Subtitle:
The fundamental unit of data in Apache Kafka's event streaming platform
Core Idea:
A Kafka event (also called a record or message) represents something that happened in the world or business, consisting of a key, value, timestamp, and optional metadata headers that are published to and consumed from Kafka topics.
Key Principles:
- Event Structure:
- Each event has a key (optional), value, timestamp, and optional headers
- Immutability:
- Events are immutable once written to Kafka, preserving their integrity
- Durability:
- Events are stored persistently and can be read multiple times by different consumers
- Ordering:
- Events with the same key are guaranteed to be processed in the order they were written
Why It Matters:
- Data Integrity:
- Provides a reliable record of what happened and when it occurred
- Replayability:
- Allows systems to replay past events for recovery or reprocessing
- Decoupling:
- Enables separation between event producers and consumers
How to Implement:
- Define Event Schema:
- Establish what information your events need to contain and their structure
- Determine Key Strategy:
- Choose keys based on how you want events to be partitioned and ordered
- Set Retention Policy:
- Configure how long events should be kept before being discarded
Example:
- Scenario:
- An online banking system tracking money transfers
- Application:
Event key: "account-12345"
Event value: {"transferAmount": 200, "toAccount": "67890", "status": "completed"}
Event timestamp: "2025-03-16T14:30:25Z"
Headers: {"correlationId": "tx-78942", "source": "mobile-app"}
- Result:
- This event captures a completed transfer transaction, allowing various systems to process it accordingly while maintaining order with other events for the same account.
Connections:
- Related Concepts:
- Kafka Topics: The organizational units where events are published
- Event Streaming: The broader concept that Kafka events enable
- Broader Concepts:
- Domain Events: Events that represent meaningful occurrences in a business domain
- Event Sourcing: Pattern where state changes are stored as a sequence of events
References:
- Primary Source:
- Apache Kafka documentation on events and messages
- Additional Resources:
- "Designing Event-Driven Systems" by Ben Stopford
Tags:
#kafka #events #data-records #messages #event-streaming
Connections:
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