Resiliency Explained — Understanding the PowerFlex Self-Healing, Self-Balancing Architecture
Wed, 15 Jul 2020 16:35:08 -0000|
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My phone rang. When I picked up it was Rob*, one of my favourite PowerFlex customers who runs his company’s Storage Infrastructure. Last year, his CTO made the decision to embrace digital transformation across the entire company, which included a software-defined approach. During that process, they selected the Dell EMC PowerFlex family as their Software-Defined Storage (SDS) infrastructure because they had a mixture of virtualised and bare-metal workloads, needed a solution that could handle their unpredictable storage growth, and also one powerful enough to support their key business applications.
During testing of the PowerFlex system, I educated Rob on how we give our customers an almost endless list of significant benefits – blazingly fast block storage performance that scales linearly as new nodes are added to the system; a self-healing & self-balancing storage platform that automatically ensures that it always gives the best possible performance; super-fast rebuilds in the event of disk or node failures, plus the ability to engineer a system that will meet or exceed his business commitments to uptime & SLAs.
PowerFlex provides all this (and more) thanks to its “Secret Sauce” – its Distributed Mesh-Mirror Architecture. It ensures there are always two copies of your application data – thus ensuring availability in case of any hardware failure. Data is intelligently distributed across all the disk devices in each of the nodes within a storage pool. As more nodes are added, the overall performance increases nearly linearly, without affecting application latencies. Yet at the same time, adding more disks or nodes also makes rebuild times during those (admittedly rare) failure situations decrease – which means that PowerFlex heals itself more quickly as the system grows!
PowerFlex automatically ensures that the two copies of each block of data that gets written to the Storage Pool reside on different SDS (storage) nodes, because we need to be able to get a hold of the second copy of data if a disk or a storage node that holds the first block fails at any time. And because the data is written across all the disks in all the nodes within a Storage Pool, this allows for super-quick IO response times, because we access all data in parallel.
Data also gets written to disk using very small chunk sizes – either 1MB or 4KB, depending on the Storage Pool type. Why is this? Doing this ensures that we always spread the data evenly across all the disk devices, automatically preventing performance hot-spots from ever being an issue in the first place. So, when a volume is assigned to a host or a VM, that data is already spread efficiently across all the disks in all Storage Nodes. For example, a 4-Node PowerFlex system, with 3 volumes provisioned from it, will look something like the following:
Figure 1: A Simplified View of a 4-Node PowerFlex System Presenting 3 Storage Volumes
Now, here is where the magic begins. In the event of a drive failure, the PowerFlex rebuild process utilizes an efficient many-to-many scheme for very fast rebuilds. It uses ALL the devices in the storage pool for rebuild operations and will always rebalance the data in the pool automatically whenever new disks or nodes are added to the Storage Pool. This means that, as the system grows, performance increases linearly – which is great for future-proofing your infrastructure if you are not sure how your system will grow. But this also gives another benefit – as your system grows in size, rebuilds get faster!
Customers like Rob typically raise their eyes at that last statement – until we provide a simple example to get the point across – and then they have a lightbulb moment. Think about what happens if we used a 4-node PowerFlex system, but only had one disk drive in each storage node. All data would be spread evenly across the 4 Nodes, but we also have some spare capacity reserved, which is also spread evenly across each drive. This spare capacity is needed to rebuild data into, in the event of a disk or a node failure and it usually equates to either the capacity of an entire node or 10% of the entire system, whichever is largest. At a superficial level, a 4-Node system would look something like this:
Figure 2: A Simplified View of a 4-Node PowerFlex System & Available Dataflows
If one of those drives (or nodes) failed, then obviously we would end up rebuilding between the three remaining disks, one disk per node:
Figure 3: Our Simplified 4-Node PowerFlex System & Available Dataflows with One Failed Disk
Now of course, in this scenario, that rebuild is going to take some time to complete. We will be performing lots of 1MB or 4KB copies between the three remaining nodes, in both directions, as we rebuild into the spare capacity available on the remaining nodes & get back to having two copies of data in order to be fully protected again. It is worth pointing out here that a node typically contains 10 or 24 drives, not just one, so PowerFlex isn’t just protecting you from “a” drive failure, we’re able to protect you from a whole pile of drives. This is not your typical RAID card schema.
Now – let the magic of PowerFlex begin! What happens if we were to add a fifth storage node into the mix? And what happens when a disk or node fails in this scenario??
Figure 4: Dataflows in a Normally Running 5-Node PowerFlex System … & Available Dataflows with One Failed Disk or Node
It should be clear for all to see that we now have more disks - and nodes - to participate in the rebuild process, making the rebuild complete substantially faster than in our previous 4 node scenario. But PowerFlex nodes do not have just a single disk inside them - They typically have 10 or 24 drive slots, hence even for a small deployment with 4 nodes, each having 10 disks, we will have data placed intelligently and evenly across all 40 drives, configured as one Storage Pool. Now, with today’s flash media, that is a heck of a lot of performance capability available at your fingertips, that can be delivered with consistent sub-millisecond latencies.
Let me also highlight the “many-to-many” rebuild scheme used by each Storage Pool. This means that any data within a Storage Pool can be rebuilt to all the other disks in the same Pool. If we have 40 drives in our pool, it means that when one drive fails, the other 39 drives will be utilised to rebuild the data of the failed drive. This results in extremely quick rebuilds that occur in parallel, with minimum impact to application performance if we lose a disk:
Figure 5: A 40-disk Storage Pool, with a Disk Failure… Showing The Magic of Parallel Rebuilds
Note that we had to over-simplify the dataflows between the disks in the figure above, because if we tried to show all the interconnects at play, we would simply have a tangle of green arrows!
Here’s another example to explain the difference between PowerFlex and conventional RAID-type drive protection. The initial rebuild test on an empty system usually takes little more than a minute for the rebuild to complete. This is because PowerFlex will only ever rebuild chunks of application data, unlike a traditional RAID controller, which will rebuild disk blocks whether they contain data or not. Why waste resources rebuilding empty zeroes of data when you need to repair from a failed disk or node as quickly as possible?
The PowerFlex Distributed Mesh-Mirror architecture is truly unique and gives our customers the fastest, most scalable and most resilient block storage platform available on the market today! Please visit www.DellTechnologies.com/PowerFlex for more information.
* Name changed to protect the innocent!
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How PowerFlex Transforms Big Data with VMware Tanzu Greenplum
Wed, 13 Apr 2022 13:16:23 -0000|
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Quick! The word has just come down. There is a new initiative that requires a massively parallel processing (MPP) database, and you are in charge of implementing it. What are you going to do? Luckily, you know the answer. You also just discovered that the Dell PowerFlex Solutions team has you covered with a solutions guide for VMware Tanzu Greenplum.
What is in the solutions guide and how will it help with an MPP database? This blog provides the answer. We look at what Greenplum is and how to leverage Dell PowerFlex for both the storage and compute resources in Greenplum.
Infrastructure flexibility: PowerFlex
If you have read my other blogs or are familiar with PowerFlex, you know it has powerful transmorphic properties. For example, PowerFlex nodes sometimes function as both storage and compute, like hyperconverged infrastructure (HCI). At other times, PowerFlex functions as a storage-only (SO) node or a compute-only (CO) node. Even more interesting, these node types can be mixed and matched in the same environment to meet the needs of the organization and the workloads that they run.
This transmorphic property of PowerFlex is helpful in a Greenplum deployment, especially with the configuration described in the solutions guide. Because the deployment is built on open-source PostgreSQL, it is optimized for the needs of an MPP database, like Greenplum. PowerFlex can deliver the compute performance necessary to support massive data IO with its CO nodes. The PowerFlex infrastructure can also support workloads running on CO nodes or nodes that combine compute and storage (hybrid nodes). By leveraging the malleable nature of PowerFlex, no additional silos are needed in the data center, and it may even help remove existing ones.
The architecture used in the solutions guide consists of 12 CO nodes and 10 SO nodes. The CO nodes have VMware ESXi installed on them, with Greenplum instances deployed on top. There are 10 segments and one director deployed for the Greenplum environment. The 12th CO node is used for redundancy.
The storage tier uses the 10 SO nodes to deliver 12 volumes backed by SSDs. This configuration creates a high speed, highly redundant storage system that is needed for Greenplum. Also, two protection domains are used to provide both primary and mirror storage for the Greenplum instances. Greenplum mirrors the volumes between those protection domains, adding an additional level of protection to the environment, as shown in the following figure:
By using this fluid and composable architecture, the components can be scaled independently of one another, allowing for storage to be increased either independently or together with compute. Administrators can use this configuration to optimize usage and deliver appropriate resources as needed without creating silos in the environment.
Testing and validation with Greenplum: we have you covered
The solutions guide not only describes how to build a Greenplum environment, it also addresses testing, which many administrators want to perform before they finish a build. The guide covers performing basic validations with FIO and gpcheckperf. In the simplest terms, these tools ensure that IO, memory, and network performance are acceptable. The FIO tests that were run for the guide showed that the HBA was fully saturated, maximizing both read and write operations. The gpcheckperf testing showed a performance of 14,283.62 MB/sec for write workloads.
Wouldn’t you feel better if a Greenplum environment was tested with a real-world dataset? That is, taking it beyond just the minimum, maximum, and average numbers? The great news is that the architecture was tested that way! Our Dell Digital team has developed an internal test suite running static benchmarked data. This test suite is used at Dell Technologies across new Greenplum environments as the gold standard for new deployments.
In this test design, all the datasets and queries are static. This scenario allows for a consistent measurement of the environment from one run to the next. It also provides a baseline of an environment that can be used over time to see how its performance has changed -- for example, if the environment sped up or slowed down following a software update.
Massive performance with real data
So how did the architecture fare? It did very well! When 182 parallel complex queries were run simultaneously to stress the system, it took just under 12 minutes for the test to run. In that time, the environment had a read bandwidth of 40 GB/s and a write bandwidth of 10 GB/s. These results are using actual production-based queries from the Dell Digital team workload. These results are close to saturating the network bandwidth for the environment, which indicates that there are no storage bottlenecks.
The design covered in this solution guide goes beyond simply verifying that the environment can handle the workload; it also shows how the configuration can maintain performance during ongoing operations.
Maintaining performance with snapshots
One of the key areas that we tested was the impact of snapshots on performance. Snapshots are a frequent operation in data centers and are used to create test copies of data as well as a source for backups. For this reason, consider the impact of snapshots on MPP databases when looking at an environment, not just how fast the database performs when it is first deployed.
In our testing, we used the native snapshot capabilities of PowerFlex to measure the impact that snapshots have on performance. Using PowerFlex snapshots provides significant flexibility in data protection and cloning operations that are commonly performed in data centers.
We found that when the first storage-consistent snapshot of the database volumes was taken, the test took 45 seconds longer to complete than initial tests. This result was because it was the first snapshot of the volumes. Follow-on snapshots during testing resulted in minimal impact to the environment. This minimal impact is significant for MPP databases in which performance is important. (Of course, performance can vary with each deployment.)
We hope that these findings help administrators who are building a Greenplum environment feel more at ease. You not only have a solution guide to refer to as you architect the environment, you can be confident that it was built on best-in-class infrastructure and validated using common testing tools and real-world queries.
The bottom line
Now that you know the assignment is coming to build an MPP database using VMware Tanzu Greenplum -- are you up to the challenge?
If you are, be sure to read the solution guide. If you need additional guidance on building your Greenplum environment on PowerFlex, be sure to reach out to your Dell representative.
Looking Ahead: Dell Container Storage Modules 1.2
Mon, 21 Mar 2022 14:31:56 -0000|
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The quarterly update for Dell CSI Drivers & Dell Container Storage Modules (CSM) is here! Here’s what we’re planning.
New CSM Operator!
Dell Container Storage Modules (CSM) add data services and features that are not in the scope of the CSI specification today. The new CSM Operator simplifies the deployment of CSMs. With an ever-growing ecosystem and added features, deploying a driver and its affiliated modules need to be carefully studied before beginning the deployment.
The new CSM Operator:
- Serves as a one-stop-shop for deploying all Dell CSI driver and Container Storage Modules
- Simplifies the install and upgrade operations
- Leverages the Operator framework to give a clear status of the deployment of the resources
- Is certified by Red Hat OpenShift
In the short/middle term, the CSM Operator will deprecate the experimental CSM Installer.
Replication support with PowerScale
For disaster recovery protection, PowerScale implements data replication between appliances by means of the the SyncIQ feature. SyncIQ replicates the data between two sites, where one is read-write while the other is read-only, similar to Dell storage backends with async or sync replication.
The role of the CSM replication module and underlying CSI driver is to provision the volume within Kubernetes clusters and prepare the export configurations, quotas, and so on.
CSM Replication for PowerScale has been designed and implemented in such a way that it won’t collide with your existing Superna Eyeglass DR utility.
A live-action demo will be posted in the coming weeks on our VP YouTube channel: https://www.youtube.com/user/itzikreich/.
Across the portfolio
In this release, each CSI driver:
- Supports OpenShift 4.9
- Supports Kubernetes 1.23
- Supports the CSI Spec 1.5
- Updates the latest UBI-minimal image
- Supports fsGroupPolicy
There are three possible options:
- None -- which means that the fsGroup directive from the securityContext will be ignored
- File -- which means that the fsGroup directive will be applied on the volume. This is the default setting for NAS systems such as PowerScale or Unity-File.
- ReadWriteOnceWithFSType -- which means that the fsGroup directive will be applied on the volume if it has fsType defined and is ReadWriteOnce. This is the default setting for block systems such as PowerMax and PowerStore-Block.
In all cases, Dell CSI drivers let kubelet perform the change ownership operations and do not do it at the driver level.
Standalone Helm install
Drivers for PowerFlex and Unity can now be installed with the help of the install scripts we provide under the dell-csi-installer directory.
Note: To ensure that you install the driver on a supported Kubernetes version, the Helm charts take advantage of the kubeVersion field. Some Kubernetes distributions use labels in kubectl version (such as v1.21.3-mirantis-1 and v1.20.7-eks-1-20-7) that require manual editing.
Volume Health Monitoring support
This feature is currently in alpha in Kubernetes (in Q1-2022), and is disabled with a default installation.
Once enabled, the drivers will expose the standard storage metrics, such as capacity usage and inode usage through the Kubernetes /metrics endpoint. The metrics will flow natively in popular dashboards like the ones built-in OpenShift Monitoring:
Pave the way for full open source!
All Dell drivers and dependencies like gopowerstore, gobrick, and more are now on Github and will be fully open-sourced. The umbrella project is and remains https://github.com/dell/csm, from which you can open tickets and see the roadmap.
Google Anthos 1.9
NFSv4 POSIX and ACL support
- In PowerScale, you can use plain ACL or built-in values such as private_read, private, public_read, public_read_write, public or custom ones;
- In PowerStore, you can use the custom ones such as A::OWNER@:RWX, A::GROUP@:RWX, and A::OWNER@:rxtncy.
For more details you can:
- Watch these great CSM demos on our VP YouTube channel: https://www.youtube.com/user/itzikreich/
- Read the FAQs
- Subscribe to Github notification and be informed of the latest releases on: https://github.com/dell/csm
- Ask for help or chat with us on Slack
Author: Florian Coulombel