{"id":6045,"date":"2026-05-02T12:03:49","date_gmt":"2026-05-02T12:03:49","guid":{"rendered":"https:\/\/dalelane.co.uk\/blog\/?p=6045"},"modified":"2026-05-02T12:03:49","modified_gmt":"2026-05-02T12:03:49","slug":"instrumenting-a-kafka-connect-connector-with-metrics","status":"publish","type":"post","link":"https:\/\/dalelane.co.uk\/blog\/?p=6045","title":{"rendered":"Instrumenting a Kafka Connect connector with metrics"},"content":{"rendered":"

Metrics can help provide operational insight over Kafka Connect connectors, informing users of how to better configure them. With simple updates, a Kafka Connect connector can be instrumented to make this possible by emitting useful metrics.<\/strong><\/p>\n

A couple years ago<\/a>, I created a simple skeleton Connect connector project<\/a> to help developers at a hackathon create their first Kafka connector.<\/p>\n

I’ve updated<\/a> the source connector<\/a> from that sample to emit metrics. In this post, I’ll walk through what I did, as an example for how to add metrics to your own Kafka connector.<\/p>\n

To add some context that might help when I go through what metrics I added, this connector periodically polled a weather API<\/a> for weather reports, and emitted a Kafka event when the weather report details changed.<\/em><\/p>\n

Check for support<\/h3>\n

Support for Connector metrics is a relatively recent addition<\/a>, so if your Connector might be used in Connect runtimes with versions before 4.2, then you need to handle the support not being there.<\/p>\n