Kafka vs. Pulsar: Choosing Your Streaming Platform

• 15/1/2024
• 5-minute read

In today’s data-driven world, processing real-time information streams has become critical for organizations looking to make timely decisions and deliver responsive applications. Two major contenders in the distributed streaming platform space—Apache Kafka and Apache Pulsar—have emerged as powerful solutions, each with its own architecture and strengths. This article explores both platforms in depth to help you determine which best suits your streaming data needs.

The Evolution of Streaming Platforms

Traditional batch processing is no longer sufficient for applications requiring real-time insights. Stream processing platforms address this limitation by enabling continuous data flow processing with minimal latency. Both Kafka and Pulsar were built to handle this paradigm but take different approaches to the challenge.

Apache Kafka: Architecture Overview

Apache Kafka, developed at LinkedIn and open-sourced in 2011, has become the de facto standard for distributed streaming. Its architecture consists of several key components:

Kafka’s design emphasizes simplicity, high throughput, and durability. Each partition is an ordered, immutable sequence of records that can only be appended to. This append-only log model provides strong ordering guarantees within partitions.

# Creating a topic in Kafka with 3 partitions and replication factor of 3
bin/kafka-topics.sh --create --topic my-topic --bootstrap-server localhost:9092 --partitions 3 --replication-factor 3

Apache Pulsar: Architecture Overview

Apache Pulsar, initially developed at Yahoo! and later open-sourced, takes a more layered approach to streaming:

This separation of storage and serving layers is a fundamental difference from Kafka’s architecture. Pulsar uses a distributed log system called BookKeeper where data is stored in segments called ledgers, allowing for virtually infinite storage capacity.

# Creating a topic in Pulsar
bin/pulsar-admin topics create persistent://tenant/namespace/my-topic

Feature Comparison

When evaluating these platforms, several key aspects deserve attention:

Messaging Models

Kafka: Primarily designed around the publish-subscribe pattern with strong ordering guarantees within partitions. Messages are retained based on configurable retention policies.

Pulsar: Supports both streaming and queuing patterns. Offers exclusive, shared, and failover subscription modes, providing flexibility for different consumption patterns.

Storage Architecture

Kafka:

• Data is stored directly on broker disks
• Each broker manages its own storage
• Partitions are the unit of distribution and parallelism
• Limited by disk space on individual brokers

Pulsar:

• Separates compute (brokers) from storage (BookKeeper)
• Data distributed across BookKeeper nodes as segments
• Topics divided into partitions, each composed of segments
• Virtually infinite retention with tiered storage options

Scalability

Kafka:

• Scales by adding brokers and redistributing partitions
• Rebalancing can be resource-intensive
• Scaling up requires careful partition management
• Topic-partition becomes the unit of parallelism

Pulsar:

• Independent scaling of compute and storage layers
• Dynamic scaling with minimal data movement
• Segment-based storage enables finer-grained distribution
• Horizontal scaling with minimal disruption

Multi-Tenancy

Kafka:

• Limited native multi-tenancy support
• Typically deployed as separate clusters per tenant
• Requires third-party management tools for robust multi-tenancy

Pulsar:

• Built-in tenant and namespace abstractions
• Resource isolation with quotas
• Authentication and authorization at tenant level
• Single cluster can safely serve multiple applications/tenants

Performance Benchmarks

Both systems can deliver impressive performance, though direct comparisons are challenging due to different architectural approaches. According to various benchmarks:

Note: Actual performance depends heavily on configuration, hardware, and workload patterns.

Use Case Suitability

Choosing between Kafka and Pulsar often comes down to specific requirements:

Consider Kafka when:

• You need a battle-tested platform with extensive ecosystem
• Your use case demands maximum throughput
• Your retention requirements are modest
• You have existing Kafka expertise or integrations
• You prefer simpler deployment and management

Consider Pulsar when:

• You need multi-tenancy in a single cluster
• You require both queuing and streaming semantics
• Your retention needs are extensive or unpredictable
• You anticipate frequently scaling your cluster
• You need geo-replication across multiple data centers

Real-World Implementation Example

Let’s look at a simple Java producer-consumer implementation for both platforms:

Kafka Producer Example

Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

Producer<String, String> producer = new KafkaProducer<>(props);
ProducerRecord<String, String> record = new ProducerRecord<>("topic-name", "key", "value");
producer.send(record);
producer.close();

Pulsar Producer Example

PulsarClient client = PulsarClient.builder()
    .serviceUrl("pulsar://localhost:6650")
    .build();

Producer<byte[]> producer = client.newProducer()
    .topic("persistent://tenant/namespace/topic-name")
    .create();

producer.send("Hello Pulsar".getBytes());
producer.close();
client.close();

While the APIs differ slightly, both platforms offer intuitive interfaces for producing and consuming messages.

Ecosystem and Community

The surrounding ecosystem often plays a crucial role in platform selection:

Kafka Ecosystem:

• Kafka Connect for data integration
• Kafka Streams for stream processing
• Schema Registry for schema evolution
• ksqlDB for SQL-like stream processing
• Mature monitoring tools

Pulsar Ecosystem:

• Pulsar IO (similar to Kafka Connect)
• Pulsar Functions for lightweight stream processing
• Built-in schema registry
• SQL interface for queries
• Native Spark and Flink connectors

Both have active communities, though Kafka’s has been established longer with more third-party tools and resources available.

Migration Considerations

If you’re considering migrating between platforms:

1. Dual Write Pattern: Write to both systems during transition
2. MirrorMaker 2: Kafka’s tool for replicating across clusters, usable for Kafka-to-Pulsar migration
3. Pulsar’s Kafka Compatibility: Supports Kafka protocol, allowing Kafka clients to connect to Pulsar

Conclusion: Making Your Decision

Both Apache Kafka and Apache Pulsar are robust, production-ready streaming platforms capable of handling demanding workloads. Your choice should be guided by:

• Specific technical requirements (retention, multi-tenancy, geo-replication)
• Operational constraints (management overhead, scaling needs)
• Existing infrastructure and team expertise
• Long-term growth and flexibility needs

For established use cases with moderate retention needs and where throughput is paramount, Kafka remains an excellent choice. For organizations requiring multi-tenancy, flexible consumption patterns, or virtually unlimited retention, Pulsar offers compelling advantages.

Whichever platform you choose, both represent the cutting edge of streaming technology and can form the backbone of a modern, event-driven architecture.

Further Reading

• Apache Kafka Documentation
• Apache Pulsar Documentation
• Benchmark: Pulsar and Kafka Performance Comparison
• Comparing Pulsar and Kafka: Segment-based Architecture Benefits
• Apache Kafka GitHub Repository