🟢 Scalable Architecture

Master distributed systems design and build applications that scale

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Scalable Architecture Curriculum

12
Architecture Units
~55
Design Patterns
15+
Scale Strategies
Distributed
Systems
1

Scalability Fundamentals

Learn the core principles of building scalable systems and applications.

  • What is scalability?
  • Horizontal vs vertical scaling
  • Scalability dimensions
  • Performance vs scalability
  • Bottleneck identification
  • Capacity planning
  • Scalability metrics
  • Design considerations
2

Load Balancing Strategies

Distribute traffic efficiently across multiple servers and services.

  • Load balancing concepts
  • Round robin algorithms
  • Weighted distribution
  • Health checks
  • Session persistence
  • Geographic distribution
  • Auto-scaling integration
  • Load balancer types
3

Database Scaling

Scale databases effectively using replication, sharding, and partitioning.

  • Database bottlenecks
  • Read replicas
  • Master-slave replication
  • Horizontal partitioning
  • Sharding strategies
  • NoSQL scaling
  • Database clustering
  • ACID vs BASE
4

Caching Patterns

Implement various caching strategies to improve performance and reduce load.

  • Caching fundamentals
  • Cache-aside pattern
  • Write-through caching
  • Write-behind caching
  • Distributed caching
  • Cache invalidation
  • CDN strategies
  • Cache hierarchies
5

Microservices Architecture

Design and implement microservices for scalable, maintainable systems.

  • Microservices principles
  • Service decomposition
  • Service communication
  • API gateway patterns
  • Service discovery
  • Data management
  • Distributed transactions
  • Service mesh
6

Message Queues & Event Streaming

Implement asynchronous communication patterns for scalable systems.

  • Message queue concepts
  • Publish-subscribe patterns
  • Event-driven architecture
  • Message brokers
  • Stream processing
  • Event sourcing
  • CQRS pattern
  • Saga patterns
7

Distributed Systems Concepts

Understand the challenges and solutions in distributed system design.

  • CAP theorem
  • Consistency models
  • Eventual consistency
  • Consensus algorithms
  • Distributed locks
  • Vector clocks
  • Split-brain problems
  • Fault tolerance
8

Auto-scaling & Elasticity

Implement automatic scaling based on demand and performance metrics.

  • Auto-scaling principles
  • Scaling triggers
  • Metrics-based scaling
  • Predictive scaling
  • Container orchestration
  • Serverless scaling
  • Cost optimization
  • Scaling policies
9

Performance Optimization

Optimize system performance through various techniques and strategies.

  • Performance profiling
  • Bottleneck analysis
  • Resource optimization
  • Code optimization
  • Database optimization
  • Network optimization
  • Memory management
  • Monitoring strategies
10

Cloud-Native Architecture

Design applications for cloud environments with native scaling capabilities.

  • Cloud-native principles
  • 12-factor app methodology
  • Container strategies
  • Kubernetes scaling
  • Serverless architectures
  • Multi-cloud strategies
  • Cloud services integration
  • Cost optimization
11

Monitoring & Observability

Implement comprehensive monitoring for scalable distributed systems.

  • Observability principles
  • Metrics collection
  • Distributed tracing
  • Log aggregation
  • Performance monitoring
  • Alerting strategies
  • SLA/SLO management
  • Capacity monitoring
12

System Design Case Studies

Apply scalability principles to real-world system design scenarios.

  • Social media platforms
  • E-commerce systems
  • Chat applications
  • Video streaming
  • Search engines
  • IoT platforms
  • Financial systems
  • Gaming backends

Unit 1: Scalability Fundamentals

Learn the core principles of building scalable systems and applications.

What is Scalability?

Understand scalability as the ability of a system to handle increased load gracefully.

Performance Capacity Load Handling Growth
Scalability is the ability of a system, network, or process to handle a growing amount of work by adding resources to the system. It's about designing systems that can grow and adapt to increased demand without compromising performance or user experience.
Scalability Benefits
Handle growth
Maintain performance
Cost efficiency
User satisfaction
Business success
// Scalability example - Load distribution
const loadBalancer = {
  servers: ['server1', 'server2',