Why database scaling handles data growth in HLD - Architecture Impact

System Overview - Why database scaling handles data growth

This system shows how database scaling helps manage increasing amounts of data. As data grows, the system uses scaling techniques to keep performance steady and avoid slowdowns.

Architecture Diagram

User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  v
Application Service
  |
  v
+-------------------+       +-------------------+
|   Cache (Redis)   |<----->|   Database Cluster |
+-------------------+       +-------------------+
                              /           \
                             /             \
                    Shard 1 DB           Shard 2 DB

Components

User

client

Sends requests to the system

Load Balancer

load_balancer

Distributes incoming requests evenly to API Gateway instances

API Gateway

api_gateway

Handles client requests, routes them to application services

Application Service

service

Processes business logic and queries data

Cache (Redis)

cache

Stores frequently accessed data to reduce database load

Database Cluster

database_cluster

Stores all persistent data, scaled horizontally by sharding

Shard 1 DB

database_shard

Holds a subset of data to distribute load

Shard 2 DB

database_shard

Holds another subset of data to distribute load

Request Flow - 10 Hops

User → Load Balancer

Load Balancer → API Gateway

API Gateway → Application Service

Application Service → Cache (Redis)

Cache (Redis) → Application Service

Application Service → Database Cluster

Database Cluster → Application Service

Application Service → Cache (Redis)

Application Service → API Gateway

API Gateway → User

Failure Scenario

Component Fails:Database Cluster

Impact:New writes fail and some reads may fail if data is not cached. Cached reads still work.

Mitigation:Use database replication and sharding to isolate failures. Cache helps serve reads during partial DB failure.

Architecture Quiz - 3 Questions

Why does the system use a cache before querying the database?

A To replace the database completely

B To store all user requests permanently

C To reduce database load and speed up data access

D To slow down the response time

Design Principle

This design shows horizontal scaling of databases using sharding to handle data growth. Caching reduces database load and improves response time. Load balancers and API gateways ensure smooth request distribution and routing.