What are the differences between Kafka, RabbitMQ, and RocketMQ?

Kafka Comparison with Other Message Queues

As a distributed streaming platform, Kafka differs significantly from traditional message queues (such as RabbitMQ, RocketMQ, ActiveMQ) in design philosophy, performance characteristics, and application scenarios. Understanding these differences is crucial for technology selection and system architecture design.

Kafka vs RabbitMQ

Architecture Design

Kafka:

• Distributed architecture, supports horizontal scaling
• Log-based storage, messages persisted to disk
• Pull model, Consumer actively pulls messages
• Stateless Broker, messages stored in file system

RabbitMQ:

• Centralized architecture, supports cluster mode
• Memory-based storage, messages stored in memory or disk
• Push model, Broker actively pushes messages
• Stateful Broker, messages stored in internal database

Performance Characteristics

Kafka:

• High throughput: single machine can reach millions of TPS
• Low latency: millisecond-level latency
• High concurrency: supports large number of concurrent connections
• Sequential read/write: leverages disk sequential read/write advantages

RabbitMQ:

• Medium throughput: single machine tens of thousands of TPS
• Low latency: microsecond-level latency
• Medium concurrency: limited concurrent connections
• Random read/write: fast memory access

Message Reliability

Kafka:

• Messages persisted to disk
• Supports replica mechanism to prevent data loss
• Supports message replay
• Configurable message retention time

RabbitMQ:

• Messages can be persisted to disk
• Supports message acknowledgment mechanism
• Supports dead letter queue
• Messages not persisted by default

Feature Characteristics

Kafka:

• Supports message replay
• Supports message compression
• Supports transactional messages
• Supports stream processing (Kafka Streams)

RabbitMQ:

• Supports message routing (Exchange, Binding)
• Supports message priority
• Supports delayed messages
• Supports message TTL

Kafka vs RocketMQ

Architecture Design

Kafka:

• Pure distributed architecture
• Decentralized design
• ZooKeeper-based coordination
• Simple storage model

RocketMQ:

• Distributed architecture
• Supports NameServer coordination
• Supports master-slave architecture
• Complex storage model

Performance Characteristics

Kafka:

• Higher throughput
• Slightly higher latency
• Strong batch processing capability
• Zero-copy technology optimization

RocketMQ:

• High throughput
• Lower latency
• Fast single message processing
• Good transactional message performance

Message Reliability

Kafka:

• Replica mechanism ensures reliability
• Supports synchronous and asynchronous replication
• Data persisted to disk
• Supports message replay

RocketMQ:

• Master-slave synchronization ensures reliability
• Supports synchronous dual-write and asynchronous replication
• Supports message flush strategies
• Supports message retry

Feature Characteristics

Kafka:

• Strong stream processing capability
• Rich ecosystem (Kafka Connect, Kafka Streams)
• Active community
• Comprehensive documentation

RocketMQ:

• Comprehensive transactional message support
• Supports message filtering
• Supports scheduled messages
• Supports message tracing

Kafka vs ActiveMQ

Architecture Design

Kafka:

• Modern distributed architecture
• Stateless design
• Strong horizontal scaling capability
• Storage and compute separation

ActiveMQ:

• Traditional message queue architecture
• Stateful design
• Vertical scaling primarily
• Storage and compute coupling

Performance Characteristics

Kafka:

• High throughput
• Low latency
• High concurrency
• Sequential read/write optimization

ActiveMQ:

• Medium throughput
• Medium latency
• Low concurrency
• Traditional database storage

Message Reliability

Kafka:

• Replica mechanism
• Persistent storage
• Message replay
• High availability

ActiveMQ:

• Persistent storage
• Message acknowledgment
• Transaction support
• Master-slave replication

Technology Selection Recommendations

Scenarios to Choose Kafka

1. Big Data Scenarios

   • Log collection
   • Real-time data analytics
   • Stream processing
   • Data pipelines

2. High Throughput Scenarios

   • Millions of messages per second
   • Batch data processing
   • Large-scale data transmission

3. Message Replay Requirements

   • Need to re-consume historical messages
   • Need multiple subscribers to consume
   • Need message persistence

4. Stream Processing Scenarios

   • Real-time computing
   • Event-driven architecture
   • Complex event processing

Scenarios to Choose RabbitMQ

1. Complex Routing Scenarios

   • Need flexible message routing
   • Need message filtering
   • Need multi-condition matching

2. Low Latency Scenarios

   • Microsecond-level latency requirements
   • High real-time requirements
   • Moderate message volume

3. Enterprise Application Scenarios

   • Enterprise-level message middleware
   • Traditional message queue requirements
   • Need rich management features

Scenarios to Choose RocketMQ

1. Financial Scenarios

   • Transactional message requirements
   • High reliability requirements
   • Message ordering requirements

2. E-commerce Scenarios

   • Order processing
   • Inventory synchronization
   • Message tracing

3. Alibaba Ecosystem

   • Using Alibaba Cloud services
   • Need Spring Cloud integration
   • Need comprehensive technical support

Scenarios to Choose ActiveMQ

1. Traditional Applications

   • JMS specification requirements
   • Traditional enterprise applications
   • Simple message queue requirements

2. Small-scale Applications

   • Small message volume
   • Simple deployment
   • Low maintenance cost

Performance Comparison Summary

Feature	Kafka	RabbitMQ	RocketMQ	ActiveMQ
Throughput	Extremely High	Medium	High	Low
Latency	Millisecond	Microsecond	Millisecond	Medium
Reliability	High	High	High	Medium
Scalability	Extremely Strong	Medium	Strong	Weak
Complexity	Medium	High	High	Medium
Ecosystem	Rich	Rich	Average	Average

Best Practices

1. Choose Based on Business Scenarios

   • Prioritize Kafka for big data scenarios
   • Prioritize RabbitMQ for complex routing scenarios
   • Prioritize RocketMQ for financial scenarios

2. Consider Team Capabilities

   • Choose technology stack familiar to the team
   • Consider learning and maintenance costs
   • Evaluate technical support capabilities

3. Evaluate Long-term Planning

   • Consider business growth requirements
   • Evaluate technology development trends
   • Plan technology evolution roadmap

By comparing the characteristics and applicable scenarios of different message queues, more reasonable technology selection decisions can be made.