Introduction
While EAV (Entity-Attribute-Value) databases often face criticism due to design flaws, a well-structured EAV schema offers a practical solution for tracking historical data and streamlining data exchange between SQL and key-value systems.
Enhanced EAV Schema for Historical Data Management
To overcome common EAV limitations and optimize historical data handling, a modified schema is proposed. This approach categorizes entity attributes by type, enabling efficient storage and indexing of specific attribute data.
Database Schema
The schema comprises several tables:
entity_type: Stores base entity types (e.g., "products," "users").
entity: Links entities to their corresponding entity types.
attr: Defines entity attributes with metadata (name, type).
attr_option, option: Handles option-based attributes and their values.
attr_int, attr_datetime, ...: Dedicated tables for distinct attribute types (integer, datetime, etc.).
attr_relation: Manages foreign key relationships between entities.
Example Queries
The following SQL queries illustrate data retrieval:
Retrieve Entity Type:
<code class="language-sql"> SELECT * FROM entity_type et LEFT JOIN entity e ON e.entity_type_id = et.id WHERE e.id = ?</code>
Retrieve Entity Attributes:
<code class="language-sql"> SELECT * FROM attr WHERE entity_id = ?</code>
Retrieve Most Recent Attribute Values:
<code class="language-sql"> SELECT * FROM attr_option WHERE entity_id = ? ORDER BY created_at DESC LIMIT 1 -- For single-value attributes SELECT * FROM attr_int WHERE entity_id = ? ORDER BY created_at DESC LIMIT 1 -- For integer attributes SELECT * FROM attr_relation WHERE entity_id = ? ORDER BY created_at DESC LIMIT 1 -- For relational attributes ...</code>
Retrieve Entity Relationships:
<code class="language-sql"> SELECT * FROM entity AS e LEFT JOIN attr_relation AS ar ON ar.entity_id = e.id WHERE ar.entity_id = 34 AND e.entity_type = 2;</code>
Potential Challenges
Despite improvements, this modified EAV approach presents some challenges:
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