Software architectures are greatly influenced by the size and scale of the software applications. With growing size, the code base becomes complex. With scale, the deployment becomes challenging. The result: debugging becomes an increasingly time-consuming process for developers.

The modern standard for observability in backend systems is: distributed traces with OpenTelemetry, plus dynamic aggregations over these events. This works very well in the world of web servers. But what about the web client? This post describes the state of OpenTelemetry support for React web clients, as of early April 2023.

As technology advances, so does the need for software engineers and DevOps teams to understand the precise inner workings of the systems they create. In 2023, observability is quickly becoming a key factor in gaining success for many businesses. The study states that many businesses are in different stages of adopting observability into their arsenal of tools, and that the need for these practices is on the rise.

At a recent training session, Jeli spent a great deal of time covering incident retrospectives and what makes an incident worthy of studying. My colleague Ben Hartshorne asked a fascinating question, which I’ll paraphrase here: That caught me by surprise. We had a great discussion, and it made me consider approaches I hadn’t before.

Elastic Stack provides many valuable insights for different users. Developers are interested in low-level metrics and debugging information. SREs are interested in seeing everything at once and identifying where the root cause is. Managers want reports that tell them how good service performance is and if the service level agreement (SLA) is met. In this post, we’ll focus on the service perspective and provide an overview of calculating an SLA.

Lightrun, the leading Developer Observability Platform for production environments, announced today that it has extended its support to include C# on its plugins for JetBrains Rider, VSCode, and VSCode.dev. With this new runtime support, .NET developers can troubleshoot their apps against.NET Framework 4.6.1+, .NET Core 2.0+, and.NET 5.0+ technologies.

When your alerts cover systems owned by different teams, who should be on call? We get this question a lot when talking about SLOs. We believe that great SLOs measure things that are close to the user experience. However, it becomes difficult to set up alerting on that SLO, because in any sufficiently complex system, the SLO is going to measure the interaction between multiple services owned by different teams.

Today, GitHub announced the public beta of Deployment Protection Rules for GitHub Actions for GitHub Enterprise users. In support of that launch, we’ve partnered with GitHub to create the Honeycomb Deployment Protection Rule (available as a GitHub App). This rule lets you run Honeycomb queries so that you can get real-time performance feedback from your services before deciding whether to prevent deployment of your code to a specific environment.

With countless observability tools, data sources, and environments to juggle, the organizations that deploy and manage today’s distributed applications often face an uphill battle to gain visibility into their application performance. That was a key takeaway from the Grafana Labs Observability Survey 2023, which incorporated input from more than 250 industry practitioners who are all too familiar with these complexities.

Do you want to confidently move workloads to the cloud without dropping or losing data? Of course, everyone does. But easier said than done. Cloud migration is tricky. There’s so much to think through and so much to worry about — how can you reconfigure architectures and data flows to ensure parity and visibility? How do you know the data in transit is safe and secure? How can you get your job done without getting in trouble with procurement?

Observability is a term that has been thrown around a lot in the past few years in the software development industry. Different people use it in different ways, but one thing that is clear is that it attempts to provide a solution to a real pain engineers are feeling. It is the pain of not knowing what is happening in the microservices architecture and how and why systems are behaving in production.

At KubeCon Europe, it was announced that Elastic Common Schema (ECS) has been accepted by OpenTelemetry (OTel) as a contribution to the project. The goal is to achieve convergence of ECS and OpenTelemetry’s Semantic Conventions (SemConv) into a single open schema that is maintained by OpenTelemetry. This FAQ details Elastic’s contribution of Elastic Common Schema to OpenTelemetry, how it will help drive the industry to a common schema, and its impact on observability and security.

At Lightstep, we’ve seen many organizations grapple with “cloud-native sticker shock” as they come to understand that these complex systems require sifting through massive amounts of data across architectures and proprietary solutions. In today’s macroeconomic environment, organizations are looking to reduce costs while driving innovation, especially when it comes to cloud-native applications.

Metrics, Logs and Traces are often referred to as The Three Pillars of “Observability“. The term observability has been used in control theory to refer to how the state of a system can be inferred from the system’s external outputs. Applied to IT, observability is how the current state of an application can be assessed based on the data it generates. Applications and the IT components they use provide outputs in the form of metrics, events, logs and traces (MELT).

Continuous Integration/Continuous Delivery (CI/CD) has now become the de-facto standard for all engineering teams seeking to keep pace with the demands of the modern economy. At Coralogix, we operate some of the most advanced build and deploy pipelines in the world. We’ve baked that knowledge into our platform with a CI/CD Observability feature called Coralogix Tagging.

Refinery, Honeycomb’s tail-based dynamic sampling proxy, often makes sampling feel like magic. This applies especially to dynamic sampling, because it ensures that interesting and unique traffic is kept, while tossing out nearly-identical “boring” traffic. But like any sufficiently advanced technology, it can feel a bit counterintuitive to wield correctly, at first. On Honeycomb’s Customer Architect team, we’re often asked to assist customers with their Refinery clusters.

Observability originated in the field of engineering and has recently gained popularity in the world of software development. Put simply, observability refers to the ability to understand the internal state of a system based on its external outputs. IBM defines it as follows: As systems have become more complex, often including remote elements in cloud-based systems, management of the systems and troubleshooting faults and downtime have also become more complex.

Well howdy there partner, Phillip here with a rootin’ tootin’ OTel update for ya, right on time for Kubecon EU!

In the race to attract and retain customers, businesses must deliver great customer experiences, release reliable products fast, and scrutinize costs to achieve consistent growth. That can either be a well-oiled machine or a tangle of disjointed communications and workflows that frustrate customers, employees, and management alike. By developing a culture of observability, you can have a framework that harmonizes the experience for everyone.

Contrary to Betteridge’s Law of Tabloid Headlines, the answer to the question, "does OpenTelemetry in.NET cause performance degradation?" is yes, but context is important. I get this question so often that I thought it was time to get some stats on it. I’ve heard comments like: I can only assume that these are based on previous versions, or things like OpenTracing / OpenCensus (the heritage frameworks that were the feeders for OpenTelemetry).

With business transaction insights in AppDynamics Cloud, you can turn cloud native chaos into business context. Here’s how. In any organization, technology plays a vital role in nearly every aspect of the business — from marketing to operations to human resources. But increasingly, its role in revenue generation is taking center stage. Profitability and growth are now in the hands of CTOs and CIOs.

Spring has sprung, and the bees have been busy. Let’s have a look at what’s new in Honeycomb at the close of March.

As companies navigate geopolitical challenges, macroeconomic headwinds, and the post-pandemic comedown, business leaders face intense pressure to drive software transformation, reduce costs, and compete faster in the cloud-transition era of “lift and shift.” Amid layoffs and a slowed pace of hiring, the demand for better tools, real-time insights, seamless experiences, and contextual analysis has skyrocketed.

TL;DR—This is a fundraising post! Yes, even in this economy. Here at Honeycomb, we've always focused more on the problems we help our customers solve rather than playing the meta game of posturing in startup-land—so these fundraising blog posts are usually the least fun to write (and read, probably). But this one is a little different.

eBPF is a powerful technical framework to see every interaction between an application and the Linux kernel it relies on. eBPF allows us to get granular visibility into network activity, resource utilization, file access, and much more. It has become a primary method for observability of our applications on premises and in the cloud. In this post, we’ll explore in-depth how eBPF works, its use cases, and how we can use it today specifically for container monitoring.

The blog will take you through best practices to observe Kafka-based solutions implemented on Confluent Cloud with Elastic Observability. (To monitor Kafka brokers that are not in Confluent Cloud, I recommend checking out this blog.) We will instrument Kafka applications with Elastic APM, use the Confluent Cloud metrics endpoint to get data about brokers, and pull it all together with a unified Kafka and Confluent Cloud monitoring dashboard in Elastic Observability.

As the world of technology continues to evolve, the demand for cutting-edge solutions to monitor and optimize system performance has never been higher. Today, we’re excited to introduce a revolutionary new concept in observability: Quantum Entangled Observability (QEO). This ground-breaking method leverages the peculiar properties of quantum mechanics to provide unparalleled insights into your systems’ inner workings.