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
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
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
Fault Tolerance vs. High Availability

Fault Tolerance and High Availability are both critical concepts in system design, especially in the context of distributed systems, cloud computing, and IT infrastructure. They are strategies used to ensure reliable and continuous operation of a system, but they address different aspects and have distinct operational focuses.

Fault Tolerance

Definition

  • Fault Tolerance refers to a system's ability to continue operating without interruption when one or more of its components fail. Fault-tolerant systems are designed to handle hardware, software, and network failures seamlessly.

Characteristics

  • Redundancy: Incorporates redundancy in system components (like servers, networks, storage) to ensure no single point of failure.
  • Automatic Failover: Automatically switches to a redundant or standby system upon the failure of a system component.
  • No Data Loss: Ensures that no data is lost in the event of a failure.
  • Cost: Generally more expensive due to the need for redundant components.

Use Cases

  • Critical applications in sectors like finance, healthcare, and aviation, where system downtime can have severe consequences.

High Availability

Definition

  • High Availability refers to a system's ability to remain operational and accessible for a very high percentage of the time, minimizing downtime as much as possible.

Characteristics

  • Uptime Guarantee: Designed to ensure a high level of operational performance and uptime (often quantified in terms of “nines” – for example, 99.999% availability).
  • Load Balancing and Redundancy: Achieved through techniques like load balancing, redundant systems, and clustering.
  • Rapid Recovery: Focuses on quickly restoring service after a failure, though a brief disruption is acceptable.
  • Cost-Effectiveness: Balances cost against the desired level of availability.

Use Cases

  • Online services, e-commerce platforms, and enterprise applications where availability is critical for customer satisfaction and business continuity.

Key Differences

  1. Objective:

    • Fault Tolerance is about continuous operation without failure being noticeable to the end-user. It is about designing the system to handle failures as they occur.
    • High Availability is about ensuring that the system is operational and accessible over a specified period, with minimal downtime. It focuses on quick recovery from failures.
  2. Approach:

    • Fault Tolerance: Involves redundancy and automatic failover mechanisms.
    • High Availability: Focuses on preventing downtime through redundant resources and rapid recovery strategies.
  3. Downtime:

    • Fault Tolerance: No downtime even during failure.
    • High Availability: Minimal downtime, but brief interruptions are acceptable.
  4. Cost and Complexity:

    • Fault Tolerance: More expensive and complex due to the need for exact replicas and seamless failover.
    • High Availability: More cost-effective, balancing the level of availability with associated costs.
  5. Data Integrity:

    • Fault Tolerance: Maintains data integrity even in failure scenarios.
    • High Availability: Prioritizes system uptime, with potential for minimal data loss in certain failure conditions.

Conclusion

While both fault tolerance and high availability are about ensuring reliable system operations, they address different levels of resilience and operational continuity. Fault tolerance is about uninterrupted operation even in the face of component failures, while high availability is about keeping the overall system operational as much as possible. The choice between them depends on the specific requirements, criticality, and budget constraints of the business or application in question.

Resilience and Error Handling
HTTP vs. HTTPS
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On this page

Fault Tolerance

Definition

Characteristics

Use Cases

High Availability

Definition

Characteristics

Use Cases

Key Differences

Conclusion