Pulsar vs Kafka

Architecture

Both Apache Kafka and Pulsar interact through topics divided into partitions. These partitions distribute data among brokers to be consumed by multiple consumers. The key difference lies in Kafka following a partition-centered design, while Pulsar adopts a multi-layered architecture.

Apache Kafka implements a monolithic architecture where partitions are stored directly on the Leader broker, and data is replicated to Follower brokers for fault tolerance. A significant drawback of Kafka is storing partitions on a locally limited disk space. Another disadvantage is the potential data loss when incoming messages halt due to the Follower broker reaching its capacity. Brokers in Kafka are not stateless, meaning if one fails, another broker needs to synchronize its state (or data) from the failed broker.

Apache Pulsar follows a Segment-centric approach where partitions are evenly distributed among Bookies (an Apache BookKeeper component). This approach eliminates the need to replicate content when memory is full, contributing to Redundancy and Scalability. Additionally, brokers in Apache Pulsar are stateless, and data is stored in Apache Bookkeeper rather than in brokers.

Message Consumption

In Apache Kafka, consumers retrieve messages from the server, and the long-polling method allows almost instant consumption of new messages.

Apache Pulsar uses the Publish-Subscribe (Pub-Sub) model. Producers publish messages, and consumers subscribe to receive them.

Retention

Both Apache Kafka and Apache Pulsar support long-term storage. However, Kafka allows an intelligent compression strategy instead of creating snapshots and leaving the topic as is. Apache Pulsar allows message deletion based on consumption. Both systems may serve the purpose, but users should consider storage features before choosing a platform.

Message Acknowledgement

Apache Kafka acknowledges messages at the Consumer Group level, separately for each partition. It is not possible for two consumers in the same consumer group to process two messages from the same partition simultaneously, ensuring message order.

Apache Pulsar allows users to add multiple consumers to a topic, and each can receive and acknowledge messages independently. Pulsar aims to manage issues like Task Queues, also known as Scheduling.

Documentation & Community Support

Compared to Apache Pulsar, Apache Kafka has a larger and more active community due to its popularity and established presence. Despite having a smaller community, Apache Pulsar provides comprehensive documentation to support developers.

Conclusion

Apache Pulsar has a clear advantage over Kafka in areas such as multi-tenancy, cost-effective storage for older data, efficient geographical replication of clusters, and combining queuing and streaming capabilities in a single system.

When it comes to trust, configuration, documentation, use cases, and support, Apache Kafka is more advantageous.