The latest News and Information on Observabilty for complex systems and related technologies.


The importance of metadata in your Kubernetes observability initiatives

Kubernetes is a popular container orchestration system at the heart of the Cloud Native Computing Foundation projects. It automates the deployment, lifecycle, and operations of containers, containerized applications, and "pods," which are groups of one or more containers. The platform itself, along with each of these workloads, may generate event data. There are different kinds of data associated with these processes.

Raw & Real Ep 7 The Tracing You Deserve So You Can Observe

Distributed tracing is key to building and operating reliable services that make your customers happy. Traces pinpoint where failures occur and what causes poor performance. With tracing and observability, you can visualize the entire life cycle of service requests and discover hidden latency, errors, and optimization opportunities monitoring can’t show you. So why doesn’t everybody do it? Setting up tracing is notoriously difficult, but it doesn’t have to be. Honeycomb Instrumentation Engineer Paul Osman has the easy-breezy steps for you to get the tracing you deserve.

Lightrun & JFrog - Achieving Complete Agility With Continuous Debugging And Continuous Observability

CI/CD has become the de facto standard for infusing the software development process with hardcoded agility. Organizations are now integrating DevOps concepts and practices into their workflows in order to get great features out of the door faster and reduce internal friction. But your ability to understand what’s going on in a production service is pre-defined by the logs, metrics and traces (i.e. the three pillars of observability) your developers pre-defined during development. There is, however, a need for agility even after the service is live - in order to adhere to strict SLAs, decrease MTTR and save on logging costs.

Infrastructure Observability with Resource Events

You may have seen the Honeycomb white paper on metrics, and want to use the power of Honeycomb with metrics. Sending infrastructure metrics data to Honeycomb has always been possible, but with our focus on debugging the user experience inside the application, it isn’t the first or most obvious thing to do. This post will discuss why we use metrics in general and how to think about metrics in Honeycomb.


What is API Observability

API Observability is a key component to properly execute APIOps Cycles and ensure your building something of value for your API users. If you’re not familiar with APIOps Cycles, take a look at this guide which provides an agile framework to quickly build APIs that are business-oriented and serve customer needs. API Observability itself is an evolution of traditional monitoring and born out of control systems theory.


Observability Matters - Why IBM's acquisition of Instana is a game changer for AIOps

Today IBM announced the definitive agreement to acquire Instana. The acquisition will help businesses better manage the complexity of modern applications that span the hybrid cloud landscape and infuse AI in all areas of IT Management. You’ll also find a blog post from Pratik Gupta, CTO Hybrid Cloud Management and IBM Distinguished Engineer, AI and Automation Platform, here.


The Role of Context and Dependencies in Observability

To be successful in Observability, you must have the ability to observe the behavior of a system and derive its health state from it. But deriving the health state of any given application entity requires the observability system to understand the context of every piece of information given, to automatically correlate all the data, and deliver hard evidence – quickly – on what is going on.


Kubernetes: Resource Contention and Change Risk Assessment

An ever-increasing number of System architectures and deployment strategies depend on Kubernetes-based environments. Kubernetes (also known as k8s) is an orchestration platform and abstract layer for containerized applications and services. As such, k8s manages and limits container available resources on the physical machine, as well as takes care of deployment, (re)start, stop, and scaling of service instances.