What are the differences between Kafka and RabbitMQ?

RabbitMQ is a general purpose message broker that supports protocols including MQTT, AMQP, and STOMP. Kafka is a durable message broker that enables applications to process, persist, and re-process streamed data. Kafka has a straightforward routing approach that uses a routing key to send messages to a topic.

How is Kafka better than RabbitMQ?

RabbitMQ queues are faster only when they’re empty, unlike Kafka that can retain lots of data with minimal overhead. Kafka is capable of processing millions of messages in a second. Though RabbitMQ can also process millions of messages in a second, it would require more resources to do so.

Why use Kafka over MQ?

Kafka was designed to deliver these distinct advantages over AMQP, JMS, etc. Kafka is highly scalable. Kafka is a distributed system, which is able to be scaled quickly and easily without incurring any downtime. Apache Kafka is able to handle many terabytes of data without incurring much at all in the way of overhead.

What happens when Kafka broker fails?

Since all the critical information is stored in the Zookeeper and it normally replicates this data across its ensemble, failure of Kafka broker / Zookeeper does not affect the state of the Kafka cluster. Kafka will restore the state, once the Zookeeper restarts. This gives zero downtime for Kafka.

Can we replace RabbitMQ with Kafka?

The use of a standardized message protocol allows you to replace your RabbitMQ broker with any AMQP based broker. Kafka uses a custom protocol, on top of TCP/IP for communication between applications and the cluster. Kafka can’t simply be removed and replaced, since its the only software implementing this protocol.

How is Kafka tolerant?

Fault tolerance in Kafka is done by copying the partition data to other brokers which are known as replicas. And each of these partitions can either be a replica or leader for the topic. All the writes and reads to a topic go through the leader and the leader coordinates to update replicas with new data.