OpenTelemetry vs. OpenTracing - differences, evolution, and ways to migrate to OpenTelemetry.

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.

OpenTelemetry is more than just becoming the open ingestion standard for observability. As one of the major Cloud Native Computing Foundation (CNCF) projects, with as many commits as Kubernetes, it is gaining support from major ISVs and cloud providers delivering support for the framework. Many global companies from finance, insurance, tech, and other industries are starting to standardize on OpenTelemetry.

OpenTelemetry (OTel) is steadily gaining broad industry adoption. As one of the major Cloud Native Computing Foundation (CNCF) projects, with as many commits as Kubernetes, it is gaining support from major ISVs and cloud providers delivering support for the framework. Many global companies from finance, insurance, tech, and other industries are starting to standardize on OpenTelemetry.

OpenTelemetry is the future of Observability, APM, Monitoring, whatever you want to call ‘the process of knowing what our software is doing.’ It’s becoming common knowledge that your time is better spent gaining experience with an open, standardized system for telemetry than closed-source or otherwise proprietary standard. This truth is so universally acknowledged that all the big players in the market have made announcements of how they’re embracing OpenTelemetry.

Application performance management (APM) has moved beyond traditional monitoring to become an essential tool for developers, offering deep insights into applications at the code level. With APM, teams can not only detect issues but also understand their root causes, optimizing software performance and end-user experiences. The modern landscape presents a wide range of APM tools and companies offering different solutions. Additionally, OpenTelemetry is becoming the open ingestion standard for APM.