When to use SQL vs NoSQL in HLD - Design Approaches Compared

Design: Database Selection Guidance System

Focus on guiding database choice based on application data and workload characteristics. Does not cover actual database implementation or migration.

Functional Requirements

FR1: Support decision making between SQL and NoSQL databases based on application needs

FR2: Handle scenarios involving structured and unstructured data

FR3: Consider scalability and performance requirements

FR4: Support transactional consistency when needed

FR5: Allow flexible schema changes for evolving data models

Non-Functional Requirements

NFR1: Must support up to 10,000 concurrent users making database queries

NFR2: API response latency should be under 200ms for decision queries

NFR3: Availability target of 99.9% uptime

NFR4: Support data volumes from small (GBs) to large (TBs) scale

Think Before You Design

Questions to Ask

❓ Question 1

❓ Question 2

❓ Question 3

❓ Question 4

❓ Question 5

Key Components

Relational Database Management System (RDBMS) for SQL

Document, Key-Value, Column, or Graph stores for NoSQL

Caching layer for performance

API layer to abstract database choice

Monitoring and metrics for performance and availability

Design Patterns

Polyglot persistence (using multiple database types)

Caching for read-heavy workloads

Eventual consistency vs strong consistency

Schema evolution and migrations

Sharding and replication for scaling

Reference Architecture

Client
  |
  v
API Layer
  |
  +-------------------+
  |                   |
SQL Database      NoSQL Database
(RDBMS)           (Document/Key-Value/Graph)

Components

API Layer

REST/GraphQL

Receives client requests and routes to appropriate database type based on data and workload

SQL Database

PostgreSQL/MySQL/SQL Server

Stores structured data requiring strong consistency and complex queries

NoSQL Database

MongoDB/Cassandra/Redis/Neo4j

Stores unstructured or semi-structured data with flexible schema and high scalability

Cache Layer

Redis/Memcached

Improves read performance for frequently accessed data

Monitoring

Prometheus/Grafana

Tracks system health, latency, and throughput

Request Flow

1. Client sends request to API Layer

2. API Layer analyzes request data type and workload needs

3. If data is structured and requires transactions, route to SQL Database

4. If data is unstructured or needs flexible schema, route to NoSQL Database

5. Cache Layer serves repeated read requests to reduce latency

6. API Layer returns data or decision guidance to client

Database Schema

SQL Database: Tables with defined columns, primary keys, foreign keys for relationships NoSQL Database: Collections or key-value pairs with flexible or dynamic schemas Relationships: SQL uses joins; NoSQL uses embedded documents or references depending on type

Scaling Discussion

Bottlenecks

SQL databases can become slow with very large datasets or high write loads

NoSQL databases may sacrifice consistency for availability

API Layer can become a bottleneck if not horizontally scalable

Cache invalidation complexity as data changes

Monitoring overhead with increasing system size

Solutions

Use sharding and read replicas to scale SQL databases horizontally

Choose NoSQL types based on consistency needs (e.g., Cassandra for availability, MongoDB for consistency)

Deploy API Layer behind load balancers and scale horizontally

Implement cache invalidation strategies like TTL and event-driven updates

Use distributed monitoring systems and aggregate metrics efficiently

Interview Tips

Time: Spend 10 minutes understanding requirements and clarifying data types and consistency needs, 20 minutes designing the architecture and explaining database choices, 10 minutes discussing scaling and trade-offs, 5 minutes summarizing.

Explain differences between SQL and NoSQL clearly

Discuss data structure, consistency, and scalability trade-offs

Show understanding of real-world use cases for each database type

Highlight importance of API abstraction and caching

Address scaling challenges and practical solutions