What is Distributed Tracing, and Why Should you Care About it?

All Things Open – Raleigh 🇺🇸

Oct. 2021

Ricardo Ferreira

Slides

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Abstract

Let’s face it: most people talking about o11y (observability) all end up talking about distributed tracing somehow. It is a technology that is radically changing the way we identify and solve technical problems. In a world where virtually all applications are born distributed — it seems to be something that you as an SRE ought to know in more detail. This talk will provide a pragmatic overview of distributed tracing by clearly articulating its motivation, problems it solves, the challenges, technologies you should use to ensure a vendor-agnostic implementation, and which aspects you should consider while picking an o11y backend. While discussing the challenges, this talk will highlight white-box versus black-box instrumentation, which is valuable knowledge to determine where the developer’s responsibility finishes and when the Ops team starts, and — when both team’s responsibilities may entangle.

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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