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Kafka Tutorial: Creating a Kafka Producer in Java

In this tutorial, we are going to create simple Java example that creates a Kafka producer. You create a new replicated Kafka topic called my-example-topic , then you create a Kafka producer that uses this topic to send records. You will send records with the Kafka producer. You will send records synchronously. Later, you will send records asynchronously.

Before you start

This tutorial is part of a series. If you are not sure what Kafka is, you should start with What is Kafka?. If you are unfamiliar with the architecture of Kafka then we suggest reading Kafka Architecture, Kafka Topics Architecture, Kafka Producer Architecture and Kafka Consumer Architecture.

Create Replicated Kafka Topic

Next, you need to create a replicated topic.

~/kafka-training/lab3/create-topic.sh

#!/usr/bin/env bash cd ~/kafka-training ## Create topics kafka/bin/kafka-topics.sh --create \ --replication-factor 3 \ --partitions 13 \ --topic my-example-topic \ --zookeeper localhost:2181 ## List created topics kafka/bin/kafka-topics.sh --list \ --zookeeper localhost:2181 

Above we create a topic named my-example-topic with 13 partitions and a replication factor of 3. Then we list the Kafka topics.

Runs create-topic.sh as follows.

Output from running create-topic.sh

~/kafka-training/lab3 $ ./create-topic.sh Created topic "my-example-topic". __consumer_offsets my-example-topic my-failsafe-topic 

Gradle Build Script

For this example, we use gradle to build the project.

~/kafka-training/lab3/solution/build.gradle

group 'cloudurable-kafka' version '1.0-SNAPSHOT' apply plugin: 'java' sourceCompatibility = 1.8 repositories < mavenCentral() >dependencies

Notice that we import the jar file kafka-clients:0.10.2.0 . Apache Kafka uses sl4j so to setup logging we use logback ( ch.qos.logback:logback-classic:1.2.2 ).

Construct a Kafka Producer

To create a Kafka producer, you will need to pass it a list of bootstrap servers (a list of Kafka brokers). You will also specify a client.id that uniquely identifies this Producer client. In this example, we are going to send messages with ids. The message body is a string, so we need a record value serializer as we will send the message body in the Kafka’s records value field. The message id (long), will be sent as the Kafka’s records key. You will need to specify a Key serializer and a value serializer, which Kafka will use to encode the message id as a Kafka record key, and the message body as the Kafka record value.

Common Kafka imports and constants

Next, we will import the Kafka packages and define a constant for the topic and a constant to define the list of bootstrap servers that the producer will connect.

KafkaProducerExample.java — imports and constants

~/kafka-training/lab3/src/main/java/com/cloudurable/kafka/KafkaProducerExample.java

 package com.cloudurable.kafka; import org.apache.kafka.clients.producer.*; import org.apache.kafka.common.serialization.LongSerializer; import org.apache.kafka.common.serialization.StringSerializer; import java.util.Properties; public class KafkaProducerExample < private final static String TOPIC = "my-example-topic"; private final static String BOOTSTRAP_SERVERS = "localhost:9092,localhost:9093,localhost:9094"; 

Notice that KafkaProducerExample imports LongSerializer which gets configured as the Kafka record key serializer, and imports StringSerializer which gets configured as the record value serializer. The constant BOOTSTRAP_SERVERS is set to localhost:9092,localhost:9093,localhost:9094 which is the three Kafka servers that we started up in the last lesson. Go ahead and make sure all three Kafka servers are running. The constant TOPIC is set to the replicated Kafka topic that we just created.

Create Kafka Producer to send records

Now, that we imported the Kafka classes and defined some constants, let’s create a Kafka producer.

KafkaProducerExample.java - Create Producer to send Records

~/kafka-training/lab3/src/main/java/com/cloudurable/kafka/KafkaProducerExample.java

public class KafkaProducerExample < . private static ProducercreateProducer() < Properties props = new Properties(); props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, BOOTSTRAP_SERVERS); props.put(ProducerConfig.CLIENT_ID_CONFIG, "KafkaExampleProducer"); props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, LongSerializer.class.getName()); props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName()); return new KafkaProducer<>(props); > 

To create a Kafka producer, you use java.util.Properties and define certain properties that we pass to the constructor of a KafkaProducer .

Above KafkaProducerExample.createProducer sets the BOOTSTRAP_SERVERS_CONFIG (“bootstrap.servers) property to the list of broker addresses we defined earlier. BOOTSTRAP_SERVERS_CONFIG value is a comma separated list of host/port pairs that the Producer uses to establish an initial connection to the Kafka cluster. The producer uses of all servers in the cluster no matter which ones we list here. This list only specifies the initial Kafka brokers used to discover the full set of servers of the Kafka cluster. If a server in this list is down, the producer will just go to the next broker in the list to discover the full topology of the Kafka cluster.

The CLIENT_ID_CONFIG (“client.id”) is an id to pass to the server when making requests so the server can track the source of requests beyond just IP/port by passing a producer name for things like server-side request logging.

The KEY_SERIALIZER_CLASS_CONFIG (“key.serializer”) is a Kafka Serializer class for Kafka record keys that implements the Kafka Serializer interface. Notice that we set this to LongSerializer as the message ids in our example are longs.

The VALUE_SERIALIZER_CLASS_CONFIG (“value.serializer”) is a Kafka Serializer class for Kafka record values that implements the Kafka Serializer interface. Notice that we set this to StringSerializer as the message body in our example are strings.

Send records synchronously with Kafka Producer

Kafka provides a synchronous send method to send a record to a topic. Let’s use this method to send some message ids and messages to the Kafka topic we created earlier.

KafkaProducerExample.java - Send Records Synchronously

~/kafka-training/lab3/src/main/java/com/cloudurable/kafka/KafkaProducerExample.java

public class KafkaProducerExample < . static void runProducer(final int sendMessageCount) throws Exception < final Producerproducer = createProducer(); long time = System.currentTimeMillis(); try < for (long index = time; index < time + sendMessageCount; index++) < final ProducerRecordrecord = new ProducerRecord<>(TOPIC, index, "Hello Mom " + index); RecordMetadata metadata = producer.send(record).get(); long elapsedTime = System.currentTimeMillis() - time; System.out.printf("sent record(key=%s value=%s) " + "meta(partition=%d, offset=%d) time=%d\n", record.key(), record.value(), metadata.partition(), metadata.offset(), elapsedTime); > > finally < producer.flush(); producer.close(); >> . 

The above just iterates through a for loop, creating a ProducerRecord sending an example message ( "Hello Mom " + index ) as the record value and the for loop index as the record key . For each iteration, runProducer calls the send method of the producer ( RecordMetadata metadata = producer.send(record).get() ). The send method returns a Java Future .

The response RecordMetadata has ‘partition’ where the record was written and the ‘offset’ of the record in that partition.

Notice the call to flush and close. Kafka will auto flush on its own, but you can also call flush explicitly which will send the accumulated records now. It is polite to close the connection when we are done.

Running the Kafka Producer

Next you define the main method.

KafkaProducerExample.java - Running the Producer

~/kafka-training/lab3/src/main/java/com/cloudurable/kafka/KafkaProducerExample.java

public static void main(String. args) throws Exception < if (args.length == 0) < runProducer(5); >else < runProducer(Integer.parseInt(args[0])); >> 

The main method just calls runProducer .

Send records asynchronously with Kafka Producer

Kafka provides an asynchronous send method to send a record to a topic. Let’s use this method to send some message ids and messages to the Kafka topic we created earlier. The big difference here will be that we use a lambda expression to define a callback.

KafkaProducerExample.java - Send Records Asynchronously with Kafka Producer

~/kafka-training/lab3/src/main/java/com/cloudurable/kafka/KafkaProducerExample.java

static void runProducer(final int sendMessageCount) throws InterruptedException < final Producerproducer = createProducer(); long time = System.currentTimeMillis(); final CountDownLatch countDownLatch = new CountDownLatch(sendMessageCount); try < for (long index = time; index < time + sendMessageCount; index++) < final ProducerRecordrecord = new ProducerRecord<>(TOPIC, index, "Hello Mom " + index); producer.send(record, (metadata, exception) -> < long elapsedTime = System.currentTimeMillis() - time; if (metadata != null) < System.out.printf("sent record(key=%s value=%s) " + "meta(partition=%d, offset=%d) time=%d\n", record.key(), record.value(), metadata.partition(), metadata.offset(), elapsedTime); >else < exception.printStackTrace(); >countDownLatch.countDown(); >); > countDownLatch.await(25, TimeUnit.SECONDS); >finally < producer.flush(); producer.close(); >> 

Notice the use of a CountDownLatch so we can send all N messages and then wait for them all to send.

Async Interface Callback and Async Send Method

Kafka defines a Callback interface that you use for asynchronous operations. The callback interface allows code to execute when the request is complete. The callback executes in a background I/O thread so it should be fast (don’t block it). The onCompletion(RecordMetadata metadata, Exception exception) gets called when the asynchronous operation completes. The metadata gets set (not null) if the operation was a success, and the exception gets set (not null) if the operation had an error.

The async send method is used to send a record to a topic, and the provided callback gets called when the send is acknowledged. The send method is asynchronous, and when called returns immediately once the record gets stored in the buffer of records waiting to post to the Kafka broker. The send method allows sending many records in parallel without blocking to wait for the response after each one.

Since the send call is asynchronous it returns a Future for the RecordMetadata that will be assigned to this record. Invoking get() on this future will block until the associated request completes and then return the metadata for the record or throw any exception that occurred while sending the record. KafkaProducer

Conclusion Kafka Producer example

We created a simple example that creates a Kafka Producer. First, we created a new replicated Kafka topic; then we created Kafka Producer in Java that uses the Kafka replicated topic to send records. We sent records with the Kafka Producer using async and sync send methods.

Review Kafka Producer

What does the Callback lambda do?

The callback gets notified when the request is complete.

What will happen if the first server is down in the bootstrap list? Can the producer still connect to the other Kafka brokers in the cluster?

The producer will try to contact the next broker in the list. Any of the brokers once contacted, will let the producer know about the entire Kafka cluster. The Producer will connect as long as at least one of the brokers in the list is running. If you have 100 brokers and two of the brokers in a list of three servers in the bootstrap list are down, the producer can still use the 98 remaining brokers.

When would you use Kafka async send vs. sync send?

If you were already using an async code (Akka, QBit, Reakt, Vert.x) base, and you wanted to send records quickly.

Why do you need two serializers for a Kafka record?

One of the serializers is for the Kafka record key, and the other is for the Kafka record value.

Related content

• What is Kafka?
• Kafka Architecture
• Kafka Topic Architecture
• Kafka Consumer Architecture
• Kafka Producer Architecture
• Kafka Architecture and low level design
• Kafka and Schema Registry
• Kafka and Avro
• Kafka Ecosystem
• Kafka vs. JMS
• Kafka versus Kinesis
• Kafka Tutorial: Using Kafka from the command line
• Kafka Tutorial: Kafka Broker Failover and Consumer Failover
• Kafka Tutorial
• Kafka Tutorial: Writing a Kafka Producer example in Java
• Kafka Tutorial: Writing a Kafka Consumer example in Java
• Kafka Architecture: Log Compaction

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