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If you’ve ever worked with your hands, you know that you can’t do the job right without the right tools. That adage carries over quite well to software development as well. The right tools can make the difference between success or failure, regardless of the underlying technology. In the Kubernetes ecosystem, more and more tools are being introduced as folks find ways to solve a common problem. This article looks are four of those tools.

At KubeCon + CloudNativeCon EU last week, Grafana Labs VP Product Tom Wilkie and Red Hat Software Engineer Frederic Branczyk, gave a keynote presentation about the future of observability and how this trifecta will evolve in 2019 and the years to come. Source: grafana.com

Recently, OpenTelemetry has been announced as a new CNCF sandbox project resulting from a merger of OpenTracing and OpenCensus [1], [2], [3], [4]. Several people have already asked me what OpenTelemetry means for the Jaeger project (incubating at CNCF), and whether it is going to replace Jaeger. I will attempt to answer these questions in this post. I’ve been working on OpenTracing from its inception at a Zipkin workshop back in the Fall of 2015.

Observability comes from the world of engineering and control theory. Control theory states that observability is itself a measure that describes “how well internal states of a system can be inferred from knowledge of external outputs”. In contrast to monitoring which is something you do, observability, is a property of a system. A system is observable if the external outputs, logging, metrics, tracing, health-checks, etc, allow you to understand its internal state.

Backups can capture subsets of the cluster’s resources, filtering by namespace, resource type, and/or label selector, providing a high degree of flexibility around what’s backed up and restored. Users of managed Kubernetes offerings often do not have access to the underlying etcd database, so direct backups/restores of it are not possible. Resources exposed through aggregated API servers can easily be backed up and restored even if they’re stored in a separate etcd database.

On Sunday 2 June, 2019, Google Cloud projects running services in multiple US regions experienced elevated packet loss as a result of network congestion for a duration of between 3 hours 19 minutes, and 4 hours 25 minutes. The duration and degree of packet loss varied considerably from region to region and is explained in detail below. Other Google Cloud services which depend on Google’s US network were also impacted, as were several non-Cloud Google services which could not fully redirect users to unaffected regions.

According to this recently completed CNCF Survey, the adoption rate of Cloud Native technologies in production is growing rapidly. Kubernetes is at the heart of this technological revolution. Naturally, the growth of cloud native technologies has been accompanied by the growth of the ecosystem that surrounds it. Of course, the complexity of cloud native technologies have increased as well. Just google for the phrase “Kubernetes is hard”, and you’ll get plenty of articles that explain this complexity problem.

Elasticsearch, following the emergence of Kubernetes as the de facto standard for orchestrating containers, launches Elastic Cloud on Kubernetes (ECK). Let’s have a look at what ECK brings to the Kubernetes ecosystem. The Elasticsearch team takes the next steptowards their commitment to make it easier for users to deploy and operate Elastic products and solutions in Kubernetes environments. Elastic Cloud on Kubernetes (ECK) is built using the Kubernetes Operator pattern, installs into your Kubernetes cluster and does more than just simplifying the task of deploying Elasticsearch and Kibana on Kubernetes.

Introduced at a Business Equipment Manufacturers Association show in New York in October 1965, this programmable desktop calculator provedan immediate success. Also known as the P101 or the Perottina (after the chief engineer who designed it, Pier Giorgio Perotto), it eventually sold more than 40,000 units, primarily in the United States but also in Europe. NASA bought a number of P101s, which were used by engineers working on the 1969 Apollo11 moon landing.

Today we announced that Solo.io is a launch partner of the Service Mesh Interface (SMI) specification in collaboration with Microsoft, Bouyant and Hashicorp with support across the cloud-native ecosystem. As part of today’s announcement, the SuperGloo project and The Service Mesh Hub are the first reference implementations of SMI available today. The Service Mesh Interface (SMI) is a specification for service meshes that run on Kubernetes and defines a common standard that can be implemented by a variety of providers.