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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. When we released the Istio operator earlier this year, our goal (besides managing Istio installation and upgrades) was to provide support for these excellent traffic routing features, while making everything more usable and UX friendly.
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At Banzai Cloud we work with Istio quite a bit and run a lot of Istio-based service meshes for our customers. Earlier this year we opensourced the Banzai Cloud Istio operator in order to simplify provisioning, management, upgrades, and multi-cluster scenarios. However, we still felt that this was not simple enough. A few months ago we announced Backyards, a method of simplifying, creating and managing Istio, with planned support for all Istio features - the more complicated, the better!
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At Banzai Cloud we develop a container management platform that provisions and orchestrates hundreds of Kubernetes clusters across six different cloud providers or hybrid clouds every day. Pipeline makes it easy for users to run applications on Kubernetes, but under the hood there is an insanely complex system, so it’s mission critical for us to have realtime information about what’s happening inside the platform and have access to historical data to investigate incidents our customers may have had.
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Many organizations are embracing a more decentralized decision making process, allowing them to better build cloud native applications, and to deliver them more rapidly and safely. Often, different teams manage different microservices, with each team able to release their microservices independent of the others. Istio is exceedingly helpful for this, since it provides low level contructs able to enforce policies in the network and configure traffic control, observability, rate limiting, circuit breaking and programmable rollouts.
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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. The contents of the posts were built around our open source Istio operator that helps installing and managing an Istio service mesh in a single or multi and hybrid-cluster setup.
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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. While a basic Istio installation is able to set up all the components needed to collect telemetry from the mesh, it’s not easy to understand how these components fit together and how to configure them in a production environment.
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One of the main features of the Banzai Cloud Pipeline platform is that it allows enterprises to run workloads cost effectively by mixing spot or preemptible instances with regular ones, without sacrificing overall reliability. The platform allows enterprises to develop, deploy and scale container-based applications and it leverages best-of-breed cloud components, such as Kubernetes, to create a highly productive yet flexible environment for developers and operation teams alike. tl;dr The Banzai Cloud Pipeline platform switched to a unified, cloud-aware spot instance termination handler to properly drain the cluster node and provide information to the monitoring system if an instance is going to be preempted from a cluster nodepool.
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Whenever we talk about Kubernetes monitoring and the topic of Prometheus comes up, so does Grafana; the two pretty much go hand in hand. Prometheus query language This article will not serve as an introduction to the powerful Prometheus query language. If you’re not comfortable with Prometheus, now would be a good time to familiarize yourself with the basics of querying and monitoring best practices with Prometheus on Kubernetes. We will only use simple queries with filters and labels in this post.
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Monitoring series: Monitoring Apache Spark with Prometheus Monitoring multiple federated clusters with Prometheus - the secure way Application monitoring with Prometheus and Pipeline Building a cloud cost management system on top of Prometheus Monitoring Spark with Prometheus, reloaded Hands on Thanos Monitoring Vault on Kubernetes using Cloud Native technologies At Banzai Cloud we are building a feature rich enterprise-grade application platform, built for containers on top of Kubernetes, called Pipeline.
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Monitoring series: Monitoring Apache Spark with Prometheus Monitoring multiple federated clusters with Prometheus - the secure way Application monitoring with Prometheus and Pipeline Building a cloud cost management system on top of Prometheus Monitoring Spark with Prometheus, reloaded Hands on Thanos Monitoring Vault on Kubernetes using Cloud Native technologies In depth application observability is a must-have feature for us; we put significant effort into monitoring all our customers’ Kubernetes deployments on the Pipeline platform, as well as the platform itself.
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