Apache Kafka – Create Safe Producer using Java

Apache Kafka Producers are going to write data to topics and topics are made of partitions. Now the producers in Kafka will automatically know to which broker and partition to write based on your message and in case there is a Kafka broker failure in your cluster the producers will automatically recover from it which makes Kafka resilient and which makes Kafka so good and used today. But how to create a safe producer in Apache Kafka? So there are two answers to this question according to Kafka Version you are using.

Prerequisites:

• Apache Kafka – Producer Acknowledgement and min.insync.replicas
• Apache Kafka – Producer Retry and max.in.flight.requests.per.connection
• Apache Kafka – Idempotent Producer

Kafka Version < 0.11

If you are using Kafka Version < 0.11 then you have to set the following producer properties in order to make your producer safe.

• acks = all (producer level) : It will ensure that data is properly replicated before an ack is received
• min.insync.replicas = 2 (broker/topic level) : It will ensure that two brokers in In-Sync Replica at least have the data after an ack
• retries = Integer.MAX_VALUE (producer level) : It will ensure that transient errors are retried indefinitely
• max.inflight.requests = 1 (producer level) : It will ensure that only one request is tried at any time, preventing message re-ordering in case of retries

Kafka Version >= 0.11

If you are using Kafka Version >= 0.11 then you have to set the following producer properties in order to make your producer safe.

• enable idempotence = true (producer level)
• acks = all (producer level) : It will ensure that data is properly replicated before an ack is received
• min.insync.replicas = 2 (broker/topic level) : It will ensure that two brokers in In-Sync Replica at least have the data after an ack
• retries = Integer.MAX_VALUE (producer level) : It will ensure that transient errors are retried indefinitely
• max.inflight.requests = 5 (producer level) : Default

Note: Running a "safe producer" might impact throughput and latency, always test it for your use cases.

Create Safe Producer using Java

To create a safe producer using java we have to set these additional properties in the code

Example Project

In this example, we are going to discuss the step-by-step implementation of how to Create a Safe Apache Kafka Producer using Java.

Step by Step Implementation

Step 1: Create a New Apache Kafka Project in IntelliJ

To create a new Apache Kafka Project in IntelliJ using Java and Maven please refer to How to Create an Apache Kafka Project in IntelliJ using Java and Maven.

Step 2: Install and Run Apache Kafka

To Install and Run Apache Kafka in your local system please refer to How to Install and Run Apache Kafka.

Step 3: Create Producer using Java

First, we have to create Producer Properties. And to create Producer Properties refer to the below code snippet

Create Producer Properties:

Properties properties = new Properties();
properties.setProperty(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServer);
properties.setProperty(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());
properties.setProperty(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, StringSerializer.class.getName());

Add Additional Properties for Creating Safe Producer:

properties.setProperty(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, "true");
properties.setProperty(ProducerConfig.ACKS_CONFIG, "all");
properties.setProperty(ProducerConfig.RETRIES_CONFIG, Integer.toString(Integer.MAX_VALUE));
properties.setProperty(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION, "5");

Create the Producer:

KafkaProducer<String, String> producer = new KafkaProducer<>(properties);

Create a Producer Record:

ProducerRecord<String, String> record =
                new ProducerRecord<>("first_neveropen_topic", "hello_neveropen");

Send data asynchronously:

producer.send(record);

Flush and Close the Producer:

producer.flush();
producer.close();

Below is the complete code. Comments are added inside the code to understand the code in more detail.

Step 4: Run the Application

Now run the application and below is the output. Please have a look at the bold properties in the below output console.

[main] INFO org.apache.kafka.clients.producer.ProducerConfig - ProducerConfig values: 
    acks = all
    batch.size = 16384
    bootstrap.servers = [127.0.0.1:9092]
    buffer.memory = 33554432
    client.dns.lookup = use_all_dns_ips
    client.id = producer-1
    compression.type = none
    connections.max.idle.ms = 540000
    delivery.timeout.ms = 120000
    enable.idempotence = true
    interceptor.classes = []
    internal.auto.downgrade.txn.commit = false
    key.serializer = class org.apache.kafka.common.serialization.StringSerializer
    linger.ms = 0
    max.block.ms = 60000
    max.in.flight.requests.per.connection = 5
    max.request.size = 1048576
    metadata.max.age.ms = 300000
    metadata.max.idle.ms = 300000
    metric.reporters = []
    metrics.num.samples = 2
    metrics.recording.level = INFO
    metrics.sample.window.ms = 30000
    partitioner.class = class org.apache.kafka.clients.producer.internals.DefaultPartitioner
    receive.buffer.bytes = 32768
    reconnect.backoff.max.ms = 1000
    reconnect.backoff.ms = 50
    request.timeout.ms = 30000
    retries = 2147483647
    retry.backoff.ms = 100
    sasl.client.callback.handler.class = null
    sasl.jaas.config = null
    sasl.kerberos.kinit.cmd = /usr/bin/kinit
    sasl.kerberos.min.time.before.relogin = 60000
    sasl.kerberos.service.name = null
    sasl.kerberos.ticket.renew.jitter = 0.05
    sasl.kerberos.ticket.renew.window.factor = 0.8
    sasl.login.callback.handler.class = null
    sasl.login.class = null
    sasl.login.refresh.buffer.seconds = 300
    sasl.login.refresh.min.period.seconds = 60
    sasl.login.refresh.window.factor = 0.8
    sasl.login.refresh.window.jitter = 0.05
    sasl.mechanism = GSSAPI
    security.protocol = PLAINTEXT
    security.providers = null
    send.buffer.bytes = 131072
    socket.connection.setup.timeout.max.ms = 30000
    socket.connection.setup.timeout.ms = 10000
    ssl.cipher.suites = null
    ssl.enabled.protocols = [TLSv1.2, TLSv1.3]
    ssl.endpoint.identification.algorithm = https
    ssl.engine.factory.class = null
    ssl.key.password = null
    ssl.keymanager.algorithm = SunX509
    ssl.keystore.certificate.chain = null
    ssl.keystore.key = null
    ssl.keystore.location = null
    ssl.keystore.password = null
    ssl.keystore.type = JKS
    ssl.protocol = TLSv1.3
    ssl.provider = null
    ssl.secure.random.implementation = null
    ssl.trustmanager.algorithm = PKIX
    ssl.truststore.certificates = null
    ssl.truststore.location = null
    ssl.truststore.password = null
    ssl.truststore.type = JKS
    transaction.timeout.ms = 60000
    transactional.id = null
    value.serializer = class org.apache.kafka.common.serialization.StringSerializer

[main] INFO org.apache.kafka.common.utils.AppInfoParser - Kafka version: 2.8.0
[main] INFO org.apache.kafka.common.utils.AppInfoParser - Kafka commitId: ebb1d6e21cc92130
[main] INFO org.apache.kafka.common.utils.AppInfoParser - Kafka startTimeMs: 1674753797898
[kafka-producer-network-thread | producer-1] INFO org.apache.kafka.clients.Metadata - [Producer clientId=producer-1] Cluster ID: OIx0v3RmSd2y0zKUaBM7-Q
[main] INFO org.apache.kafka.clients.producer.KafkaProducer - [Producer clientId=producer-1] Closing the Kafka producer with timeoutMillis = 9223372036854775807 ms.
[main] INFO org.apache.kafka.common.metrics.Metrics - Metrics scheduler closed
[main] INFO org.apache.kafka.common.metrics.Metrics - Closing reporter org.apache.kafka.common.metrics.JmxReporter
[main] INFO org.apache.kafka.common.metrics.Metrics - Metrics reporters closed
[main] INFO org.apache.kafka.common.utils.AppInfoParser - App info kafka.producer for producer-1 unregistered

Process finished with exit code 0

And you can see the message in the Kafka consumer console. Run this command

kafka-console-consumer --bootstrap-server 127.0.0.1:9092 --topic first_gfg_topic

Output: