Operations | Monitoring | ITSM | DevOps | Cloud

This post was written by David Chang, Staff Software Engineer at Pinterest, and originally posted on the Pinterest engineering blog on Medium. Develocity, formerly known as Gradle Enterprise, is a powerful tool that speeds up local and CI build time, helps troubleshoot your builds, and analyzes your data. At Pinterest, we have a dedicated team, Mobile Builds, and we ensure that developers can build fast and often. This enables developers to be more productive by getting faster feedback on their code.

We’re always interested in improving the signal-to-noise ratio of our internal telemetry at Honeycomb. In an effort to reduce the amount of noise in our logs, we looked at reducing and deduplicating the logs emitted by our infrastructure and applications.

In the world of digital language learning, Duolingo stands out as a beacon of innovation and user engagement. With millions of users worldwide, their platform is designed not only to teach languages, but also to create a fun and engaging learning experience. Running on the robust AWS cloud infrastructure, Duolingo manages vast amounts of data and user interactions daily. As the company experienced rapid growth, Duolingo remained steadfast in their commitment to delivering a high-quality user experience.

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.

Earlier this year, we introduced relational fields. Relational fields enable you to query spans based on their relationship to one other within a trace, rather than only in isolation. We’ve now expanded this feature and introduced four new prefixes: child., none., any2., and any3.. Previously, you could use root., parent., and any. to query on the root span of your target span’s trace, the parent span of your target span, and any other span in the same trace as your target span.