Core Concepts
Apache Kafka is a distributed streaming platform originally developed by LinkedIn and later contributed to the Apache Software Foundation. It is primarily used for building real-time data pipelines and streaming applications.
Key Features
- High Throughput: Kafka can handle millions of messages per second
- Low Latency: Message transmission latency is typically at the millisecond level
- Scalability: Easily scale the cluster by adding Brokers
- Persistence: Messages are persisted to disk, supporting data replay
- Fault Tolerance: Data loss is prevented through replica mechanisms
Core Components
- Producer: Message producer responsible for sending messages to the Kafka cluster
- Broker: Kafka server node responsible for storing and forwarding messages
- Topic: Message topic, the unit of message classification
- Partition: Topic partition that improves concurrent processing capability
- Consumer: Message consumer that reads messages from Topics
- Consumer Group: Consumer group that implements message load balancing
Working Principle
Kafka uses a publish-subscribe model where Producers send messages to specific Topics, and Consumers subscribe to and consume messages from Topics. Each Topic can be divided into multiple Partitions distributed across different Brokers, enabling parallel processing.
Use Cases
- Log collection systems
- Real-time data analytics
- Stream processing
- Message queuing
- Event sourcing
Kafka's design makes it an ideal choice for processing large-scale real-time data streams, widely used in internet, finance, IoT, and other fields.