Matchups: Apache Kafka vs Apache Pulsar | Real Time Data Platforms Comparison

Overview

Apache Kafka is a distributed streaming platform designed for high-throughput, fault-tolerant event streaming, using a log-based architecture.

Apache Pulsar is a multi-tenant, distributed messaging system with a segmented log architecture, optimized for flexibility and tiered storage.

Both handle large-scale event streaming: Kafka focuses on throughput and ecosystem, Pulsar on multi-tenancy and storage efficiency.

Fun Fact: Kafka was originally developed at LinkedIn for log processing!

Section 1 - Architecture

Kafka publish/subscribe (Java):

Properties props = new Properties(); props.put("bootstrap.servers", "localhost:9092"); KafkaProducer producer = new KafkaProducer<>(props); producer.send(new ProducerRecord<>("topic", "event"));

Pulsar publish/subscribe (Java):

PulsarClient client = PulsarClient.builder() .serviceUrl("pulsar://localhost:6650").build(); Producer producer = client.newProducer().topic("topic").create(); producer.send("event".getBytes());

Kafka uses a broker-based, append-only log with partitions, tightly coupled storage and compute. Pulsar separates compute (brokers) and storage (BookKeeper), enabling dynamic scaling and multi-tenancy. Kafka is simpler to deploy, Pulsar more flexible.

Scenario: A 1M-event/sec pipeline—Kafka excels in single-tenant throughput, Pulsar in multi-tenant isolation.

Pro Tip: Use Pulsar’s geo-replication for cross-region data sync!

Section 2 - Performance

Kafka achieves 1M events/sec (e.g., 10ms latency) with optimized partitioning and batching, ideal for high-throughput workloads.

Pulsar handles 500K events/sec (e.g., 15ms latency), with segmented logs and tiered storage reducing tail latency for diverse workloads.

Scenario: A 100K-user analytics pipeline—Kafka delivers raw speed, Pulsar ensures consistent performance under variable loads.

Key Insight: Kafka’s batching minimizes network overhead for large-scale streaming!

Section 3 - Scalability

Kafka scales horizontally across 100+ brokers, handling 10TB+ datasets, with ZooKeeper for coordination.

Pulsar scales across 50+ brokers, managing 5TB+ datasets, with BookKeeper enabling independent storage scaling.

Scenario: A 1PB event store—Kafka scales with broker additions, Pulsar with storage tiering. Kafka is robust, Pulsar is adaptive.

Advanced Tip: Use Kafka’s dynamic partition rebalancing for seamless scaling!

Section 4 - Ecosystem and Use Cases

Kafka integrates with Kafka Streams, Connect, and Spark for analytics and ETL, ideal for log aggregation (e.g., 1M logs/sec).

Pulsar supports Functions, IO connectors, and Presto, suited for multi-tenant apps (e.g., 10K tenants).

Kafka powers data pipelines (e.g., Netflix analytics), Pulsar excels in messaging (e.g., Comcast IoT). Kafka is analytics-focused, Pulsar is tenant-driven.

Example: Uber uses Kafka for event streaming; Yahoo uses Pulsar for pub/sub!

Section 5 - Comparison Table

Aspect	Apache Kafka	Apache Pulsar
Architecture	Log-based, coupled	Segmented, decoupled
Performance	1M events/sec	500K events/sec
Scalability	Broker-based	Storage-separated
Ecosystem	Kafka Streams, Spark	Functions, Presto
Best For	Analytics, pipelines	Multi-tenant, IoT

Kafka drives throughput; Pulsar enhances flexibility.

Conclusion

Kafka and Pulsar are leading streaming platforms with distinct strengths. Kafka excels in high-throughput data pipelines and analytics, ideal for large-scale, single-tenant systems. Pulsar is best for multi-tenant, flexible messaging with tiered storage, suited for diverse workloads.

Choose based on needs: Kafka for raw performance and ecosystem, Pulsar for multi-tenancy and storage efficiency. Optimize with Streams (Kafka) or Functions (Pulsar). Hybrid approaches (e.g., Kafka for analytics, Pulsar for IoT) can work.

Pro Tip: Use Pulsar’s tiered storage to offload cold data to S3!

Tech Matchups: Apache Kafka vs. Apache Pulsar