That said, as Kubernetes adoption keeps growing in 2025, selecting the right storage solution is more important than ever. Enterprise-level features, data encryption, and high availability are at the forefront of the requirements we want to look into. The same is true for the ability to attach to multiple clients simultaneously and the built-in data loss protection.

Simplyblock: Enterprise Storage Platform for Kubernetes

Simplyblock™ is an enterprise-grade storage platform designed to cater to high-performance and scalability needs for storage in Kubernetes environments. Simplyblock is fully optimized to take advantage of modern NVMe devices. It utilizes the NVMe/TCP protocol to share its shared volumes between the storage cluster and clients, providing superior throughput and lower access latency than the alternatives.

Simplyblock is designed as a cloud-native solution and is highly integrated with Kubernetes through its Simplyblock CSI driver. It supports dynamic provisioning, snapshots, clones, volume resizing, fully integrated encryption at rest, and many more. One benefit of simplyblock is its use of NVMe over TCP which is integrated into the Linux and Windows (Server 2025 or later) kernels, meaning no additional drivers. This also means it is easy to use simplyblock volumes outside of Kubernetes if you also operate virtual machines and would like to unify your storage. Furthermore, simplyblock volumes support read-write multi-attach. That means they can be attached to multiple pods, VMs, or both at the same time, making it easy to share data.

Its scale-out architecture provides full multi-tenant isolation, meaning that many customers can share the same storage backend. Logical volumes can be encrypted at rest either by an encryption key per tenant or even per logical volume, providing the strongest isolation option.

Deployment-wise, simplyblock offers the best of both worlds: disaggregated and hyperconverged setups. Simplyblock’s storage engine can be deployed on either a set of separate storage nodes, building a disaggregated storage cluster, or on Kubernetes worker nodes to utilize the worker node-local storage. Simplyblock also supports a mixed setup, where workloads can benefit from ultra-low latency with worker node-local storage (node-affinity) and the security and “spill-over” of the infinite storage from the disaggregated cluster.

As the only solution presented here, simplyblock favors erasure coding over replication for high availability and fault tolerance. Erasure coding is quite similar to RAID and uses parity information to achieve data loss protection. Simplyblock distributes this parity information across cluster nodes for higher fault tolerance. That said, erasure coding has a configuration similar to a replication factor, defining how many chunks and parity information will be used per calculation. This enables the best trade-off between data protection and storage overhead, enabling secure setups with as little as 50% additional storage requirements.

Furthermore, simplyblock provides a full multi-tier solution that caters to diverse storage needs in a single system. It enables you to utilize ultra-fast flash storage devices such as NVMe alongside slower SATA/SAS SSDs. At the same time, you can manage your long-term (cold) storage with traditional HDDs or QLC-based flash storage (slower but very high capacity).

Simplyblock is a robust choice if you need scalable, high-performance block storage for use cases such as databases, CDNs (Content Delivery Network), analytics solutions, and similar. Furthermore, simplyblock offers high throughput and low access latency. With its use of erasure coding, simplyblock is a great solution for companies seeking cost-effective storage while its ease of use allows organizations to adapt quickly to changing storage demands. For businesses seeking a modern, efficient, and Kubernetes-optimized block storage solution, simplyblock offers a compelling combination of features and performance.

Portworx: Kubernetes Storage and Data Management

Portworx is a cloud-native, software-defined storage platform that is highly integrated with Kubernetes. It is an enterprise-grade, closed-source solution that was acquired and is in active development by Pure Storage. Hence, its integration with the Pure Storage hardware appliances enables a performant, scalable storage option with integrated tiering capabilities.

Portworx integrated with Kubernetes through its native CSI driver and provides important CSI features such as dynamic provisioning, snapshots, clones, resizing, and persistent or ephemeral volumes. Furthermore, Portworx supports data-at-rest encryption and disaster recovery using synchronous and asynchronous cluster replication.

To enable fault tolerance and high availability, Portworx utilizes replicas, storing copies of data on different cluster nodes. This multiplies the required disk space by the replication factor. For the connection between the storage cluster and clients, Portworx provides access via iSCSI, a fairly old protocol that isn’t necessarily optimized for fast flash storage.

For connections between Pure’s FlashArray and Portworx, you can use NVMe/TCP or NVMe-RoCE (NVMe with RDMA over Converged Ethernet) — a mouthful, I know.

Encryption at rest is supported with either a unique key per volume or a cluster-wide encryption key. For storage-client communication, while iSCSI should be separated into its own VLAN, remember that iSCSI itself isn’t encrypted, meaning that encryption in transit isn’t guaranteed (if not pushed through a secured channel).

As mentioned, Portworx distinguishes itself by integrating with Pure Storage appliances. This integration enables organizations to leverage the performance and reliability of Pure’s flash storage systems. This makes Portworx a compelling choice for running critical stateful applications such as databases, message queues, and analytics platforms in Kubernetes, especially if you don’t fear operating hardware appliances. While available as a pure software-defined storage solution, Portworx excels in combination with Pure’s hardware, making it a great choice for databases, high-throughput message queues, and analytical applications on Kubernetes.

Ceph: Open-source, Distributed Storage System

Ceph is a highly scalable and distributed storage solution. Run as a company-backed open-source project, Ceph presents a unified storage platform with support for block, object, and file storage. That makes it a versatile choice for a wide range of Kubernetes applications.

Ceph’s Kubernetes integration is provided through the ceph-csi driver, which brings dynamically provisioned persistent volumes and automatic lifecycle management. CSI features supported by Ceph include snapshotting, cloning, resizing, and encryption.

The architecture of Ceph is built to be self-healing and self-managing, mainly designed to enable infinite disk space scalability. The provided access latency, while not on the top end, is good enough for many use cases. Running workloads like databases, which love high IOPS and low latency, can feel a bit laggy, though. Finally, high availability and fault tolerance are implemented through replication between Ceph cluster nodes.

From the security end, Ceph supports encryption at rest via a few different options. I’d recommend using the LUKS-based (Linux Unified Key Setup) setup as it supports all of the different Ceph storage options. The communication between cluster nodes, as well as storage and client, is not encrypted by default. If you require encryption in transit (and you should), utilize SSH and SSL termination via HAproxy or similar solutions. It’s unfortunate that a storage solution as big as Ceph has no such built-in support. The same goes for multi-tenancy, which can be achieved using RADOS namespaces but isn’t an out-of-the-box solution.

Ceph is an excellent choice as your Kubernetes storage when you are looking for an open-source solution with a proven track record of enterprise deployments, infinite storage scalability, and versatile storage types. It is not a good choice if you are looking for high-performance storage with low latency and millions of IOPS.

Moreover, due to its community-driven development and support, Ceph can be operated as a cost-effective and open-source alternative to proprietary storage solutions. Whether you’re deploying in a private data center, a public cloud, or a hybrid environment, Ceph’s adaptability is a great help for managing storage in containerized ecosystems.

Commercial support for Ceph is available from Red Hat.

Longhorn: Cloud-native Block Storage for Kubernetes

Longhorn is an open-source, cloud-native storage solution specifically designed for Kubernetes. It provides block storage that focuses on flexibility and ease of use. Therefore, Longhorn deploys straight into your Kubernetes cluster, providing worker node-local storage as persistent volumes.

As a cloud-native storage solution, Longhorn provides its own CSI driver, highly integrated with Longhorn and Kubernetes. It enables dynamic provisioning and management of persistent volumes, snapshots, clones, and backups. For the latter, people seem to have some complications with restores, so make sure to test your recovery processes.

For communication between storage and clients, Longhorn uses the iSCSI protocol. A newer version of the storage engine is in the works, which enables NVMe over TCP, however, at the time of writing, this engine isn’t yet production-ready and is not recommended for production use.

Anyhow, Longhorn provides good access latency and throughput, making it a great solution for mid-size databases and similar workloads. Encryption at rest can be set up but isn’t as simple as with some alternatives. High availability and fault tolerance is achieved by replicating data between cluster nodes. That means, as with many other solutions, the required storage is multiplied by the replication factor. However, Longhorn supports incremental backups to external storage systems like S3 for easy data protection and fast recoverability in disaster scenarios.

Longhorn is a full open-source project under the Cloud Native Computing Foundation (CNCF). It was originally developed by Rancher and is backed by SUSE. Hence, it’s commercially available with enterprise support as SUSE Storage.

Longhorn is a good choice if you want a lightweight, cloud-native, open-source solution that runs hyper-converged with your cluster workloads. It is usually used for smaller deployments or home labs — widely discussed on Reddit. Generally, it is not considered as robust as Ceph and, hence, is not recommended for mission-critical enterprise production workloads.

NFS: File Sharing Solution for Enterprises with Heterogeneous Environments

NFS (Network File System) is a well-known and widely adopted file-sharing protocol, inside and outside Kubernetes. That said, NFS has a proven track record showing its simplicity, reliability, and ability to provide shared access to persistent storage.

One of the main features of NFS is its ability to simultaneously attach volumes to many containers (and pods) with read-write access. That enables easy sharing of configuration, training data, or similar shared data sets between many instances or applications.

There are quite a few different options for integrating NFS with Kubernetes. The two main ones are the Kubernetes NFS Subdir External Provisioner. Both automatically create NFS subdirectories when new persistent volumes are requested, and the csi-driver-nfs. In addition, many storage solutions provide optimized NFS CSI drivers designed to provision shares for their respective solutions automatically. Such storage options include TrueNAS, OpenEBS, Dell EMC, and others.

High availability is one of the elements of NFS that isn’t simple, though. To make automatic failover work, additional tools like Corosync or Pacemaker need to be configured. On the client side, automount should be set up to handle automatic failover and reconnection. NFS is an old protocol from a time when those additional steps were commonplace. Today, they feel frumpy and out of place, especially compared to available alternatives.

While multi-tenancy isn’t strictly supported by NFS, using individual shares could be seen as a viable solution. However, remember that shares aren’t secured in any way. Authentication requires additional setups such as Kerberos. File access permissions shouldn’t be used as a sufficient setup for tenant isolation.

Encryption at rest with NFS comes down to the backing storage solution. NFS, as a sharing protocol, doesn’t offer anything by itself. Encryption in transit is supported, either via Kerberos or other means like TLS via stunnel. The implementation details differ per NFS provider, though. You should consult your provider’s manual.

NFS is your Kubernetes storage of choice if you need a simple, scalable, and shared file storage system that integrates seamlessly into your existing infrastructure. In the best case, you already have an NFS server set up and ready to go. Installing the CSI driver and configuring the storage class is all you need. While NFS might be a bottleneck for high-performance systems such as databases, many applications work perfectly fine. Imagine you need to scale out a WordPress-based website. There isn’t an easier way to share the same writable storage to many WP instances. That said, for organizations looking for a mature, battle-tested storage option to deliver shared storage with minimal complexity, NFS is the choice.

Make Your Kubernetes Storage Choice in 2025

Selecting the right storage solution for your Kubernetes persistent volume isn’t easy. It is an important choice to ensure performance, scalability, and reliability for your containerized workloads. Solutions like simplyblock™, Portworx, Ceph, Longhorn, and NFS offer a great set of features and are optimized for different use cases.

NFS is loved for its simplicity and easy multi-attach functionality. It is a great choice for all use cases needing shared write access. It’s not a great fit for high throughput and super-low access latency, though.

Ceph, on the other hand, is great if you need infinite scalability and don’t fear away from a slightly more complicated setup and operation. Ceph provides a robust choice for all use cases, as well as high-performance databases and similar IO-intensive applications.

Longhorn and Portworx are generally good choices for almost all types of applications. Both solutions provide good access latency and throughput. If you tend to buy hardware appliances, Portworx, in combination with Pure Storage, is the way to go. If you prefer pure software-defined storage and want to utilize storage available in your worker nodes, take a look at Longhorn.

Last but not least, simplyblock is your choice when running IO-hungry databases in or outside Kubernetes. Its use of the NVMe/TCP protocol makes it a perfect choice for pure container storage, as well as mixed environments with containers and virtual machines. Due to its low storage overhead for data protection, simplyblock is a great, cost-effective, and fast storage solution. And a bit of capacity always remains for all other storage needs, meaning a single solution will do it for you.

As Kubernetes evolves, leveraging the proper storage solution will significantly improve your application performance and resiliency. To ensure you make an informed decision for your short and long-term storage needs, consider factors like workload complexity, deployment scale, and data management needs.

Whether you are looking for a robust enterprise solution or a more simple and straightforward setup, these five options are all strong contenders to meet your Kubernetes storage demands in 2025.

The post 5 Storage Solutions for Kubernetes in 2025 appeared first on simplyblock.

One of the most important questions to answer when designing an application or infrastructure is the architecture approach to system scalability. Traditionally, systems used the scale-up approach or vertical scalability. Many modern systems use a scale-out approach, especially in the cloud-native ecosystem. Also called horizontal scalability.

Understanding the Basics

Understanding the fundamental concepts is essential when discussing system architectures. Hence, let’s briefly overview the two approaches before exploring them in more depth.

• With Scale Up (Vertical Scalability), you increase resources (typically CPU, memory, and storage) in the existing system to improve performance and capacity.
• With Scale Out (Horizontal Scalability), you add additional nodes or machines to the existing workforce to distribute the workload across multiple systems.

Both architectural approaches have their respective advantages and disadvantages. While scale-up architectures are easier to implement, they are harder to scale at a certain point. On the other hand, scale-out architectures are more complex to implement but scale almost linearly if done right.

Vertical Scaling (Scale Up) Architectures: The Traditional Approach

Vertical scaling, commonly known as scaling up, involves adding more resources to an existing system to increase its power or capacity.

Think of it as upgrading your personal computer. Instead of buying a second computer, you add more RAM or install a faster processor or larger storage device. In enterprise storage systems, this typically means adding more CPU cores, memory, or storage drives to an existing server. Meanwhile, for virtual machines it usually involves increasing the host machine’s assigned resources.

To clarify, let’s use a real-world example from the storage industry. With a ZFS-based SAN (Storage Area Network) system, a scaling up system design is required. Or as Jason Lohrey wrote: «However, ZFS has a significant issue – it can’t scale out. ZFS’s biggest limitation is that it is “scale-up” only.» ZFS, as awesome as it is, is limited to a single machine. That said, increasing the storage capacity always means adding larger or more disks to the existing machine. This approach maintains the simplicity of the original architecture while increasing storage capacity and potentially improving performance.

Strengths of Vertical Scaling

Today, many people see the vertical scalability approach as outdated and superfluous. That is, however, not necessarily true. Vertical scaling shines in several scenarios.

First, implementing a scale-up system is generally more straightforward since it doesn’t require changes to your application architectures or complex data distribution logic. When you scale up a transactional database like PostgreSQL or MySQL, you essentially give it more operational resources while maintaining the same operational model.

Secondly, the management overhead is lower. Tasks such as backups, monitoring, and maintenance are straightforward. This simplicity often translates to lower operational costs despite the potentially higher hardware costs.

Here is a quick overview of all the advantages:

1. Simplicity: It’s straightforward to implement since you’re just adding resources to an existing system
2. Lower Complexity: Less architectural overhead since you’re working with a single system
3. Consistent Performance: Lower latency due to all resources being in one place
4. Software Compatibility: Most traditional software is designed to run on a single system
5. Lower Initial Costs: Often cheaper for smaller workloads due to simpler licensing and management

Weaknesses and Limitations of Scale-Up Architectures

Like anything in this world, vertical scaling architectures also have drawbacks. The most significant limitation is the so-called physical ceiling. A system is limited by its server chassis’s space capacity or the hardware architecture’s limitation. You can only add as much hardware as those limitations allow. Alternatively, you need to migrate to a bigger base system.

Traditional monolithic applications often face another challenge with vertical scaling: adding more resources doesn’t always translate to linear performance improvements. For example, doubling the CPU cores might yield only a 50% performance increase due to software architecture limitations, especially resource contention.

Here is a quick overview of all the disadvantages:

1. Hardware Limits: The physical ceiling limits how much you can scale up based on maximum hardware specifications
2. Downtime During Upgrades: Usually requires system shutdown for hardware upgrades
3. Cost Efficiency: High-end hardware becomes exponentially more expensive
4. Single Point of Failure: No built-in redundancy
5. Limited Flexibility: Cannot easily scale back down when demand decreases

When to Scale Up?

After all that, here is when you really want to go with a scale-up architecture:

• You have traditional monolithic applications
• You look for an easier way to optimize for performance, not capacity
• You’re dealing with applications that aren’t designed for distributed computing
• You need a quick solution for immediate performance issues

Horizontal Scaling (Scale Out) Architectures: The Distributed Approach

The fundamentally different approach is the horizontal scaling or scale-out architecture. Instead of increasing the available resources on the existing system, you add more systems to distribute the load across them. This is actually similar to adding additional workers to an assembly line rather than trying to make one worker more efficient.

Consider a distributed storage system like simplyblock or a distributed database like MongoDB. When you scale out these systems, you add more nodes to the cluster, and the workload gets distributed across all nodes. Each node handles a portion of the data and processing, allowing the system to grow almost limitlessly.

Advantages of Horizontal Scaling

Large-scale deployments and highly distributed systems are the forte of scale-out architectures. As a simple example, most modern web applications utilize load balancers. They distribute the traffic across multiple application servers. This allows us to handle millions of concurrent requests and users. Similarly, distributed storage systems like simplyblock scale to petabytes of data by adding additional storage nodes.

Secondly, another significant advantage is improved high availability and fault tolerance. In a properly designed scale-out system, if one node fails, the system continues operating. While it may degrade to a reduced service, it will not experience a complete system failure or outage.

To bring this all to a point:

1. Near-Infinite Scalability: Can continue adding nodes as needed
2. Better Fault Tolerance: Built-in redundancy through multiple nodes
3. Cost Effectiveness: Can use commodity hardware
4. Flexible Resource Allocation: Easy to scale up or down based on demand
5. High Availability: No single point of failure

The Cost of Distribution: Weakness and Limitations of Horizontal Scalability

The primary challenge when considering scale-out architectures is complexity. Distributed systems must maintain data consistency across system boundaries, handle network communications or latencies, and handle failure recovery. Multiple algorithms have been developed over the years. The most commonly used ones are Raft and Paxos, but that’s a different blog post. Anyhow, this complexity typically requires more sophisticated management tools and distributed systems expertise. Normally also for the team operating the system.

The second challenge is the overhead of system coordination. In a distributed system, nodes must synchronize their operations. If not careful, this can introduce latency and even reduce the performance of certain types of operations. Great distributed systems utilize sophisticated algorithms to prevent these issues from happening.

Here is a quick overview of the disadvantages of horizontal scaling:

1. Increased Complexity: More moving parts to manage
2. Data Consistency Challenges: Maintaining consistency across nodes can be complex
3. Higher Initial Setup Costs: Requires more infrastructure and planning
4. Software Requirements: Applications must be designed for distributed computing
5. Network Overhead: Communication between nodes adds latency

Kubernetes: A Modern Approach to Scaling

Kubernetes has become the de facto platform for container orchestration. It comes in multiple varieties, in its vanilla form or as the basis for systems like OpenShift or Rancher. Either way, it can be used for both vertical and horizontal scaling capabilities. However, Kubernetes has become a necessity when deploying scale-out services. Let’s look at how different workloads scale in a Kubernetes environment.

Scaling Stateless Workloads

Stateless applications, like web servers or API gateways, are natural candidates for horizontal scaling in Kubernetes. The Horizontal Pod Autoscaler (HPA) provided by Kubernetes automatically adjusts the number of pods based on metrics such as CPU or RAM utilization. Custom metrics as triggers are also possible.

Horizontally scaling stateless applications is easy. As the name suggests, stateless applications do not maintain persistent local or shared state data. Each instance or pod is entirely independent and interchangeable. Each request to the service contains all the required information needed for processing.

That said, automatically scaling up and down (in the meaning of starting new instances or shutting some down) is part of the typical lifecycle and can happen at any point in time.

Scaling Stateful Workloads

Stateful workloads, like databases, require more careful consideration.

A common approach for more traditional databases like PostgreSQL or MySQL is to use a primary-replica architecture. In this design, write operations always go to the primary instance, while read operations can be distributed across all replicas.

On the other hand, MongoDB, which uses a distributed database design, can scale out more naturally by adding more shards to the cluster. Their internal cluster design uses a technique called sharding. Data is assigned to horizontally scaling partitions distributed across the cluster nodes. Shard assignment happens either automatically (based on the data) or by providing a specific shard key, enabling data affinity. Adding a shard to the cluster will increase capacity when additional scale is necessary. Data rebalancing happens automatically.

Why we built Simplyblock on a Scale-Out Architecture

Stateful workloads, like Postgres or MySQL, can scale out by adding additional read-replicas to the cluster. However, every single instance needs storage to store its very own data. Hence, the need for scalable storage arrives.

Simplyblock is a cloud-native and distributed storage platform built to deliver scalable performance and virtually infinite capacity for logical devices through horizontal scalability. Unlike traditional storage systems, simplyblock distributes data across all cluster nodes, multiplying the performance and capacity.

Designed as an NVMe-first architecture, simplyblock using the NVMe over Fabrics protocol family. This extends the reach of the highly scalable NVMe protocol over network fabrics such as TCP, Fibre Channel, and others. Furthermore, it provides built-in support for multi-pathing, enabling seamless failover and load balancing.

The system uses a distributed data placement algorithm to spread data across all available cluster nodes, automatically rebalancing data when nodes are added or removed. When writing data, simplyblock splits the item into multiple, smaller chunks and distributes them. This allows for parallel access during read operations. The data distribution also provides redundancy, with parity information stored on other nodes in the cluster. This protects the data against individual disk and node failures.

Using this architecture, simplyblock provides linear capacity and performance scalability by pooling all available disks and parallelizing access. This enables simplyblock to scale from mere terabytes to multiple petabytes while maintaining performance, consistency, and durability characteristics throughout the cluster-growth process.

Building Future-Proof Infrastructure

To wrap up, when you build out a new system infrastructure or application, consider these facts:

1. Workload characteristics: CPU-intensive workloads might benefit more from vertical scaling. Distributing operations comes with its own overhead. If the operation itself doesn’t set off this overhead, you might see lower performance than with vertical scaling. On the other hand, I/O-heavy workloads might perform better with horizontal scaling. If the access patterns are highly parallelizable, a horizontal architecture will most likely out scale a vertical one.
   
2. Growth patterns: Predictable, steady growth might favor scaling up, while rapid growth patterns might necessitate the flexibility of scaling out. This isn’t a hard rule, though. A carefully designed scale-out system will provide a very predictable growth pattern and latency. However, the application isn’t the only element to take into account when designing the system, as there are other components, most prominently the network and network equipment.
   
3. Future-Proofing: Scaling out often requires little upfront investment in infrastructure but higher investment in development and expertise. It can, however, provide better long-term cost efficiency for large deployments. That said, buying a scale-out solution is a great idea. With a storage solution like simplyblock, for example, you can start small and add required resources whenever necessary. With traditional storage solutions, you have to go with a higher upfront cost and are limited by the physical ceiling.
   
4. Operational Complexity: Scale-up architectures are typically easier to manage, while a stronger DevOps or operations team is required to handle scale-out solutions. That’s why simplyblock’s design is carefully crafted to be fully autonomous and self-healing, with as few hands-on requirements as possible.

The Answer Depends

That means there is no universal answer to whether scaling up or out is better. A consultant would say, “It depends.” Seriously, it does. It depends on your specific requirements, constraints, and goals.

Many successful organizations use a hybrid approach, scaling up individual nodes while also scaling out their overall infrastructure. The key is understanding the trade-offs and choosing the best approach to your needs while keeping future growth in mind. Hence, simplyblock provides the general scale-out architecture for infinite scalability. It also provides a way to utilize storage located in Kubernetes worker nodes as part of the storage cluster to provide the highest possible performance. At the same time, it maintains the option to spill over when local capacity is reached and the high durability and fault tolerance of a fully distributed storage system.

Remember, the best scaling strategy aligns with your business objectives while maintaining performance, reliability, and cost-effectiveness. Whether you scale up, out, or both, ensure your choice supports your long-term infrastructure goals.

The post Scale Up vs Scale Out: System Scalability Strategies appeared first on simplyblock.

The debate over the optimal storage solution has been ongoing. Local instance storage on AWS (i.e. ephemeral NVMe disk attached to EC2 instance) brings remarkable cost-performance ratios. It offers 20 times better performance and 10 times lower access latency than EBS. It’s a powerhouse for quick, ephemeral storage needs. In simple words, local NVME disk is very fast and relatively cheap, but not scalable and not persistent.

Recently, Vantage posted an article titled “Don’t use EBS for Cloud Native Services“. We agree with this problem statement, however we also strongly believe that there is a better solution that using Local NVMe SSD Storage on AWS as an alternative to EBS. Local NVMe to EBS is not like comparing apples to apples, but more like apples to oranges.

The Local Instance NVMe Storage Advantage

Local storage on AWS excels in speed and cost-efficiency, delivering performance that’s 20 times better and latency that’s 10 times lower compared to EBS. For certain workloads with temporary storage needs, it’s a clear winner. But, let’s acknowledge the reasons why data centers have traditionally separated storage and compute.

Overcoming Traditional Challenges of Local Storage

Simplyblock provides an innovatrive approach that marries the cost and performance advantages of local instance storage with the benefits of pooled cloud storage. It offers the best of both worlds—high-speed, low-latency performance near to local storage, coupled with the robustness and flexibility of pooled cloud storage.

Why Choose simplyblock on AWS?

1. Performance and Cost Efficiency: Enjoy the benefits of local storage without compromising on scalability, reliability, and high availability.
2. Data Protection: simplyblock employs advanced data protection mechanisms,
   ensuring that your data survives any instance outage.
3. Seamless Operations: Upgrade and maintain compute instances without impacting storage, ensuring continuous operations.
4. Data Services Galore: Unlock the potential of various data services without affecting local CPU performance.

While local instance storage has its merits, the future lies in a harmonious blend of the speed of local storage and the resilience of cloud-pooled storage. With simplyblock, we transcend the limitations of local NVMe disk, providing you with a storage solution that’s not just powerful but also versatile, scalable, and intelligently designed for the complexities of the cloud era.

The post Local NVMe Storage on AWS – Pros and Cons appeared first on simplyblock.

In this episode of the simplyblock Cloud Commute Podcast, host Chris Engelbert interviews Michael Schmidt, co-founder of simplyblock. Michael shares insights into the evolution of storage technologies and how simplyblock is pushing boundaries with software-defined storage (SDS) to replace outdated hardware-defined systems. If you’re curious about how cloud storage is transforming through SDS and how it’s creating new possibilities for scalability and efficiency, this episode is a must-listen.

This interview is part of the simplyblock Cloud Commute Podcast, available on Youtube, Spotify, iTunes/Apple Podcasts, and our show site.

Key Takeaways

What is simplyblock, and how does it Differ from Traditional Storage Technologies?

Michael Schmidt explained that simplyblock is built on the idea that hardware-defined storage systems are becoming outdated. The traditional storage models, like SAN (Storage Area Networks), are slow-moving, expensive, and difficult to scale in cloud environments. Simplyblock, in contrast, leverages software-defined storage (SDS), making it more flexible, scalable, and hardware-agnostic. The key advantage is that SDS allows organizations to operate independently of the hardware lifecycle and seamlessly scale their storage without the limitations of physical systems.

How does simplyblock Offer better Storage Performance for Kubernetes Clusters?

Simplyblock is optimized for Kubernetes environments by integrating a CSI (Container Storage Interface) driver. Michael noted that deploying simplyblock on Kubernetes allows users to take advantage of local disk storage, NVMe devices, or standard GP3 volumes within AWS. This integration simplifies scaling and enhances storage performance with minimal configuration, making it highly adaptable for workloads that require high-speed, reliable storage.

In addition to highlighting the key takeaways, it’s essential to provide context that enriches the listener’s understanding of the episode. By offering this added layer of information, we ensure that when you tune in, you’ll have a clearer grasp of the nuances behind the discussion. This approach helps shed light on the reasoning and perspective behind the thoughtful questions posed by our host, Chris Engelbert. Ultimately, this allows for a more immersive and insightful listening experience.

Key Learnings

What are the Advantages of Software-defined Storage Compared to Hardware-defined Storage?

Software-defined storage offers flexibility by decoupling storage from physical hardware. This results in improved scalability, lifecycle management, and cost-effectiveness.

Simplyblock Insight:

Software-defined storage systems like simplyblock allow for hardware-agnostic scalability, enabling businesses to avoid hardware refresh cycles that burden CAPEX and OPEX budgets. SDS also opens up the possibility for greater automation and better integration with existing cloud infrastructures.

What is Thin Provisioning in Cloud Storage?

Thin provisioning allows cloud users to allocate storage without consuming the full provisioned capacity upfront, optimizing resource usage.

Simplyblock Insight:

Thin provisioning has been standard in enterprise storage systems for years, and simplyblock brings this essential feature to the cloud. By offering thin provisioning in its cloud-native architecture, simplyblock ensures that businesses can avoid over-provisioning and reduce storage costs, only paying for the storage they use. This efficiency significantly benefits organizations with unpredictable storage needs.

Additional Nugget of Information

Why are SLAs Important in Software-defined Storage, and how does Simplyblock Ensure Performance Reliability?

Service Level Agreements (SLAs) are crucial in software-defined storage because they guarantee specific performance metrics, such as IOPS (input/output operations per second), latency, and availability. In traditional hardware-defined storage systems, performance metrics were easier to predict due to standardized hardware configurations. However, with software-defined storage, where hardware can vary, SLAs provide customers with a level of assurance that the storage system will meet their needs consistently, regardless of the underlying infrastructure.

Conclusion

Michael Schmidt’s discussion offers a fascinating look at the evolving landscape of cloud storage. It’s clear that simplyblock is addressing key challenges by combining the flexibility of software-defined storage with the power of modern cloud-native architectures. Whether you’re managing large-scale Kubernetes deployments or trying to cut infrastructure costs, simplyblock’s approach to scalability and performance could be just what you need.

If you’re considering how to future-proof your storage solutions or make them more cost-efficient, the insights shared in this episode will be valuable. Be sure to explore the simplyblock platform and stay connected for more episodes of the Cloud Commute Podcast. We’re constantly bringing in experts to discuss the cutting-edge technologies shaping tomorrow’s infrastructure. Don’t miss out!

The post Origins of simplyblock and the Evolution of Storage Technologies appeared first on simplyblock.

For environments with high-performance requirements such as transactional databases, where low-latency access and optimized storage costs are key, alternative solutions are essential. This is where simplyblock steps in, offering a new way to manage storage that addresses common pain points in traditional EBS or local NVMe disk usage—such as limited scalability, complex resizing processes, and the cost of underutilized storage capacity.

What is Simplyblock?

Simplyblock is known for providing top performance based on distributed (clustered) NVMe instance storage at low cost with great data availability and durability. Simplyblock provides storage to Linux instances and Kubernetes environments via the NVMe block storage and NVMe over Fabrics (using TCP/IP as the underlying transport layer) protocols and the simplyblock CSI Driver.

Simplyblock’s storage orchestration technology is fast. The service provides access latency between 100 us and 500 us, depending on the IO access pattern and deployment topology. That means that simplyblock’s access latency is comparable to, or even lower than on Amazon EBS io2 volumes, which typically provide between 200 us to 300 us.

To make sure we only provide storage which will keep up, we test simplyblock extensively. With simplyblock you can easily achieve more than 1 million IOPS at a 4KiB block size on single EC2 compute instances. This is several times higher than the most scalable Amazon EBS volumes, io2 Block Express. On the other hand, simplyblock’s cost of capacity is comparable to io2. However, with simplyblock IOPS come for free – at absolutely no extra charge. Therefore, depending on the capacity to IOPS ratio of io2 volumes, it is possible to achieve cost advantages up to 10x .

For customers requiring very low storage access latency and high IOPS per TiB, simplyblock provides the best cost efficiency available today.

Why Simplyblock over Simple Amazon EBS?

Many customers are generally satisfied with the performance of their gp3 EBS volumes. Access latency of 6 to 10 ms is fine, and they never have to go beyond the included 3,000 IOPS (on gp2 and gp3). They should still care for simplyblock, because there is more. Much more.

Benefits of Thin Provisioning

With gp3, customers have to pay for provisioned rather than utilized capacity (~USD 80 per TiB provisioned). According to our research, the average utilization of Amazon EBS gp3 volumes is only at ~30%. This means that customers are actually paying more than three times the price per TiB of utilized storage. That said, due to the low utilization below one-third, the actual price comes down to about USD 250 per TiB. The higher the utilization, the closer a customer would be to the projected USD 80 per TiB.

In addition to the price inefficiency, customers also have to manage the resizing of gp3 volumes when utilization reaches the current capacity limit. However, resizing has its own number of limitations in EBS it is only possible once every six hours. To mitigate potential issues during that time, volumes are commonly doubled in size.

On the other hand, simplyblock provides thin provisioned logical volumes. This means that you can provision your volumes nearly without any restriction in size. Think of growable partitions that are sliced out of the storage pool. Logical volumes can also be over-provisioned, meaning, you can set the requested storage capacity to exceed the storage pool’s current size. There is no charge for the over-provisioned capacity as long as you do not use it.

That said, simplyblock thinly provisions NVMe volumes from a storage pool which is either made up of distributed local instance storage or gp3 volumes. The underlying pool is resized before it runs out of storage capacity.

These means enable you to save massively on storage, while also simplifying your operations. No more manual or script-based resizing! No more custom alerts before running out of storage.

Benefits of Storage Tiering

But if you feel there should be even more potential to save on storage, you are absolutely right!

The total data stored on a single EBS volume has very different access patterns. Let’s explore together what the average database setup looks like. The typical corporate’s transactional database will easily qualify as a “hot” storage. It is commonly stored on SSD-based EBS volumes. Nobody would think of putting this database to slow file storage stored on HDD or Amazon S3.

In reality, however, data that belongs to a database is never homogeneous when it comes to performance requirements. There is, for example, the so-called database transaction log, often referred to as write-ahead log (WAL) or simply a database journal. The WAL is quite sensitive to access latency and requires a high IOPS rate for writes. On the other hand, the log is relatively small compared to the entire dataset in the database.

Furthermore, some other data files store tablespaces and index spaces. Many of them are read so frequently that they are always kept in memory. They do not depend on storage performance. Others are accessed less frequently, meaning they have to be loaded from storage every time they’re accessed. They require solid storage performance on read.

Last but not least, there are large tables which are commonly used for archiving or document storage. They are written or read infrequently and typically in large IO sizes (batches). While throughput speed is relevant for accessing this data, access latency is not.

To support all of the above use cases, simplyblock supports automatic tiering. Our tiering will place less frequently accessed data to either Amazon EBS (st2) or Amazon S3, called warm storage. The tiering implementation is optimized for throughput, hence large amounts of data can be written or read in parallel. Simplyblock automatically identifies individual segments of data, which qualify for tiering, and moves them automatically to secondary storage, and only after tiering was successful, cleaning them up on the “hot” tier. This reduces the storage demand in the hot pool.

The AWS cost ratio between hot and warm storage is about 5:1, cutting cost to about 20% for tiered data. Tiering is completely transparent to you and data is automatically read from tiered storage when requested.

Based on our observations, we often see that up to 75% of all stored data can be tiered to warm storage. This creates another massive potential in storage costs savings.

How to Prevent Data Duplication

But there is yet more to come.

The AWS’ gp3 volumes do not allow multi-attach, meaning the same volume cannot be attached to multiple virtual machines or containers at the same time. Furthermore, its reliability is also relatively low (indicated at 99.8% – 99.9%) compared to Amazon S3.

That means neither a loss of availability nor a loss of data can be ruled out in case of an incident.

Therefore, additional steps need to be taken to increase availability of the storage consuming service, as well as the reliability of the storage itself. The common measure is to employ storage replication (RAID-1, or application-level replication). However, this leads to additional operational complexity, utilization of network bandwidth, and to a duplication of storage demand (which doubles the storage capacity and cost).

Simplyblock mitigates the requirement to replicate storage. First, the same thinly provisioned volume can be attached to more than one Amazon EC2 instance (or container) and, second, the reliability of each individual volume is higher (99.9999%) due to the internal use of erasure coding (parity data) to protect the data.

Multi-attach helps to cut the storage cost by half.

The Cost of Backup

Last but not least, backups. Yes there is even more.

A snapshot taken from an Amazon EBS volume is stored in an S3-like storage. However, AWS charges significantly more per TiB than for the same storage directly on S3. Actually about 3.5 times.

Snapshots taken from simplyblock logical volumes, however, are stored into a standard Amazon S3 bucket and based on the standard S3 pricing, giving you yet another nice cost reduction.

Near-Zero RPO Disaster Recovery

Anyhow, there is one more feature that we really want to talk about. Disaster recovery is an optional feature. Our DR comes at a minimum RPO and can be deployed without any redundancy on either the block storage or the compute layer between zones. Additionally, no data transfers between zones are needed.

Simplyblock employs asynchronous replication to store any change on the storage pool to an S3 bucket. This enables a fully crash-consistent and near-real-time option for disaster recovery. You can bootstrap and restart your entire environment after a disaster. This works in the same or a different availability zone and without having to take care of backup management yourself.

And if something happened, accidental deletion or even a successful ransomware attack which encrypted your data. Simplyblock is here to help. Our asynchronous replication journal provides full Point-in-Time-Recovery functionality on the block storage layer. No need for your service or database to support it. Just rewind the storage to whatever point in time in the past.

It also utilizes write- and deletion-protected on its S3 bucket making the journal itself resilient to ransomware attacks. That said, simplyblock provides a sophisticated solution to disaster recovery and cybersecurity breaches without the need for manual backup management.

Simplyblock is Storage Optimization – just for you

Simplyblock provides a number of advantages for environments that utilize a large number of Amazon EBS gp2 or gp3 volumes. Thin provisioning enables you to consolidate unused storage capacity and minimize the spent. Due to the automatic pool enlargement (increasing the pool with additional EBS volumes or storage nodes), you’ll never run out of storage space but also only require the least amount.

Together with automatic tiering, you can move infrequently used data blocks to warm or even cold storage. Fully transparent to the application. The same is true for our disaster recovery. Built into the storage layer, every application can benefit from point in time recovery, removing almost all RPO (Recovery Point Objective) from your whole infrastructure. And with consistent snapshots across volumes, you can enable a full-blown infrastructure recovery in case of an availability zone outage, right from ground up.

With simplyblock you get more features than mentioned here. Get started right away and learn about our other features and benefits.

The post Simplyblock for AWS: Environments with many gp2 or gp3 Volumes appeared first on simplyblock.

Simplyblock provides sophisticated block storage-level Ransomware protection and mitigation. Together with recovery options, simplyblock enables Point-in-Time Recovery (PITR) for any service or solution storing data.

What is Ransomware?

Ransomware is a type of malicious software (also known as malware) designed to block access to a computer system and/or encrypt data until a ransom is paid to the attacker. Cybercriminals typically carry out this type of attack by demanding payment, often in cryptocurrency, in exchange for providing a decryption key to restore access to the data or system.

Statistics show a significant rise in ransomware cyber attacks: ransomware cases more than doubled in 2023, and the amount of ransom paid reached more than a billion dollars—and these are only official numbers. Many organizations prefer not to report breaches and payments, as those are illegal in many jurisdictions.

The Danger of Ransomware Increases

The number and sophistication of attack tools have also increased significantly. They are becoming increasingly commoditized and easy to use, drastically reducing the skills cyber criminals require to deploy them.

There are many best practices and tools to protect against successful attacks. However, little can be done once an account, particularly a privileged one, has been compromised. Even if the breach is detected, it is most often too late. Attackers may only need minutes to encrypt important data.

Storage, particularly backups, serves as a last line of defense. After a successful attack, they provide a means to recover. However, there are certain downsides to using backups to recover from a successful attack:

• The latest backup does not contain all of the data: Data written between the last backup and the time the attack is unrecoverably lost. Even the loss of one hour of data written to a database can be critical for many enterprises.
• Backups are not consistent with each other: The backup of one database may not fit the backup of another database or a file repository, so the systems will not be able to integrate correctly after restoration.
• The latest backups may already contain encrypted data. It may be necessary to go back in time to find an older backup that is still “clean.” This backup, if available at all, may be linked to substantial data loss.
• Backups must be protected from writes and delete operations; otherwise, they can be destroyed or damaged by attackers. Attackers may also damage the backup inventory management system, making it hard or impossible to locate specific backups.
• Human error in Backup Management may lead to missing backups.

Simplyblock for Ransomware Protection and Mitigation

Simplyblock provides a smart solution to recover data after a ransomware attack, complementing classical backups.

In addition to writing data to hot-tier storage, simplyblock creates an asynchronously replicated write-ahead log (WAL) of all data written. This log is optimized for high throughput to secondary (low IOPS) storage, such as Amazon S3 or HDD pools, like AWS’ EBS st2 service. If this secondary storage supports write and deletion protection for pre-defined retention periods, as with S3, it is possible to “rewind” the storage to the point immediately before the attack. This performs a data recovery with near-zero RPO (Recovery Point Objective).

A recovery mechanism like this is particularly useful in combination with databases. Before the attack can start, database systems typically have to be stopped. This is necessary as all data and WAL files are in use by the database. This allows for automatically identifying a consistent recovery point with no data loss.

In the future, simplyblock plans to enhance this functionality further. A multi-stage attack detection mechanism will be integrated into the storage. Additionally, deletion protection after clearance from attack within a historical time window and precise automatic identification of attack launch points to locate recovery points.

Furthermore, simplyblock will support partial restore of recovery points to enable different service’ data on the same logical volumes to be restored from individual points in time. This is important since encryption of one service might have started earlier or later than for others, hence the point in time to rewind to must be different.

Conclusion

Simplyblock provides a complementary recovery solution to classical backups. Backups support long-term storage of full recovery snapshots. In contrast, write-ahead log-based recovery is specifically designed for near-zero RPO recovery right after a Ransomware attack starts and enables quick and easy recovery for data protection.

While many databases and data-storing services, such as PostgreSQL, may provide the possibility of Point-in-Time Recovery, the WAL segments need to be stored outside the system as soon as they are closed. That said, the RPO would come down to the size of a WAL segment, whereas with simplyblock, due to its copy-on-write nature, the RPO can be as small as one committed write.

Learn more about simplyblock and its other features like thin-provisioning, immediate clones and branches, encryption, compression, deduplication, and more. Or just get started right away and find the best Ransomware attack protection and mitigation to date.

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Why is Vendor Lock-In Dangerous?

For most companies, data is the most crucial part of their business. Therefore, it is dangerous to forfeit the control on how to store this incredibly important good and storage vendor lock-in poses a significant risk to these businesses. When a company becomes overly reliant on a single storage provider or technology, it can find itself trapped in an inflexible situation with far-reaching consequences.

The dangers of vendor lock-in manifest in several ways:

1. Limited flexibility when requirements change or scalability needs grow.
2. Potential cost increases when vendors decide for sudden price rise, knowing that customers depend on their services and migration is complicated or tortuous.
3. Innovation constraints where other vendors provide advanced features.
4. Data migration challenges when moving large amounts of data from one system to another which can be complex and expensive.
5. Reduced bargaining power due to limited alternatives and complex migrations.
6. Business continuity risks if a vendor faces issues or goes out of business.
7. Compatibility problems due to proprietary formats or API which limited interoperability and compatibility.

These factors can lead to increased operational costs, decreased competitiveness, and potential disruptions to business continuity. As such, it’s crucial for organizations to carefully consider their storage strategies and implement measures to mitigate risks of vendor lock-in.

Migrating Block Storage on Linux

The interfaces provided by a database system are extremely complex compared to block storage. While some of it is standardized in SQL, there are a lot of system specifics in data and administrative interfaces. Migrating a database system from one to another—or even upgrading a release—requires entire projects.

On the other hand, the block storage interface on Linux is extremely simple in its essence, it allows you to write, read, or delete (trim) a range of blocks. The NVMe protocol itself is a bit more complicated, but is fully standardized (industry standard, managed by NVM Express, Inc.) and the majority of advanced features are neither required nor used. Most commonly they aren’t even accessible through the Linux block storage interface.

In essence, to migrate block storage under Linux, just follow a few simple steps, which have to be performed volume-by-volume: Take your volume offline Create your new volume of the same size or larger Copy or replicate the data on block-level (under Linux just use dd) Potentially resize the filesystem (if necessary) Verify the results In some cases, it is even possible to lower or eliminate the down-time and perform online replication. For example, the Linux Volume Manager (LVM) provides a feature to move data between physical volumes for a particular logical volume under the hood and while the volume is online (pvmove).

When operating in a Kubernetes-based environment, this simple migration is still perfectly available when using file or block storage.

Migration to Simplyblock

Migrating from any block storage to a simplyblock logical volume (virtual NVMe block device) is simple and supported through sbcli (the simplyblock command line interface).

Plain Linux

Within a plain linux environment, it is possible to use sbcli migrate with an input of a list of block storage volumes. The necessary and corresponding simplyblock logical volumes are created first. Those volumes may be of the same size or larger. The source volumes are then unmounted, and volume level replication takes place. Finally, source volumes may be deleted and replicated volumes are mounted instead.

Kubernetes

To migrate existing PVCs (Persistent Volume Claim) from any underlying storage, we need to first replicate them into simplyblock. Simplyblock’s internal Kubernetes job sbcli ‑ migrate can automatically select all PVs (Persistent Volume) of a particular type, storage class, or label. During the migration, PVCs may still be active, meaning that PVs can be mounted, but pods must be stopped.

Simplyblock will then create corresponding volumes and PVCs. Afterwards it will replicate the source’s content over to the new volumes, and deploy them under the same mount points.

Optionally, it is possible to resize the volumes during this process and to automatically delete the originals when the process finishes successfully.

Migration from Simplyblock

Migrating a specific volume away from simplyblock is just as easy. Outside of Kubernetes, using dd is the easiest way with the source and destination volumes being unmounted and just copied.

Inside a Kubernetes environment, the process of migrating block and file storage is straight-forward, too.

Individual PVs can simply be backed up after deleting the PVC. Make sure that the lifecycle of the PV and PVC aren’t bound, otherwise the PV will be deleted by Kubernetes in the process. Afterwards, the PV can be restored to new volumes and eventually re-mounted as a new PVC.

Velero is a tool that greatly helps to simplify this process.

Simplyblock: Storage without Vendor Lock-in

Utilizing block storage brings the best of all worlds. Easy migration options, compatibility due to standardized interfaces, and the possibility to choose the best tool for the job by mixing different block storage options.

Simplyblock embraces the fact that there is no one-fits-all solution and enables full interoperability and compatibility with the default standard interfaces in modern computing systems, such as block storage and the NVMe protocol. Hence, simplyblock’s logical volumes provide an easy migration path from and to simplyblock.

However, simplyblock logical volumes provide additional features that make users want to stay.

Simplyblock volumes are full copy-on-write block storage devices which enable immediate snapshots and clones. Those can be used for fast backups, or to implement features such as database branching, enabling fast turn-around times when implementing customer facing functionality.

Furthermore, multi-tenancy (including encryption keys per volume) and thin provisioning enable storage virtualization with overprovisioning. Making use of the fact that a typical storage utilization is around 30% brings down bundled storage requirements by 70% and provides a great way to optimize for cost efficiency. Additional features such as deduplication can decrease storage needs even further.

All this and more makes simplyblock, the intelligent storage orchestrator, the perfect storage solution for database operators and everyone who operates stateful Kubernetes workloads that require high performance and low latency block or file storage.

The post Avoiding Storage Lock-in: Block Storage Migration with Simplyblock appeared first on simplyblock.

For decades, traditional methods like replication have been the go-to solution for protecting against data loss or corruption. In recent years, however, a more efficient and resource-friendly technique has become more prevalent—erasure coding. This innovative approach ensures data integrity, optimizes storage capacity, and reduces the chances of catastrophic data loss. Let us delve into the elements of erasure codes, exploring what they are and how they revolutionize how we protect our digital assets.

What is Erasure Coding?

Like many more commonly known technologies, such as RAID or replication/mirroring, erasure coding is a data protection method. It is a class of high-performance Forward Error Correction (FEC). A simplified explanation would say that it breaks down data into smaller pieces, does some mathematical magic, and writes the pieces to different disks. It doesn’t sound too complicated.

What that really means is slightly more involved, though. Erasure code schemes break down pieces of information, such as files, into fragments (sometimes called chunks), which are enriched with redundancy information (meaning fragments are extended with results of multiple mathematical equations) and eventually distributed across multiple storage nodes and disks.

Unlike traditional replication, which duplicates the entire data, erasure coding allows for more efficient storage utilization. This method employs advanced mathematical algorithms to create parity fragments, which can later be used to reconstruct the original data even if some fragments are lost or corrupted.

The Core Principles of Erasure Coding

While it may sound like erasure coding is the new kid on the block, it was actually invented in 1960 by Irving Reed and Gustave Solomon. Together, they created a new encoding mechanism known as the Reed-Solomon code. Today, this algorithm is widely used in a wide variety of systems, including distributed storage solutions, communication services, and aerospace systems.

These days, while there are many more erasure coding schemes, the three most common ones are:

• The Reed-Solomon code is simple and efficient, can be applied to a wide range of applications, and is very common for simple data storage solutions, such as DVD and Blu-Ray disks.
• The low-density parity check (LDPC or Gallager code), which is more complex but shows better performance in certain use cases, such as 10GBASE-T (10 Gbit/s Ethernet).
• The turbo codes, originally invented by Claude Berrou in 1991, are more complex than LDPC but provide the best performance of data protection to efficiency ratio, and are widely used in mobile communications technologies such as UMTS and LTE.

Anyhow, all the different implementations combine a set of particular features. Storage solutions that utilize erasure coding for data protection most commonly use either a Reed-Solomon or LDPC algorithm.

Data Fragmentation

Erasure coding begins by breaking down the original data into smaller fragments. These fragments are the building blocks that will be distributed across the storage nodes. The size and number of these fragments depend on the specific erasure coding scheme being used.

Parity Creation

Parity fragments (sometimes called coding chunks) are generated using mathematical functions that operate on the original data fragments. These parity fragments are calculated so that any combination of original fragments and parity fragments can be used to reconstruct the original data. This redundancy is the key to the ability of erasure coding to tolerate the loss of information pieces without actual data loss.

Distribution across Nodes

Once the data and parity fragments are created, they are distributed across different storage nodes and disks. This distribution ensures that a failure in one node does not result in the loss of the entire dataset. Each node stores a unique combination of data and parity fragments.

Reconstruction Mechanism

In the event of a node failure or data loss, the erasure coding system can reconstruct the missing or corrupted fragments using the available fragments stored on other nodes. The mathematical relationships established during the parity creation phase facilitate this reconstruction process.

Erasure Coding Profile

Common to all erasure coding algorithms are two specific numbers, called K and M . K defines the amount of fragments the original piece of information is split into, meaning that a K=3 says to split the original object, say a file, into three fragments. M, on the other hand, defines how many parity fragments are distributed. A M=2 means that the parity information is stored on two different systems. In a configuration of K=3, M=2 a storage cluster would need five servers to store the data fragments and and parity fragments.

Advantages of Erasure Coding

Erasure coding provides several advantages over more traditional data protection mechanisms, such as RAID or replication.

Optimized Storage Utilization

Erasure coding significantly reduces the amount of storage space required compared to traditional replication methods. While replication duplicates data in its entirety, erasure coding introduces redundancy at the fragment level, allowing for more efficient use of storage resources.

Editor’s Note: Our Erasure Coding Calculator can help you determine the erasure coding overhead.

Fault Tolerance

The distributed nature of erasure coding ensures that the failure of a single storage node does not result in data loss. The original data can be reconstructed as long as the required number of fragments is available across the surviving nodes. This fault tolerance is crucial for systems requiring high availability and reliability.

Cost-Effective Scalability

Traditional replication can become prohibitively expensive as data volumes grow. Erasure coding provides a cost-effective alternative, allowing organizations to scale their storage infrastructure without a linear cost increase.

Reduced Bandwidth Requirements

Transmitting and storing parity fragments instead of full data copies reduces the bandwidth and storage requirements. This is particularly advantageous in scenarios where network bandwidth or storage capacity is a limiting factor.

Use Cases of Erasure Coding

Erasure coding has many use cases, not only in the storage ecosystem but also in communication. UMTS, LTE, and certain satellite communication systems use different erasure code schemes to implement forward error correction.

Anyhow, in terms of storage solutions, next to consumer storage media such as DVD, there are three main storage alternatives that heavily benefit from erasure codes, both in terms of reliability or durability, as well as storage efficiency.

Cloud Storage

Erasure coding is widely adopted in cloud storage environments, where cost efficiency and fault tolerance are paramount. These solutions leverage erasure codes to ensure data durability and availability across many storage nodes or data centers.

Distributed File Systems

Systems like Hadoop Distributed File System (HDFS) and Ceph rely on erasure coding for fault tolerance and efficient storage utilization. It enables these systems to handle large-scale data processing and storage requirements.

Object Storage

Erasure coding optimizes storage space without compromising data integrity, making it an ideal choice for long-term data retention. Object storage platforms, commonly used for archival and backup purposes, benefit from this storage space savings.

Challenges and Considerations of Erasure Coding

While erasure coding offers numerous advantages, it’s important to consider that there is always good and bad news. That said, it has some characteristics that need to be understood.

Computational Overhead

The encoding and decoding processes involve more or less complex mathematical calculations, which can introduce computational overhead. However, advancements in hardware and algorithm optimization have mitigated this concern to a great extent.

Latency

The additional steps involved in the encoding and decoding processes can introduce latency. Organizations must carefully evaluate their performance requirements and select an erasure code scheme that aligns with their needs. Anyhow, the typically distributed nature of the storage using erasure coding commonly mitigates this issue by parallelizing storage requests.

Algorithm and Scheme Selection

Different erasure coding algorithms and schemes offer varying levels of efficiency, fault tolerance, and complexity. Choosing the right scheme requires thoroughly understanding the specific use case and performance considerations.

Erasure Coding and Simplyblock

Erasure coding is a powerful and efficient way to protect data, offering a great balance between storage efficiency, fault tolerance, and cost-effectiveness. As organizations grapple with the ever-growing volumes of data, adopting erasure coding becomes not just a choice but a strategic imperative. That said, it reshapes the data storage landscape, ensuring that our digital assets remain secure, resilient, and accessible in the face of evolving challenges.

That’s why simplyblock utilizes erasure coding for data protection and fault tolerance in our clustered storage solution, enabling “more bang for the buck” in terms of storage efficiency combined with the industry-standard NVMe over TCP protocol. Simplyblock enables logical devices that are high-performance, predictable, low-latency, and cost-effective, available as Kubernetes Persistent Volumes, for the easiest access possible. Not to mention all the other cool features such as compression, deduplication, encryption, thin provisioning, and more; learn more now.

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