Pros of Kafka

• Core Apache Kafka project with full source code and implementation details
• Extensive documentation and community support for the core Kafka platform
• Provides the foundational streaming platform that other tools build upon

Cons of Kafka

• Steeper learning curve for beginners compared to example-focused repositories
• Less focus on practical examples and quick-start guides
• Requires more setup and configuration to get running

Code Comparison

Kafka (producer example):

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer = new KafkaProducer<>(props);

Examples (producer example):

String bootstrapServers = "localhost:9092";
Properties props = new Properties();
props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());

The Examples repository focuses on practical implementations and tutorials, making it easier for developers to get started with Kafka quickly. However, the Kafka repository provides a deeper understanding of the core platform and its internals, which is valuable for advanced users and contributors.

Pros of spring-kafka

• Tighter integration with Spring Framework, offering seamless development for Spring-based applications
• More comprehensive documentation and extensive community support
• Provides higher-level abstractions and utilities specific to Spring applications

Cons of spring-kafka

• Limited to Spring ecosystem, less flexible for non-Spring projects
• May have a steeper learning curve for developers not familiar with Spring
• Fewer examples of integration with other Kafka-related tools and services

Code Comparison

spring-kafka:

@KafkaListener(topics = "myTopic")
public void listen(String message) {
    System.out.println("Received: " + message);
}

examples:

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("group.id", "test-group");
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList("myTopic"));

Summary

spring-kafka is ideal for Spring-based applications, offering tight integration and Spring-specific features. examples provides a broader range of Kafka usage examples across different languages and frameworks, making it more versatile for diverse projects. The choice between them depends on your project's ecosystem and requirements.

Pros of sarama

• Focused Go client library for Apache Kafka
• More lightweight and specialized for Kafka interactions
• Active development with frequent updates and bug fixes

Cons of sarama

• Limited to Go language, unlike the multi-language examples in examples
• Lacks comprehensive tutorials and demo applications
• Steeper learning curve for beginners compared to examples

Code Comparison

sarama (Go):

producer, err := sarama.NewSyncProducer([]string{"localhost:9092"}, nil)
if err != nil {
    log.Fatalln(err)
}
defer func() { _ = producer.Close() }()

msg := &sarama.ProducerMessage{Topic: "test", Value: sarama.StringEncoder("test message")}

examples (Java):

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
Producer<String, String> producer = new KafkaProducer<>(props);
producer.send(new ProducerRecord<>("test", "test message"));

Both repositories serve different purposes. sarama is a dedicated Go client library for Kafka, offering a more focused and lightweight approach. examples provides a broader range of examples across multiple languages and Kafka-related technologies, making it more suitable for learning and exploring various Kafka use cases.

Pros of kafka-docker

• Lightweight and focused specifically on Kafka Docker setup
• Easy to customize and extend for specific use cases
• Widely adopted in the community with frequent updates

Cons of kafka-docker

• Limited to Kafka-only setup, lacking additional ecosystem components
• Less comprehensive documentation compared to examples
• May require more manual configuration for advanced scenarios

Code Comparison

kafka-docker:

version: '2'
services:
  zookeeper:
    image: wurstmeister/zookeeper
    ports:
      - "2181:2181"
  kafka:
    build: .
    ports:
      - "9092:9092"

examples:

version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:latest
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181
      ZOOKEEPER_TICK_TIME: 2000
  kafka:
    image: confluentinc/cp-kafka:latest
    depends_on:
      - zookeeper
    environment:
      KAFKA_BROKER_ID: 1
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181

The kafka-docker repository provides a simpler Docker Compose configuration, focusing solely on Kafka and Zookeeper. In contrast, the examples repository offers a more comprehensive setup with additional configuration options and environment variables, reflecting its broader scope within the Confluent ecosystem.

While kafka-docker is ideal for quick Kafka setups and experimentation, examples provides a more production-ready configuration with additional components and best practices from Confluent.

Pros of docker-kafka

• Lightweight and focused solely on Kafka containerization
• Easier to set up and run for simple Kafka environments
• Suitable for quick prototyping and development purposes

Cons of docker-kafka

• Limited scope compared to the comprehensive examples in examples
• Lacks advanced configurations and integrations with other Confluent ecosystem components
• May not be suitable for production-grade deployments without additional customization

Code Comparison

examples:

---
version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.0
    hostname: zookeeper
    container_name: zookeeper
    ports:
      - "2181:2181"

docker-kafka:

FROM openjdk:8-jre

ENV KAFKA_VERSION=2.8.0
ENV SCALA_VERSION=2.13

RUN apt-get update && apt-get install -y wget
RUN wget -q https://archive.apache.org/dist/kafka/${KAFKA_VERSION}/kafka_${SCALA_VERSION}-${KAFKA_VERSION}.tgz

The examples repository provides a more comprehensive set of examples and configurations for various Confluent Platform components, making it suitable for learning and implementing complex Kafka-based solutions. It offers a wider range of use cases and integrations with other Confluent tools.

On the other hand, docker-kafka focuses specifically on containerizing Kafka, providing a simpler and more lightweight solution for running Kafka in Docker. It's ideal for developers who need a quick Kafka setup for testing or development purposes but may require additional work for production environments.

Pros of containers

• Broader scope, covering a wide range of containerized applications
• More frequent updates and active maintenance
• Larger community and user base, leading to better support and resources

Cons of containers

• Less focused on specific technologies or use cases
• May require more configuration for specialized deployments
• Potentially steeper learning curve for beginners due to its breadth

Code comparison

examples:

---
version: '2'
services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.3.0
    hostname: zookeeper
    container_name: zookeeper

containers:

FROM docker.io/bitnami/minideb:bullseye

ARG TARGETARCH

LABEL org.opencontainers.image.base.name="docker.io/bitnami/minideb:bullseye" \
      org.opencontainers.image.created="2023-06-22T14:04:44Z" \
      org.opencontainers.image.description="Application packaged by VMware, Inc" \

Summary

While examples focuses on Confluent Platform and related technologies, containers offers a broader range of containerized applications. examples provides more specialized and targeted examples for Kafka and streaming data ecosystems, while containers offers a wider variety of containerized applications with frequent updates and a larger community. The choice between the two depends on the specific needs of the project and the level of specialization required.