Getting Started with Apache Kafka: Core Concepts and Use Cases

Kafka vs RabbitMQ: When to Use Each

Both Apache Kafka and RabbitMQ are popular tools for handling messages, but they serve different purposes. Choosing the right one depends on your use case.

When to Use Kafka

• High Throughput and Scalability: You need to process millions of messages per second.
• Event Streaming: Data must be consumed in real-time and possibly by multiple consumer groups.
• Data Retention: Messages should be stored for hours, days, or even weeks so consumers can re-read them.
• Replayability: Consumers might need to “rewind” and process data from the past.
• Big Data Pipelines: Integration with systems like Spark, Flink, or Hadoop for analytics.

Example: Collecting logs from thousands of servers and analyzing them in real time.

When to Use RabbitMQ

• Simple Messaging: You just need reliable delivery of messages between services.
• Request/Response or RPC: A service calls another and waits for a reply.
• Complex Routing: Built-in exchange types (direct, topic, fanout) allow flexible message routing.
• Short-Lived Messages: Messages don’t need to be stored for long; once consumed, they are gone.
• Smaller Workloads: Works well for moderate message volumes with simpler requirements.

Example: Sending order confirmation emails where each message is consumed once and then discarded.

RabbitMQ Streams vs Kafka

What RabbitMQ Streams Add

• Persistent log storage: Similar to Kafka, messages are kept on disk and can be replayed.
• Sequential access: Messages are read in order, like a log.
• High throughput: Streams are optimized for faster message handling compared to classic queues.
• Replay capability: Consumers can re-read old messages by specifying an offset.

How It Differs from Kafka

• Architecture: RabbitMQ Streams are an add-on to RabbitMQ’s traditional broker, while Kafka was built for streaming from day one.
• Ecosystem: Kafka has a mature ecosystem (Connect, Streams API, ksqlDB, Schema Registry), while RabbitMQ Streams focus mainly on extending RabbitMQ.
• Scaling: Kafka partitions and broker clusters are designed to scale horizontally across very large data volumes; RabbitMQ Streams scale, but not as seamlessly as Kafka in massive data pipelines.
• Maturity: Kafka is proven in high-load environments (LinkedIn, Netflix, Uber), while RabbitMQ Streams are still newer and less battle-tested.

When to Use RabbitMQ Streams

• You are already using RabbitMQ and need streaming features without migrating to Kafka.
• Your workloads are medium-scale and don’t require Kafka’s full ecosystem.
• You want both traditional queueing and streaming in one system.