Kafka vs. Rabbitmq

Kafka

• high-throughput ingestion, fault-tolerant
• it is a log of persistent/durable events appended each time into a topic, with little overhead
• events are persistent and replayed until a retention period has passed
• can hold a large amount of durable data
• topics are splitter into partitions by a partition key, where the insertion order is kept
• a single event can be consumed by different consumer groups
• a consumer group can consume partitions in parallel.
• each consumer group has its own offset in each partition.
• consumers pull messages

Rabbitmq

• general-purpose message queues
• fast when the topics/queues are empty as it keeps messages in memory (have a look at lazy queues)
• to scale out and keep the queues not large, simply add more consumers
• when a message is acknowledged/consumed by a client, it gets removed
• flexibly route messages: direct, topic, fanout, and header exchanges
• support message priority
• messages are pushed to consumers

When to use RabbitMQ or Apache Kafka - CloudAMQP

RabbitMQ vs. Kafka

Kafka vs. RabbitMQ: Why Use Kafka?

Benchmarking Kafka vs. Pulsar vs. RabbitMQ: Which is Fastest?

How to choose the number of topics/partitions in a Kafka cluster? - Confluent

Part 4: RabbitMQ Exchanges, routing keys and bindings - CloudAMQP