Apache Pulsar vs Kafka: Which is Better? [5 Critical Differences]

By: Published: January 13, 2022

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The continuous flow of data from diverse sources that may be stored, processed, and evaluated in real-time is known as Streaming Data or Stream Processing. Networking devices, applications, server log files, financial transactions, website activity, and other sources provide Data Streams that are aggregated to provide Real-time Insights and Analytics. Streaming Data solutions allow businesses to consume and analyze data in real-time, unlike Traditional Solutions that require data to be ingested and processed before being used.

Now, you can transfer data between applications using a Message Queuing system. They enable the applications to focus on the data themselves rather than how it will be shared and transferred. Message systems like Apache Kafka & Apache Pulsar enable remote communication and data transfer.

In this article, you will be introduced to the comparative study of Pulsar vs Kafka. Moreover, you will also be introduced to Apache Pulsar, Apache Kafka, and their key features. Read along to learn more about the comparative study of Pulsar vs Kafka!

Table of Contents 

What is Apache Pulsar?

Pulsar is a Multi-tenant, Cloud-native, and Open-source Server-to-Server Messaging System developed by Yahoo in 2013. Since its contribution to Apache Software Foundation (ASF) in 2016, Pulsar has grown in popularity. The Apache Pulsar Messaging System was designed to fill in the gaps of existing Open-source Messaging solutions like Multi-tenancy, Geo-Replication, and Durability.

Apache Pulsar is a Distributed Pub-sub Messaging system. The Publishers or Senders don’t send messages (or events) to specific publishers or receivers. Instead, the consumers subscribe to the topics they’re interested in and receive messages every time an event associated with that topic is published. The Pub-Sub method doesn’t require extensive Queueing or Batching. It also offers low Publish and End-to-end Latency, guaranteed Message Delivery, and Zero Data Loss.

The Two-layer Architecture separates the storage of messages from their delivery, resulting in a system that combines the flexibility and high-level techniques of Messaging, Queuing, and Lightweight computing with scalable log storage mechanisms. Apache Pulsar can thus Dynamically Scale up or Down without causing any downtime.

Apache Pulsar incorporates the best features of Traditional Messaging systems like RabbitMQ and Pub-sub (publish-subscribe) systems like Apache Kafka. With high performance, Cloud-native package, you get the best of both worlds.

5 Key Features of Apache Pulsar 

Following are some important features to keep a note of:

1) Schema Registry 

In any messaging system, Producers and Consumers must use the same language. The Pulsar software comes with a preinstalled schema registry. All you need to do is register the schema with a Pulsar Topic, and it will enforce the rules according to the schema.

Pulsar adopted two basic approaches for safety in Messaging: the Client-side Approach and the Server-side Approach. In the first approach, both Producers and Consumers are responsible for serializing and deserializing messages. In contrast, Producers and Consumers inform the system about data types transmitted via the topic in the second approach, ensuring type safety and synchronization.

2) Geo-Replication

Replicating messages to remote locations is crucial to Disaster Recovery and enabling applications to operate globally. By using Geo-replication, applications can connect to the local cluster while sending and receiving data to the rest of the world. Pulsar supports Geo-replication, allowing messages published to a Topic to be automatically replicated to the configured remote Geo-location without complicated setups or add-ons.

3) IO Connector 

The primary purpose of a messaging system is to bind together Data-intensive Systems like databases and stream processors. Apache Pulsar comes with a wide range of ready-made connectors, including MySQL, MongoDB, Cassandra, RabbitMQ, Kafka, Flume, Redis, and many others. These I/O connectors make it easy to bind various systems together.

4) Real-Time Compute

Pulsar can perform user-defined computations on the messages, eliminating the need to use an external computational system to perform fundamental transformations, such as data enrichment, filtering, and aggregation.

5) Scalable Storage 

Pulsar’s independent storage layer, coupled with support for tiered storage, allows you to keep messaging data indefinitely. Apache Pulsar does not have a physical limit on how much data it can retain and ingest.

What is Apache Kafka?

Apache Kafka is an Open-source, Distributed, Partitioned, & Replicated Commit-log-based Publish-Subscribe Messaging System. By offering a real-time Publish-Subscribe solution, Apache Kafka can use data volumes that grow in the order of magnitude larger than the actual data to overcome the challenges of consuming them. Apache Kafka supports parallel data loading in Hadoop Systems as well.

Apache Kafka combines Queuing and Publish-subscribe Messaging models that allow for data to be distributed across many consumers. It also uses a partitioned log model to stitch the two messaging solutions so there can be multiple subscribers for the same topic, and they are assigned a partition for better Scalability. 

5 Key Features of Apache Kafka

Below are some important notable features of Kafka:

1) Real-Time

Event-based systems, such as Complex Event Processing (CEP) systems, require that messages produced by producer threads be visible immediately to consumer threads.

2) Multiple Client Support 

It is easy to integrate clients from different platforms such as Java, .NET, PHP, Ruby, and Python with Apache Kafka.

3) Persistent Messaging 

Despite vast volumes of stored messages, Apache Kafka provides Constant-time Performance even with large batches of data in the TB range. Apache Kafka persists messages on disk and replicates them within the Cluster to prevent data loss.

4) High Throughput

Kafka is designed with Big Data in mind and can handle hundreds of megabytes of R/W Commands/second from many clients on commodity hardware.

5) Distributed

Apache Kafka’s cluster-centric design explicitly distributes messages across Kafka servers and maintains per-partition ordering semantics across consumer machines. The Kafka cluster can grow transparently and elastically without any downtime.

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Apache Pulsar vs Kafka: What are the 5 Key Differences?

Here are some major key differences to be noticed

1) Pulsar vs Kafka: Architecture

Pulsar vs Kafka - Pulsar vs Kafka: Architecture
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Both Apache Kafka and Pulsar interact through topics that are split up into partitions. Further, these partitions distribute data across nodes to be consumed by multiple consumers. The rudimentary difference is the architectural approach, where Kafka follows a Partition-centred Design, whereas Pulsar follows a Multi-layered Architecture Design. 

Apache Kafka follows a Monolithic Architecture where partitions are directly stored to the Leader Node, and data is replicated to the Replica Node for fault tolerance. The biggest drawback of Kafka is that the partition is stored on a local disk that has limited space. Another disadvantage of Kafka is that once the Replica Node size is filled, incoming messages will halt, leading to data loss. In Kafka, Brokers aren’t stateless, which means another Broker must synchronize state from the current broker if it fails. 

Apache Pulsar follows a Segment-centric Approach where Partitions are subdivided into segments evenly distributed across Bookies. This approach helps Redundancy and Scaling, removing the need to replicate content when the memory is maxed out. Further, Brokers are stateless in Apache Pulsar Architecture. Apache Pulsar maintains state, but the data is stored in Apache Bookkeeper rather than Brokers.

2) Pulsar vs Kafka: Message Consumption 

Consumers pull messages from the Server when using Apache Kafka. The Long-polling method ensures that new messages are consumed almost immediately.

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

3) Pulsar vs Kafka: Retention 

Apache Kafka and Pulsar both support long-term storage, but Kafka allows a smart compaction strategy instead of creating snapshots and leaving the Topic as is. Apache Pulsar provides for the deletion of messages based on consumption. Both systems will likely do the job, but users must consider storage capabilities before selecting a platform. 

4) Pulsar vs Kafka: Message Acknowledgement

Pulsar vs Kafka - Pulsar vs Kafka: Message Acknowledgement
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Apache Kafka acknowledges messages at the Consumer Group Level for each partition separately. It is not possible for two Consumers of the same Consumer Group to process two messages from the same partition simultaneously. Partitioning ensures that messages arrive in order. 

Pulsar vs Kafka - Pulsar vs Kafka: Message
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Whereas Apache Pulsar allows users to add multiple consumers to one topic and retrieve messages simultaneously, each of which can be acknowledged individually. The purpose of Pulsar is to manage issues as Task Queues, also known as Scheduling.

5) Pulsar vs Kafka: Documentation & Community Support 

Compared to Pulsar, Apache Kafka has a much larger and more active community because it is more popular and established. Despite the smaller size of the community, Apache Pulsar provides extensive documentation to support developers.


In this article, you have learned about the comparative understanding of Apache Pulsar vs Kafka. This article also provided information on Apache Pulsar, Kafka and their key features. Apache Pulsar has a clear advantage over Kafka in Separating Tenants, Storing Older Data on Cheaper Storage, efficiently Replicating Clusters across geographic boundaries, consolidating Queueing and Streaming capabilities into one system. 

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Share your experience of understanding the comparative study of Apache Pulsar vs Kafka in the comment section below! We would love to hear your thoughts.

Freelance Technical Content Writer, Hevo Data

Srishty loves breaking down complexities of data integration and analysis, through her detailed and comprehensive content to help data teams in understanding intricate subjects and solve business problems.

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