Why Relational Databases Struggle with Relationships
Posted: Tue May 20, 2025 6:46 am
Edges always have a direction (e.g., "Person A FRIENDS_WITH Person B" is different from "Person B FRIENDS_WITH Person A" in some contexts, though friendship is usually bidirectional).
Edges must connect exactly two nodes.
Edges can also have properties that describe the nature of the relationship (e.g., a "PURCHASED" edge might have properties like date: "2025-05-18", quantity: 2, price: 500 BDT).
Properties: Key-value pairs that provide descriptive information about nodes or edges.
This simple yet powerful model allows for an incredibly intuitive and expressive way to represent highly complex and deeply interconnected datasets.
The Graph Advantage:
While relational databases use foreign keys to link tables and home owner data represent relationships, this approach becomes inefficient and unwieldy when dealing with deep or complex connections:
Expensive Joins: In a relational database, traversing relationships means performing costly JOIN operations between tables. As the number of "hops" or connections increases (e.g., finding "friends of friends of friends"), the number of joins explodes, leading to exponential performance degradation. A query that requires 5-7 joins in a relational database can become a performance bottleneck.
Rigid Schema for Relationships: Changing or adding new types of relationships in an RDBMS often requires altering table schemas, which can be a complex and time-consuming process, especially in large-scale production environments.
Difficulty with Multi-Hop Queries: Expressing and optimizing queries that involve many indirect connections is challenging in SQL.
Lack of Contextual Understanding: Relational models primarily focus on the individual data points. The relationships are secondary and are derived through joins, making it harder to grasp the overall network structure or patterns.
Edges must connect exactly two nodes.
Edges can also have properties that describe the nature of the relationship (e.g., a "PURCHASED" edge might have properties like date: "2025-05-18", quantity: 2, price: 500 BDT).
Properties: Key-value pairs that provide descriptive information about nodes or edges.
This simple yet powerful model allows for an incredibly intuitive and expressive way to represent highly complex and deeply interconnected datasets.
The Graph Advantage:
While relational databases use foreign keys to link tables and home owner data represent relationships, this approach becomes inefficient and unwieldy when dealing with deep or complex connections:
Expensive Joins: In a relational database, traversing relationships means performing costly JOIN operations between tables. As the number of "hops" or connections increases (e.g., finding "friends of friends of friends"), the number of joins explodes, leading to exponential performance degradation. A query that requires 5-7 joins in a relational database can become a performance bottleneck.
Rigid Schema for Relationships: Changing or adding new types of relationships in an RDBMS often requires altering table schemas, which can be a complex and time-consuming process, especially in large-scale production environments.
Difficulty with Multi-Hop Queries: Expressing and optimizing queries that involve many indirect connections is challenging in SQL.
Lack of Contextual Understanding: Relational models primarily focus on the individual data points. The relationships are secondary and are derived through joins, making it harder to grasp the overall network structure or patterns.