Hidden Secrets about Instrumenting JVMs for OpenTelemetry

JNation 2021 – Virtual

Jun. 2021

Ricardo Ferreira

Slides

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Abstract

OpenTelemetry is the new-kid-on-the-block in API to instrument distributed applications to send telemetry data (logs, metrics, and traces) to backends for visualization and monitoring. It was created to be a de-facto replacement to standards such as OpenTracing and OpenCensus. The support for Java in OpenTelemetry is generous. It supports both automatic and manual instrumentation. With automatic instrumentation, developers can have known libraries and frameworks instrumented without writing a single code line. Manual support also allows them to decide which parts of the code need instrumentation, and there is a rich set of APIs available to use. This talk will open the pandora box for both options. It will reveal which knobs are available to use that make a tremendous difference in how the applications report telemetry data — both in terms of performance and details. Knowing these hidden secrets will grant developers fine control over their applications and how observability stacks show them to the world.

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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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