In the land of the sizing, the one-partition Kafka topic is king

Strange Loop 2022 – St. Louis 🇺🇸

Sep. 2022

Ricardo Ferreira

Slides

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Abstract

Every technology has that key concept that people struggle to understand. With databases, is which join clause to use for fetching data from multiple tables. Containers are tricky when you have to pick a storage type given some persistence requirements. With Apache Kafka, the winner is how many partitions to set for a topic. Why this is important? You may ask. Well, sizing Kafka partitions wrongly affects many aspects of the system, such as consistency, concurrency, and durability. Worse, it may also affect how much load Kafka can handle. Hence why often the decision about how many partitions to set for a topic is handled by Ops teams, as we see this to be only an infrastructure matter. In reality, this is an architectural design decision that affects even the amount of code you write. This session will peel off the concept of partitions and explain it from the perspective of the Kafka cluster and its clients. By using a what-if presentation style, it will explain the overall impact on the system given a number. This will help you build more confidence about how to size Kafka partitions correctly, and to spot a poor decision when you see one.

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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