Why Pulsar Might Outperform Kafka: Key Advantages and Drawbacks

Pulsar is a Kafka‑like message middleware, open‑sourced by Yahoo, designed to address many of Kafka’s shortcomings.

1. Kafka Overview

Kafka was created in 2011 by LinkedIn and has become the dominant messaging system, supporting many features such as:

Schema Registry

Kafka Connect for integrating other data sources

Kafka Streams for distributed stream processing

KSQL for SQL‑like queries on topics

…

Kafka is fast, easy to install, and popular across a wide range of use cases.

2. Kafka Pain Points

Scaling is difficult because brokers store data; moving a broker requires costly partition and replica re‑replication.

Lacks true multi‑tenant isolation.

Asynchronous replication can lead to data loss.

Requires careful planning of broker, topic, partition, and replica counts to avoid scaling issues.

Offset‑based consumption can be cumbersome for simple messaging needs.

Cluster rebalancing impacts producer and consumer performance.

MirrorMaker geo‑replication has limitations; companies like Uber built custom solutions.

3. Pulsar Overview

Pulsar was created by Yahoo in 2013, donated to the Apache Foundation in 2016, and is now a top‑level Apache project.

Companies such as Yahoo, Verizon, and Twitter use Pulsar to handle massive messaging workloads.

Pulsar claims to be faster than Kafka, cheaper to operate, and to resolve many of Kafka’s pain points.

It can function both as a distributed log system like Kafka and as a simple message broker similar to RabbitMQ.

Pulsar offers multiple subscription types, delivery guarantees, and retention policies.

4. Pulsar Features

Built‑in Multi‑Tenant Support : Different teams can share a cluster while remaining isolated, with authentication, authorization, and quota controls.

Multi‑Layer Architecture : Uses Apache BookKeeper as a separate ledger layer; brokers are decoupled from storage, enabling easier scaling, rebalancing, and maintenance.

Tiered Storage : Old data can be automatically moved to external storage (e.g., Amazon S3) without client changes, allowing cheap cold‑data retention.

Functions : Lightweight compute API that lets developers run simple processing tasks without deploying a full stream‑processing engine.

Security : Built‑in proxy, multi‑tenant security mechanisms, and pluggable authentication.

Fast Rebalancing : Partitions are split into smaller chunks, making rebalancing quick.

Multi‑System Integration : Can integrate easily with Kafka, RabbitMQ, and other messaging systems.

Multi‑Language Support : Client libraries are available for Go, Java, Scala, Node.js, Python, and more.

5. Conclusion

Pulsar indeed addresses many of Kafka’s shortcomings, as Yahoo built it specifically to solve those problems.

However, Pulsar’s adoption is far behind Kafka’s, and Kafka benefits from Confluent’s professional support, which Pulsar lacks. This leads to fewer extensions, a smaller talent pool, and other challenges.

Both Pulsar and Kafka have strengths and weaknesses; no technology is perfect. Choosing the right solution depends on the specific requirements and constraints of your project.

Reference: https://itnext.io/pulsar-advantages-over-kafka-7e0c2affe2d6