# Architectual scalability

# Overview

Scalability is the ability of a system to handle an increased workload without losing performance

Crash course Architectual scalability

TIP

AVOID TIGHT COUPLING

Data layer

Replication
- Leader follower
- Multi-leader
- Leaderless
Sharding
- Scalability
- Improved perf
- Availability
Distributed cache
- Cache clusters
- Key Distribution
- Cache Invalidation
CQRS Pattern
- Optimized perf
- Scaling reads & writes
- Flexibility in storage solutions

DB Scaling

Denormalization: Reduce complex joins to improve query performance
Indexing: analyzae the query patterns of the app & create the right index
Marterizalized views: pre-compute complex query results & store for faster access
Vertical scaling: boost the DB server by adding more CPU, RAM, ...
Sharding: Load resources that the page will need before they're needed
Replication: Create replicas of the primary db on different servers for scaling the reads
Database caching: Store frequently accessed data in a faster storage layer

LB use cases

Load balancing use cases:

API Gateway: A Centralized Hub for API Management

API Gateway use cases

API Gateway use cases:

Build an ecosystem: Partners collaborate to provide better integrations in the ecosystem
Shareholder for the Economy: Monetization of APIs for developers & developer teams
Multi-platform integration: Provide seamless compatibility among different platforms