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 Read Strategies

Here are the two famous cache read strategies:

Read through cache

A read-through cache strategy is a caching mechanism where the cache itself is responsible for retrieving the data from the underlying data store when a cache miss occurs. In this strategy, the application requests data from the cache instead of the data store directly. If the requested data is not found in the cache (cache miss), the cache retrieves the data from the data store, updates the cache with the retrieved data, and returns the data to the application.

This approach helps to maintain consistency between the cache and the data store, as the cache is always responsible for retrieving and updating the data. It also simplifies the application code since the application doesn't need to handle cache misses and data retrieval logic. The read-through cache strategy can significantly improve performance in scenarios where data retrieval from the data store is expensive, and cache misses are relatively infrequent.

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Read aside cache

A read-aside cache strategy, also known as cache-aside or lazy-loading, is a caching mechanism where the application is responsible for retrieving the data from the underlying data store when a cache miss occurs. In this strategy, the application first checks the cache for the requested data. If the data is found in the cache (cache hit), the application uses the cached data. However, if the data is not present in the cache (cache miss), the application retrieves the data from the data store, updates the cache with the retrieved data, and then uses the data.

The read-aside cache strategy provides better control over the caching process, as the application can decide when and how to update the cache. However, it also adds complexity to the application code, as the application must handle cache misses and data retrieval logic.

You should use cache-aside when you need caching but also need to ensure that a failure of the cache won’t take down your whole system – the application can always go to the DB if needed. This approach can also be beneficial in scenarios where the application wants to optimize cache usage based on specific data access patterns.

Cache Invalidation
Cache Coherence and Consistency Models
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Read through cache

Read aside cache