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
Cache Replacement Policies

When implementing caching, it’s important to have a cache replacement policy to determine which items in the cache should be removed when the cache becomes full. Here are some of the most common cache replacement policies:

1. Least Recently Used (LRU)

LRU is a cache replacement policy that removes the least recently used item from the cache when it becomes full. This policy assumes that items that have been accessed more recently are more likely to be accessed again in the future.

2. Least Frequently Used (LFU)

LFU is a cache replacement policy that removes the least frequently used item from the cache when it becomes full. This policy assumes that items that have been accessed more frequently are more likely to be accessed again in the future.

3. First In, First Out (FIFO)

FIFO is a cache replacement policy that removes the oldest item from the cache when it becomes full. This policy assumes that the oldest items in the cache are the least likely to be accessed again in the future.

4. Random Replacement

Random replacement is a cache replacement policy that removes a random item from the cache when it becomes full. This policy doesn’t make any assumptions about the likelihood of future access and can be useful when the access pattern is unpredictable.

Comparison of different replacement policies

Each cache replacement policy has its advantages and disadvantages, and the best policy to use depends on the specific use case. LRU and LFU are generally more effective than FIFO and random replacement since they take into account the access pattern of the cache. However, LRU and LFU can be more expensive to implement since they require maintaining additional data structures to track access patterns. FIFO and random replacement are simpler to implement but may not be as effective in optimizing cache performance. Overall, the cache replacement policy used should be chosen carefully to balance the trade-off between performance and complexity.

Types of Caching
Cache Invalidation
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On this page

  1. Least Recently Used (LRU)
  1. Least Frequently Used (LFU)
  1. First In, First Out (FIFO)
  1. Random Replacement

Comparison of different replacement policies