Pros of awesome-compose

• Offers a wide variety of Docker Compose examples for different technologies and stacks
• Provides a learning resource for Docker Compose best practices and configurations
• Includes examples for popular web frameworks, databases, and development tools

Cons of awesome-compose

• Not focused on a specific technology stack or platform
• May require more setup and configuration for production use
• Lacks the integrated ecosystem approach of cp-all-in-one

Code Comparison

awesome-compose example (Python Flask with Redis):

version: '3'
services:
  web:
    build: .
    ports:
      - "5000:5000"
  redis:
    image: "redis:alpine"

cp-all-in-one example (Kafka and Zookeeper setup):

version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.2
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

Summary

awesome-compose is a versatile collection of Docker Compose examples for various technologies, making it an excellent learning resource. However, it may require more setup for production use. cp-all-in-one, on the other hand, provides a more focused and integrated approach for Confluent Platform components but is limited to that specific ecosystem.

Pros of containers

• Broader scope: Offers containers for various applications beyond Kafka ecosystem
• More frequent updates: Actively maintained with regular releases
• Flexibility: Can be used independently or as part of larger Bitnami stack

Cons of containers

• Less Kafka-specific: May require more configuration for Kafka-centric deployments
• Steeper learning curve: Wider range of options can be overwhelming for beginners
• Limited integration: Lacks tight integration with Confluent-specific tools

Code comparison

cp-all-in-one:

---
version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.0
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

containers:

---
version: '2'
services:
  kafka:
    image: bitnami/kafka:latest
    environment:
      - KAFKA_CFG_ZOOKEEPER_CONNECT=zookeeper:2181
      - ALLOW_PLAINTEXT_LISTENER=yes

The cp-all-in-one repository focuses specifically on Confluent Platform components, providing a streamlined setup for Kafka and related services. It offers a more opinionated and integrated approach, ideal for users wanting a quick start with Confluent's ecosystem.

containers, on the other hand, provides a wider range of containerized applications, including Kafka. It offers more flexibility and options but may require additional configuration for a complete Kafka setup. This repository is better suited for users who need a variety of containerized applications or prefer more control over their deployment.

Pros of kafka-docker

• Lightweight and focused solely on Kafka, making it easier to understand and customize
• More flexibility in configuring Kafka settings and cluster topology
• Actively maintained with frequent updates and community contributions

Cons of kafka-docker

• Lacks additional components like Schema Registry, Kafka Connect, and ksqlDB
• Requires more manual setup and configuration for advanced features
• May not be as production-ready out of the box compared to cp-all-in-one

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-all-in-one:

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 snippets show that both repositories use Docker Compose to set up Kafka, but cp-all-in-one includes additional services and configuration options for a more comprehensive Kafka ecosystem.

Pros of docker-kafka

• Lightweight and focused solely on Kafka, making it easier to understand and customize
• Provides a simple Docker setup for Kafka and Zookeeper, ideal for development and testing
• Actively maintained by Spotify, benefiting from their expertise in large-scale Kafka deployments

Cons of docker-kafka

• Limited to basic Kafka functionality, lacking additional components like Schema Registry or Kafka Connect
• May require more manual configuration for advanced use cases or production environments
• Does not include a comprehensive suite of tools for managing and monitoring Kafka clusters

Code Comparison

docker-kafka:

version: '3'
services:
  zookeeper:
    image: wurstmeister/zookeeper
  kafka:
    image: wurstmeister/kafka
    environment:
      KAFKA_ADVERTISED_HOST_NAME: localhost

cp-all-in-one:

version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.2
  kafka:
    image: confluentinc/cp-kafka:7.3.2
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181

The cp-all-in-one repository provides a more comprehensive Kafka ecosystem with additional components and tools, making it suitable for production-like environments. However, docker-kafka offers a simpler setup focused on core Kafka functionality, which can be beneficial for specific use cases or learning purposes.

Pros of fast-data-dev

• Includes a wider range of Kafka ecosystem tools and connectors out-of-the-box
• Provides a web UI for easier management and monitoring
• Designed for quick setup and experimentation with minimal configuration

Cons of fast-data-dev

• Less frequently updated compared to cp-all-in-one
• May not always align with the latest Confluent Platform versions
• Limited to development and testing use cases, not suitable for production

Code Comparison

fast-data-dev:

FROM landoop/fast-data-dev:latest
ENV ADV_HOST=127.0.0.1
EXPOSE 2181 3030 8081-8083 9581-9585 9092

cp-all-in-one:

version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.0
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000

Both repositories aim to provide a comprehensive Kafka development environment, but they differ in their approach and included components. fast-data-dev offers a more user-friendly experience with its web UI and pre-configured tools, making it ideal for quick prototyping and learning. cp-all-in-one, being part of the official Confluent Platform, provides a more production-like setup and is regularly updated to match the latest Confluent releases. The choice between the two depends on the specific use case, with fast-data-dev being more suitable for rapid development and experimentation, while cp-all-in-one is better for those seeking a closer representation of a production Confluent environment.