Interoperability and Protocol Integration Between Apache Pulsar and Apache Kafka

The landscape of modern event streaming is dominated by two massive, yet architecturally distinct, ecosystems: Apache Kafka and Apache Pulsar. As enterprises seek to modernize their data pipelines, a critical technical challenge has emerged: how to leverage the specialized advantages of Pulsar—such as multi-tenancy and decoupled storage—without rewriting the massive body of existing logic built on the Kafka client API. The intersection of these two technologies is not merely a matter of data movement; it is a complex study in protocol translation, client-side abstraction, and broker-side implementation. Understanding how Apache Pulsar interacts with the Kafka ecosystem requires a deep examination of three distinct integration vectors: the Kafka client compatibility wrapper, the KoP (Kafka on Pulsar) protocol handler, and the Kafka Source connector. Each method offers a different level of abstraction, varying degrees of performance, and specific use cases ranging from simple application migration to high-throughput data ingestion.

The Pulsar Kafka Compatibility Wrapper

For development teams heavily invested in the Apache Kafka Java client API, the barrier to entry for Pulsar often feels insurmountable. Re-architecting microservices to move from Kafka's consumer/producer models to Pulsar’s subscription-based model requires significant engineering hours. To mitigate this, Apache Pulsar provides a compatibility wrapper designed to allow existing Kafka-based applications to communicate with a Pulsar broker with minimal code intervention.

The mechanism relies on substituting the standard Kafka client libraries with specific Pulsar-maintained artifacts. These artifacts act as a translation layer, intercepting Kafka-specific API calls and translating them into Pulsar's internal wire protocol. This allows the business logic—the "how" of data processing—to remain untouched while the "where" of data storage changes from Kafka to Pulsar.

There are two primary artifacts used for this purpose: pulsar-client-kafka and pulsar-client-kafka-original. These reside in a dedicated repository under apache/pulsar-adapters. It is a critical technical requirement that developers pin their dependencies correctly to avoid runtime exceptions or classpath conflicts.

Table 1: Dependency Management for Kafka-to-Pulsar Migration

A vital nuance for DevOps engineers is the versioning constraint. Although a Pulsar cluster might be running on version 3.x or even 4.x, the Kafka compatibility artifacts are not updated in lockstep with the main broker releases. Currently, the last released version on Maven Central is 2.11.0. Therefore, even when deploying a modern Pulsar backend, the application's pom.xml must remain pinned to version 2.11.0 to ensure stable communication.

The migration process follows a strict two-step dependency replacement strategy to ensure the application can successfully resolve classes during the build phase.

Step 1: Removal of the standard Kafka client. In the existing pom.xml file, the following dependency must be identified and removed:

xml <dependency> <groupId>org.apache.kafka</groupId> <artifactId>kafka-clients</artifactId> <version>0.10.2.1</version> </dependency>

Step 2: Inclusion of the Pulsar Kafka wrapper. The developer must then inject the following dependency into the configuration:

xml <dependency> <groupId>org.apache.pulsar</groupId> <artifactId>pulsar-client-kafka</artifactId> <version>2.11.0</version> </dependency>

By performing this swap, the application's existing code functions without any internal changes, effectively masking the underlying complexity of the Pulsar broker from the application layer.

Protocol-Level Integration via KoP (Kafka on Pulsar)

While the client wrapper works at the application layer, KoP (Kafka on Pulsar) operates at the protocol layer. This is a fundamentally different approach that provides native Apache Kafka protocol support directly on the Pulsar brokers. Instead of changing how the application thinks (the client API), KoP changes how the broker listens.

KoP is implemented as a Pulsar protocol handler plugin, identified by the protocol name kafka. When the Pulsar broker initializes, it loads this handler, enabling it to understand the Kafka wire protocol. This allows "pure" Kafka clients—those that have no knowledge of Pulsar—to connect to a Pulsar cluster as if it were a standard Kafka broker. This is particularly powerful for migration scenarios where legacy systems or third-party tools cannot be modified to use Pulsar-specific libraries.

By implementing the Kafka wire protocol on Pulsar, KoP leverages existing Pulsar components to handle the heavy lifting of data management. It utilizes Pulsar's topic discovery mechanisms, the ManagedLedger distributed log library, and its cursor management system. This ensures that while the interface looks like Kafka, the backend benefits from Pulsar's architecture.

Table 2: KoP Strategic Advantages and Functional Benefits

Users migrating to KoP can unlock several high-level features that were previously unavailable in a standard Kafka environment. These include streamlined operations through multi-tenancy, simplified scaling due to Pulsar's rebalance-free architecture, and serverless event processing via Pulsar Functions.

However, KoP is not without its technical requirements and versioning considerations. It is compatible with Kafka clients version 0.9 or higher. A significant complication arises with Kafka clients 3.2.0 and above due to the introduction of KIP-679. To maintain compatibility with these newer clients, specific configurations must be manually enabled within the KoP setup:

yaml kafkaTransactionCoordinatorEnabled=true brokerDeduplicationEnabled=true

It should be noted that the development trajectory for KoP has shifted. While KoP was a primary method for integration, it has now been archived. Organizations seeking this level of integration are encouraged to move toward KSN (Kafka on StreamNative), which serves as the evolved successor for cloud-native Kafka-on-Pulsar implementations.

Ingesting Kafka Data into Pulsar via Source Connectors

For scenarios where the goal is not to replace Kafka but to bridge it—moving data from an existing Kafka cluster into a Pulsar cluster for specialized processing—the Pulsar IO Kafka Source connector is the professional standard. This is an "ingestion" model rather than a "protocol" model.

This method is essential for organizations that have a "source of truth" in Kafka but want to utilize Pulsar's advanced features (like tiered storage or Pulsar Functions) for downstream analytics and real-time processing. The process involves deploying a Pulsar connector that acts as a Kafka consumer and a Pulsar producer simultaneously.

Implementation Workflow for Local Testing

To validate a Kafka-to-Pulsar pipeline, an engineer can simulate the entire environment using Docker. This involves spinning up a standalone Pulsar instance and a Kafka producer to test the end-to-end flow.

First, the Pulsar container must be initialized with the necessary ports and volume mappings for data persistence:

bash docker pull apachepulsar/pulsar:latest docker run -d -it -p 6650:6650 -p 8080:8080 -v $PWD/data:/pulsar/data --name pulsar-kafka-standalone apachepulsar/pulsar:latest bin/pulsar standalone -

Once the broker is active, the specific Kafka Source connector file (pulsar-io-kafka.nar) and the configuration file (kafkaSourceConfig.yaml) must be moved into the container environment to allow Pulsar to interface with the external Kafka broker.

bash docker cp pulsar-io-kafka.nar pulsar-kafka-standalone:/pulsar docker cp kafkaSourceConfig.yaml pulsar-kafka-standalone:/pulsar/conf -

The next phase involves initiating the local run of the source connector. This command instructs Pulsar to act as a bridge, pulling data from a Kafka topic and pushing it into a Pulsar topic.

bash docker exec -it pulsar-kafka-standalone /bin/bash ./bin/pulsar-admin source localrun \ --archive $PWD/pulsar-io-kafka.nar \ --tenant public \ --namespace default \ --name kafka \ --destination-topic-name my-topic \ --source-config-file $PWD/conf/kafkaSourceConfig.yaml \ --parallelism 1

To verify the flow, a Python-based Kafka producer can be used to send messages into the Kafka side of the bridge:

python from kafka import KafkaProducer producer = KafkaProducer(bootstrap_servers='localhost:9092') future = producer.send('my-topic', b'hello world') future.get()

Finally, a Pulsar client can be used to subscribe to the destination topic and confirm the message has successfully migrated from the Kafka ecosystem into the Pulsar ecosystem.

Architectural Comparison: The Foundational Divergence

To understand why these integration layers exist, one must understand the fundamental architectural differences between the two systems. While both are used for event streaming, they are not identical.

Kafka is often described as a "pure distributed log." Its design philosophy is centered on high-throughput, high-scale event streaming through a single-layer architecture. In Kafka, the broker is responsible for both the data processing logic (to a degree) and the storage of the log. This simplicity makes Kafka exceptionally efficient for smaller deployments where operational overhead must be kept to a minimum.

Pulsar, conversely, is more of a hybrid system. It sits between a traditional messaging system like RabbitMQ and a distributed log like Kafka. Pulsar's architecture is multi-layered, separating the serving layer (Brokers) from the storage layer (Apache BookKeeper). This separation is the primary driver behind Pulsar's ability to offer features like infinite retention via tiered storage and its unique "rebalance-free" scaling capability. While Kafka requires partition rebalancing when a new node is added to a cluster—a resource-intensive process—Pulsar simply adds new storage nodes, and the brokers immediately begin utilizing them.

Table 3: Functional Comparison Matrix

Deployment and Ecosystem Considerations

When deciding between the two, the "simplicity vs. capability" trade-off is paramount. Kafka's ecosystem is significantly more mature and widespread. If a team requires immediate access to a vast array of third-party connectors and community-tested patterns, Kafka is the safer bet. Furthermore, because Kafka's ecosystem is so large, finding managed service providers and professional support is much easier.

Pulsar's strength lies in its versatility. It is designed for the "brave at heart" who require a system that can simultaneously handle queuing (point-to-point) and event streaming (pub-sub) within the same infrastructure. Pulsar's ability to handle geo-replication at the namespace level via a few CLI commands provides a level of operational agility that Kafka, which relies on external tools like MirrorMaker, struggles to match.

Strategic Analysis of Integration Paths

The choice of integration method—Wrapper, KoP, or Source Connector—is not arbitrary; it is a strategic decision based on the lifecycle stage of the application.

The Compatibility Wrapper is the optimal choice for "Application Modernization." It is best suited for teams who are moving toward a cloud-native or multi-tenant infrastructure but are constrained by the development time required to rewrite complex Java consumers. It provides a low-risk, high-reward bridge that preserves the investment in existing codebases.

KoP (and its successor KSN) is the choice for "Ecosystem Coexistence." This is the preferred method for organizations that have a heterogeneous environment where various legacy tools (like older versions of Confluent's ecosystem) must communicate with a modern Pulsar backend. By solving the problem at the protocol level, it eliminates the need to touch a single line of client code, making it the most "transparent" integration method.

The Kafka Source Connector is the choice for "Hybrid Data Pipelines." It is not an alternative to migration, but a tool for coexistence. This is the professional standard for companies that are not ready to abandon Kafka as their primary event backbone but want to leverage Pulsar's serverless processing (Pulsar Functions) or its tiered storage for long-term analytical auditing.

In conclusion, the interoperability between Apache Pulsar and Apache Kafka represents the cutting edge of data engineering. Whether through the application-level abstraction of the Java wrapper, the protocol-level translation of KoP, or the data-level movement of the IO connectors, the ability to bridge these two worlds allows organizations to capture the high-throughput strengths of Kafka while adopting the elastic, multi-tenant, and serverless capabilities of Pulsar.

Sources

1. Apache Pulsar Documentation: Kafka Adaptors
2. GitHub: StreamNative KoP Repository
3. Apache Pulsar Documentation: Kafka Source Connector
4. Milvus: How is Apache Pulsar different from Apache Kafka
5. Quix: Kafka vs. Pulsar Comparison
6. Confluent: Compare Kafka vs. Pulsar