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Kubernetes: Overcoming complexity – Gigaom



Kubernetes was created in 2014 so that administrators can easily run distributed systems. Thanks to its 100% open source nature, it can run both locally and in the cloud, including public, hybrid and multi-cloud environments.

Kubernetes has been rapidly accepted since its introduction. Because it was specially developed for handling containers on a scale, it is both quick and easy, can be used on any VM or bare metal server and offers robust container-centered functions. Kubernetes can be executed equally well on any VM infrastructure that is huge from a DevOps perspective, because the portability of containers should match the portability of the container manager.

Despite all its advantages, Kubernetes offers unique challenges. Running stateful services such as databases on Kubernetes requires certain container-native storage systems. Without this technology, certain problems often occur: stuck volumes, downtime, over-provisioning, data loss, and manual backups and migrations.

The hardware challenges that make Kubernetes so difficult are described below. Let̵

7;s take a look at what companies need to do to build a successful deployment.

What does Kubernetes Storage have to do?

To be successful, the Kubernetes memory needs six core functions (see illustration 1):

Self service: The ability to provision and manage containerized granular storage as needed.

Application conscious: Kubernetes-based applications consist of multiple containers that run on a fleet of hosts. This means that storage operations such as backup, restore, DR, and encryption must be applied at the application level and not at the machine level, as is the case with conventional storage.

Automation: A layer that can perform the details of the memory implementation as well as other operations that need to be performed, e.g. B. BC / DR. Automation eliminates most manual processing and offers important advantages.

Infrastructure independent SLAs: A consistent level of service, regardless of what infrastructure is used, or not tying SLAs directly to part of the infrastructure such as platform services.

Cost optimization: Uses processes that can improve cost efficiency. This includes automating provisioning systems to ensure that no more memory is allocated than is required.

How to meet requirements

Below are the key attributes of the best Kubernetes native storage systems and their proper use in production.

Kubernetes-native: A subsystem, including storage and database systems, specially developed for the Kubernetes platform. For example, storage operations must be container-granular and include additional operations at the namespace level.

Performance on the Kubernetes scale: Storage typically needs to be scalable to more than 1,000 volumes per node and thousands of concurrent operations per minute. Non-Kubernetes storage systems are often designed to process a relatively small number of large volumes that are managed by an administrator, as opposed to a large number of small to medium volumes that are automatically managed by Kubernetes.

Application oriented: The storage must have an application or solution focus, as opposed to an infrastructure focus. Container granular storage should be able to be provisioned when the application is deployed, if needed.

Comprehensive native Kubernetes storage stack: The ability to support both transactional and analytics-oriented storage systems, including focus on performance, BC / DR, data security, and data compliance, as well as the ability to support real-time backups, as shown in Figure 2.

Understand it correctly

There are many ways to solve Kubernetes memory, and most work in proof-of-concept environments. However, most are not optimal for the solution set you are looking for. Choosing the right path is critical to the overall success of your Kubernetes-based applications where storage is on the critical path. The paths that are available to you can be seen in Figure 3.

Traditional storage approaches: These systems are not Kubernetes-enabled, but Kubernetes-based applications can use them. These usually come in two forms: native storage systems for local systems or a cloud-provided storage system such as cloud block storage, an object-based storage system or a cloud-native database.

Container orchestration-native: These storage systems are more commonly referred to as Kubernetes native and were specially developed for a specific container orchestration engine.

This Kubernetes-native approach does not require a translation layer between VM storage and container storage, as is the case with the conventional approach. As a result, many of the above requirements and features are supported by this container orchestration approach and Kubernetes’ core storage technology.

Hybrid storage solutions: These provide support for native and non-native Kubernetes storage. They are usually not the solution of choice because you are trying to do everything on two or more different platforms.

Conclusion

Kubernetes is changing the way distributed systems are run and opening up new opportunities for cloud-based databases. However, in order to use this technology optimally, the system must meet its core requirements.

Storage systems are not complex core systems, but require special consideration due to their impact on the applications and business systems that use them. The switch to multi-cloud and container orchestration creates an incentive for the switch to Kubernetes-native storage.




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