Gartner® has published the 2022 Magic Quadrant™ for APM and Observability, an annual report that evaluates vendors in this category. We’re honored that Datadog has been recognized as a “Leader” within this Magic Quadrant report for the second consecutive year, with the highest position for Ability to Execute.
Honeycomb ruffled the first of many feathers nearly seven years ago when we coined the term “observability” in talking about production code. Today, we get to celebrate a major victory in our push for the term to break free from its unsuitable parent category, Application Performance Monitoring (APM).
I use .NET and I keep seeing something called `Activity` but in OpenTelemetry there is only talk about “Span” and “Trace,” why? And what should I be using? This is understandable, and has caused confusion since that decision was made by Microsoft back in 2018/19 (I believe). I’ll do my best to provide some guidance on what the distinction is, and also when each is useful.
Monitoring and analyzing log files to identify and resolve issues make up log observability. Log analytics is the process of extracting insights from log data. Logs are a valuable source of information for IT operations teams, as they provide insight into what is happening on a system or network. Logs can monitor system performance, troubleshoot problems, and identify security incidents. Logs are a vital part of application performance management.
Monitoring is often not the first thing on the mind of the modern developer. Yet, it’s necessary at many points of the software development lifecycle, including: before deprecating an API, before launching a new feature, after launching the feature, and more. In fact, monitoring needs can vary much more than the classic Ops monitoring. My podcast guest Liran Haimovitch is the co-founder and CTO of Rookout, a live data collection and debugging platform.
A couple of weeks ago, we had a great time hosting the workshop you can see below with Vlad Mihalcea. It was loads of fun and I hope to do this again soon! In this workshop we focused on Spring Boot performance but most importantly on Hibernate performance, which is a common issue in production environments. It’s especially hard to track since issues related to data are often hard to perceive when debugging locally.
If there was a question on if an enterprise needed an observability pipeline in 2019 or 2020, we now know the answer is: yes. The observability data management methods of the 2010s aren’t going to work in the 2020s. Data is growing too fast for us to ignore, and the need to gain intelligence from said data continues to grow in importance. Data (and access to it) is becoming a competitive edge for many enterprises today.
Today’s modern applications contain a broad set of microservices, with containers and serverless becoming the architectures of choice for many cloud applications. Both architectures facilitate highly scalable systems, and while which approach to take is routinely debated, containers and serverless technologies are being used in tandem more and more.
What's the first small thing to do in o11y that would teach me something, bring something valuable, and open the way for something else? Observability doesn’t have to be a big, company-wide project. It can be useful locally and individually. A little playing around can get you some crucial insight into how your software works. Try it as a team, or in a pair, or by yourself. It takes 3 steps: Step 1 is easy. The other two might take ten minutes, or maybe more like a day.
