Pros of Compose

• More general-purpose, supporting a wide range of applications and services
• Extensive documentation and large community support
• Simpler syntax for defining multi-container applications

Cons of Compose

• Less specialized for Confluent Platform components
• May require additional configuration for Kafka-specific features
• Doesn't provide pre-built images optimized for Confluent Platform

Code Comparison

cp-docker-images:

---
version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

Compose:

version: '3'
services:
  zookeeper:
    image: zookeeper:latest
    ports:
      - "2181:2181"
    environment:
      ZOO_MY_ID: 1

cp-docker-images focuses on Confluent Platform components with pre-configured images, while Compose offers a more flexible approach for defining multi-container applications. cp-docker-images provides optimized configurations for Kafka ecosystem services, whereas Compose requires manual setup but allows for greater customization across various technologies. The code examples illustrate the difference in specifying a Zookeeper service, with cp-docker-images using Confluent's custom image and Compose using a generic Zookeeper image.

Pros of containers

• Broader scope: Covers a wide range of applications and services beyond just Confluent Platform
• More frequent updates: Generally receives more regular updates and maintenance
• Simplified configuration: Often provides easier setup and configuration options for various applications

Cons of containers

• Less specialized: May not offer as deep integration with Confluent-specific features
• Community support: Might have less focused community support for Confluent-related issues
• Potential compatibility issues: Could face challenges with certain Confluent Platform versions or features

Code comparison

cp-docker-images:

zookeeper:
  image: confluentinc/cp-zookeeper:${CONFLUENT_VERSION}
  environment:
    ZOOKEEPER_CLIENT_PORT: 2181
    ZOOKEEPER_TICK_TIME: 2000

containers:

zookeeper:
  image: bitnami/zookeeper:${ZOOKEEPER_VERSION}
  environment:
    ALLOW_ANONYMOUS_LOGIN: "yes"
    ZOO_PORT_NUMBER: 2181

The code snippets show differences in image naming conventions and environment variable configurations between the two repositories. cp-docker-images uses Confluent-specific images and configurations, while containers uses more generic Bitnami images with slightly different environment variable names.

Pros of kafka-docker

• Lightweight and simple setup, ideal for development and testing
• Highly customizable through environment variables
• Active community support and frequent updates

Cons of kafka-docker

• Limited enterprise features compared to cp-docker-images
• Less comprehensive documentation
• May require additional configuration for production-ready setups

Code Comparison

kafka-docker:

version: '2'
services:
  kafka:
    image: wurstmeister/kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_ADVERTISED_HOST_NAME: localhost
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181

cp-docker-images:

version: '2'
services:
  kafka:
    image: confluentinc/cp-kafka:latest
    ports:
      - "9092:9092"
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092

The code comparison shows that both repositories use similar Docker Compose configurations. However, cp-docker-images uses Confluent's official Kafka image, which includes additional enterprise features and optimizations. The kafka-docker repository uses a community-maintained image that focuses on simplicity and flexibility.

Pros of docker-kafka

• Simpler and more lightweight, focusing solely on Kafka
• Easier to understand and modify for basic Kafka setups
• Faster startup times due to fewer components

Cons of docker-kafka

• Limited to Kafka only, lacking additional Confluent Platform components
• Less frequent updates and maintenance compared to cp-docker-images
• May not include the latest Kafka features and improvements

Code Comparison

cp-docker-images:

version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
  kafka:
    image: confluentinc/cp-kafka:latest
    depends_on:
      - zookeeper

docker-kafka:

version: '2'
services:
  kafka:
    image: spotify/kafka:latest
    environment:
      ADVERTISED_HOST: kafka
      ADVERTISED_PORT: 9092

The cp-docker-images example shows a more comprehensive setup with separate Zookeeper and Kafka services, while the docker-kafka example provides a simpler, all-in-one Kafka container. cp-docker-images offers more configuration options and follows Confluent's best practices, whereas docker-kafka aims for simplicity and ease of use in development environments.

Pros of fast-data-dev

• All-in-one solution with pre-configured components for quick setup
• Includes a web UI for easier management and monitoring
• Supports multiple Kafka versions out of the box

Cons of fast-data-dev

• Less flexibility for customization compared to individual components
• May include unnecessary services for specific use cases
• Potentially larger image size due to bundled components

Code Comparison

fast-data-dev:

FROM alpine:3.8
RUN apk add --no-cache curl openjdk8-jre bash coreutils
ENV KAFKA_VERSION=2.0.0 SCALA_VERSION=2.11

cp-docker-images:

FROM centos:7
ARG KAFKA_VERSION=5.3.1
ARG SCALA_VERSION=2.12
ENV KAFKA_HOME=/opt/kafka

The fast-data-dev Dockerfile uses Alpine Linux as the base image, resulting in a smaller overall image size. It also includes additional tools like curl and bash. The cp-docker-images Dockerfile uses CentOS 7 as the base image and focuses on Kafka-specific components.

fast-data-dev provides a more comprehensive solution for quick development and testing, while cp-docker-images offers greater flexibility for production deployments and custom configurations.