๐ฅ๐ฎ๐ฏ๐ฏ๐ถ๐๐ ๐ค ๐๐. ๐๐ฎ๐ณ๐ธ๐ฎ ๐๐. ๐๐ฐ๐๐ถ๐๐ฒ๐ ๐ค: ๐ง๐ผ๐ฝ ๐ณ ๐๐ถ๐ณ๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ๐
๐ฃ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ฎ๐ป๐ฑ ๐ฆ๐ฐ๐ฎ๐น๐ฎ๐ฏ๐ถ๐น๐ถ๐๐: Kafka is designed for high throughput and horizontal scalability, making it well-suited for handling large volumes of data. RabbitMQ and ActiveMQ both offer high performance, but Kafka generally outperforms them in terms of throughput, particularly in scenarios with high data volume.
๐น๐ ๐ฒ๐๐๐ฎ๐ด๐ฒ ๐ฃ๐ฟ๐ถ๐ผ๐ฟ๐ถ๐๐: RabbitMQ and ActiveMQ support message prioritization, allowing messages with higher priority to be processed before those with lower priority. Kafka does not have built-in message priority support.
๐น๐ ๐ฒ๐๐๐ฎ๐ด๐ฒ ๐ข๐ฟ๐ฑ๐ฒ๐ฟ๐ถ๐ป๐ด: RabbitMQ and ActiveMQ guarantee message ordering within a single queue or topic, respectively. Kafka ensures message ordering within a partition but not across partitions within a topic.
๐น๐ ๐ฒ๐๐๐ฎ๐ด๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น: RabbitMQ uses a queue-based message model following the Advanced Message Queuing Protocol (AMQP), while Kafka utilizes a distributed log-based model. ActiveMQ is built on the Java Message Service (JMS) standard and also uses a queue-based message model.
๐น๐๐๐ฟ๐ฎ๐ฏ๐ถ๐น๐ถ๐๐: All three message brokers support durable messaging, ensuring that messages are not lost in case of failures. However, the mechanisms for achieving durability differ among the three, with RabbitMQ and ActiveMQ offering configurable durability options and Kafka providing built-in durability through log replication.
๐น๐ฅ๐ฒ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป: RabbitMQ supports replication through Mirrored Queues, while Kafka features built-in partition replication. ActiveMQ uses a Primary-Replica replication mechanism.
๐น๐ฆ๐๐ฟ๐ฒ๐ฎ๐บ ๐ฃ๐ฟ๐ผ๐ฐ๐ฒ๐๐๐ถ๐ป๐ด: Kafka provides native stream processing capabilities through Kafka Streams, similarly RabbitMQ offers stream processing too, while ActiveMQ relies on third-party libraries for stream processing.
๐นKafka: Kafka supports message prioritization using partitions. By default, Kafka distributes messages evenly across all partitions, but you can also use custom partitioning strategies to prioritize certain messages over others. Additionally, Kafka allows you to set different priorities for each partition, which can be used to prioritize messages for certain consumers or processing pipelines.
Ref:
โจ RabbitMQ vs. Kafka vs. ActiveMQ: A Battle of Messaging Brokers:
