

Google Container Engine users can now set up container clusters with nodes sporting multiple configurations within them.
Google’s support for heterogeneous clusters comes by way of a new feature called Node Pools, which moved into general availability late last week.
Fabio Yeon, software engineer at Google’s Cloud Platform group, said in a blog post that node pools can be thought of as a collection of machines with the same features and configuration within a cluster. They allow users to set up container clusters with multiple pools, each one running machines with various configurations.
“Now instead of a uniform cluster where all the nodes are the same, you can have multiple node pools that better suit your needs,” Yeon said. He gave the example of an organization that wants to beef up CPU performance from an existing cluster. Instead of setting up a brand new cluster, they can now simply add a node pool and put bigger machines with it, Yeon explained.
Yeon said there were many other benefits to node pools beside this. For example, administrators can exercise more control when they’re upgrading nodes in a cluster to the latest Kubernetes version. When a new version of Kubernetes is available, all admins have to do is set up a new node in an existing cluster, and it can be automatically updated to the new version by the cluster master software.
Node pools also support multizone cluster configurations, which are useful for enterprises that run container nodes in different zones as a precaution against outages. When an administrator creates new node pools in one region or zone, those node pools can be automatically recreated in the other regions and zones as well.
Node pools are a welcome addition that users have been anticipating for a long time, according to various threads on the Google Container forum. As one commenter noted all the way back in December 2014: “In production we run a mix of server types with fairly different processing, I/O, memory requirements. Ideally, it would be possible to match the node shape to the workload.”
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