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
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
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
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
What are Indexes?
Types of Indexes
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Difference Between Long-Polling, WebSockets, and Server-Sent Events
What is Quorum?
What is Heartbeat?
What is Checksum?
Uses of Checksum
What is Leader and Follower Pattern?
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?
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
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
Why is Caching Important?

Caching plays a critical role in improving system performance and user experience in software engineering. By storing frequently accessed data in a cache, applications can reduce the response time and latency of operations, resulting in faster and more efficient processing. Here are some reasons why caching is important:

1. Reduced latency

By serving data from the cache, which is typically faster to access than the original data source, caching can significantly reduce the time it takes to retrieve the data.

2. Improved system performance

Caching can significantly improve the performance of an application by reducing the number of times data needs to be fetched from its original source. Since cached data can be retrieved faster than from the original source, this results in a significant reduction in processing time, which leads to a more responsive application.

3. Reduced network load

Caching can also reduce network load by minimizing the amount of data that needs to be transmitted over the network. Since cached data is stored locally, there is no need to fetch data from the original source, reducing the amount of data that needs to be transferred over the network.

4. Increased scalability

Caching can improve the scalability of an application by reducing the load on the original source. By storing frequently accessed data in a cache, the original source is less likely to be overwhelmed with requests, making it more scalable.

5. Better user experience

Faster response times and reduced latency can lead to a better user experience. Applications that load quickly and respond to user requests in a timely manner are more likely to be used and preferred by users.

Introduction to Caching
Types of Caching
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  1. Reduced latency
  1. Improved system performance
  1. Reduced network load
  1. Increased scalability
  1. Better user experience