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Tech Matchups: Apache Pulsar vs. Apache Flink

Overview

Apache Pulsar is an open-source, distributed messaging and streaming platform with a segmented log architecture, optimized for multi-tenancy and tiered storage.

Apache Flink is an open-source stream processing framework designed for real-time, stateful data processing with a dataflow architecture.

Both enable real-time data pipelines: Pulsar focuses on event streaming and storage, Flink on advanced stream processing.

Fun Fact: Pulsar’s segmented logs were designed for cloud-native scalability!

Section 1 - Architecture

Pulsar publish (Java):

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

Flink stream processing (Java):

StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); DataStream stream = env.addSource(new PulsarSourceBuilder() .setServiceUrl("pulsar://localhost:6650") .setTopic("topic") .build()); stream.map(s -> s.toUpperCase()).print(); env.execute();

Pulsar’s architecture decouples compute (brokers) and storage (BookKeeper), using segmented logs for multi-tenancy and tiered storage, enabling flexible event streaming. Flink uses a dataflow architecture with stateful operators and checkpointing, designed for real-time processing with complex transformations (e.g., joins, windowing). Pulsar stores and streams events, Flink processes them for analytics.

Scenario: A 500K-event/sec analytics pipeline—Pulsar stores multi-tenant events, Flink processes them for real-time insights.

Pro Tip: Use Pulsar’s schema registry to ensure type-safe data for Flink!

Section 2 - Performance

Pulsar achieves 500K events/sec with 15ms latency for storage (e.g., 10 brokers, SSDs), optimized for multi-tenant streaming with consistent tail latency.

Flink processes 500K events/sec with 20ms latency (e.g., 10 nodes, SSDs), excelling in stateful analytics but with higher compute overhead.

Scenario: A 50K-user recommendation system—Pulsar delivers scalable event streams, Flink provides low-latency analytics. Pulsar’s performance is storage-focused, Flink’s is processing-focused.

Key Insight: Flink’s state backend optimizes complex stream computations!

Section 3 - Scalability

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

Flink scales across 50+ nodes, processing 1TB+ datasets, with dynamic task distribution and state management for computational scalability.

Scenario: A 2TB analytics pipeline—Pulsar scales for event storage, Flink for processing throughput. Pulsar is storage-intensive, Flink is compute-intensive.

Advanced Tip: Use Flink’s savepoints for seamless pipeline upgrades!

Section 4 - Ecosystem and Use Cases

Pulsar integrates with Pulsar Functions, IO connectors, and Presto for stream processing, ideal for multi-tenant messaging (e.g., 10K tenants at Comcast).

Flink supports Table API, SQL, and integrations with Pulsar and Hadoop, suited for real-time analytics (e.g., 100K events/sec at eBay).

Pulsar powers messaging (e.g., Yahoo pub/sub), Flink excels in stream analytics (e.g., recommendation systems). Pulsar is storage-driven, Flink is analytics-driven.

Example: Comcast uses Pulsar for IoT; eBay uses Flink for real-time analytics!

Section 5 - Comparison Table

Aspect Apache Pulsar Apache Flink
Architecture Segmented, decoupled Dataflow, stateful
Performance 500K events/sec, 15ms 500K events/sec, 20ms
Scalability Storage-separated, 5TB+ Node-based, 1TB+
Ecosystem Functions, Presto Table API, SQL
Best For Streaming, IoT Stream processing, analytics

Pulsar drives event streaming; Flink enhances real-time analytics.

Conclusion

Apache Pulsar and Apache Flink are complementary technologies for real-time data pipelines. Pulsar excels in multi-tenant, scalable event streaming and storage, ideal for IoT and messaging. Flink is best for stateful, real-time stream processing, offering advanced analytics capabilities.

Choose based on needs: Pulsar for event streaming and storage, Flink for processing and analytics. Optimize with Pulsar’s schema registry for typed data or Flink’s SQL API for analytics. They are often used together (e.g., Pulsar for storage, Flink for processing).

Pro Tip: Use Flink’s watermarking for handling out-of-order events!