregation. 3. Common errors such as syntax errors, data type mismatch and permission problems can be debugged through syntax checking, data type conversion and permission management. 4. Performance optimization suggestions include using indexes, avoiding full table scanning, optimizing JOIN operations and using transactions to ensure data consistency.

InnoDB achieves atomicity through undolog, consistency and isolation through locking mechanism and MVCC, and persistence through redolog. 1) Atomicity: Use undolog to record the original data to ensure that the transaction can be rolled back. 2) Consistency: Ensure the data consistency through row-level locking and MVCC. 3) Isolation: Supports multiple isolation levels, and REPEATABLEREAD is used by default. 4) Persistence: Use redolog to record modifications to ensure that data is saved for a long time.

MySQL's position in databases and programming is very important. It is an open source relational database management system that is widely used in various application scenarios. 1) MySQL provides efficient data storage, organization and retrieval functions, supporting Web, mobile and enterprise-level systems. 2) It uses a client-server architecture, supports multiple storage engines and index optimization. 3) Basic usages include creating tables and inserting data, and advanced usages involve multi-table JOINs and complex queries. 4) Frequently asked questions such as SQL syntax errors and performance issues can be debugged through the EXPLAIN command and slow query log. 5) Performance optimization methods include rational use of indexes, optimized query and use of caches. Best practices include using transactions and PreparedStatemen

MySQL is suitable for small and large enterprises. 1) Small businesses can use MySQL for basic data management, such as storing customer information. 2) Large enterprises can use MySQL to process massive data and complex business logic to optimize query performance and transaction processing.

InnoDB effectively prevents phantom reading through Next-KeyLocking mechanism. 1) Next-KeyLocking combines row lock and gap lock to lock records and their gaps to prevent new records from being inserted. 2) In practical applications, by optimizing query and adjusting isolation levels, lock competition can be reduced and concurrency performance can be improved.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

PhpStorm Mac version
The latest (2018.2.1) professional PHP integrated development tool

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

WebStorm Mac version
Useful JavaScript development tools

Notepad++7.3.1
Easy-to-use and free code editor

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.