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The sidecar concept in Kubernetes is getting more and more popular, and for a good reason. In the container world, it's a common principle that a container should address a single concern only, but do it well. The sidecar pattern helps achieving this principle by decoupling the main business logic from supplementary tasks that extend the original functionality. In Kubernetes, a pod is a group of one or more containers with shared storage and network.
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Today we are happy to announce the 1.1 release of Backyards, Banzai Cloud's automated and operationalized service mesh product built on Istio. This is an announcement post describing the new features of Backyards. If you're not familiar with Backyards yet, and want to know why we decided to build this product, we suggest reading the blog post about the first major release. Check out Backyards in action on your own clusters: curl https://getbackyards.
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When someone first hears about Istio's sidecar concept, the first two questions are usually about its effect on resource consumption and request latency or throughput. In this post we'll discuss the first of those questions: resource consumption of Istio proxies and how it can be reduced with the new Sidecar custom resource. Previously we had a Kafka on Istio benchmark post that was about throughput, and we'll take a deeper look at the most important HTTP request metrics in a forthcoming article (stay tuned).
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Check out Backyards in action on your own clusters: curl https://getbackyards.sh | sh && backyards install -a --run-demo What to know more? Get in touch with us, schedule a demo or delve into the details of the latest release. Istio has been rightfully praised for ushering in free observability and secure service to service communication. Other, more significant features, however, are what truly make Istio the Swiss army knife of service mesh operators; when it comes to meeting SLOs like uptime, latency and error rates, the ability to manage traffic between services is absolutely critical.
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There is a newer version of Backyards available, read more here: Announcing Backyards 1.1 Today we are happy to announce the first major release of Backyards, Banzai Cloud's automated service mesh product. Before jumping into the details, we need to answer an obvious question: Why another service mesh product? The service mesh became one of the most hyped things lately, and it may seem that a new product is released every other week.
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Check out Backyards in action on your own clusters: curl https://getbackyards.sh | sh && backyards install -a --run-demo What to know more? Get in touch with us, or delve into the details of the latest release. In the last few months we wrote a lot of different blog posts about the Istio service mesh. We started with a simple Istio operator, then went on with different multi-cluster service mesh topologies, Istio CNI and a telemetry deep dive.
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Check out Backyards in action on your own clusters: curl https://getbackyards.sh | sh && backyards install -a --run-demo What to know more? Get in touch with us, or delve into the details of the latest release. One of the core features of the Istio service mesh is the observability of network traffic. Because all service-to-service communication is going through Envoy proxies, and Istio's control plane is able to gather logs and metrics from these proxies, the service mesh can give you deep insights about your network.
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Check out Backyards in action on your own clusters: curl https://getbackyards.sh | sh && backyards install -a --run-demo What to know more? Get in touch with us, or delve into the details of the latest release. Two weeks ago we announced the first release of our Istio operator. Since then we've added support for Istio's preliminary 1.1 release. This post will detail how and why you should try it.
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Check out Backyards in action on your own clusters: curl https://getbackyards.sh | sh && backyards install -a --run-demo What to know more? Get in touch with us, or delve into the details of the latest release. Service mesh has, without question, been one of the most vigorously debated and obsessed over topics of discussion in recent memory. It seems like, whichever way you turn, you run into heated arguments between those developers that are convinced that service mesh will outgrow even Kubernetes, and the naysayers convinced that, outside of use in a few large companies, service mesh is impractical to the point of uselessness.
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Our last two blog posts about the Kubernetes scheduler explained how taints and tolerations and different types of affinities are working. In today's post we are going one layer deeper and we'll discuss how to implement and deploy a custom Kubernetes scheduler. Writing a scheduler may sound intimidating at first, but if you'll follow this article you'll realise that creating something that works and schedules pods based on some simple rules is quite easy.
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