Kafka vs Pulsar: Key Differences and When to Use Each

Both Kafka and Pulsar are distributed messaging systems designed for high-throughput data streaming. Kafka uses a partitioned log model with brokers storing data, while Pulsar separates storage and serving layers for better scalability and multi-tenancy. Choose Kafka for mature ecosystem and simple use cases, and Pulsar for advanced features like geo-replication and flexible messaging models.

Quick Comparison

This table summarizes key factors to quickly compare Kafka and Pulsar.

Factor	Kafka	Pulsar
Architecture	Monolithic broker handles storage and serving	Decoupled serving (brokers) and storage (BookKeeper) layers
Message Model	Topic partitions with simple pub-sub	Supports pub-sub and queue models with flexible subscription types
Scalability	Scales by adding brokers and partitions	Easier horizontal scaling with separate storage layer
Geo-Replication	Available via MirrorMaker (external tool)	Built-in geo-replication with multi-region support
Latency	Low latency, optimized for throughput	Comparable latency with added flexibility
Ecosystem & Community	Large, mature, widely adopted	Growing, newer but rapidly evolving

Key Differences

Kafka uses a monolithic architecture where brokers handle both message storage and serving. This design is simple and effective for many use cases but can limit scalability and flexibility. In contrast, Pulsar separates the serving layer (brokers) from the storage layer (Apache BookKeeper), allowing independent scaling and better fault isolation.

Pulsar supports multiple messaging models including traditional pub-sub and queue semantics with different subscription types like exclusive, shared, and failover. Kafka mainly focuses on partitioned logs with consumer groups for parallelism.

For geo-replication, Pulsar has built-in support that is easier to configure and manage, while Kafka relies on external tools like MirrorMaker. Kafka has a larger ecosystem and community due to its longer presence, making it easier to find integrations and support.

Code Comparison

Here is a simple example of producing and consuming messages in Kafka using Java.

java

import org.apache.kafka.clients.producer.*;
import org.apache.kafka.clients.consumer.*;
import org.apache.kafka.common.serialization.StringSerializer;
import org.apache.kafka.common.serialization.StringDeserializer;
import java.time.Duration;
import java.util.Collections;
import java.util.Properties;

public class KafkaExample {
    public static void main(String[] args) {
        String topic = "test-topic";

        // Producer config
        Properties producerProps = new Properties();
        producerProps.put("bootstrap.servers", "localhost:9092");
        producerProps.put("key.serializer", StringSerializer.class.getName());
        producerProps.put("value.serializer", StringSerializer.class.getName());

        Producer<String, String> producer = new KafkaProducer<>(producerProps);
        producer.send(new ProducerRecord<>(topic, "key1", "Hello Kafka"));
        producer.close();

        // Consumer config
        Properties consumerProps = new Properties();
        consumerProps.put("bootstrap.servers", "localhost:9092");
        consumerProps.put("group.id", "test-group");
        consumerProps.put("key.deserializer", StringDeserializer.class.getName());
        consumerProps.put("value.deserializer", StringDeserializer.class.getName());
        consumerProps.put("auto.offset.reset", "earliest");

        Consumer<String, String> consumer = new KafkaConsumer<>(consumerProps);
        consumer.subscribe(Collections.singletonList(topic));

        ConsumerRecords<String, String> records = consumer.poll(Duration.ofSeconds(5));
        for (ConsumerRecord<String, String> record : records) {
            System.out.println("Received: " + record.value());
        }
        consumer.close();
    }
}

Output

Received: Hello Kafka

Pulsar Equivalent

Here is the equivalent example for producing and consuming messages in Pulsar using Java.

java

import org.apache.pulsar.client.api.*;

public class PulsarExample {
    public static void main(String[] args) throws PulsarClientException {
        String serviceUrl = "pulsar://localhost:6650";
        String topic = "persistent://public/default/test-topic";

        PulsarClient client = PulsarClient.builder()
                .serviceUrl(serviceUrl)
                .build();

        Producer<byte[]> producer = client.newProducer()
                .topic(topic)
                .create();

        producer.send("Hello Pulsar".getBytes());
        producer.close();

        Consumer<byte[]> consumer = client.newConsumer()
                .topic(topic)
                .subscriptionName("test-subscription")
                .subscriptionType(SubscriptionType.Exclusive)
                .subscribe();

        Message<byte[]> msg = consumer.receive();
        System.out.println("Received: " + new String(msg.getData()));
        consumer.acknowledge(msg);

        consumer.close();
        client.close();
    }
}

Output

Received: Hello Pulsar

When to Use Which

Choose Kafka when you need a mature, widely supported streaming platform with a large ecosystem and simple architecture. It is ideal for high-throughput event processing and log aggregation where your scaling needs are moderate and you prefer a stable, battle-tested solution.

Choose Pulsar when you require advanced features like multi-tenancy, geo-replication out of the box, or need to scale storage and serving independently. Pulsar is better for complex messaging patterns, large-scale deployments, and when you want flexibility in subscription models.

Key Takeaways

Kafka uses a monolithic broker design; Pulsar separates storage and serving layers for better scalability.

Pulsar supports multiple messaging models and built-in geo-replication; Kafka relies on external tools for replication.

Kafka has a larger ecosystem and is simpler to start with; Pulsar offers advanced features for complex use cases.

Use Kafka for mature, stable streaming needs; use Pulsar for flexible, large-scale, multi-tenant environments.