Apache Kafka - Distributed Event Streaming Platform
What is Apache Kafka?
Apache Kafka is a distributed event streaming platform capable of handling trillions of events per day. It's designed for high-throughput, fault-tolerant, real-time data pipelines and streaming applications.
Think of Kafka as a distributed commit log where producers write events and consumers read them in real-time, with events stored durably for replay.
Why Use Kafka?
Real-Time Data Streaming
- Low Latency: Millisecond latency for event processing
- High Throughput: Millions of events per second
- Durable: Events persisted to disk, replicated across brokers
- Scalable: Linearly scalable by adding brokers
Decoupled Architecture
- Publish-Subscribe: Multiple consumers read same stream
- Message Retention: Replay historical events
- Fault Tolerant: No single point of failure
- Exactly-Once Semantics: Guaranteed message delivery
Event-Driven Applications
- Stream Processing: Real-time analytics with Kafka Streams
- Event Sourcing: Store state changes as events
- CQRS: Command Query Responsibility Segregation
- Microservices Communication: Async messaging between services
Core Concepts
Topics
Categories or feeds to which records are published:
Producers
Applications that publish events to topics:
Consumers
Applications that subscribe to topics and process events:
Partitions
Ordered, immutable sequences of records within a topic:
- Parallel Processing: Multiple consumers per topic
- Ordering Guarantee: Within partition only
- Scalability: Distribute load across partitions
Consumer Groups
Multiple consumers working together to consume a topic:
- Load Balancing: Partitions distributed across consumers
- Fault Tolerance: Automatic rebalancing on failure
- Horizontal Scaling: Add consumers to increase throughput
When to Use Kafka
Perfect For:
- Real-Time Analytics - Process events as they occur
- Event Sourcing - Capture state changes as events
- Log Aggregation - Centralize logs from multiple services
- Stream Processing - Real-time ETL pipelines
- Microservices - Async communication between services
- Activity Tracking - User behavior, clickstreams
- IoT Data - Sensor data streaming
Not Ideal For:
- Request-Response - Use REST/gRPC instead
- Batch Processing - Use Spark/Airflow for batch
- Small Scale - Overkill for <1000 events/sec
- Simple Queuing - Use RabbitMQ/SQS for simple queues
Kafka in Your Data Stack
Key Advantages
vs. Traditional Message Queues (RabbitMQ, SQS)
- Retention: Messages retained for replay
- Throughput: 100x higher throughput
- Scalability: Horizontally scalable
- Order: Ordering within partitions
vs. Batch Processing (Spark, Airflow)
- Latency: Milliseconds vs minutes/hours
- Freshness: Real-time vs scheduled
- Use Case: Streaming vs batch