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HLDsystem_design~12 mins

Read-heavy vs write-heavy systems in HLD - Architecture Patterns Compared

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System Overview - Read-heavy vs write-heavy systems

This system compares two types of data workloads: read-heavy and write-heavy. Read-heavy systems serve many data requests from users, focusing on fast data retrieval. Write-heavy systems handle frequent data updates or inserts, focusing on efficient data storage and consistency.

Key requirements include optimizing performance for the dominant operation type and ensuring data reliability.

Architecture Diagram
User
  |
  v
Load Balancer
  |
  v
API Gateway
  |
  +---------------------+
  |                     |
  v                     v
Read Service         Write Service
  |                     |
  v                     v
Cache                 Database
  |                     |
  v                     v
Database Replica     Write-Ahead Log
Components
User
client
Initiates requests to the system
Load Balancer
load_balancer
Distributes incoming requests evenly to API Gateway
API Gateway
api_gateway
Routes requests to appropriate services based on operation type
Read Service
service
Handles read requests, optimized for fast data retrieval
Write Service
service
Handles write requests, optimized for data consistency and durability
Cache
cache
Stores frequently read data to reduce database load and latency
Database
database
Primary data storage for both reads and writes
Database Replica
database_replica
Read-only copy of the database to serve read-heavy traffic
Write-Ahead Log
log
Ensures durability and consistency of write operations
Request Flow - 18 Hops
User → Load Balancer
Load Balancer → API Gateway
API Gateway → Read Service
Read Service → Cache
Cache → Read Service
Read Service → Database Replica
Database Replica → Read Service
Read Service → Cache
Read Service → API Gateway
API Gateway → Load Balancer
Load Balancer → User
API Gateway → Write Service
Write Service → Write-Ahead Log
Write Service → Database
Database → Database Replica
Write Service → API Gateway
API Gateway → Load Balancer
Load Balancer → User
Failure Scenario
Component Fails:Cache
Impact:Read requests experience higher latency as cache misses increase and database replicas handle all reads directly.
Mitigation:System continues serving reads from database replicas; cache rebuilds gradually as new data is requested.
Architecture Quiz - 3 Questions
Test your understanding
Which component primarily improves read performance in a read-heavy system?
ACache
BWrite-Ahead Log
CLoad Balancer
DWrite Service
Design Principle
This architecture shows how separating read and write paths optimizes system performance for different workloads. Caches and replicas speed up reads, while write-ahead logs and primary databases ensure write durability and consistency.