Container Native Storage (CNS)
What is CNS?#
Container Native Storage (CNS) is software that includes microservice-based storage controllers that are orchestrated by Kubernetes. These storage controllers can run anywhere that Kubernetes can run which means any cloud or even bare metal server or on top of a traditional shared storage system. Critically, the data itself is also accessed via containers as opposed to being stored in an off-platform shared scale out storage system.
CNS is a pattern very much in line with the trend towards disaggregated data and the rise of small, autonomous teams running small, loosely coupled workloads. For example, my team might need Postgres for our microservice, and yours might depend on Redis and MongoDB. Some of our use cases might require performance, some might be gone in 20 minutes, others are write-intensive, others read-intensive, and so on. In a large organization, the technology that teams depend on will vary more and more as the size of the organization grows and as organizations increasingly trust teams to select their tools.
CNS means that developers can work without worrying about the underlying requirements of their organizations' storage architecture. To CNS, a cloud disk is the same as a SAN which is the same as bare metal or virtualized hosts. Developers and Platform SREs do not have meetings to select the next storage vendor or to argue for settings to support their use case, instead, developers remain autonomous and can spin up their own CNS containers with whatever storage is available to the Kubernetes clusters.
CNS reflects a broader trend of solutions – many of which are now part of Cloud Native Computing Foundation (CNCF) – that reinvent particular categories or create new ones – by being built on Kubernetes and microservice and that deliver capabilities to Kubernetes based microservice environments. For example, new projects for security, DNS, networking, network policy management, messaging, tracing, logging, and more have emerged in the cloud-native ecosystem and often in CNCF itself.
Advantages of CNS#
Agility#
Each storage volume in CNS has a containerized storage controller and corresponding containerized replicas. Hence, maintenance and tuning of the resources around these components are truly agile. The capability of Kubernetes for rolling upgrades enables seamless upgrades of storage controllers and storage replicas. Resources such as CPU and memory can be tuned using container cGroups.
Granularity of Storage Policies#
Containerizing the storage software and dedicating the storage controller to each volume brings maximum granularity to storage policies. With CNS architecture, you can configure all storage policies on a per-volume basis. In addition, you can monitor storage parameters of every volume and dynamically update storage policies to achieve the desired result for each workload. The control of storage throughput, IOPS, and latency increases with this additional level of granularity in the volume storage policies.
Avoids Lock-in#
Avoiding cloud vendor lock-in is a common goal for many Kubernetes users. However, the data of stateful applications often remains dependent on the cloud provider and technology or an underlying traditional shared storage system, NAS or SAN. With the CNS approach, storage controllers can migrate the data in the background per workload and live migration becomes simpler. In other words, the granularity of control of CNS simplifies the movement of stateful workloads from one Kubernetes cluster to another in a non-disruptive way.
Cloud Native#
CNS containerizes the storage software and uses Kubernetes Custom Resource Definitions (CRDs) to represent low-level storage resources, such as disks and storage pools. This model enables storage to be integrated into other cloud-native tools seamlessly. The storage resources can be provisioned, monitored, and managed using cloud-native tools such as Prometheus, Grafana, Fluentd, Weavescope, Jaeger, and others.
Similar to hyperconverged systems, the storage and performance of a volume in CNS are scalable. As each volume has its storage controller, the storage can scale up within the permissible limits of the storage capacity of a node. As the number of container applications increases in a given Kubernetes cluster, more nodes are added, which increases the overall availability of storage capacity and performance, thereby making the storage available to the new application containers. This process of scalability is similar to successful hyperconverged systems like Nutanix.
Lower Blast Radius#
As the CNS architecture is per workload and components are loosely coupled, CNS has a much smaller blast radius than a typical distributed storage architecture.
CNS can deliver high availability through synchronous replication from storage controllers to storage replicas. The metadata required to maintain the replicas is simplified to saving the information of the nodes that have replicas and information about the status of replicas to help with quorum. If a node fails, the storage controller, which is a stateless container in this case, is spun on a node where a second or third replica is running and data continues to be available. Hence, with CNS the blast radius is much lower and also localized to the volumes that have replicas on that node.