Instrumenting Scala Spark Applications with OpenTelemetry: A Practical Guide

"Observability is no longer optional in modern data engineering. When your Spark jobs process millions of records across distributed clusters, understanding what's happening under the hood becomes critical. This guide will show you how to implement custom OpenTelemetry tracing in a Sample Scala Spark application, giving you complete visibility into your data pipelines. If you are not interested in the article, please skip to the bottom for reference of my github account."

"By the end of this tutorial, you'll have a fully instrumented Scala Spark application that: Creates custom trace spans for business operations Integrates OpenTelemetry with Spark's internal event system (using Spark Listeners API) Captures DataFrame metrics and job metadata Exports telemetry data to an OpenTelemetry Collector Runs everything in a containerized environment This isn't just theory - you'll have working code that demonstrates real-world instrumentation patterns."

Observability is essential for modern data engineering, especially when Spark jobs process millions of records across distributed clusters. Manual OpenTelemetry tracing can be implemented in a Scala Spark application to create custom trace spans for business operations, integrate with Spark Listeners API, capture DataFrame metrics and job metadata, export telemetry to an OpenTelemetry Collector, and operate in a containerized environment. Manual instrumentation ensures guaranteed coverage, captures business context and custom attributes, and provides fine-grained control across driver and executors. Manual instrumentation explicitly bridges Spark internal events with OpenTelemetry spans despite higher upfront work requirements.

Read at Medium