Apache Pulsar vs Kafka: Features, Advantages, and Practical Guide

Apache Pulsar is an Apache top‑level project that offers a cloud‑native, distributed messaging platform with decoupled compute and storage, multi‑tenant support, persistent storage, and geo‑replication.

Kafka basics : Kafka, created by LinkedIn in 2011, is a widely used distributed log system with a rich ecosystem (Schema Registry, Connect, Streams, KSQL). While fast and easy to install, it suffers from operational complexity, scaling difficulties, and limited multi‑tenant isolation.

Kafka pain points include hard scaling due to broker‑storage coupling, expensive storage, potential message loss on replica lag, complex partition planning, and cumbersome rebalancing.

Pulsar basics : Originating from Yahoo! in 2013 and donated to Apache in 2016, Pulsar separates storage (Apache BookKeeper) from brokers, enabling horizontal scaling, fast rebalancing, and high reliability. It supports both log‑style streaming and traditional queue semantics.

Pulsar features include built‑in multi‑tenant isolation, tiered storage, virtual topics, schema registry, various subscription types, server‑side deduplication, integrated Prometheus metrics, and native support for functions (serverless compute) in Java, Python, and Go.

Getting started (requires JDK):

$ wget https://archive.apache.org/dist/pulsar/pulsar-2.6.1/apache-pulsar-2.6.1-bin.tar.gz

$ wget https://archive.apache.org/dist/pulsar/pulsar-2.6.1/connectors/{connector}-2.6.1.nar

$ bin/pulsar standalone

$ bin/pulsar-client produce my-topic --messages "hello-pulsar"

$ bin/pulsar-client consume my-topic -s "first-subscription"

Akka Streams example (Scala) :

val topic = Topic("persistent://standalone/mytopic")
val consumerFn = () => client.consumer(ConsumerConfig(topic, subscription))
import com.sksamuel.pulsar4s.akka._
val pulsarSource = source(consumerFn, Some(MessageId.earliest))

Sink example:

val topic = Topic("persistent://standalone/mytopic")
val producerFn = () => client.producer(ProducerConfig(topic))
import com.sksamuel.pulsar4s.akka._
val pulsarSink = sink(producerFn)

Full Akka example:

object Example {
  import com.sksamuel.pulsar4s.{ConsumerConfig, MessageId, ProducerConfig, PulsarClient, Subscription, Topic}
  import org.apache.pulsar.client.api.Schema
  implicit val system: ActorSystem = ActorSystem()
  implicit val materializer: ActorMaterializer = ActorMaterializer()
  implicit val schema: Schema[Array[Byte]] = Schema.BYTES
  val client = PulsarClient("pulsar://localhost:6650")
  val intopic = Topic("persistent://sample/standalone/ns1/in")
  val outtopic = Topic("persistent://sample/standalone/ns1/out")
  val consumerFn = () => client.consumer(ConsumerConfig(topics = Seq(intopic), subscriptionName = Subscription("mysub")))
  val producerFn = () => client.producer(ProducerConfig(outtopic))
  val control = source(consumerFn, Some(MessageId.earliest))
    .map(msg => ProducerMessage(msg.data))
    .to(sink(producerFn)).run()
  Thread.sleep(10000)
  control.stop()
}

Pulsar Function example (Python) :

def process(input):
    return "{}!".format(input)

Pulsar Function example (Go) :

package main
import (
    "context"
    "fmt"
    "github.com/apache/pulsar/pulsar-function-go/pf"
)
func HandleRequest(ctx context.Context, in []byte) error {
    fmt.Println(string(in) + "!")
    return nil
}
func main() {
    pf.Start(HandleRequest)
}

Function deployment via Pulsar‑Admin CLI:

$ bin/pulsar-admin functions create \
    --py ~/router.py \
    --classname router.RoutingFunction \
    --tenant public \
    --namespace default \
    --name route-fruit-veg \
    --inputs persistent://public/default/basket-items

$ bin/pulsar-admin functions create \
    --name my-effectively-once-function \
    --processing-guarantees EFFECTIVELY_ONCE

Advantages of Pulsar over Kafka include richer feature set (functions, multi‑tenant, schema registry, tiered storage), flexible subscription models, multiple persistence options, no need for pre‑planned scaling, support for both queue and stream semantics, decoupled storage for better scalability, easier operations, SQL integration via Presto, lower storage cost, higher performance in benchmarks, serverless functions, built‑in load balancer and metrics, superior geo‑replication, unlimited topics, and Kafka compatibility.

Disadvantages are relatively fewer community resources, the need for additional components (BookKeeper), fewer client plugins, and less managed cloud offering compared to Confluent.

Typical use cases cover pub/sub messaging, distributed logs, event sourcing, microservices communication, SQL analytics, and serverless functions.

When to consider Pulsar : when you need both queue and stream capabilities, easy geo‑replication, multi‑tenant isolation, long‑term retention without off‑loading, high performance, or a unified platform for multiple messaging patterns.

Conclusion : While Kafka remains mature and widely adopted, its operational complexity and slower feature evolution open space for Pulsar, which offers a modern, scalable, and feature‑rich alternative; however, a careful evaluation, benchmarking, and proof‑of‑concept are recommended before migration.