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System Design Simulation

Uptime

100.0%

Users / req/s

0 /s

Avg Latency

45ms

Balance

$2,000

Ops Cost

-$0/s

Simulation Stage

STAGE 1

The Reddit Rush

Your simple blog was posted on Reddit! Upgrade compute capabilities and keep the server from melting under 200 req/s.

Scenario Goal: Survive 45 seconds at 95% uptime
0s elapsed Goal: 45s

Architectural Requirements

Senior Architect Log

[SYSTEM] Reverting back to standard visual environment. Monitoring active state.

Deployment Architecture Blueprint

Click components to deploy upgrades, scale, or configure settings.

Simulation Hot

Global Client Base

Target Request Ingress

Spike Latency 15ms
Failure Rate 0.0%

Edge Content CDN

CDN Cache

Absorbs static file assets (40% load reduction)

Hit Ratio 85%
Edge Locations 32 Points

NGINX Load Balancer

Load Balancer

Routes incoming requests evenly

Algorithm Round Robin
Active Routes 1 Server

Auto-Scaling Web Servers

Node cluster executing backend request handling

Redis In-Memory Cache

Redis Cache

Absorbs 80% of backend database read actions

Total Memory 8 GB
Hit Ratio 78%

Database Cluster

Single SQLite Node

Database Load: 0%
Capacity limit 120 req/s
DB Scale Tier I

System Bottleneck!

Your database is crying. Add Redis Cache to filter read requests directly from RAM memory!

System Design Manual

01. Vertical vs Horizontal Scaling

Vertical Scaling (Scaling Up): Adding more power (CPU, RAM) to an existing server. It has hard physical limits and represents a Single Point of Failure (SPOF).

Horizontal Scaling (Scaling Out): Deploying multiple server units. Combats traffic limits perfectly but requires a Load Balancer to split the requests.

02. Load Balancers (LB)

An entry point proxy that forwards client requests evenly to backing application servers. Without a load balancer, even with multiple servers running, only Server 1 receives incoming packets, causing a single instance crash.

03. Redis In-Memory Caching

Databases read from spinning or solid-state disks, which is slow and limited in active IOPS. A cache acts as a temporary memory space (RAM) intercepting standard read commands, directly absorbing up to 80% of database load.

04. CDNs (Content Delivery Networks)

Global edge servers distributing static assets (HTML, images, videos) closer to the physical users. They dramatically cut load speeds (latency) and instantly relieve backend server farms of resource requests.

05. Database Scaling Patterns

SQLite: Ideal for prototyping. Local file-based, collapses immediately under multiple active read/write actions.

Master-Slave Replica Cluster: Separates read/write workloads. Writes hit the master node; reads distribute to slave copies, scaling query capacities.

Sharded Database: Splits records across multiple distinct physical servers using a hash key, allowing massive enterprise scale operations.