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December 22 2015: I kept this blog in draft for 6 months. Now I am releasing it as NetApp acquires Solidfire.
The above is an old Chinese adage which means “True Gold fears no Fire“. That is how I would describe my revisited view and assessment of SolidFire, a high performance All-Flash array vendor which is starting to make its presence felt in South Asia.
I first blogged about SolidFire 3 years ago, and I have been following the company closely as more and more All-Flash array players entered the market over the 3 years. Many rode on the hype and momentum of flash storage, and as a result, muddied and convoluted the storage infrastructure market understanding. It seems to me spin marketing ruled the day and users could not make a difference between vendor A and vendor B, and C and D, and so on….
I have been often asked, which is the best All-Flash array today. I have always hesitated to say which is the best because there aren’t much to say, except for 2-3 well entrenched vendors. Pure Storage and EMC XtremIO come to mind but the one that had stayed under the enterprise storage radar was SolidFire, until now.
I hate the fact that I am bursting the big bubble brewing about Hyper Convergence (HC). I urge all to look past the hot air and hype frenzy that are going on, because in the end, the HC platforms have to be aligned and congruent to the organization’s data architecture and business plans.
The announcement of Gartner’s latest Magic Quadrant on Integrated Systems (read hyper convergence) has put Nutanix as the leader of the pack as of August 2015. Clearly, many of us get caught up because it is the “greatest feeling in the world”. However, this faux feeling is not reality because there are many factors that made the pack leaders in the Magic Quadrant (MQ).
First of all, the MQ is about market perception. There is no doubt that the pack leaders in the Leaders Quadrant have earned their right to be there. Each company’s revenue, market share, gross margin, company’s profitability have helped put each as leaders in the pack. However, it is also measured by branding, marketing, market perception and acceptance and other intangible factors.
Secondly, VMware EVO: Rail has split the market when EMC has 3 HC solutions in VCE, ScaleIO and EVO: Rail. Cisco wanted to do their own HC piece in Whiptail (between the 2014 MQ and 2015 MQ reports), and closed down Whiptail when their new CEO came on board. NetApp chose EVO: Rail and also has the ever popular FlexPod. That is why you see that in this latest MQ report, NetApp and Cisco are interpreted independently whereas in last year’s report, it was Cisco/NetApp. Market forces changed, and perception changed. Continue reading
It has been quite an interesting 2 decades.
In the beginning (starting in the early to mid-90s), SAN (Storage Area Network) was the dominant architecture. DAS (Direct Attached Storage) was on the wane as the channel-like throughput of Fibre Channel protocol coupled by the million-device addressing of FC obliterated parallel SCSI, which was only able to handle 16 devices and throughput up to 80 (later on 160 and 320) MB/sec.
NAS, defined by CIFS/SMB and NFS protocols – was happily chugging along the 100 Mbit/sec network, and occasionally getting sucked into the arguments about why SAN was better than NAS. I was already heavily dipped into NFS, because I was pretty much a SunOS/Solaris bigot back then.
When I joined NetApp in Malaysia in 2000, that NAS-SAN wars were going on, waiting for me. NetApp (or Network Appliance as it was known then) was trying to grow beyond its dot-com roots, into the enterprise space and guys like EMC and HDS were frequently trying to put NetApp down.
“It’s a toy…” was the most common jibe I got in regular engagements until EMC suddenly decided to attack Network Appliance directly with their EMC CLARiiON IP4700. EMC guys would fondly remember this as the “NetApp killer“. Continue reading
We are all familiar with the concept of data archiving. Passive data gets archived from production storage and are migrated to a slower and often, cheaper storage medium such tapes or SATA disks. Hence the terms nearline and offline data are created. With that, IT constantly reminds users that the archived data is infrequently accessed, and therefore, they have to accept the slower access to passive, archived data.
The business conditions have certainly changed, because the need for data to be 100% online is becoming more relevant. The new competitive nature of businesses dictates that data must be at the fingertips, because speed and agility are the new competitive advantage. Often the total amount of data, production and archived data, is into hundred of TBs, even into PetaBytes!
The industries I am familiar with – Oil & Gas, and Media & Entertainment – are facing this situation. These industries have a deluge of files, and unstructured data in its archive, and much of it dormant, inactive and sitting on old tapes of a bygone era. Yet, these files and unstructured data have the most potential to be explored, mined and analyzed to realize its value to the organization. In short, the archived data and files must be democratized!
The flip side is, when the archived files and unstructured data are coupled with a slow access interface or unreliable storage infrastructure, the value of archived data is downgraded because of the aggravated interaction between access and applications and business requirements. How would organizations value archived data more if the access path to the archived data is so damn hard???!!!
An interesting solution fell upon my lap some months ago, and putting A and B together (A + B), I believe the access path to archived data can be unbelievably of high performance, simple, transparent and most importantly, remove the BLOODY PAIN of FILE AND DATA MIGRATION! For storage administrators and engineers familiar with data migration, especially if the size of the migration is into hundreds of TBs or even PBs, you know what I mean!
I have known this solution for some time now, because I have been avidly following its development after its founders left NetApp following their Spinnaker venture to start Avere Systems.
The last of the Storage Field Day 6 on November 7th took me and the other delegates to NEC. There was an obvious, yet eerie silence among everyone about this visit. NEC? Are you kidding me?
NEC isn’t exactly THE exciting storage company in the Silicon Valley, yet I was pleasantly surprised with their HydraStorprowess. It is indeed quite a beast, with published numbers of backup throughput of 4PB/hour, and scales to 100PB of capacity. Most impressive indeed, and HydraStor deserves this blogger’s honourable architectural dissection.
HydraStor is NEC’s grid-based, scale-out storage platform with an object storage backend. The technology, powered by the DynamicStor ™ software, a distributed file system laid over the HydraStor grid architecture. At the same time, it has the DataRedux™ technology that provides the global in-line deduplication as the HydraStor ingests data for data protection, replication, archiving and WORM purposes. It is a massive data consolidation platform, storing gazillion loads of data (100PB you say?) for short-term and long-term retention and recovery.
The architecture is indeed solid, and its data availability goes beyond traditional RAID-level resiliency. HydraStor employs their proprietary erasure coding, called Distributed Resilient Data™. The resiliency knob can be configured to withstand 6 concurrent disks or nodes failure, but by default configured with a resiliency level of 3.
We can quickly deduce that DynamicStor™, DataRedux™ and Distributed Resilient Data™ are the technology pillars of HydraStor. How do they work, and how do they work together?
Let’s look a bit deeper into the HydraStor architecture.
HydraStor is made up of 2 types of nodes:
- Accelerator Nodes
- Storage Nodes
The Accelerator Nodes (AN) are the access nodes. They interface with the HydraStor front end, which could be CIFS, NFS or OST (Open Storage Technology). The AN nodes chunks the in-coming data and performs in-line deduplication at a very high speed. It can reach speed of 300TB/hour, which is blazingly fast!
The AN nodes also runs DynamicStor™, handling the performance heavy-lifting portion of HydraStor. The chunked data from the AN nodes are then passed on to the Storage Nodes (SN), where they are further “deduped in-line” to determined if the chunks are unique or not. It is a two-step inline deduplication process. Below is a diagram showing the ANs built above the SNs in the HydraStor grid architecture.
The HydraStor grid architecture is also a very scalable architecture, allow the dynamic scale-in and scale-out of both ANs and SNs. AN nodes and SN nodes can be added or removed into the system, auto-configuring and auto-optimizing while everything stays online. This capability further strengthens the reliability and the resiliency of the HydraStor.
Moving on to DataRedux™. DataRedux™ is HydraStor’s global in-line data deduplication technology. It performs dedupe at the sub-file level, with variable length window. This is performed at the AN nodes and the SN nodes level,chunking and creating unique hash values. All unique chunks are further compressed with a modified LZ compression algorithm, shrinking the data to its optimized footprint on the disk storage. To maintain the global in-line deduplication, the hash table is available across the HydraStor cluster.
The unique data chunk resulting from deduplication and compression are then written to disks using the configured Distributed Resilient Data™ (DRD) algorithm, at its set resiliency level.
At the junction of DRD, with erasure coding parity, the data is broken up into multiples of fragments and assigned a parity to a grouping of fragments. If the resiliency level is set to 3 (the default), the data is broken into 12 pieces, 9 data fragments + 3 parity fragments. The 3 parity fragments corresponds to the resiliency level of 3. See diagram below of the 12 fragments spread across a group of selected disks in the storage pool of the Storage Nodes.
If the HydraStor experiences a failure in the disks or nodes, and has resulted in the loss of a fragment or fragments, the DRD self-healing function will auto-rebuild and auto-reconfigure the recovered fragments in another set of disks, maintaining the level of 3 parities.
The resiliency level, as mentioned earlier, can be set up to 6, boosting the HydraStor survival factor of 6 disks or nodes failure in the grid. See below of how the autonomous DRD recovery works:
Despite lacking the razzle dazzle of most Silicon Valley storage startups and upstarts, credit be given where credit is due. NEC HydraStor is indeed a strong show stopper.
However, in a market that is as fickle as storage, deduplication solutions such as HydraStor, EMC Data Domain, and HP StoreOnce, are being superceded by Copy Data Management technology, touted by Actifio. It was rumoured that EMC restructured their entire BURA (Backup Recovery Archive) division to DPAD (Data Protection and Availability Division) to go after the burgeoning copy data management market.
It would be good if NEC can take notice and turn their HydraStor “supertanker” towards the Copy Data Management market. That would be something special to savour.
P/S: NEC. Sorry about the title. I just couldn’t resist it 😉
I was reading a great article by Frank Denneman about storage intelligence moving up the stack. It was pretty much in line with what I have been observing in the past 18 months or so, about the storage pendulum having swung back to DAS (direct attached storage). To be more precise, the DAS form factor I am referring to are physical server hardware that houses many disk drives.
Like it or not, the hypervisor has become the center of the universe in the IT space. VMware has become the indomitable force in the hypervisor technology, with Microsoft Hyper-V playing catch-up. The seismic shift of these 2 hypervisor technologies are leading storage vendors to place them on to the altar and revering them as deities. The others, with the likes of Xen and KVM, and to lesser extent Solaris Containers aren’t really worth mentioning.
This shift, as the pendulum swings from networked storage back to internal “direct-attached” storage are dictated by 4 main technology factors:
- The x86 server architecture
- Scale-out architecture
- Flash-based storage technology
Anyone remember Thumper? Not the Disney character from the Bambi movie!
When the SunFire X4500 (aka Thumper) was first released in (intermission: checking Wiki for the right year) in 2006, I felt that significant wound inflicted in the networked storage industry. Instead of the usual 4-8 hard disk drives in the all the industry servers at the time, the X4500 4U chassis housed 48 hard disk drives. The design and architecture were so astounding to me, I even went and bought a 1U SunFire X4150 for my personal server collection. Such was my adoration for Sun’s technology at the time.
The Riverbed SteelFusion (aka Granite) impressed me the moment it was introduced to me 2 years ago. I remembered that genius light bulb moment well, in December 2012 to be exact, and it had left its mark on me. Like I said last week in my previous blog, the SteelFusion technology is unique in the industry so far and has differentiated itself from its WAN optimization competitors.
To further understand the ability of Riverbed SteelFusion, a deeper inspection of the technology is essential. I am fortunate to be given the opportunity to learn more about SteelFusion’s technology and here I am, sharing what I have learned.
What does the technology of SteelFusion do?
Riverbed SteelFusion takes SAN volumes from supported storage vendors in the central datacenter and projects the storage volumes (aka LUNs)to applications and hosts at the remote branches. The technology requires a paired relationship between SteelFusion Core (in the centralized datacenter) and SteelFusion Edge (at the branch). Both SteelFusion Core and Edge are fronted respectively by the Riverbed SteelHead WAN optimization device, to deliver the performance required.
The diagram below gives an overview of how the entire SteelFusion network architecture is like:
The word “CONVERGENCE” is boiling over as the IT industry goes gaga over darlings like Simplivity and Nutanix, and the hyper-convergence market. Yet, if we take a step back and remove our emotional attachment from the frenzy, we realize that the application and implementation of hyper-convergence technologies forgot one crucial element – The other people and the other offices!
ROBOs (remote offices branch offices) are part of the organization, and often they are given the shorter end of the straw. ROBOs are like the family’s black sheeps. You know they are there but there is little mention of them most of the time.
Of course, through the decades, there are efforts to consolidate the organization’s circle to include ROBOs but somehow, technology was lacking. FTP used to be a popular but crude technology that binds the branch offices and the headquarter’s operations and data services. FTP is still used today, in countries where network bandwidth costs a premium. Data cloud services are beginning to appear of part of the organization’s outreaching strategy to include ROBOs but the fear of security weaknesses, data breaches and misuses is always there. Often, concerns of the weaknesses of the cloud overcome whatever bold strategies concocted and designed.
For those organizations in between, WAN acceleration/optimization techonolgy is another option. Companies like Riverbed, Silverpeak, F5 and Ipanema have addressed the ROBOs data strategy market well several years ago, but the demand for greater data consolidation and centralization, tighter and more effective data management and data control to meet the data compliance and data governance requirements, has grown much more sophisticated and advanced. Continue reading
Early last week, I had a catch up with my friend. He was excited to share with me the new company he just joined. It was ProphetStor. It was a catchy name and after our conversation, I have decided to spend a bit of my weekend afternoon finding out more about the company and its technology.
From another friend at FalconStor, I knew of this company several months ago. Ex-FalconStor executives have ventured to found ProphetStor as the next generation of storage resource orchestration engine. And it has found a very interesting tack to differentiate from the many would-bes of so-called “software-defined storage” leaders. ProphetStor made their early appearance at the OpenStack Summit in Hong Kong back in November last year, positioning several key technologies including OpenStack Cinder, SNIA CDMI (Cloud Data Management Interface) and SMI-S (Storage Management Initiative Specification) to provide federation of storage resources discovery, provisioning and automation.
The federation of storage resources and services solution is aptly called ProphetStor Federator. The diagram I picked up from the El Reg article presents the Federator working with different OpenStack initiatives quite nicely below: There are 3 things that attracted me to the uniqueness of ProphetStor.
1. The underlying storage resources, be it files, objects, or blocks, can be presented and exposed as Cinder-style volumes.
2. The ability to define the different performance capabilities and SLAs (IOPS, throughput and latency) from the underlying storage resources and matching them to the right application requirements.
3. The use of SNIA of SMI-S and CDMI Needless to say that the Federator software will abstract the physical and logical structures of any storage brands or storage architectures, giving it a very strong validation of the “software-defined storage (SDS)” concept.
While the SDS definition is still being moulded in the marketplace (and I know that SNIA already has a draft SDS paper out), the ProphetStor SDS concept does indeed look similar to the route taken by EMC ViPR. The use of the control plane (ProphetStor Federator) and the data plane (underlying physical and logical storage resource) is obvious.
I wrote about ViPR many moons ago in my blog and I see ProphetStor as another hat in the SDS ring. I grabbed the screenshot (below) from the ProphetStor website which I thought did beautifully explained what ProphetStor is from 10,000 feet view.
The Cinder-style volume is a class move. It preserves the sanctity of many enterprise applications which still need block storage volumes but now it comes with a twist. These block storage volumes now will have different capability and performance profiles, tagged with the relevant classifications and SLAs.
And this is where SNIA SMI-S discovery component is critical because SMI-S mines these storage characteristics and presents them to the ProphetStor Federator for storage resource classification. For storage vendors that do not have SMI-S support, ProphetStor can customize the relevant interfaces to the proprietary API to discover the storage characteristics.
On the north-end, SNIA CDMI works with the ProphetStor Federator’s Offer & Provisioning functions to bundle wrap various storage resources for the cloud and other traditional storage network architectures.
I have asked my friend for more technology deep-dive materials (he has yet to reply me) of ProphetStor to ascertain what I have just wrote. (Simon, you have to respond to me!)
This is indeed very exciting times knowing ProphetStor as one of the early leaders in the SDS space. And I like to see ProphetStor go far with this.
Now let us pray … because the prophet has arrived.