ActiveMQ vs Kafka vs RSMQ: What are the differences?

# Introduction
This Markdown code provides a comparison between ActiveMQ, Kafka, and RSMQ, summarizing key differences between these messaging systems.

1. **Message Processing Model**: ActiveMQ follows the traditional message queue model, where messages are stored until consumed by subscribers. Kafka, on the other hand, uses a distributed commit log approach where data is stored permanently and replicated across multiple servers, enabling high throughput and low latency. RSMQ utilizes a simple Redis-based message queue system that prioritizes simplicity and speed but may lack the advanced features of ActiveMQ and Kafka.

2. **Scalability**: ActiveMQ can scale vertically by adding more resources to a single broker but can face limitations in horizontal scaling due to potential bottlenecks. Kafka is highly scalable horizontally, allowing users to add more brokers to increase throughput and storage capacity easily. RSMQ provides decent scalability for simpler use cases but may not be as suitable for extremely high-volume applications compared to Kafka.

3. **Use Cases**: ActiveMQ is commonly used for traditional message queuing applications, supporting features like point-to-point and publish-subscribe messaging patterns. Kafka is preferred for use cases requiring high throughput, durability, and fault-tolerance, such as real-time data processing and event streaming. RSMQ is best suited for lightweight applications that prioritize simplicity and performance over advanced functionality and scalability.

4. **Storage Model**: ActiveMQ stores messages in-memory or on disk, providing flexibility in configuring storage options based on performance requirements. Kafka stores messages on disk persistently, ensuring durability and fault tolerance even in the event of failures. RSMQ leverages Redis for message storage, offering high performance through in-memory caching but may have limitations in terms of disk-based persistence and long-term data retention.

5. **Consumer Groups**: ActiveMQ supports consumer groups for load balancing messages across multiple consumers within a queue, ensuring efficient message processing. Kafka introduces the concept of consumer groups along with offset management, enabling parallel processing of messages by multiple consumer instances while maintaining message ordering and fault tolerance. RSMQ does not inherently support consumer groups, which may limit its capabilities for complex message processing scenarios.

6. **Ecosystem Integration**: ActiveMQ has a rich ecosystem with support for various protocols and integrations with popular frameworks and tools in the Java ecosystem. Kafka offers extensive ecosystem integration with connectors for integrating with databases, stream processing frameworks, and monitoring tools, making it a popular choice for building data pipelines. RSMQ, being a simpler message queue system based on Redis, may have limitations in terms of ecosystem integrations and community support compared to ActiveMQ and Kafka.

In Summary, this Markdown code outlined key differences between ActiveMQ, Kafka, and RSMQ, covering aspects like message processing models, scalability, use cases, storage models, consumer groups, and ecosystem integrations.