0Pricing
Apache Kafka & Stream Processing Fundamentals · Lesson

Producing Messages to Kafka

Explore how to write applications that send data to Kafka topics efficiently and reliably.

Producing Messages to Kafka is a free Apache Kafka & Stream Processing Fundamentals lesson on CoddyKit — lesson 1 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the Apache Kafka & Stream Processing Fundamentals learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Meet the Kafka Producer

In this lesson, we'll learn how to send messages (also called records) to Kafka topics. This is the job of a Kafka Producer.

Think of a producer as an application or service that generates data. It then sends this data to a Kafka cluster, where it's stored in specific topics for other applications to read.

  • Producers generate data.
  • Topics organize data streams.
  • Brokers store the data.

Producer Client Basics

To send data, your application uses a Kafka Producer client library. This library handles all the complex interactions with the Kafka brokers.

It takes care of:

  • Finding the right Kafka broker.
  • Serializing your data into bytes.
  • Retrying failed send operations.
  • Balancing message distribution.

We'll use Java examples, but the concepts apply across languages.

Essential Producer Config

Before a producer can send messages, it needs some basic configuration. The two most important settings are:

  • bootstrap.servers: A comma-separated list of host/port pairs for Kafka brokers. The producer uses these to discover the full cluster.
  • key.serializer and value.serializer: Classes that convert your message's key and value objects into byte arrays, which is how Kafka stores data.

Without these, the producer won't know where to send messages or how to format them.

Producer Config Example

Here's how you might set up these properties in Java:

import java.util.Properties;

public class ProducerConfigDemo {
  public static void main(String[] args) {
    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");

    System.out.println("Producer properties configured!");
    // In a real app, you'd create a KafkaProducer with these props
  }
}

Crafting Your Message: ProducerRecord

When you send data to Kafka, you don't just send a string. You send a ProducerRecord. This object encapsulates your message and its metadata.

A ProducerRecord requires:

  • The topic name where the message will be sent.
  • An optional key: Used for partitioning messages. Messages with the same key go to the same partition.
  • The value: The actual data you want to send.

Keys are important for ensuring ordering for related data within a topic.

Sending Messages (Blocking)

The simplest way to send a message is using the send() method. If you want to wait for the message to be acknowledged by Kafka, you can call .get() on the returned Future object.

This makes the send operation synchronous (blocking). It's easy to understand, but can be slow if you're sending many messages.

import org.apache.kafka.clients.producer.*;
import java.util.Properties;

public class SyncProducer {
  public static void main(String[] args) throws Exception {
    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");

    try (KafkaProducer<String, String> producer = new KafkaProducer<>(props)) {
      ProducerRecord<String, String> record = new ProducerRecord<>(
        "my-topic", "key1", "Hello Sync Kafka!");
      
      RecordMetadata metadata = producer.send(record).get(); // Blocks here
      System.out.println("Sent message: " + metadata.topic() + "-" + metadata.partition());
    }
  }
}

Sending Messages (Non-Blocking)

For better performance, Kafka producers are designed to send messages asynchronously. When you call send(), it adds the message to a buffer and returns immediately.

The actual sending happens in the background. This allows your application to continue processing without waiting for each message to be delivered.

import org.apache.kafka.clients.producer.*;
import java.util.Properties;

public class AsyncProducer {
  public static void main(String[] args) {
    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");

    try (KafkaProducer<String, String> producer = new KafkaProducer<>(props)) {
      ProducerRecord<String, String> record = new ProducerRecord<>(
        "my-topic", "key2", "Hello Async Kafka!");
      
      producer.send(record); // Returns immediately
      System.out.println("Message queued for sending.");
      // In a real app, you'd send many messages here
    }
  }
}

Handling Send Results with Callbacks

Since send() is asynchronous, how do you know if a message was successfully sent or if an error occurred? You use a Callback.

The callback function is executed once Kafka acknowledges the message or if an error prevents it from being sent. This is crucial for error handling and logging.

import org.apache.kafka.clients.producer.*;
import java.util.Properties;

public class CallbackProducer {
  public static void main(String[] args) {
    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");

    try (KafkaProducer<String, String> producer = new KafkaProducer<>(props)) {
      ProducerRecord<String, String> record = new ProducerRecord<>(
        "my-topic", "key3", "Hello Callback Kafka!");
      
      producer.send(record, new Callback() {
        @Override
        public void onCompletion(RecordMetadata metadata, Exception exception) {
          if (exception == null) {
            System.out.println("Message sent successfully to topic " + metadata.topic());
          } else {
            System.err.println("Error sending message: " + exception.getMessage());
          }
        }
      });
      // Must flush or close producer to ensure callback is triggered in short programs
      producer.flush(); 
    }
  }
}

Ensuring Delivery: Acks Configuration

Producer reliability is controlled by the acks configuration. This setting determines how many acknowledgments a producer needs from Kafka brokers before considering a message 'sent'.

  • acks=0: Producer sends and doesn't wait for any acknowledgment. Fastest, but lowest durability (messages might be lost).
  • acks=1: Producer waits for the leader broker to acknowledge receipt. Good balance of speed and durability.
  • acks=all (or -1): Producer waits for all in-sync replicas to acknowledge. Slowest, but highest durability (messages are very unlikely to be lost).

Producer Best Practices

To ensure your Kafka producers are efficient and robust:

  • Always close the producer: Call producer.close() when your application shuts down. This flushes any buffered messages and releases resources.
  • Batching: Kafka producers automatically batch messages for efficiency. You can tune linger.ms and batch.size for optimal throughput.
  • Error Handling: Implement robust error handling in your callbacks to deal with transient network issues or permanent errors.

Proper configuration and resource management are key to a healthy Kafka application.

Quick Check: Producers

Which producer configuration property specifies how many acknowledgments the producer needs from Kafka brokers before considering a message successfully sent?

Producer Summary

You've learned the fundamentals of producing messages to Kafka!

  • Producers send data to topics.
  • Key configurations include bootstrap.servers and serializers.
  • Messages are wrapped in ProducerRecord objects.
  • You can send messages synchronously (blocking) or asynchronously (non-blocking).
  • Callbacks are used to handle asynchronous send results.
  • The acks setting controls message durability.

Next, we'll dive into how applications read these messages using Kafka Consumers!

Frequently asked questions

Is the “Producing Messages to Kafka” lesson free?

Yes — the full text of “Producing Messages to Kafka” is free to read here on the web, and the Apache Kafka & Stream Processing Fundamentals course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the Apache Kafka & Stream Processing Fundamentals course, upgrade to CoddyKit PRO.

What will I learn in “Producing Messages to Kafka”?

Explore how to write applications that send data to Kafka topics efficiently and reliably. You practise Apache Kafka & Stream Processing Fundamentals with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start Apache Kafka & Stream Processing Fundamentals?

No prior experience is required. Apache Kafka & Stream Processing Fundamentals on CoddyKit is structured for beginners through advanced learners; this is — lesson 1 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Producing Messages to Kafka” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this Apache Kafka & Stream Processing Fundamentals lesson?

Yes. Every Apache Kafka & Stream Processing Fundamentals lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Producing Messages to Kafka
  2. Consuming Messages from Kafka
  3. Understanding Partitions & Offsets
  4. Message Keys and Partitioning Strategies
← Back to Apache Kafka & Stream Processing Fundamentals