Apache Pulsar and Apache Kafka are both popular distributed messaging systems, but they differ in their architecture and features. While Kafka is designed primarily as a log-based messaging system where producers write messages to topics that consumers read from, Pulsar offers a more flexible architecture that naturally supports both topics and queues. Pulsar distinguishes itself with a multi-layered design that separates message storage from serving, leading to better scalability and lower latency.
One significant difference between the two is how they handle data retention and message delivery. Kafka stores messages in a commit log, with data retention policies based on time or size, but it primarily works with topics that allow for a single consumer group to read from them. In contrast, Pulsar supports both topics and subscription models, allowing multiple subscription types such as exclusive, shared, and failover. This makes Pulsar a good fit for scenarios where you need different consumer behaviors for the same message stream, often simplifying application logic. For example, if you have multiple services needing to consume the same message but with different processing rules, Pulsar's subscription model can simplify managing those requirements.
Another notable difference lies in the ease of scaling. Kafka typically requires careful management of brokers and partitions, particularly as the workload grows. Scaling Kafka often involves balancing partitions across multiple brokers, which can be complex. Meanwhile, Pulsar is designed to scale horizontally with minimal manual intervention. It can handle a large number of topics and subscriptions without the complexities of rebalancing partitions. This makes Pulsar a more attractive option for dynamic environments where workloads can change rapidly, allowing developers to focus more on application development rather than infrastructure management.