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In production systems mostly when working in microservices architecture, it is soon realized that monitoring each service individually is often not enough to troubleshoot complex issues. It becomes a necessity to have a full picture of the whole call stack through the entire application with detailed information on service topologies, network latencies and individual request durations. This is usually where distributed tracing comes to the rescue. In this post, the concept of distributed tracing will be introduced in microservices architecture.
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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest versions of Istio as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.3 support for the Banzai Cloud Istio operator. In this post, we’ll be outlining how to easily upgrade Istio control planes to 1.3 with the Banzai Cloud Istio operator, within a single-mesh multi-cluster topology or across a multi-cloud or hybrid-cloud service mesh.
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Since releasing our open-source Istio operator, we’ve been doing our best to add support for the latest Istio versions as rapidly as possible. Today, we’re happy to announce that we have added Istio 1.2 support for the Banzai Cloud Istio operator. When we added Istio 1.1 support for the operator, we wrote a detailed blog post about how to employ a seamless Istio control plane upgrade in a single-mesh, single-cluster setup.
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A few weeks ago we announced Backyards, Banzai Cloud’s automated service mesh built on top of our Istio operator, which greatly simplifies the complex management of service meshes across multi and hybrid-cloud environments. Backyards is integrated into Banzai Cloud’s container management platform, Pipeline. However, it also works, and is available, as a standalone product. Naturally, using Backyards with Pipeline provides users with a variety of specific benefits (like managing applications in a multi-cloud world) but Backyards works on any Kubernetes installation.
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Service mesh probably needs no introduction. But, just to recap, let’s define it as a highly configurable, dedicated and low‑latency infrastructure layer designed to handle and provide reliable service-to-service communication, implemented as lightweight network proxies deployed alongside application code. Typical examples of mesh services are service discovery, load balancing, encryption, observability (metrics and traces) and security (authn and authz). Circuit breakers, service versioning, and canary releases are frequent use cases, all of which are part of any modern cloud-native microservice architecture.
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Hybrid- and multi-cloud are quickly becoming the new norm for enterprises, just as service mesh is becoming essential to the cloud native computing environment. From the very beginning, the Pipeline platform has supported multiple cloud providers and wiring them together at multiple levels (cluster, deployments and services) was always one of the primary goals. We supported setting up multi-cluster service meshes from the first release of our open source Istio operator.
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When we added support for Istio’s service mesh in the Pipeline platform, we experienced first hand how the deployment and management of Istio can become increasingly complex. We realized that we weren’t the only ones managing Istio with Helm dealing with these problems - that demand was emerging for an Istio operator (e.g. #9333). We decided to build an Istio operator of our own, and more than a month ago we open sourced it.
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At Banzai Cloud we are building a feature rich enterprise-grade application and devops container management platform, called Pipeline and a CNCF certified Kubernetes distribution, PKE. Security is one of our main areas of focus, and we strive to automate and enable those security patterns we consider essential for all the enterprises that use Pipeline. For us, Istio is no exception, in that we apply the best available security practices to the service mesh, while maintaining the sleekest, most automated user experience possible.
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One of the core features of Pipeline, Banzai Cloud’s application and devops container management platform, is multi-dimensional autoscaling based on default and custom metrics. Upon our introduction of custom metrics, we opted for an approach that relied on the Prometheus Adapter to gather metrics from Prometheus. Since then, a lot of our customers have begun using Hoizontal Pod Autoscaling, and most of them have been satisfied with only basic CPU & memory metrics.
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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. In creating the operator, our main goal was to simplify the deployment and management of Istio’s components. This release is still in alpha, and its main goal is still to replace Helm charts as a preferred means of installing Istio, but it provides a few additional features we think you’ll find convenient.
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