# Kafka Event Streaming

> Apache Kafka Event Streaming

- Skill: `j4flmao/kafka-event-streaming` (Agent Skill)
- Install (CLI): `npx skillmds@latest add j4flmao/kafka-event-streaming`
- Raw SKILL.md: https://api.skillmd.com/api/skills/j4flmao/kafka-event-streaming/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: j4flmao (https://skillmd.com/u/j4flmao)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/j4flmao/kafka-event-streaming

---

# Apache Kafka Event Streaming

## Core Concepts

Kafka is a distributed, high-throughput, append-only commit log used for event streaming.

### 1. Topics, Partitions, and Offsets
- **Topics:** Logical categories for messages.
- **Partitions:** Topics are split into Partitions for massive parallelism. Messages in a partition are strictly ordered.
- **Offsets:** A sequential ID assigned to each message. Consumers track their progress by saving their current offset.

### 2. Consumer Groups
A Consumer Group is a cluster of workers. Each partition in a topic is assigned to exactly ONE consumer within the group. If you have 4 partitions, adding a 5th consumer does nothing (it sits idle).

### Kafka Architecture Map

```mermaid
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    subgraph Producers ["Event Producers"]
        A["Web Server (Clickstream)"]
    end
    
    subgraph KafkaCluster ["Kafka Cluster (Brokers)"]
        subgraph Topic ["Topic: page_clicks"]
            B["Partition 0 (Leader: Broker 1)"]
            C["Partition 1 (Leader: Broker 2)"]
        end
    end
    
    subgraph Consumers ["Consumer Group A"]
        D["Consumer 1"]
        E["Consumer 2"]
    end
    
    A -->|"Produce (Hash by UserID)"| B
    A -->|"Produce (Hash by UserID)"| C
    
    B -.->|"Pull Data (Offset 10)"| D
    C -.->|"Pull Data (Offset 15)"| E
```