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It’s no news that for quite a while our Kafka on Kubernetes take, Supertubes has been happily running inside an Istio-based service mesh, in both single or multi-cluster setups across hybrid clouds. While we have touched on several aspects of the advantages Istio gave us, this post’s aim is to collect some of the issues, cornerstones and benefits. We see the service mesh as a key component of every modern Cloud Native stack.
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It's been some time since we open sourced our Kafka Operator, an operator designed from square one to take advantage of the full potential of Kafka on Kubernetes, and have built Supertubes on top to manage Kafka for our customers. One of the most requested enterprise feature has been the implementation of rolling upgrades. Built on the Banzai Cloud's Kafka operator, Supertubes adds support and orchestrates these upgrades. Accordingly, in today's blog we're going to take a deep dive into the technical details of how the operator handles an update and how you can use Supertubes to seamlessly upgrade to the newest 2.
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In one of the earlier Kafka disaster recovery on Kubernetes with CSI post we discussed how brokers of an Apache Kafka clusters can be backed up and restored leveraging Kubernetes volume snapshots. While this solution provides a good enough disaster recovery option (and a super quick recovery), it doesn't help when the entire Kubernetes cluster hosting the Kafka cluster is lost. In this post, we show you how to: back up a Kafka cluster to a remote Kafka cluster running on a separate Kubernetes cluster, and also how to recover the lost Kafka cluster with Supertubes.
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Apache Kafka was designed with a heavy emphasis on fault-tolerance and high-availability in mind, and thus provides different methods of ensuring enterprise-grade resiliency such as: replication factor - which defines how many partition replicas of a topic should be kept, each one being stored on a different broker. A replication factor of N allows for the loss of N-1 brokers. The new brokers will replace the lost brokers until the number of remaining broker(s) reaches the replication factor, or the number of copies of the partitions of a topic.
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Apache Kafka is a distributed streaming platform used to build reliable, scalable and high-throughput real-time streaming systems. Its capabilities, while impressive, can be further improved through the addition of Kubernetes. Accordingly, we've built an open-source Kafka operator and Supertubes to run and seamlessly operate Kafka on Kubernetes through its various features, like fine-grain broker configuration, metrics based scaling with rebalancing, rack awareness, and graceful rolling upgrades - just to name a few.
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A while ago we published some benchmarks and sizing about our experience of running Apache Kafka over a service mesh with the Banzai Cloud Kafka and Istio operator, orchestrated by our automated and operationalized service mesh, Backyards. The reasons for such a setup were many, and there are more details in the Running Apache Kafka over Istio - benchmark post, but let me recap some of our initial reasons, and how we evolved from there.
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One of the earliest features of our hybrid cloud container management platform, Pipeline, was to standardize the running of applications on Kubernetes with a framework we called Spotguides. Spotguides allowed us to build domain specific knowledge into apps, avoid writing boilerplate code, and go from commit to a production ready deployment in minutes. All of this while meeting enterprise-grade requirements such as federated monitoring, centralized log collection, security scans, cluster lifecycle management and lots more.
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Our thinking that there was a hunger for an operator that makes easy the provisioning and operating Kafka clusters on Kubernetes which is not based on Kubernetes stateful sets, proved to be correct as shortly after we released the first version our open-source Banzai Cloud Kafka Operator a community started to build around it. We received lots of valuable feedback that helps to shape the future of the Kafka operator and also feature contributions from the community.
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One of the key features of our container management platform, Pipeline, as well as our CNCF certified Kubernetes distribution, PKE, is their ability to form and run seamlessly across multi- and hybrid-cloud environments. While the needs of Pipeline users vary depending on whether they employ a single or multi-cloud approach, they usually build upon one or more of these key features: Multi-cloud application management An Istio based automated service mesh for multi and hybrid cloud deployments Federated resource and application deployments built on Kubernetes federation v2 As Istio operator-based multi-cluster and multi/hybrid-cloud adoption increased, so did the demand for the ability to run distributed or decentralized applications wired into a service mesh.
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If you're reading this post, you're likely already familiar with our container management platform, Pipeline, and our CNCF certified Kubernetes distribution, PKE: you probably already know how we make it possible to spin up clusters across five cloud providers and on-premise, in multi-cloud but also hybrid-cloud environments. But whether these are single or multi-cluster topologies, resilience is key. We at Banzai Cloud believe this is the case not just for infrastructural components but for entire managed application environments, like Apache Kafka.
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