Do It Yourself: Programmable Metrics using OpenTelemetry

Berlin Buzzwords 2022 Berlin 🇩🇪

Jun. 2022

Ricardo Ferreira

Slides

Abstract

Using metrics to measure how good or bad things are going is a proven way to ensure a software-based system is going in the right direction. Most metrics are created and monitored automatically by agent technologies installed in our infrastructure, making us hostages of the set of metrics that these agents are programmed to address. But what if you need to handle your own set of metrics? This is a question that often drives developers mad because they fear spending development cycles building something that will end up being locked into a particular monitoring/observability vendor. But OpenTelemetry — a CNCF observability framework that provides a vendor-neutral approach to tackle metrics, logging, and tracing needs, can change everything. This talk will explain how the OpenTelemetry framework allows the creation of custom metrics in a standard, scalable, and reusable way. It will provide an example in Java of a set of metrics that are continuously updated based on the execution of the code and how to hook that data with a compatible observability backend.

Video

© 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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