Podman and Buildah for Docker users

I was asked recently on Twitter to better explain Podman and Buildah for someone familiar with Docker. Though there are many blogs and tutorials out there, which I will list later, we in the community have not centralized an explanation of how Docker users move from Docker to Podman and Buildah. Also what role does Buildah play? Is Podman deficient in some way that we need both Podman and Buildah to replace Docker?
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Infrastructure monitoring: Defense against surprise downtime

Infrastructure monitoring is an integral part of infrastructure management. It is an IT manager’s first line of defense against surprise downtime. Severe issues can inject considerable downtime to live infrastructure, sometimes causing heavy loss of money and material. Source: opensource.com

Intro to Redis Cluster Sharding – Advantages, Limitations, Deploying & Client Connections

Redis Clusteris the native sharding implementation available within Redis that allows you to automatically distribute your data across multiple nodes without having to rely on external tools and utilities. At ScaleGrid, werecently added support forRedis Clusterson our platform through our fully managedRedis hostingplans. In this post, we’re going to introduce you to the advanced Redis Cluster sharding opportunities, discussits advantages and limitations, when you should deploy, and how to connect to your Redis Cluster.
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Cilium 1.4: Multi-Cluster Service Routing, DNS Authorization

We are excited to announce the Cilium 1.4 release. The release introduces several new features as well as optimization and scalability work. The highlights include the addition of global services to provide Kubernetes service routing across multiple clusters, DNS request/response aware authorization and visibility, transparent encryption (beta), IPVLAN support for better performance and latency (beta), integration with Flannel, GKE on COS support, AWS metadata based policy enforcement (alpha) as well as significant efforts into optimizing memory and CPU usage.
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Introducing Ludwig, a Code-Free Deep Learning Toolbox

Over the last decade, deep learning models have proven highly effective at performing a wide variety of machine learning tasks in vision, speech, and language. At Uber we are using these models for a variety of tasks, including customer support, object detection, improving maps, streamlining chat communications, forecasting, and preventing fraud. Many open source libraries, including TensorFlow, PyTorch, CNTK, MXNET, and Chainer, among others, have implemented the building blocks needed to build such models, allowing for faster and less error-prone development.
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Kubernetes network deep dive: Did you make the right choice?

Kubernetes networking design can be intimidating, especially when you are the one to make decisions for cluster-level network choices. In this session, we will discuss how these choices will affect cluster routing and load balancing, focusing on KubeProxy modes(iptables vs IPVS) and network solutions. The main purpose of this blog is to help Kubernetes users to get comfortable with K8S major network components, common usage patterns, and corresponding troubleshooting tools.
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Kubernetes Metrics and Monitoring

This post explores the current state of metrics and monitoring in Kubernetes by walking through the gradual thought process that I experienced when learning this topic. Kubernetes needs some metrics for it’s basic out-of-the-box functionality, like autoscaling and scheduling. This is regardless of any monitoring solution you may want for the purpose of troubleshooting and alerting. The case for Kubernetes is often being referred to as the ‘core metrics pipeline’ in contrast to a general monitoring solution.
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Kubernetes Operations: Prioritize Workload in Overcommitted Clusters

One of the benefits in adopting a system like Kubernetes is facilitating burst-able and scalable workload. Horizontal application scaling involves adding or removing instances of an application to match demand. Kubernetes Horizontal Pod Autoscaler enables automated pod scaling based on demand. This is cool, however can lead to unpredictable load on the cluster, which may put the cluster into an overcommitted state. The following image represents a three node cluster that runs three applications.
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Use Istio traffic mirroring for quicker debugging

Often when an error occurs, especially in production, one needs to debug the application to create a fix. Unfortunately the input that created the issue is gone. And the test data on file does not trigger the error (otherwise it would have been fixed before delivery). Likewise if one is creating new code, one often wants to see what values a client can supply (and to be honest I have used more than once WireShark to see what is being sent).
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Kubernetes Ingress Controllers: How to choose the right one: Part 1

In this article, I will share my experiences with 3 major types of Kubernetes ingress solutions. Let’s go through their pros and cons and find out which one suits your needs. How does it work behind thescene? First, Let’s deploy a hello-world service with 2 Pods running in demo namespace. Next, We apply the hello-world ingress resource file as below. Let’s take a look when an Ingress resource is deployed, how does the ingress controller translate it into Nginx configuration?
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