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A few weeks ago we discussed the way that we integrated Kubernetes federation v2 into Pipeline, and took a deep dive into how it works. This is the next post in our federation multi cloud/cluster series, in which we’ll dig into some real world use cases involving one of Kubefed’s most interesting features: Replica Scheduling Preference. tl;dr: ReplicaScheduler helps balance replicas between federated clusters, rebalancing if replicas on one or more clusters become (or are) unschedulable We’ll take a deep dive into how the ReplicaScheduler works And we’ll present some examples that we’re proof-of-concepting with our customers Note that every multicloud or hybrid cloud use case requires different architectural approaches - built on our cluster group feature, the Pipeline platform supports multiple scenarios, while maintaining the same clean and consistent UX experience

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At Banzai Cloud we are building a managed Cloud Native application and devops platform called Pipeline. Pipeline supercharges the development, deployment and scaling of container-based applications with native support for multi and hybrid-cloud environments. The Pipeline platform provides support for advanced scheduling that enables enterprises to run their workflows in an efficient way by scheduling workflows to nodes that meet the needs of the workflow (e.g.: CPU, memory, network, IO, spot price, etc).

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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 Apache Spark CI/CD workflow howto 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 CI/CD flow for Zeppelin notebooks

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As 2017 comes to an end, we’re looking back at the three blog posts that were most popular with our readers. We can’t go too far back (though we’ve had 13 posts and one release already), since we founded our startup just a little over one month ago (on November 20, 2017, to be precise), but during this short period we’ve achieved a whole lot, and laid the foundation for some exciting new projects we plan to ship out early next year.

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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 Apache Spark CI/CD workflow howto 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 CI/CD flow for Zeppelin notebooks

Read more...

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 Apache Spark CI/CD workflow howto 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 CI/CD flow for Zeppelin notebooks

Read more...