Grokking System Design Fundamentals
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System Design Fundamentals
Introduction to System Design
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Caching
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
CAP & PACELC Theorems
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Databases (SQL vs. NoSQL)
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
Indexes
What are Indexes?
Types of Indexes
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Long-Polling vs. WebSockets vs. Server-Sent Events
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Quorum
What is Quorum?
Heartbeat
What is Heartbeat?
Checksum
What is Checksum?
Uses of Checksum
Leader and Follower
What is Leader and Follower Pattern?
Security
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
Distributed File Systems
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Misc Concepts
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Quiz
Microservices vs. Serverless Architecture

Microservices and Serverless Architecture are two popular approaches in designing scalable, modern applications. They share some principles but differ significantly in how they are structured and managed.

Microservices

Definition

  • Microservices architecture is a method of developing software systems that structures an application as a collection of loosely coupled services, which are fine-grained and independently deployable.

Characteristics

  • Modularity: The application is broken down into smaller, independent services.
  • Scalability: Each service can be scaled independently, based on demand.
  • Deployment: Services are deployed individually, allowing for continuous integration and continuous delivery (CI/CD).
  • Language Agnostic: Each microservice can be written in a different programming language, depending on its requirements.

Use Cases

  • Large applications where different modules have differing requirements or scaling needs.
  • Teams that prefer to work independently on different parts of an application.

Example

  • An e-commerce application where inventory management, order processing, and user authentication are developed and operated as separate microservices.

Serverless Architecture

Definition

  • Serverless Architecture refers to a cloud computing model where the cloud provider dynamically manages the allocation and provisioning of servers. A serverless model allows developers to build and run applications without managing infrastructure.

Characteristics

  • No Server Management: Developers don't need to manage the server infrastructure.
  • Auto-scaling: Automatically scales up or down to handle the workload.
  • Cost-Effective: Generally billed based on the actual usage, not on pre-allocated capacity.
  • Event-Driven: Often used for applications that respond to events and triggers.

Use Cases

  • Applications with variable or unpredictable workloads.
  • Lightweight APIs, web apps, or automated tasks that run in response to events or requests.

Example

  • A photo processing function that automatically resizes images when uploaded to a cloud storage, triggered each time a new photo is uploaded.

Key Differences

  1. Infrastructure Management:

    • Microservices: Requires managing the infrastructure, although this can be abstracted away using containers and orchestration tools like Kubernetes.
    • Serverless: No infrastructure management; the cloud provider handles it.
  2. Scalability:

    • Microservices: Scalability is managed by the development team, although it allows for fine-tuned control.
    • Serverless: Automatic scalability based on demand.
  3. Cost Model:

    • Microservices: Costs are based on the infrastructure provisioned, regardless of usage.
    • Serverless: Pay-per-use model, often based on the number of executions and the duration of execution.
  4. Development and Operational Complexity:

    • Microservices: Higher operational complexity due to the need to manage multiple services and their interactions.
    • Serverless: Simpler from an operational standpoint, but can have limitations in terms of function execution times and resource limits.
  5. Use Case Suitability:

    • Microservices: Suitable for large, complex applications where each service may have different resource requirements.
    • Serverless: Ideal for event-driven scenarios, short-lived jobs, or applications with highly variable traffic.

Conclusion

While both microservices and serverless architectures offer ways to build scalable, modern applications, they cater to different needs. Microservices provide greater control over each service component but require managing the infrastructure. Serverless architectures abstract away the infrastructure concerns, offering a simpler model for deploying code, especially for event-driven applications or those with fluctuating workloads. The choice between the two often depends on the specific requirements of the application, team capabilities, and the desired level of control over the infrastructure.

Push vs. Pull Notification Systems
Message Queues vs. Service Bus
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On this page

Microservices

Definition

Characteristics

Use Cases

Example

Serverless Architecture

Definition

Characteristics

Use Cases

Example

Key Differences

Conclusion