4 articles
Thursday, Feb 27, 2025
In the relentless pursuit of performance, our industry often gravitates toward seemingly obvious solutions: more cores, more threads, more concurrency. Yet Redis—one of the most performant databases in the world—has maintained its commitment to a primarily single-threaded execution model. As various Redis forks emerge claiming dramatic performance improvements through multi-threading, I want to explore why Redis’s core architectural choices remain fundamentally sound, even at scale.
When Salvatore Sanfilippo designed Redis, he made a choice that seemed to contradict conventional wisdom: a single-threaded event loop at the core of a high-performance database. This wasn’t a limitation to be overcome—it was a deliberate architectural decision that eliminated entire classes of complexity:
Tuesday, Mar 3, 2020
Recently I had to build an application that would store and order players in a Redis Sorted Set, but using multiple columns as criteria for the score, instead of just one. After trying to find a proper solution on the web and failing to do so; I decided to put my head to work and figure out an way. This post will show how did I accomplished that.
It turns out that my application has players with the following fields:
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.
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.
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.
