Adopting OpenTelemetry in Confluent and Beyond ft. Xavier Léauté


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By Confluent, original creators of Apache Kafka® and Original creators of Apache Kafka®. Discovered by Player FM and our community — copyright is owned by the publisher, not Player FM, and audio is streamed directly from their servers. Hit the Subscribe button to track updates in Player FM, or paste the feed URL into other podcast apps.

Collecting internal, operational telemetry from Confluent Cloud services and thousands of clusters is no small feat. Stakeholders need to rely on the same data to make operational decisions. Whether it be metrics from clusters in Confluent Cloud or traces from our internal service, they all provide valuable insights not only to engineering teams but also to customers for their own operations and for business reporting needs. Traditionally, this data needs to be collected in multiple ways to satisfy all the different requirements. We leverage third-party vendors for our operational needs, which usually means deploying vendor agents or libraries in addition to our own, as we also need to collect some of the same data to expose to customers.

However, this sometimes leads to discrepancies between various systems, which are often hard to reconcile and make it harder to troubleshoot issues across engineering, data science, and other teams.

One of the earliest software engineers at Confluent, Xavier Léauté is no stranger to this. At Confluent, he leads our observability engineering efforts in Confluent Cloud.

With OpenTelemetry, we can collect data in a vendor-agnostic way. It defines a standard format that all our services can use to expose telemetry, and it provides Go and Java libraries that we can use to instrument our services. Many vendors already integrate with OpenTelemetry, which gives us the flexibility to try out different observability solutions with minimal effort, without the need to rewrite applications or deploy new agents. This means that the same data we send to third parties can also be collected internally (in our own clusters).

The same source of data can then be leveraged in many different ways:

  • Using Kafka Connect, we can send this data to our data warehouse and data science teams in real time to derive many of the metrics that we use to track the health of our cloud business
  • That very same data also powers our Cloud Metrics API to provide our customers visibility into their infrastructure
  • Engineers and support teams can collect more fine-grained data to troubleshoot incidents or understand low-level application behavior

We’ve also adopted the same approach for on-prem customers, which enables us to collect telemetry into our cloud and help them troubleshoot issues, leveraging the same infrastructure that we already built for Cloud.

Regarding OpenTelemetry efforts in Apache Kafka®, we’re working on KIP-714 which will allow us to collect Kafka client metrics to help better understand client-side problems without the need to instrument client applications. Our ultimate goal has always been to migrate to OpenTelemetry, which is now underway. We’d like to make a way for direct integration with OpenTelemetry in Kafka, based on the work that we’ve done at Confluent.


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