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Containers are not built to persist data. When a container is created, it only runs the process it hosts and terminates. Any data that it creates or processes are also discarded when the container exits. Containers are built to be ephemeral as this makes them lightweight and helps to keep containerized workloads independent of a host’s filesystem which results in flexible, portable and platform-agnostic applications. These benefits, however, creates a few challenges as well when it comes to orchestrating storage for containerized workloads: The need for appropriate tools that enable data sharing across immutable containers Options for backup and discovery in the event of application failure Means to get rid of stored data once it is no longer needed so that hosts can efficiently handle newer workloads In Kubernetes, PODs are also ephemeral. Kubernetes supports various options to persist data for containerized workloads in different formats. This article explores various tools and strategies that facilitate persistent data storage in Kubernetes. How Kubernetes handles Persistent Storage Kubernetes supports multiple options for the requisition and consumption of storage resources. The basic building block of Kubernetes storage architecture is the volume. This section explores central Kubernetes storage concepts and other integrations that allow for the provisioning of highly available storage for containerized applications. Kubernetes Storage Primitives: Volume A volume is a directory that contains data that can be consumed by containers running in a POD. To attach a volume to a specific POD, it is specified in .spec.volumes and is mounted to containers by specifying in .spec.containers[*].volumeMounts. Kubernetes supports different types of volumes depending on the medium hosting them and their contents. There are two main classes of volumes: Ephemeral volumes - These share the life of a POD and are destroyed as soon as the POD ceases to exist. Persistent Volumes - Exist beyond a POD’s lifetime. Persistent Volume and Persistent Volume Claims A Persistent Volume (PV) is a Kubernetes resource that represents a unit of storage available to the cluster. The lifecycle of a PV is independent of the POD consuming it, so data stored in the PV is available even after containers restart. A PV is an actual Kubernetes object that captures details of how a volume implements storage and is configured using a YAML file with specifications similar to: YAML apiVersion: v1 kind: PersistentVolume metadata: name: darwin-volume labels: type: local spec: storageClassName: dev capacity: storage: 10Gi accessModes: - ReadWriteMany hostPath: path: "/mnt/data" A Persistent Volume Claim (PVC) is the Kubernetes object that PODs use to request a specific portion of storage. The PVC is also a Kubernetes resource and can have specifications similar to: YAML apiVersion: v1 kind: PersistentVolumeClaim metadata: name: darwin-claim spec: storageClassName: dev accessModes: - ReadWriteMany resources: requests: storage: 3Gi A PVC can be attached to a pod with a specification similar to: YAML spec: volumes: - name: darwin-storage persistentVolumeClaim: claimName: darwin-claim containers: - name: darwin-container image: nginx ports: - containerPort: 80 Once the PVC is created in the cluster via the kubectl apply command, the Kubernetes Master Node searches for a PV that meets the requirements listed by the claim. If an appropriate PV exists with the same storageClassName specification, the PV is bound to the volume. Supported Persistent Volumes in Kubernetes Kubernetes supports different kinds of PVs for containerized applications. These include: awsElasticBlockStore (EBS) azureDisk azureFile cephfs Cinder gcePersistentDisk glusterfs hostPath Iscsi portworxVolume storageOS vsphereVolume Storage Classes Every application typically requires storage with different properties to run different workloads. PVCs allow for the static provision of abstracted storage, which restricts the volume’s properties of size and access modes. The StorageClass resource allows cluster administrators to offer volumes with different properties such as performance, access modes or size without exposing the implementation of abstracted storage to users. Using the storageClass resource, cluster administrators can describe the different flavors of storage on offer, mapping to different quality-of-service levels or security policies. The storageClass resource is defined using three main specifications: Provisioner- determines the type of volume plugin used to avail Persistent Volumes Reclaim Policy- tells the cluster how to handle a volume after a PVC releases the PV it is attached to. Parameters- properties of the volumes accepted by a storage class Since the StorageClass lets PVCs access volume resources with minimal human intervention, it enables dynamic provisioning of storage resources. Storage Architecture Kubernetes applications run in containers hosted in PODs. Each POD uses a PVC to request a specific portion of PV. PVs are managed by a control plane, which calls volume plugin API actions using logic used to implement storage. The volume plugins, therefore, allow for access to physical storage. The architecture of Kubernetes storage in clusters: A typical Kubernetes Storage Cluster Storage Plugins Kubernetes supports different storage plugins- managed solutions built with a focus on enabling persistent storage for Kubernetes applications. With third-party plugins, Kubernetes developers and administrators can focus on an enhanced user experience, while vendors configure storage systems. Kubernetes supports all three types of file systems: File Storage Systems - Stores data as a single piece of information organized in a folder accessible through paths. It uses a logical hierarchy to store large arrays of files, making access and navigation simpler. Block Storage - Data is segregated into distinct clusters (blocks). Each block has a unique identifier so that storage drivers can store information in convenient chunks without needing file structures. Block storage offers granular control which is desirable for such use-cases as Mail Servers, Virtual Machines and Databases. Object Storage - Isolates data in encapsulated containers known as objects. Each object gets a unique ID which is stored in a flat memory model. This makes data easier to find in a large pool and also allows for the storage of data in different deployment environments. Object storage is most appropriate for highly flexible and scalable workloads such as Big Data, web applications and backup archives. Container Storage Interface Container Storage Interface (CSI) standardizes the management of persistent storage for containerized applications so that storage plugins can be developed for any container runtime or orchestrator. The CSI is a standard interface that allows storage systems to be exposed to containerized workloads. With CSI, storage product vendors can develop plugins and drivers that work across different orchestration platforms. CSI also defines Remote Procedure Calls (RPCs) that enable various storage-related tasks. These tasks include: Dynamic volume provisioning Attaching and detaching nodes to volumes Mounting and unmounting volumes from nodes Consumption of volumes Identification of local storage providers Creating and deleting volume snapshots With the CSI providing a standard approach to the implementation and consumption of storage by containerized applications, a number of solutions have been developed to enable persistent storage. Some top cloud-native storage solutions include: OpenEBS Developed by Mayadata, OpenEBS is an open-source storage solution that entirely runs within the user space as Container Attached Storage. It allows for automated provisioning and high availability through replicated and dynamic Persistent Volumes. Some key features of OpenEBS include: Open-source and vendor-agnostic Utilizes hyperconverged infrastructure Supports both local and replicated volumes Built using microservices-based CAS architecture Portworx Portworx is an end-to-end storage solution for Kubernetes that offers granular storage, data security, migration across multiple cloud platforms and disaster recovery options. Portworx is built for containers from the ground up, making it a popular choice for cloud-native storage. Some features include: Elastic scaling with container-optimized volumes Uses multi-writer shared volumes Storage-aware and application-aware I/O tuning Enables data encryption at volume, storage class and cluster levels Ceph Ceph is founded on the Reliable Autonomic Distributed Object Store (RADOS) to provide pooled storage in a single unified cluster that is highly available, flexible and easy to manage. Ceph relies on the RADOS block storage system to decouple the namespace from underlying hardware to enable the creation of extensive, flexible storage clusters. Ceph features include: Uses the CRUSH algorithm for High Availability Supports file, block and object-storage systems Open-source StorageOS StorageOS is a complete cloud-native software-defined storage platform for running stateful Kubernetes applications. The solution is orchestrated as a cluster of containers that monitor and maintain the state of volumes and cluster nodes. Some features of StorageOS include: Reduces latency by enforcing data locality Uses in-memory caching to speed up volume access Enforces high availability using synchronous replication Utilizes standard AES encryption LongHorn Longhorn is a distributed, lightweight and reliable block storage solution for Kubernetes. It is built using container constructs and orchestrated using Kubernetes, making it a popular cloud native storage solution. Features of LongHorn include: Distributed, enterprise-grade storage with no single point of failure Change block detection for backup Automated, non-disruptive upgrades Incremental snapshots of storage for recovery Directory Mounts Kubernetes uses a hostPath volume to mount a directory from the host’s file system directly to a POD. This is mostly applicable for development and testing on single-node clusters. HostPath volumes are referenced via static provisioning. While not useful in a production environment, this method of persistence is beneficial for several use-cases, including: Running the container advisor (cAdvisor) inside a container When running a container that requires access to Docker internals Allowing a POD to specify whether a given volume path should exist before the POD starts running Containers are immutable, necessitating orchestration mechanisms that allow for the persistence of data they generate and process. Kubernetes uses volume primitives to enable the storage of cluster data. These include Volumes, Persistent Volumes and Persistent Volume Claims. Kubernetes also supports third-party storage vendors through the CSI. For single-node clusters, the hostPath volume attaches PODs directly to a node’s file system, facilitating development and testing. This article has already been published on https://blog.mayadata.io/the-importance-of-persistent-storage-in-kubernetes-mayadata-openebs and has been authorized by MayaData for a republish.

Create an AWS EKS Cluster and give logging/debugging access to the developer's EC2 who does not have access in AWS Console or are not added as an IAM user.

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Introduction- The NVMe Protocol Non-Volatile Memory express (NVMe) is a storage access protocol that lets the CPU access SSD memory through the Peripheral Component Interconnect Express (PCIe). Through a set of protocols and technologies, NVMe dramatically accelerates the way data is transmitted, stored and retrieved. With NVMe, the CPU accesses data on SSDs directly, enabling maximum SSD utilization and flexible scalability. NVMe allows for Storage Disaggregation and can be combined with Kubernetes for scale-out applications. This blog explores how NVMe redefines storage orchestration in Kubernetes. Advantages of NVMe for Distributed Storage: By using the PCIe interface to connect CPUs to SSDs, NVMe removes layers connecting compute to storage, allowing efficient storage abstraction and disaggregation. This offers various benefits for modern data centers, including: Efficient Memory Transfer - NVMe uses one ring per CPU to communicate directly with SSD storage, reducing the internal locking speeds for Input-Output controllers. NVMe also supports message signaled interrupts to prevent CPU bottlenecks, making storage efficient and scalable. NVMe reduces latency by combining message signaled interrupts with the large number of cores in CPUs to enable I/O parallelism. NVMe offers massive Queue Parallelism - Unlike SATA which supports a maximum 32 commands per queue, NVMe utilizes a private queuing which provides up to 64 thousand commands per queue over 64 thousand queues. This is because Each I/O controller gets its own set of queues, which linearly increases throughput with the number of CPU cores available. NVMe offers improved Security - The NVMe over Fabric specification supports secure tunneling protocols produced by reputable security communities such as the Trusted Computing Group (TCG). This means that NVMe enables enterprise-grade security features such as Access Control, Data Encryption at REST, Purge-Level Erase and Crypto-erase among others. NVMe relies on an efficient command set - The protocol relies on a simple, streamlined command set which halves the number of CPU instructions needed to process I/O requests. Besides offering lower latencies, this scheme enables advanced features such as power management and reservations, which extends the benefits beyond input-output operations. NVMe-oF Non Volatile Memory express-over Fabrics (NVMe-oF) is a specification that allows CPUs to connect to SSD Storage devices across a network fabric. This is designed to harness the benefits of the NVMe protocol over a Storage Area Network (SAN). The host computer can target an SSD storage device using an MSI-X based command while the network can be implemented using various networking protocols, including Fiber Channel, Ethernet or Infiniband. NVMe-oF has found wider popularity in modern networks since it allows software organizations to implement scaled out storage for highly-distributed, highly-available applications. By extending the NVMe protocol to SAN devices, NVMe-oF makes CPU usage efficient while improving connection speeds between applications on servers and storage. NVMe-oF supports various data transfer mechanisms, such as: RDMA Fiber Channel TCP/IP NVMe-oF interfaces networked flash storage with compute servers, enabling applications to run on shared network storage, thereby providing additional network consolidation for data centers. The SSD targets can be shared dynamically among application workloads, allowing for the efficient consumption of resources, flexibility and scalability. Kubernetes Orchestration and Storage Persistence While containers are transient, Kubernetes enables stateful applications by providing abstractions that reference a physical storage device. A containerized application is virtually isolated from other processes and applications running on other containers. This makes the Kubernetes environment highly flexible and scalable, as it allows applications to run in virtual machines, bare metal systems, supported cloud systems, or a combination of various deployments. While there are benefits to this approach, it also presents a challenge when there is the need to store and share data between containers. Kubernetes offers various abstractions and options for attaching container PODs to physical storage, such as: Volumes Persistent Volumes & Persistent Volume Claims Storage Classes The Container Storage Interface (CSI) and Storage Plugins Challenges of Orchestration using Direct Attached Storage (DAS) While Direct Attached Storage (DAS) offers a simple, highly available and quick storage, DAS alone is not sufficient to run Kubernetes clusters. This is because DAS devices have a limited storage capacity that cannot be dynamically provisioned to match stateful Kubernetes workloads. Additionally, DAS doesn’t incorporate networking capabilities or facilitate data access by different user groups since storage is only directly accessible to individual servers/desktop machines, while Kubernetes orchestrates on distributed clusters. NVMe for Kubernetes NVMe extends the low latency of DAS to Network Attached Storage devices by connecting servers to SSDs over a high-speed PCIe-oF interface. This makes NVMe an efficient option to provide storage for dynamic, extensible and flexible stateful applications running on Kubernetes. The Container Storage Interface (CSI) standard connects these pooled NVMe devices to Kubernetes clusters running stateful applications. By combining the low-latency networked storage offered by NVMe-oF and the flexibility of the CSI plugin, organizations can provide an efficient, agile and demand driven storage solution for Kubernetes applications. NVMe-oF Persistent Volumes To avoid the bottlenecks of running NVMe SSDs on a single, local server, several organizations are working to enable an NVMe-oF plugin for Kubernetes storage. Kubernetes enables the use of REST APIs to allow control of the storage provisioner through the NVMe-oF protocol. The storage provisioner then creates standard Volume API objects that can be used to attach a portion of pooled NVMe SSDs to a POD. Kubernetes PODs and other resources can then read and write data onto this pooled storage like any persistent volume object. OpenEBS created by MayaData is a popular agile storage stack for stateful Kubernetes applications that need minimal latency. The software infrastructure and plugins from OpenEBS integrate perfectly with the rapid, disaggregated physical storage offered by NVMe-oF. Integrating NVMe SSDs with OpenEBS plugins allows for simpler storage configurations for loosely coupled applications with stateful workloads. OpenEBS is one of the popular open-source, agile storage stacks for performance-sensitive databases orchestrated by Kubernetes. Mayastor, OpenEBS’s latest storage engine, delivers very low overhead versus the performance capabilities of underlying devices. While OpenEBS Mayastor does not require NVMe devices and does not require the workloads to access data via NVMe, an end to end deployment from a workload running a container supporting NVMe over TCP through the low overhead OpenEBS Mayastor and ultimately NVMe devices will understandably perform as close as possible to the theoretical maximum performance of the underlying devices.To learn more about how OpenEBS Mayastor, leveraging NVMe as a protocol, performs when leveraging some of the fastest NVMe devices currently available on the market, visit this article. OpenEBS Mayastor builds a foundational layer that enables workloads to coalesce and control storage as needed in a declarative, Kubernetes-native way. While doing so, the user can focus on what's important, that is, deploying and operating stateful workloads. If you’re interested in trying out Mayastor for yourself, instructions for how to set up your own cluster, and run a benchmark like `fio` may be found at https://docs.openebs.io/docs/next/mayastor.html. References Mayastor NVMe-oF TCP performance - https://openebs.io/blog/mayastor-nvme-of-tcp-performance/ Lightning-fast storage solutions with OpenEBS Mayastor and Intel Optane - https://mayadata.io/assets/pdf/product/intel-and-mayadata-benchmarking-of-openEBS-mayastor.pdf This article has already been published on https://blog.mayadata.io/the-benefits-of-using-nvme-for-kubernetes and has been authorized by MayaData for a republish.

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