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
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
RabbitMQ vs. Kafka vs. ActiveMQ

Here are the top differences between RabbitMQ, Kafka, and ActiveMQ:

  1. Performance and Scalability: Kafka is designed for high throughput and horizontal scalability, making it well-suited for handling large volumes of data. RabbitMQ and ActiveMQ both offer high performance, but Kafka generally outperforms them in terms of throughput, particularly in scenarios with high data volume.

  2. Message Ordering: RabbitMQ and ActiveMQ guarantee message ordering within a single queue or topic, respectively. Kafka ensures message ordering within a partition but not across partitions within a topic.

  3. Message Priority: RabbitMQ and ActiveMQ support message prioritization, allowing messages with higher priority to be processed before those with lower priority. Kafka does not have built-in message priority support.

  4. Message Model: RabbitMQ uses a queue-based message model following the Advanced Message Queuing Protocol (AMQP), while Kafka utilizes a distributed log-based model. ActiveMQ is built on the Java Message Service (JMS) standard and also uses a queue-based message model.

  5. Durability: All three message brokers support durable messaging, ensuring that messages are not lost in case of failures. However, the mechanisms for achieving durability differ among the three, with RabbitMQ and ActiveMQ offering configurable durability options and Kafka providing built-in durability through log replication.

  6. Message Routing: RabbitMQ provides advanced message routing capabilities through exchanges and bindings, while ActiveMQ uses selectors and topics for more advanced routing. Kafka's message routing is relatively basic and relies on topic-based partitioning.

  7. Replication: RabbitMQ supports replication through Mirrored Queues, while Kafka features built-in partition replication. ActiveMQ uses a Master-Slave replication mechanism.

  8. Stream Processing: Kafka provides native stream processing capabilities through Kafka Streams, similarly RabbitMQ offers stream processing too, while ActiveMQ relies on third-party libraries for stream processing.

  9. Latency: RabbitMQ is designed for low-latency messaging, making it suitable for use cases requiring near-real-time processing.

  10. License: RabbitMQ is licensed under the Mozilla Public License, while both Kafka and ActiveMQ are licensed under the Apache 2.0 License.

Popular Messaging Queue Systems
Scalability and Performance
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