Relationships (association, aggregation, composition) in LLD - Scalability & System Analysis

Scalability Analysis - Relationships (association, aggregation, composition)

Growth Table: Relationships in System Design

Users / Objects	100	10,000	1,000,000	100,000,000
Number of Relationship Links	~1,000	~100,000	~10,000,000	~1,000,000,000
Storage for Relationship Metadata	MBs	GBs	TBs	Petabytes
Query Complexity	Simple joins	Moderate joins, indexing needed	Complex joins, caching required	Distributed joins, sharding mandatory
Latency Impact	Negligible	Noticeable without optimization	High without caching and sharding	Critical, requires advanced optimization
Data Integrity Checks	Simple constraints	Automated constraints, triggers	Distributed consistency challenges	Eventual consistency, complex coordination

First Bottleneck

The first bottleneck is the database handling relationship data. As relationships grow, join queries become expensive. Especially for composition where lifecycle dependencies require strict integrity, the database load increases. Without proper indexing and caching, query latency spikes.

Scaling Solutions

• Indexing: Add indexes on foreign keys to speed up joins.
• Caching: Cache frequent relationship queries to reduce DB hits.
• Read Replicas: Use replicas to distribute read load.
• Sharding: Partition data by user or object to reduce join scope.
• Denormalization: Store some relationship data redundantly to avoid complex joins.
• Eventual Consistency: For aggregation, relax strict consistency to improve performance.
• Lifecycle Management: For composition, use transactional or distributed transactions carefully.

Back-of-Envelope Cost Analysis

Assuming 1 million users with 10 relationships each:

• Relationships: 10 million links
• Storage: ~100 bytes per link -> ~1 GB total
• Queries: 10,000 QPS (e.g., 10,000 active users each querying once per second)
• Bandwidth: 1 KB per query -> ~10 MB/s network traffic
• Database: Needs to handle 10,000 QPS, likely requiring sharding and replicas

Interview Tip

Start by explaining the types of relationships and their impact on data integrity and lifecycle. Then discuss how relationship volume affects database load. Finally, propose scaling strategies like indexing, caching, and sharding, explaining why each fits the problem.

Self Check

Your database handles 1000 QPS for relationship queries. Traffic grows 10x. What do you do first?

Answer: Add read replicas and implement caching to reduce direct database load before considering sharding or denormalization.

Key Result

Relationship data first stresses the database with complex joins and integrity checks; indexing, caching, and sharding are key to scaling.