Kafka and C++: Building Robust Event-Driven Applications

Introduction

Apache Kafka is a powerful distributed streaming platform for building real-time data pipelines and scalable event-driven applications. With its capabilities of high-throughput message handling, fault tolerance, and flexible data processing, Kafka has become a popular choice in a wide range of industries.

C++ is a powerful language known for its performance and control, making it well-suited to work with Kafka in scenarios where speed and efficiency are paramount. In this blog post, we’ll dive into using Kafka with C++:

Key Kafka C++ Libraries

1. librdkafka is the official C/C++ client library provided by Apache Kafka. It offers a comprehensive set of features for producing and consuming messages and administering Kafka clusters.
2. cppkafka: A higher-level C++ wrapper built on top of librdkafka, providing a more user-friendly and object-oriented interface.
3. Other Libraries: You may also find libraries like modern-cpp-kafka, which offer additional features and abstractions.

Choosing a Library

For direct control and maximum customization, librdkafka is the way to go. If you prefer a simpler API and easier development, consider cppkafka.

Setting Up

• Install librdkafka or cppkafka. Refer to their official documentation for instructions based on your operating system and build environment.
• Include headers: In your C++ code, include the necessary headers for your chosen library.

Basic Producer Example (Using librdkafka)

C++

#include <cstdlib>

#include <iostream>

#include <librdkafka/rdkafkacpp.h>

int main() {

    // Configuration

    std::string brokers = “localhost:9092”;

    std::string topic = “my-topic”;

    RdKafka::Conf *conf = RdKafka::Conf::create(RdKafka::Conf::CONF_GLOBAL);

    if (conf->set(“bootstrap.servers”, brokers, errstr_t()) != RdKafka::Conf::CONF_OK) {

        std::cerr << errstr() << std::endl;

        exit(1); 

    }

    // Create Producer

    RdKafka::Producer *producer = RdKafka::Producer::create(conf, errstr_t());

    if (!producer) {

        std::cerr << errstr() << std::endl;

        exit(1);

    }

    // Send Message

    std::string message = “Hello from Kafka C++ Producer”;

    RdKafka::ErrorCode resp = 

           producer->produce(topic, RdKafka::Topic::PARTITION_ANY,

                             RdKafka::Producer::RK_MSG_COPY,

                             message.c_str(), message.size(),

                             NULL, NULL);

    // Delivery report callback (optional)    

    // …

    producer->poll(0); // Handle message delivery in background

    // Clean up

    delete producer;

    delete conf;

    return 0;

}

Use code 

content_copy

Basic Consumer Example (Using librdkafka)

C++

// … headers, config setup similar to producer

RdKafka::Consumer *consumer = RdKafka::Consumer::create(conf, errstr_t());

if (!consumer) {

    // … Handle error

}

std::vector<std::string> topics = {“my-topic”};

RdKafka::ErrorCode err = consumer->subscribe(topics);

if (err) {

   // … Handle error

}

// Message consumption Loop

while (true) {

    RdKafka::Message *msg = consumer->consume(1000); // Timeout

    switch (msg->err()) {

        case RdKafka::ERR_NO_ERROR:

            std::cout << “Message: ” << static_cast<char *>(msg->payload()) << std::endl;

            break;

        // … Proper error handling

    }

    delete msg;

}

consumer->close();

delete consumer;

delete conf;

Use code 

content_copy

Best Practices

• Error Handling: Kafka operations can fail. Always handle errors gracefully.
• Delivery Guarantees: Understand producer delivery guarantees (at most once, at least once, exactly once).
• Consumer Groups: Utilize consumer groups for scalability and distributed processing.