Kubernetes, the popular container management system, offers enterprises many benefits: agility, scalability, faster development, and management. Consequently, the open source orchestration software has quickly been gaining traction. One ripple effect is that many enterprise storage systems are struggling to keep pace with its growth. As a result, enterprises need to look at their storage solutions and deploy a Kubernetes compatible, true scale-out storage system that doesn’t introduce performance bottlenecks.
Companies deploy containers for many reasons, starting with portability. Once written, a containerized application can be deployed anywhere – public cloud, hybrid, on-prem – with little to no change to the underlying code
Container Acceptance Rapidly Grows
Because of that feature, many companies rapidly embraced the technology. A 2021 survey by the Cloud Native Computing Foundation (CNCF) found that most organizations (93%) now run containers in production. That number was up just 1% from 83% in 2020, but up 9% in 2019. For even more context, production container usage was just 23% when CNCF completed its first survey in March 2016.
In theory, containers abstract away computer infrastructure. With Kubernetes, a developer describes the amount of memory and compute power needed, and the system allocates storage automatically.
New Management Challenges Arise
But writing an application is only one step that enterprises need to take in order to take advantage of this modern technology. They also must manage it, which means making sure that system resources match application needs and do not create any performance bottlenecks.
Kubernetes was designed with that goal in mind. It is a container management system designed to function across clusters of nodes. Its Container Storage Interface (CSI), an industry standard, enables storage vendors to develop plugins for exposing block and filesystem storage systems to containers. In essence, CSI provides an extensible layer to manage Kubernetes volumes.
A History Lesson
In theory, containers and Kubernetes offer unmatched scalability, but that premise is not completely accurate. While containers offer developers the ability to run on any infrastructure, that ability does not mean that applications automatically perform well in any case. In fact, in many instances, they do not. Putting high-performance applications on low performance storage infrastructure is similar to building a Formula race car with regular tires. It runs fast theoretically, but not in practice because the tires do not match the horsepower.
The same holds true with enterprise storage. Here, the moral is: If corporations scale-out their applications, they also need to scale-out their storage. History is a good teacher here: such lessons were learned in the past with Virtual Machines. Enterprises gradually understood that block storage was not suited to high-performance applications for a variety of reasons: no sharing/trapped data, loss of context, and I/O patterns.
Containers deployments are following a similar path even though they are very different, more lightweight, and work on a higher abstraction level than VMs. This new approach does make it so much easier to share data and run workloads natively on different infrastructures. Here, IO context is saved and can be used to optimize workloads automatically, e.g., storing Hadoop workloads on HDD and databases on SSD.
But this higher level of abstraction creates storage challenges. Many companies experience performance problems, especially when they try to share data among pods.
What is Missing?
So, what do companies need to overcome the storage limitations? They require a scale-out storage system built with the same hyperscaler concepts as Kubernetes. Such a distributed file system delivers container-native storage and includes a CSI plugin for persistent volumes with auto deployment and quotas. In addition, the product supports static volumes and snapshots.
Corporations gain flexibility. The system drops in and serves as a Network File System (NFS) replacement and includes deep integration with Kubernetes and Docker. They run databases, scale-out applications, and big data analytics on a consistent infrastructure and no longer worry about container data storage performance issues.
Companies mirror container flexibility in their storage clusters. As noted, portability is a key container concept: Now, the storage system itself is containerized and runs inside of containers, providing businesses with storage flexibility, enabling rolling updates, and simplifying storage management.
The storage system is reliable. Dynamic and policy-driven provisioning and full-fledged monitoring and management enable developers to grow storage capacity along with the container platform and benefit from linear scale-out. The system delivers low-latency and parallel throughput performance for containerized applications, plus its built-in fault tolerance ensures high availability.
The distributed file system includes smart replication and erasure coding, ensuring that data is safe and secure. Consequently, no data is lost when containers fail. Persistent volumes, full fault tolerance, and implicit locking protect information. Smart replication, policy-driven provisioning, failover, and rolling updates guarantee zero downtime.
Businesses gain operational efficiency. Container management platforms, such as Kubernetes, are popular because they include automation tools. With them, companies greatly streamline container management and scheduling. The underlying storage system automatically completes storage tasks.
The Quobyte Difference
Quobyte’s reliable, unconditionally simple, hyperscale, High-Performance Computing (HPC) storage delivers the performance that containers need. Quobyte’s Software-defined Storage (SDS) frees companies from past constraints, so storage resources are allocated with a few mouse clicks rather than a long, complex series of commands, resulting in exponential (10X) productivity increases.
Quobyte’s Health Manager includes near real-time analytics. Its web console, APIs, and CLI make it easy for system administrators to access all of the information needed to manage container storage proactively. The solution includes deep integration with Kubernetes as well as other platforms. Technicians gain access to data through native clients for Linux, Windows, and macOS, or they use S3, NFS, SMB, and Hadoop.
With it, businesses make end users and management happy because they deliver high performance storage along with container portability and flexibility. Enterprises gain the power to run massive scale-out workloads. Data sets can be used by 1 or 10,000 pods, for large workloads, like analytics workloads and machine learning. They even serve millions of requests per day for complex business systems and popular consumer applications. With Quobyte, enterprises realize the potential benefits that containers offer without falling prey to any of its hidden storage flaws.