Apache Pulsar vs Apache Kafka: Architecture, Performance, and Advantages

Since its creation by LinkedIn in 2011, Apache Kafka has been the dominant large‑scale messaging system, handling millions of messages daily for companies like Twitter and Uber, but its architecture increasingly struggles with the billions‑level message growth seen today.

Kafka faces numerous pain points: difficult horizontal scaling due to broker‑based persistence, partition rebalancing overhead, ISR leader election issues, complex capacity planning, performance degradation during rebalancing, potential data loss during expansion, lack of native offset management, disk‑space pressure, unreliable MirrorMaker replication, reliance on external stream processors (Storm, Spark), and limited multi‑tenant isolation.

Apache Pulsar emerged as a modern alternative, originally developed by Yahoo in 2013 and donated to the Apache Foundation in 2016; it is now a top‑level Apache project adopted by Yahoo, Twitter, and many other firms.

Pulsar’s architecture separates compute and storage by using Apache BookKeeper for durable, low‑latency storage, making brokers stateless and enabling seamless horizontal scaling without moving data. It supports tiered storage (e.g., Amazon S3) for virtually unlimited retention, allowing topics to serve as a data lake while still providing real‑time access.

Performance benchmarks from GigaOm show Pulsar delivering up to 2.5× higher throughput and 40% lower latency than Kafka for single‑partition, 100‑byte messages, with the ability to publish over 220,000 messages per second.

Pulsar also offers built‑in multi‑tenant isolation, fine‑grained access control, native TLS/JWT authentication, optional end‑to‑end encryption, and a rich ecosystem of client libraries (Java, Go, Python, C++, WebSocket). Its Functions framework provides serverless stream processing (FaaS) similar to AWS Lambda, with examples such as a Java word‑count function:

package org.example.functions;
import org.apache.pulsar.functions.api.Context;
import org.apache.pulsar.functions.api.Function;
import java.util.Arrays;
public class WordCountFunction implements Function<String, Void> {
    // This is invoked every time messages published to the topic
    @Override
    public Void process(String input, Context context) throws Exception {
        Arrays.asList(input.split(" ")).forEach(word -> {
            String counterKey = word.toLowerCase();
            context.incrCounter(counterKey, 1);
        });
        return null;
    }
}

Pulsar SQL, powered by the Presto engine, enables ad‑hoc queries across stored messages, e.g.:

show tables in pulsar."public/default"

Cross‑region replication is native and tenant‑aware, ensuring message integrity across clusters, while security features such as TLS, JWT, and optional encryption protect data in transit and at rest.

For Kafka users, migration to Pulsar is straightforward via built‑in connectors that can ingest Kafka topics directly or import existing data, minimizing disruption.

In summary, Pulsar addresses the core limitations of Kafka—scalability, storage management, multi‑tenant isolation, and operational complexity—while delivering superior performance and a richer feature set, making it a compelling choice for modern large‑scale streaming architectures.