14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups) • High-Level System Design (HLD): Distributed Systems at Scale • Rishabh Ki Kaksha

High-Level System Design (HLD): Distributed Systems at Scale › 14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups)

50 mins

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Course Syllabus

Module 1: Distributed Systems Core & Scalability Fundamentals

01. Scalability Dimensions: Vertical Scaling vs Horizontal Scaling & Latency vs Throughput 02. CAP Theorem vs PACELC Theorem: Consistency, Availability & Partition Tolerance Tradeoffs 03. Consistency Models: Strong Consistency, Eventual Consistency, Read-Your-Writes & Monotonic Reads

Module 2: Networking, Load Balancing & Reverse Proxies

04. Layer 4 vs Layer 7 Load Balancing: Round Robin, Weighted, Least Connections & IP Hash 05. High-Availability Load Balancers: Keepalived, VRRP, Anycast DNS & Failover Clustering 06. API Gateway Pattern: Authentication, Rate Limiting, Request Routing & Circuit Breakers

Module 3: Database Scaling: Sharding, Replication & Distributed Transactions

07. Database Replication: Master-Slave vs Multi-Master & Synchronous vs Asynchronous Replication 08. Database Sharding: Horizontal Partitioning, Directory-Based Sharding & Rebalancing Hotspots 09. Distributed Transactions: Two-Phase Commit (2PC) vs Saga Pattern (Choreography vs Orchestration)

Module 4: Caching Architectures, CDN & Consistent Hashing

10. Caching Strategies: Cache-Aside, Read-Through, Write-Through, Write-Around & Write-Back 11. Cache Invalidation Nightmares: Cache Avalanche, Cache Stampede, Thundering Herd & Cache Penetration 12. Consistent Hashing with Virtual Nodes (Solving Cache Clustering & Dynamic Scaling) 13. Content Delivery Networks (CDN): Edge Caching, Dynamic Site Acceleration & Origin Shielding

Module 5: Message Brokers, Event-Driven Architecture & Stream Processing

14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups) 15. Exactly-Once Processing Semantics (EOS), Idempotency Keys & Deduplication Architecture 16. Distributed Unique ID Generators: Twitter Snowflake, UUIDv7 & Database Ticket Servers

Module 6: FAANG Classic HLD Architectural Case Studies

17. System Design Case Study 1: URL Shortener (TinyURL / Bitly) at 100M Daily Requests 18. System Design Case Study 2: Distributed Rate Limiter & Web Application Firewall 19. System Design Case Study 3: Real-Time Chat & Notification System (WhatsApp / Slack) 20. System Design Case Study 4: Social Media News Feed (Twitter/Instagram) with Fan-out on Write/Read 21. System Design Case Study 5: Video Streaming Platform (YouTube / Netflix) Transcoding Architecture 22. System Design Case Study 6: Ride Hailing Geospatial Dispatch Architecture (Uber / Ola)

Interactive Lesson Studio

# 14. Message Queues vs Event Streams: RabbitMQ vs Apache Kafka (Partitioning, Consumer Groups)

> High-Level Design (HLD) Notes by Rishabh - Topic 14

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### 1. Message Queue (RabbitMQ) vs Distributed Event Log (Kafka) ⚔️

*   **RabbitMQ (Smart Broker, Dumb Consumer):** Message deliver hone aur consumer dwara acknowledge hone ke baad **Queue se delete ho jata hai** (Transient task distribution).
*   **Apache Kafka (Dumb Broker, Smart Consumer):** **Immutable Append-Only Commit Log on Disk**; messages delete nahi hote (Retention: 7 days to years). Multiple independent consumer groups log ko kisi bhi offset se replay kar sakte hain!

```
[ RabbitMQ ] : Message ➔ Deliver ➔ Ack ➔ Deleted from Broker
[ Kafka ]    : [Event 0 | Event 1 | Event 2 | Event 3 | Event 4 (Append-only on Disk)]
                     ▲                 ▲
               Consumer Grp 1    Consumer Grp 2 (Tracks its own Offset!)
```

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### 2. Kafka Partitioning & Horizontal Scaling Architecture 📈

*   **Topic:** Event stream category (e.g. `orders`).
*   **Partitions:** Topic multiple ordered append-only commit logs me split hota hai across disk nodes.
*   **Strict Ordering Guarantee:** Single partition ke andar ordering 100% strict guaranteed rehti hai (`Partition Key = userId`).

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### 3. Master Comparison Matrix

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### 4. Interview Ke 3 Sawal Is Topic Se

1. **Kafka itna blazing fast kyu hai ($10^6$ msgs/sec)?** -> Sequential Disk I/O, Zero-Copy OS system calls (`sendfile`), aur Page Cache kernel memory architecture ki wajah se.
2. **Consumer Group rebalancing Kafka me kab trigger hoti hai?** -> Jab koi naya consumer join karta hai ya existing consumer crash/heartbeat fail hota hai, partitions re-assign kiye jaate hain.
3. **RabbitMQ me Dead Letter Exchange (DLX) ka kya role hota hai?** -> Rejected, failed ya expired (TTL) messages ko quarantine queue me move karna taaki failure analysis ho sake.

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**Next Topic:** 15. Exactly-Once Processing Semantics (EOS), Idempotency Keys & Deduplication Architecture

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