Lecture Notes: Kafka vs RabbitMQ

Introduction

• Focus: Differences between Kafka and RabbitMQ, specifically stream processing systems vs. traditional message queues.
• Misconception: Not interchangeable, they serve different purposes and choosing the incorrect one can lead to problems.

Design Differences

Kafka

• Stream Processing System: Handles streams of events and distributes them to many consumers.
• High Throughput: Keeps messages until they expire, allowing replay.
• Fan-Out by Default: Multiple consumers can get copies of the same record.

RabbitMQ

• Message Queuing System: Holds messages until ready to be processed, then sends to a processor.
• Complex Message Routing: Routes specific messages based on properties.
• Single Consumer Delivery: Each message goes to exactly one consumer.
• Handles Moderate Data Volumes: Fast but less throughput than Kafka.

Consumer Patterns

• Kafka: Each consumer gets all messages if connected to the same queue.
• RabbitMQ: Each message goes to one consumer.
  • Flexible patterns possible, but less scalable.

Use Cases

Kafka

• Event Distribution: Distributes events to logging, data analytics, real-time updates.
• Simple Service Jobs: Suitable for services requiring minimal scaling.

RabbitMQ

• Message Processing: Suitable for processing jobs, expandable to many processors.

Message Routing

Kafka

• Producer Responsibility: Determines message routing.
• High Throughput: No internal routing workload, scalable.
• Producer Setup: Messages are sent to queues based on properties.

RabbitMQ

• Exchanges: Handle message routing, support complex routing and duplication.
• Consumer Control: Consumers control which messages to consume.

Choosing Kafka vs RabbitMQ

Kafka

• Uniform, Short Processing Messages: Good for streaming events to multiple places.
• High Throughput: Necessary for extremely fast data handling.

RabbitMQ

• Long-Running Tasks: Handles complex routing well.
• Sporadic Data Flows: Works well when data flow is inconsistent.

Acknowledgement Mechanisms

Kafka

• Offset Log: Consumers track message offsets.
• Automatic Resend: If a consumer fails, messages are resent based on offset.

RabbitMQ

• Acknowledgements Required: Consumers poll and acknowledge data processing.
• Reliability: Waits and retries if no acknowledgement.

Example Use Cases

Kafka

• Stream Data Analysis: Real-time data processing using Kafka-compatible tools.
• Event Bus: Capturing events for multiple systems.
• Logging: Stream of logs captured and processed.
• Real-Time Communication: Streaming real-time events to users.

RabbitMQ

• Job Worker Systems: Cues and processes jobs in clusters.
• Microservices Decoupling: Handles simple inter-service communication.

Conclusion

• Further Learning: Interviewpen.com offers more on system design, data structures, and AI support tools.