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?
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.
