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Helm version 3 has been out officially for some time (release blog post was published on Wed, Nov 13, 2019). We've been using Helm since the early days of Kubernetes, and it's been a core part of our Pipeline container management platform since day one. We've been making the switch to Helm 3 for a while and, as the title of this post indicates, today we'll be digging into some details of our experience in transitioning between versions, and of using Helm as a Kubernetes release manager.
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At Banzai Cloud we write Kubernetes tools that, no surprise, require other tools to get installed. These tools handle resource lifecycle operations like the installation, upgrade, and removal of various components that need to be integrated together. Kubernetes Operators solve all these and even more for a given application's domain, but if the focus is simply on installation, upgrade and removal of a set of components - which are in our case operators themselves (operating operators), then we need a tool with different characteristics at hand.
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About a year ago, we wanted to publish a central Helm chart repository. It seemed like the easiest way to do that was from a single source, so we migrated all of our Helm charts to a central Git repository. The idea was to use CircleCI to build every chart, then upload the resultant charts to S3 and serve them from there. It wasn't a perfect solution, though, since it made coordination with application releases and tracking issues more difficult.
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At Banzai Cloud we are passionate about observability, and we expend a great amount of effort to make sure we always know what's happening inside our Kubernetes clusters. All clusters provisioned with Pipeline - our multi- and hybrid-cloud container management platform - are provided with, and rely upon, each of the three pillars of observability: federated monitoring, centralized log collection and traces. In order to automate log collection on Kubernetes, we opensourced a logging-operator built on the Fluent ecosystem.
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This is the second part of a very popular post, Helm from basics to advanced. In the previous post (we highly suggest you read it, if you haven't done so already) we covered Helm's basics, and finished with an examination of design principles. In this post, we'd like to continue our discussion of Helm by exploring best practices and taking a look at some common mistakes. If you are looking for a place to securely store your Helm charts, remember that Banzai Cloud runs a free Helm Chart repository service.
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Update: Logging operator v3 (released March, 2020) We're constantly improving the logging-operator based on feature requests of our ops team and our customers. The main features of version 3.0 are: Log routing based on namespaces Excluding logs Select (or exclude) logs based on hosts and container names Logging operator documentation is now available on the Banzai Cloud site. Check The Kubernetes logging operator reloaded post for details.
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In one of our previous posts about creating Helm Charts for Kubernetes, we outlined what we consider the best practices for creating Helm charts. We've been using Helm in production and investing our time in creating Helm charts (available on the Banzai Cloud Charts GitHub repository) since Banzai Cloud's inception. Creating Helm Charts is one thing; storing and serving them is another. We'd like to reduce the burden this places on the user, so today marks the launch of our Helm Chart repository service, which you can use to store and serve public Helm Charts for free.
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Banzai Cloud is on a mission to simplify the development, deployment, and scaling of complex applications and to bring the full power of Kubernetes to all developers and enterprises. Banzai Cloud’s Pipeline provides a platform which allows enterprises to develop, deploy and scale container-based applications. It leverages best-of-breed technology from the Cloud Native Foundation ecosystem to create a highly productive, yet flexible environment for developers and operation teams alike. One of the key tools we use from the Kubernetes ecosystem is Helm.
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Apache Spark on Kubernetes series: Introduction to Spark on Kubernetes Scaling Spark made simple on Kubernetes The anatomy of Spark applications on Kubernetes Monitoring Apache Spark with Prometheus Spark History Server on Kubernetes Spark scheduling on Kubernetes demystified Spark Streaming Checkpointing on Kubernetes Deep dive into monitoring Spark and Zeppelin with Prometheus Apache Spark application resilience on Kubernetes Apache Zeppelin on Kubernetes series: Running Zeppelin Spark notebooks on Kubernetes Running Zeppelin Spark notebooks on Kubernetes - deep dive
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If you are looking to try out an automated way to provision and manage Kafka on Kubernetes, please follow this Kafka on Kubernetes the easy way link. At Banzai Cloud we use Kafka internally a lot. We have some internal systems and customer reporting deployments where we rely heavily on Kafka deployed to Kubernetes. We practice what we preach and all these deployments (not just the external ones) are done using our application platform, Pipeline.
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