Stating the case for a Storage Appliance approach

I was in Indonesia last week to meet with iXsystems™‘ partner PT Maha Data Solusi. I had the wonderful opportunity to meet with many people there and one interesting and often-replayed question arose. Why aren’t iX doing software-defined-storage (SDS)? It was a very obvious and deliberate question.

After all, iX is already providing the free use of the open source TrueNAS® CORE software that runs on many x86 systems as an SDS solution and yet commercially, iX sell the TrueNAS® storage appliances.

This argument between a storage appliance model and a storage storage only model has been debated for more than a decade, and it does come into my conversations on and off. I finally want to address this here, with my own views and opinions. And I want to inform that I am open to both models, because as a storage consultant, both have their pros and cons, advantages and disadvantages. Up front I gravitate to the storage appliance model, and here’s why.

My story of the storage appliance begins …

Back in the 90s, most of my work was on Fibre Channel and NFS. iSCSI has not existed yet (iSCSI was ratified in 2003). It was almost exclusively on the Sun Microsystems® enterprise storage with Sun’s software resell of the Veritas® software suite that included the Sun Volume Manager (VxVM), Veritas® Filesystem (VxFS), Veritas® Replication (VxVR) and Veritas® Cluster Server (VCS). I didn’t do much Veritas® NetBackup (NBU) although I was trained at Veritas® in Boston in July 1997 (I remembered that 2 weeks’ trip fondly). It was just over 2 months after Veritas® acquired OpenVision. Backup Plus was the NetBackup.

Between 1998-1999, I spent a lot of time working Sun NFS servers. The prevalent networking speed at that time was 100Mbits/sec. And I remember having this argument with a Sun partner engineer by the name of Wong Teck Seng. Teck Seng was an inquisitive fella (still is) and he was raving about this purpose-built NFS server he knew about and he shared his experience with me. I detracted him, brushing aside his always-on tech orgasm, and did not find great things about a NAS storage appliance. Auspex™ was big then, and I knew of them.

I joined NetApp® as Malaysia’s employee #2. It was an odd few months working with a storage appliance but after a couple of months, I started to understand and appreciate the philosophy. The storage Appliance Model made sense to me, even through these days.

Continue reading

Unstructured Data Observability with Datadobi StorageMAP

Let’s face it. Data is bursting through its storage seams. And every organization now is storing too much data that they don’t know they have.

By 2025, IDC predicts that 80% the world’s data will be unstructured. IDC‘s report Global Datasphere Forecast 2021-2025 will see the global data creation and replication capacity expand to 181 zettabytes, an unfathomable figure. Organizations are inundated. They struggle with data growth, with little understanding of what data they have, where the data is residing, what to do with the data, and how to manage the voluminous data deluge.

The simple knee-jerk action is to store it in cloud object storage where the price of storage is $0.0000xxx/GB/month. But many IT departments in these organizations often overlook the fact that that the data they have parked in the cloud require movement between the cloud and on-premises. I have been involved in numerous discussions where the customers realized that they moved the data in the cloud moved too frequently. Often it was an erred judgement or short term blindness (blinded by the cheap storage costs no doubt), further exacerbated by the pandemic. These oversights have resulted in expensive and painful monthly API calls and egress fees. Welcome to reality. Suddenly the cheap cloud storage doesn’t sound so cheap after all.

The same can said about storing non-active unstructured data on primary storage. Many organizations have not been disciplined to practise good data management. The primary Tier 1 storage becomes bloated over time, grinding sluggishly as the data capacity grows. I/O processing becomes painfully slow and backup takes longer and longer. Sounds familiar?

The A in ABC

I brought up the ABC mantra a few blogs ago. A is for Archive First. It is part of my data protection consulting practice conversation repertoire, and I use it often to advise IT organizations to be smart with their data management. Before archiving (some folks like to call it tiering, but I am not going down that argument today), we must know what to archive. We cannot blindly send all sorts of junk data to the secondary or tertiary storage premises. If we do that, it is akin to digging another hole to fill up the first hole.

We must know which unstructured data to move replicate or sync from the Tier 1 storage to a second (or third) less taxing storage premises. We must be able to see this data, observe its behaviour over time, and decide the best data management practice to apply to this data. Take note that I said best data management practice and not best storage location in the previous sentence. There has to be a clear distinction that a data management strategy is more prudent than to a “best” storage premises. The reason is many organizations are ignorantly thinking the best storage location (the thought of the “cheapest” always seems to creep up) is a good strategy while ignoring the fact that data is like water. It moves from premises to premises, from on-prem to cloud, cloud to other cloud. Data mobility is a variable in data management.

Continue reading

All the Sources and Sinks going to Object Storage

The vocabulary of sources and sinks are beginning to appear in the world of data storage as we witness the new addition of data processing frameworks and the applications in this space. I wrote about this in my blog “Rethinking data. processing frameworks systems in real time” a few months ago, introducing my take on this budding new set of I/O characteristics and data ecosystem. I also started learning about the Kappa Architecture (and Lambda as well), a framework designed to craft and develop a set of amalgamated technologies to handle stream processing of a series of data in relation to time.

In Computer Science, sources and sinks are considered external entities that often serve as connectors of input and output of disparate systems. They are often not in the purview of data storage architects. Also often, these sources and sinks are viewed as black boxes, and their inner workings are hidden from the views of the data storage architects.

Diagram from https://developer.here.com/documentation/get-started/dev_guide/shared_content/topics/olp/concepts/pipelines.html

The changing facade of data stream processing presents the constant motion of data, the continuous data being altered as it passes through the many integrated sources and sinks. We are also see much of the data processed in-memory as much as possible. Thus, the data services from a traditional storage model of SAN and NAS may straggle with the requirements demanded by this new generation of data stream processing.

As the world of traditional data storage processing is expanding into data streams processing and vice versa, and the chatter of sources and sinks can no longer be ignored.

Continue reading

I built a 6-node Gluster cluster with TrueNAS SCALE

I haven’t had hands-on with Gluster for over a decade. My last blog about Gluster was in 2011, right after I did a proof-of-concept for the now defunct, Jaring, Malaysia’s first ISP (Internet Service Provider). But I followed Gluster’s development on and off, until I found out that Gluster was a feature in then upcoming TrueNAS® SCALE. That was almost 2 years ago, just before I accepted to offer to join iXsystems™, my present employer.

The eagerness to test drive Gluster (again) on TrueNAS® SCALE has always been there but I waited for SCALE to become GA. GA finally came on February 22, 2022. My plans for the test rig was laid out, and in the past few weeks, I have been diligently re-learning and putting up the scope to built a 6-node Gluster clustered storage with TrueNAS® SCALE VMs on Virtualbox®.

Gluster on OpenZFS with TrueNAS SCALE

Before we continue, I must warn that this is not pretty. I have limited computing resources in my homelab, but Gluster worked beautifully once I ironed out the inefficiencies. Secondly, this is not a performance test as well, for obvious reasons. So, this is the annals along with the trials and tribulations of my 6-node Gluster cluster test rig on TrueNAS® SCALE.

Continue reading

Nakivo Backup Replication architecture and installation on TrueNAS – Part 1

Backup and Replication software have received strong mandates in organizations with enterprise mindsets and vision. But lower down the rung, small medium organizations are less invested in backup and replication software. These organizations know full well that they must backup, replicate and protect their servers, physical and virtual, and also new workloads in the clouds, given the threat of security breaches and ransomware is looming larger and larger all the time. But many are often put off by the cost of implementing and deploying a Backup and Replication software.

So I explored one of the lesser known backup and recovery software called Nakivo® Backup and Replication (NBR) and took the opportunity to build a backup and replication appliance in my homelab with TrueNAS®. My objective was to create a cost effective option for small medium organizations to enjoy enterprise-grade protection and recovery without the hefty price tag.

This blog, Part 1, writes about the architecture overview of Nakivo® and the installation of the NBR software in TrueNAS® to bake in and create the concept of a backup and replication appliance. Part 2, in a future blog post, will cover the administrative and operations usage of NBR.

Continue reading

Please cultivate 3-2-1 and A-B-C of Data Management

My Sunday morning was muddled 2 weeks ago. There was a frenetic call from someone whom I knew a while back and he needed some advice. Turned out that his company’s files were encrypted and the “backups” (more on this later) were gone. With some detective work, I found that their files were stored in a Synology® NAS, often accessed via QuickConnect remotely, and “backed up” to Microsoft® Azure. I put “Backup” in inverted commas because their definition of “backup” was using Synology®’s Cloud Sync to Azure. It is not a true backup but a file synchronization service that often mislabeled as a data protection backup service.

All of his company’s projects files were encrypted and there were no backups to recover from. It was a typical ransomware cluster F crime scene.

I would have gloated because many of small medium businesses like his take a very poor and lackadaisical attitude towards good data management practices. No use crying over spilled milk when prevention is better than cure. But instead of investing early in the prevention, the cure would likely be 3x more expensive. And in this case, he wanted to use Deloitte® recovery services, which I did not know existed. Good luck with the recovery was all I said to him after my Sunday morning was made topsy turvy of sorts.

NAS is the ransomware goldmine

I have said it before and I am saying it again. NAS devices, especially the consumer and prosumer brands, are easy pickings because there was little attention paid to implement a good data management practice either by the respective vendor or the end users themselves. 2 years ago I was already seeing a consistent pattern of the heightened ransomware attacks on NAS devices, especially the NAS devices that proliferated the small medium businesses market segment.

The WFH (work from home) practice trigged by the Covid-19 pandemic has made NAS devices essential for businesses. NAS are the workhorses of many businesses after all.  The ease of connecting from anywhere with features similar to the Synology® QuickConnect I mentioned earlier, or through VPNs (virtual private networks), or a self created port forwarding (for those who wants to save a quick buck [ sarcasm ]), opened the doors to bad actors and easy ransomware incursions. Good data management practices are often sidestepped or ignored in exchange for simplicity, convenience, and trying to save foolish dollars. Until ….

Continue reading

Exploring the venerable NFS Ganesha

As TrueNAS® SCALE approaches its General Availability date in less than 10 days time, one of the technology pieces I am extremely excited about in TrueNAS® SCALE is the NFS Ganesha server. It is still early days to see the full prowess of NFS Ganesha in TrueNAS® SCALE, but the potential of Ganesha’s capabilities in iXsystems™ new scale-out storage technology is very, very promising.

NFS Ganesha

I love Network File System (NFS). It was one of the main reasons I was so attracted to Sun Microsystems® SunOS in the first place. 6 months before I graduated, I took a Unix systems programming course in C in the university. The labs were on Sun 3/60 workstations. Coming from a background of a VAX/VMS system administrator in the school’s lab, Unix became a revelation for me. It completely (and blissfully) opened my eyes to open technology, and NFS was the main catalyst. Till this day, my devotion to Unix remained sacrosanct because of the NFS spark aeons ago.

I don’t know NFS Ganesha. I knew of its existence for almost a decade, but I have never used it. Most of the NFS daemons/servers I worked with were kernel NFS, and these included NFS services in Sun SunOS/Solaris, several Linux flavours – Red Hat®, SuSE®, Ubuntu, BSD variants in FreeBSD and MacOS, the older Unices of the 90s – HP-UX, Ultrix, AIX and Irix along with SCO Unix and Microsoft® XenixNetApp® ONTAP™, EMC® Isilon (very briefly), Hitachi® HNAS (née BlueArc) and of course, in these past 5-6 years FreeNAS®/TrueNAS™.

So, as TrueNAS® SCALE beckons, I took to this weekend to learn a bit about NFS Ganesha. Here are what I have learned.

Continue reading

Crash consistent data recovery for ZFS volumes

While TrueNAS® CORE and TrueNAS® Enterprise are more well known for its NAS (network attached storage) prowess, many organizations are also confidently placing their enterprise applications such as hypervisors and databases on TrueNAS® via SANs (storage area networks) as well. Both iSCSI and Fibre Channel™ (selected TrueNAS® Enterprise storage models) protocols are supported well.

To reliably protect these block-based applications via the SAN protocols, ZFS snapshot is the key technology that can be dependent upon to restore the enterprise applications quickly. However, there are still some confusions when it comes to the state of recovery from the ZFS snapshots. On that matter, this situations are not unique to the ZFS environments because as with many other storage technologies, the confusion often stem from the (mis)understanding of the consistency state of the data in the backups and in the snapshots.

Crash Consistency vs Application Consistency

To dispel this misunderstanding, we must first begin with the understanding of a generic filesystem agnostic snapshot. It is a point-in-time copy, just like a data copy on the tape or in the disks or in the cloud backup. It is a complete image of the data and the state of the data at the storage layer at the time the storage snapshot was taken. This means that the data and metadata in this snapshot copy/version has a consistent state at that point in time. This state is frozen for this particular snapshot version, and therefore it is often labeled as “crash consistent“.

In the event of a subsystem (application, compute, storage, rack, site, etc) failure or a power loss, data recovery can be initiated using the last known “crash consistent” state, i.e. restoring from the last good backup or snapshot copy. Depending on applications, operating systems, hypervisors, filesystems and the subsystems (journals, transaction logs, protocol resiliency primitives etc) that are aligned with them, some workloads will just continue from where it stopped. It may already have some recovery mechanisms or these workloads can accept data loss without data corruption and inconsistencies.

Some applications, especially databases, are more sensitive to data and state consistencies. That is because of how these applications are designed. Take for instance, the Oracle® database. When an Oracle® database instance is online, there is an SGA (system global area) which handles all the running mechanics of the database. SGA exists in the memory of the compute along with transaction logs, tablespaces, and open files that represent the Oracle® database instance. From time to time, often measured in seconds, the state of the Oracle® instance and the data it is processing have to be synched to non-volatile, persistent storage. This commit is important to ensure the integrity of the data at all times.

Continue reading

How well do you know your data and the storage platform that processes the data

Last week was consumed by many conversations on this topic. I was quite jaded, really. Unfortunately many still take a very simplistic view of all the storage technology, or should I say over-marketing of the storage technology. So much so that the end users make incredible assumptions of the benefits of a storage array or software defined storage platform or even cloud storage. And too often caveats of turning on a feature and tuning a configuration to the max are discarded or neglected. Regards for good storage and data management best practices? What’s that?

I share some of my thoughts handling conversations like these and try to set the right expectations rather than overhype a feature or a function in the data storage services.

Complex data networks and the storage services that serve it

I/O Characteristics

Applications and workloads (A&W) read and write from the data storage services platforms. These could be local DAS (direct access storage), network storage arrays in SAN and NAS, and now objects, or from cloud storage services. Regardless of structured or unstructured data, different A&Ws have different behavioural I/O patterns in accessing data from storage. Therefore storage has to be configured at best to match these patterns, so that it can perform optimally for these A&Ws. Without going into deep details, here are a few to think about:

  • Random and Sequential patterns
  • Block sizes of these A&Ws ranging from typically 4K to 1024K.
  • Causal effects of synchronous and asynchronous I/Os to and from the storage

Continue reading

OpenZFS with Object Storage

At AWS re:Invent last week, Amazon Web Services announced Amazon FSx for OpenZFS. This is the 4th managed service under the Amazon FSx umbrella, joining NetApp® ONTAP™, Lustre and Windows File Server. The highly scalable OpenZFS filesystem can provide high throughput and IOPS bandwidth to Amazon EC2, ECS, EKS and VMware® Cloud on AWS.

I am assuming the AWS OpenZFS uses EBS as the block storage backend, given the announcement that it can deliver 4GB/sec of throughput and 160,000 IOPS from the “drives” without caching. How the OpenZFS is provisioned to the AWS clients is well documented in this blog here. It is an absolutely joy (for me) to see the open source OpenZFS filesystem getting the validation and recognization from AWS. This is one hell of a filesystem.

But this blog isn’t about AWS FSx for OpenZFS with block storage. It is about what is coming, and eventually AWS FSx for OpenZFS could expand into AWS’s proficient S3 storage as well.  Can OpenZFS integrate with an S3 object storage backend? This blog looks into the burning question.

In the recently concluded OpenZFS Developer Summit 2021, one of the topics was “ZFS on Object Storage“, and the short answer is a resounding YES!

OpenZFS Developer Summit 2021

Continue reading