A few days ago I had posted part 1 of my quest to move my cloud to Kubernetes. Now the next parts need to be done, namely choosing the right storage facility.
First of all, I have to decide what kinda facility is most useful for me. Since I am limited in my finances to run this cloud, I cannot host a huge storage array like Purestorage etc. There are many solutions with many features, but they have one thing in common: A certain amount of costs associated. Also, since I am hosting my cloud with Hetzner, I am limited there too. Although Hetzner provides relatively cheap storage boxes and now also object storage, they do not offer NFS or any similar network storage. FTP, SCP, CIFS etc. are not suitable for my task of multiple hosts accessing the file system with reasonable performance simultaneously. For me right now, there are some options to choose from Network File System (NFS), distributed file system like Glusterfs or Ceph, or better yet, Kubernetes-integrated solutions like OpenEBS or Longhorn.
NFS comes pretty much with every Linux distribution and is relatively easy to setup. However it is not that much scalable. Usually it suns on a single host, so if that host goes down, your data is not accessible and the Kubernetes cluster probably widely broken, at least for applications which rely on shared storage.
Glusterfs is reliant and scalable, but works much better with large files. Ceph is as resiliant and scalable, but also supports random access patterns pretty well. Here is what Grok ( the ai built into X, formerly twitter) has to say about this:
NFS (Network File System)
- Pros:
- Simplicity: NFS is easy to set up and manage, especially if you're already familiar with it. It's a good choice for smaller clusters or environments where simplicity is key.
- Cost-effective: It can leverage existing hardware without the need for specialized storage solutions.
- Cons:
- Single Point of Failure: Traditional NFS setups can have a single server as a point of failure, though this can be mitigated with high-availability configurations.
- Performance: NFS can struggle with high concurrency and may not scale well for very large or distributed clusters due to its centralized nature.
- Best Practices: Use NFS for environments where reliability isn't the highest concern or where you have a robust high-availability setup. Consider using NFS-Ganesha for better integration with Kubernetes.
GlusterFS
- Pros:
- Distributed: GlusterFS offers distributed file system capabilities, providing better scalability than traditional NFS for horizontal growth across nodes.
- Flexibility: It supports multiple volumes and can handle both block and file storage.
- Cons:
- Complexity: GlusterFS can be complex to configure, especially for achieving high performance and reliability.
- Performance Overhead: For numerous small files, GlusterFS can be slower due to its architecture.
- Best Practices: GlusterFS is suitable for scenarios where you need to scale storage horizontally and can handle the setup complexity. It's particularly useful if you're working with large datasets but not under stringent performance requirements for small file operations. The integration with Kubernetes through Heketi can automate volume provisioning.
Ceph
- Pros:
- Scalability and Reliability: Ceph offers excellent scalability and high reliability with features like self-healing, replication, and erasure coding.
- Versatile: It provides block, object, and file storage all in one, making it very versatile for different types of workloads.
- Cons:
- Complexity: Setting up a Ceph cluster can be quite involved, requiring careful planning for hardware and network configurations.
- Resource Intensive: It needs significant resources, both in terms of hardware and ongoing management.
- Best Practices: Ceph is ideal for large-scale, high-availability environments where you need robust, reliable storage. Rook is a good operator for managing Ceph on Kubernetes, simplifying deployment and management. Consider the overhead of managing Ceph but also the benefits if your use case demands high performance and scalability.
Conclusion
- NFS for simpler, smaller setups where ease of use is prioritized over scalability.
- GlusterFS for scenarios where you need distributed file storage with good scalability but are willing to manage some complexity.
- Ceph when you're looking for a comprehensive solution with the highest scalability and resilience, although it requires more initial setup and resources.
But then there is yet another solution available: OpenEBS:
What is OpenEBS?
OpenEBS is an open-source Container Attached Storage (CAS) solution specifically designed for Kubernetes. It provides persistent storage by running storage services as containers within the Kubernetes cluster, allowing for tight integration with Kubernetes' management and scaling features.
Key Features and Benefits:
- Container-Native: OpenEBS is deployed and managed like any other container in Kubernetes, offering a seamless experience for DevOps teams familiar with Kubernetes.
- Dynamic Provisioning: Supports automatic provisioning of storage volumes based on Persistent Volume Claims (PVCs), which simplifies the management of storage resources.
- Multiple Storage Engines:
- Local PV Hostpath: Utilizes local storage on each node, excellent for testing or non-critical data.
- Local PV LVM, ZFS: Can use local disks for better performance with features like snapshots.
- Mayastor: A newer engine providing high-performance, distributed block storage with NVMe-oF capabilities.
- Replicated PV (Jiva, cStor): Offers data replication for higher availability, suitable for stateful applications like databases.
- High Availability: Through replication (like Jiva or Mayastor), OpenEBS can provide data redundancy across nodes, enhancing data durability.
- Cloud-Native Architecture: It fits well into cloud-native paradigms, reducing lock-in to specific cloud storage solutions.
- Snapshot and Cloning: Supports snapshots for backups and cloning for faster deployments or testing.
- Integration: Works with popular Kubernetes tools like Prometheus for monitoring, Velero for backup, and supports CSI (Container Storage Interface) for broader compatibility.
Considerations:
- Performance: The performance can vary based on the chosen engine. Local storage engines are fast for single node use but don't provide redundancy. Replicated engines like Jiva and Mayastor might introduce some latency due to network overhead.
- Complexity for Advanced Features: While basic setup and operation are straightforward, leveraging advanced features or managing a large-scale deployment might require more expertise.
- Resource Usage: Each storage controller runs as a pod, which means it consumes cluster resources. This is generally less than traditional SAN/NAS but still a consideration for resource-constrained clusters.
- Community and Support: Being an open-source project under CNCF (Cloud Native Computing Foundation), it has a vibrant community but also means professional support might be through third parties or additional services.
Experience and Reviews:
- Posts found on X and various web sources mention OpenEBS for its ease of use, particularly with local storage scenarios, and its integration capabilities with Kubernetes. However, there are concerns about performance when compared to some proprietary solutions for high-throughput applications, and some users have noted that setup for replicated storage can be complex for those new to storage management.
Conclusion:
If you're building or managing a Kubernetes environment with a focus on cloud-native principles, OpenEBS could be an excellent choice, especially for developers and smaller teams looking for simplicity in managing stateful applications. However, for high-performance requirements or very large-scale deployments, you might want to benchmark OpenEBS against your specific use cases or consider it alongside other solutions like Ceph for comprehensive storage needs. Remember, the choice often comes down to balancing simplicity, performance, scalability, and the specific needs of your applications.
And yet another solution came up: Longhorn:
Key Features:
- Cloud-Native: Longhorn is built to run directly within a Kubernetes cluster, leveraging Kubernetes' own mechanisms for scheduling, scaling, and management.
- Persistent Storage: Provides persistent volumes for stateful applications running in Kubernetes, ensuring data durability.
- High Availability: Longhorn automatically replicates data across multiple nodes in the cluster, ensuring that your data remains available even if a node fails.
- Snapshots and Backups: Offers both snapshots for quick recovery and backups to external storage solutions like NFS or S3 for disaster recovery.
- Dynamic Volume Provisioning: Integrates with Kubernetes storage classes for automatic provisioning of volumes as needed.
- UI Management: Comes with an intuitive web UI for managing volumes, snapshots, backups, and monitoring the health of the storage system.
- Upgrade Without Disruption: Longhorn supports in-place, non-disruptive upgrades, meaning you can update the storage software without affecting the running workloads.
- CSI (Container Storage Interface) Support: Uses the Kubernetes CSI for seamless integration and management.
Advantages:
- Simplicity: One of Longhorn's biggest selling points is its ease of installation and operation. It can be deployed with simple kubectl commands or through Helm charts.
- Scalability: Scales with your Kubernetes cluster, leveraging Kubernetes for orchestration.
- Cost-Effective: Being open-source and utilizing existing hardware, it can be cost-effective compared to proprietary solutions or external storage arrays.
- Versatility: Works across different environments (bare-metal, VMs, cloud) without lock-in to a specific cloud provider's storage solution.
Considerations:
- Performance: While Longhorn is efficient for many use cases, complex or very high-performance needs might require tuning or might be better served by other solutions.
- Resource Overhead: Each node in the cluster runs Longhorn components, which consume resources (CPU, memory, storage). This should be factored into your node sizing.
- Complex Configurations: For advanced configurations or very large-scale deployments, there could be a learning curve or management overhead.
- Backup and Recovery: While Longhorn provides excellent backup solutions, ensure your backup strategy (like where backups are stored) aligns with your disaster recovery needs.
Deployment and Management:
- Installation: Can be done via Helm, kubectl, or the Rancher UI if you're using Rancher for cluster management. It requires minimal prerequisites (like a Kubernetes cluster and some storage on each node).
- Management: The Longhorn UI simplifies many management tasks, but for automation or integration into CI/CD pipelines, you might need to use its API or command-line tools.
Real-World Experience:
- Posts found on X and various web sources suggest Longhorn is particularly valued in scenarios where ease of use, cost, and avoiding vendor lock-in are priorities. It's praised for its straightforward deployment and the ability to manage storage directly from the Kubernetes control plane. However, some users have pointed out that for very specific, high-performance storage needs, additional tuning or alternative solutions might be necessary.
Conclusion:
Longhorn is an excellent choice for organizations looking for a straightforward, Kubernetes-centric storage solution that provides resilience, scalability, and ease of management. It's especially suited for environments where you want to keep operations within the Kubernetes ecosystem, reducing the complexity of managing separate storage infrastructures. However, as with any storage solution, it's wise to test in your specific use case to ensure it meets performance and reliability expectations.
So, where does this lead me? My basic cluster is already running with some applications. However they are currently tied to particular nodes, which is very unwanted. Ceph seems the best choice overall for performance reasons, but has a huge learning curve and management requirements. NFS is well known and reliable enough, but lacks scalability.
Well, back to the drawing board. I will need to research a little about these options, and how hard or easy they can be implemented.