Kafka vs RabbitMQ vs Pulsar

Kafka vs RabbitMQ vs Pulsar for production workloads

These three systems solve different messaging problems even though they all move events. This comparison focuses on durability, ordering, and operational overhead with a Python-centric view.

Prerequisites

• Python 3.11+
• Access to Kafka, RabbitMQ, and Pulsar clusters
• Basic familiarity with publish/subscribe concepts

Quick comparison

Feature	Kafka	RabbitMQ	Pulsar
Primary model	Log-based stream	Brokered queue	Log-based stream with segments
Storage	Brokers	Optional (memory/disk)	Brokers + BookKeeper
Ordering	Partition-ordered	Queue-ordered	Partition-ordered
Retention	Time or size	Ack-based	Time or size
Replay	Native	Limited	Native

Python connectivity examples

Kafka producer with kafka-python:

from kafka import KafkaProducer

producer = KafkaProducer(bootstrap_servers=["localhost:9092"])
producer.send("orders", b"order-1")
producer.flush()

RabbitMQ publisher with pika:

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters("localhost"))
channel = connection.channel()
channel.queue_declare(queue="orders", durable=True)
channel.basic_publish(exchange="", routing_key="orders", body=b"order-1")
connection.close()

Pulsar producer with pulsar-client:

import pulsar

client = pulsar.Client("pulsar://localhost:6650")
producer = client.create_producer("orders")
producer.send(b"order-1")
client.close()

When to choose each system

• Kafka: event streaming, analytics, and replay-heavy workloads.
• RabbitMQ: task queues, request-reply, and low-latency routing.
• Pulsar: multi-tenant streaming with tiered storage and geo-replication.

Operational considerations

• Kafka needs careful tuning of partitions and retention.
• RabbitMQ scales best with multiple queues and sharding.
• Pulsar introduces BookKeeper and more moving parts but simplifies storage scaling.

Things to remember

• Kafka and Pulsar are better for durable replay and event sourcing.
• RabbitMQ excels at fine-grained routing and traditional messaging patterns.
• Align your choice with data retention, ordering, and replay requirements.