It’s very popular to push logs, in a formatted way, to the console output of an application (sometimes referred to as stdout). Although using a push-based approach like OTLP over gRPC/HTTP is preferred and has more benefits, there are many legacy systems that still use this approach. These systems typically use a JSON output for their logs. So, how do we get these JSON logs into a backend analysis system like Honeycomb that primarily accepts OTLP data?

For football fans worldwide, staying connected to their favorite teams, players, and matches is a passion—and OneFootball delivers exactly that. The platform is a one-stop shop for football fans to follow their teams, get up-to-date information, and immerse themselves in global football culture. With over 100 million users spanning multiple continents, OneFootball is an essential companion for fans to track live scores, player stats, breaking news, and more.

Many businesses rely on cloud infrastructure to power their software solutions. The cloud today makes it easier than ever to create services and components, increasingly the complexity of software. With more and often smaller processes, cloud-native architectures have driven the need for better insights into our software—a way to look into how these processes fit together.

We’ve been talking about observability 2.0 a lot lately; what it means for telemetry and instrumentation, its practices and sociotechnical implications, and the dramatically different shape of its cost model. With all of these details swimming about, I’m afraid we’re already starting to lose sight of what matters.

In the software space, we spend a lot of time defining the terminology that describes our roles, implementations, and ways of working. These terms help us share fundamental concepts that improve our software and let us better manage our software solutions. To optimize your software solutions and help you implement system observability, this blog post will share the key differences between logs vs traces.

About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.