The goal is to set up a Kafka Cluster using Docker and create Kafka producers and consumers using Python and Express. Here's an introduction and an overview of the setup and implementation.

Project Overview

Kafka Cluster Setup with Docker: Apache Kafka is a distributed event streaming platform. For this project, you'll use Docker to spin up a Kafka cluster. Docker allows you to create isolated environments that can run Kafka and Zookeeper (which manages Kafka brokers).

Kafka Producer & Consumer: Kafka Producers are applications that send messages (records) to Kafka topics, while Kafka Consumers read and process those messages. In this project, you'll implement both using Python scripts and an Express.js application.

Steps Breakdown

1. Setting Up Kafka Cluster using Docker Install Docker: First, you need Docker installed on your system. Install Docker.

Clone the Project: Get the project directory from your version control (e.g., GitHub).

Update the Docker Compose File:

You will use Docker Compose to manage multiple containers, including Kafka and Zookeeper. Update the advertised.listeners in the Kafka service definition with the correct Advertised IP address. This will be the IP address that clients (producers and consumers) use to connect to Kafka. Bring Up the Kafka Cluster:

Run the following command to spin up the Kafka cluster:

docker-compose up

This will start the Kafka broker and the Zookeeper service in Docker containers.

2. Kafka Producer & Consumer with Python (Using Jupyter Notebook) Run Jupyter Lab: Use Docker to run a Jupyter Lab environment, which is ideal for interactive experimentation.

Run the following command to start a Jupyter notebook server:

docker run --net=host -p 10001:8888 jupyter/scipy-notebook:2023-02-28

Once the server is up, you can access it by opening localhost:10001 in your browser.

Producer Script: Import the Kafka producer script. This script sends messages to the Kafka topic you've configured.

Consumer Script: Similarly, import the Kafka consumer script to read messages from the Kafka topic.

3. Kafka Producer & Consumer using Express App Update Configuration: In the provided Express app, update the configuration to match your Kafka setup. Make sure the Kafka broker details are correct, including IP and port.

Install Dependencies: Run the following command to install the required npm packages for your Express app, including the Kafka libraries:

npm install

Start the Express Server: After configuring, start the Express.js server. This app will handle Kafka messages by producing or consuming them depending on the routes you define.

4. Monitoring the Kafka Cluster Kafka cluster monitoring is essential for ensuring the health and performance of your Kafka brokers, topics, and messages.

To set up monitoring, you can use tools like:

Kafka Manager: A web-based tool that helps you monitor Kafka topics, brokers, and clusters. Prometheus and Grafana: For real-time monitoring and visualization of Kafka metrics. You'll find detailed instructions in the linked documentation on how to integrate Kafka with monitoring tools like Prometheus and Grafana.

Summary Kafka Cluster: Use Docker to set up Kafka and Zookeeper. Kafka Producer/Consumer: Use Python scripts within a Jupyter Lab environment for experimentation. Set up an Express.js app to serve as another Kafka producer/consumer. Monitoring: Implement cluster monitoring with tools like Prometheus and Grafana. This project combines containerization (Docker) and messaging (Kafka) to build a robust, scalable data pipeline with both experimentation (Python) and real-time services (Express.js).