In OpenTelemetry metrics, there are two temporalities, Delta and Cumulative and the OpenTelemetry community has a good guide on the different trade-offs of each. However, the guide tackles the problem from the SDK end. It does not cover the complexity that arises from the collection pipeline. This post takes that into account and covers the architecture and considerations that are involved end-to-end for picking the temporality.

Apache Kafka, born at LinkedIn in 2010, has revolutionized real-time data streaming and has become a staple in many enterprise architectures. As it facilitates seamless processing of vast data volumes in distributed ecosystems, the importance of visibility into its operations has risen substantially. In this blog, we’re setting our sights on the step-by-step deployment of a containerized Kafka cluster, accompanied by a Python application to validate its functionality. The cherry on top?

ClickHouse database has been used as a remote storage server for Jaeger traces for quite some time, thanks to a gRPC storage plugin built by the community. Lately, we have decided to make ClickHouse one of the core storage backends for Jaeger, besides Cassandra and Elasticsearch. The first step for this integration was figuring out an optimal schema design. Also, since ClickHouse is designed for batch inserts, we also needed to consider how to support that in Jaeger.

Six months ago I attempted to get OpenTelemetry (OTEL) metrics working in JavaScript, and after a couple of days of getting absolutely no-where, I gave up. But here I am, back for more punishment... but this time I found success! In this article I demonstrate how to instrument a Node.js application for traces using OpenTelemetry and to export the resulting spans to Jaeger. For simplicity, I'm going to export directly to Jaeger (not via the OpenTelemetry Collector).

Debugging errors in distributed systems can be a challenging task, as it involves tracing the flow of operations across numerous microservices. This complexity often leads to difficulties in pinpointing the root cause of performance issues or errors. OpenTelemetry provides instrumentation libraries in most programming languages for tracing.

When the services in your distributed application interact with a database, you need telemetry that gives you end-to-end visibility into query performance to troubleshoot application issues. But often there are obstacles: application developers don’t have visibility into the database or its infrastructure, and database administrators (DBAs) can’t attribute the database load to specific services.

Performance testing plays a critical role in application reliability. It enables developers and engineering teams to catch issues before they reach production or impact the end-user experience. Understanding performance test results and acting on them, however, has always been a challenge. This is due to the visibility gap between the black-box data from performance testing and the internal white-box data of the system being tested.