OpenTelemetry for Dummies: Instrumenting Go Apps

GopherCon Europe – Virtual

May. 2021

Ricardo Ferreira

Slides

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Abstract

Though tracing technologies are not necessarily a new concept only in the recent years it gained enough traction to become one of the key dimensions required to build observability stacks. Undoubtedly the major force behind this traction are the standards created to help developers to collect telemetry data from their applications, such as OpenTelemetry—an observability framework for cloud-native software. OpenTelemetry provides a single set of APIs, libraries, agents, and collectors that ensures technology agnostic collection of traces and metrics—but the implementation for each programming language is different. While Java has an agent capable of automatically instrumenting the JVM with additional bytecode, other programming languages like Go have to handle this instrumentation manually. But Go developers have nothing to fear. This talk will explain in a for-the-rest-of-us style how to instrument applications written in Go and how to send the telemetry data to a backend using a collector.

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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