Apache Kafka Has a Free Event Streaming Platform — Handle Millions of Events per Second

Apache Kafka processes millions of events per second with persistent, distributed, fault-tolerant message storage. The backbone of event-driven architecture.

Why Kafka?

RabbitMQ: great for task queues. But when you need to:

• Process 1M+ events/second
• Replay events from any point in time
• Fan out one event to 10 different consumers
• Keep events for days/weeks/forever

Kafka was built for exactly this.

Core Concepts

Topics — named streams of events (like database tables)
Producers — write events to topics
Consumers — read events from topics
Consumer groups — parallel processing with automatic load balancing
Partitions — topics split across brokers for parallelism
Retention — events stored for configured time (hours, days, forever)

What You Get for Free

# Docker Compose (quickest start)
version: '3'
services:
  kafka:
    image: confluentinc/cp-kafka:7.5.0
    ports: ['9092:9092']
    environment:
      KAFKA_NODE_ID: 1
      KAFKA_PROCESS_ROLES: broker,controller
      KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,CONTROLLER://0.0.0.0:9093
      KAFKA_CONTROLLER_QUORUM_VOTERS: 1@kafka:9093
      CLUSTER_ID: MkU3OEVBNTcwNTJENDM2Qk

Producer (Node.js):

import { Kafka } from 'kafkajs';
const kafka = new Kafka({ brokers: ['localhost:9092'] });
const producer = kafka.producer();

await producer.send({
  topic: 'user-events',
  messages: [{ key: 'user-123', value: JSON.stringify({ action: 'purchase', amount: 99.99 }) }],
});

Consumer:

const consumer = kafka.consumer({ groupId: 'analytics-group' });
await consumer.subscribe({ topic: 'user-events' });
await consumer.run({
  eachMessage: async ({ message }) => {
    const event = JSON.parse(message.value.toString());
    console.log('Event:', event);
  },
});

Real Use Cases

• Event sourcing — store every state change, rebuild state from events
• Real-time analytics — process clickstreams, transactions, IoT data
• Microservice communication — decouple services with event-driven messaging
• Change data capture — stream database changes to other systems
• Log aggregation — centralize logs from hundreds of services

Performance

• Millions of events/second per cluster
• Millisecond latency for produce and consume
• Horizontal scaling — add brokers for more throughput
• Data retention — keep events for days, weeks, or indefinitely

If your system processes more than 1,000 events/second — you'll eventually need Kafka.

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