Writing Custom Sink Connectors for Pulsar I/O

Pulsar Summit Europe 2021 – Virtual

Oct. 2021

Ricardo Ferreira

Slides

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Abstract

You currently have massive amounts of data sitting on Apache Pulsar and likely — even more data coming in every second. But part of your project requires this data to be sent to some system to explore the dataset further. You look into the Pulsar website for built-in connectors to that particular system and realize that there are none. What a bummer. But you don’t have to give up on Pulsar because of this. You can write custom sink connectors for Pulsar I/O that work just like the built-in ones, and all you will need is a bit of Java development experience and creativity. The rest you can leave to this talk. This talk will explain how to start developing your custom connector, show how the code looks like, demonstrate how to deploy, and discuss some of the design decisions that your custom connector may need to address.

Video

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© 2018 - 2026 Ricardo Ferreira

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Open Source

I contribute to LangChain4j, an idiomatic open source Java library for building LLM-powered applications on the JVM. Recent work includes adding native vector search embedding stores so developers can build RAG, recommendation engines, and AI memory systems.

I also ported RedisVL to Go, an open source, AI-native client that brings vector search, semantic caching, LLM memory, semantic routing, rerankers, and an MCP server to the Redis ecosystem for Golang developers.

Speaking

I’ve been speaking at conferences since 2008 and doing it full time as part of my work since 2018. Some of the events I’ve spoken at include AWS re:Invent, Microsoft Ignite, Google Cloud Next, KubeCon, Oracle OpenWorld, QCon, Strange Loop, Kafka Summit, Pulsar Summit, JavaOne, DevNexus, JFokus, JNation, and All Things Open.

Recordings of my talks live on my YouTube channel, and the code I write for talks, demos, and tutorials is on my GitHub.

Who am I?

I work at the intersection of distributed systems, AI, and data infrastructure, turning complex technology into things developers can understand, adopt, and build with.

Lately, that means hands-on AI engineering: building vector search, semantic caching, agent memory, and RAG into the data layer; contributing to LangChain4j and RedisVL for Golang; and figuring out how to make AI agents secure enough to ship.

The AI-native work isn’t a pivot. It draws on the same systems-design foundation I’ve built for 20+ years, moving data fast, at scale, close to compute, watching where systems break; now applied to vectors and agents. I have worked on event streaming with Apache Kafka and Flink at AWS and Confluent; observability at Elastic; as well as RDBMS, NoSQL, and Big Data at Oracle. That foundation is exactly what separates AI demos that work on stage from AI systems that survive production.

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