I like LTFS (Linear Tape File System). I was hoping it would take off but it has not. And looking at its future, its significance is becoming less and less relevant. I look if Cloud has been a factor in the possible demise of LTFS in the next few years.
What is LTFS?
In a nutshell, Linear Tape File System makes LTO tapes look like a disk with a file system. It takes a tape and divides it into 2 partitions:
Index Partition (XML Index Schema with file names, metadata and attributes details)
Data Partition (where the data resides)
Diagram from https://www.snia.org/sites/default/orig/SDC2011/presentations/tuesday/DavidPease_LinearTape_File_System.pdf
It has a File System module which is implemented in supported OS of Unix/Linux, MacOS and Windows. And the mounted file system “tape partition” shows up as a drive or device.
There were many attempts to kill off tapes and so far, none has been successful.
The flash frenzy has reached its zenith in 2016. We now no longer are interested in listening to storage technology vendors touting the power of solid state storage (NAND Flash included) over spinning drives.
The capacity of 3D NAND Flash SSDs has reached a whopping 15.3TB (that is even bigger than the 12TB 7200RPM HDDs of today), and with deduplication and compression, the storage efficiency has reached a conservative 4:1 or 5:1. Effective capacity of most mid-end storage arrays can easily reach 1-2 Petabytes.
And flash and hybrid platforms have reached maturity in these few short years. So what is next?
The landscape has obviously changed. The performance landscape, the capacity landscape and all related to the storage data points have changed. And the speed of SSDs together with the up-and-coming NVMe and NVDIMM technology in new storage array controllers are also shifting the data bottlenecks to another part of the architecture. The development of I/O communications and interfaces has to change as well, to take advantage of the asynchronous I/Os in storage tiering and caching using NAND Flash.
With this mature and well understood landscape, it is time to take Flash to the next level. This next level comes in the form of an exciting end-user conference in Singapore on 25th April 2017. It is called FlashForward.
The 2016 FlashForward event in Europe has already garnered great support from the cream of the storage technologists around the world, and had fantastic feedbacks from the end-user attendees. That FlashForward event has also seen the birth of an international business and technology exchange in its inaugural introduction. Yes, it is time to learn from the field experts, and it is time to build on the Flash Platform for new Data Services.
From the sponsorship package brochure I have received, it is definitely an event not to be missed.
The FlashForward Conference in Singapore is exquisitely procured by Evito Ltd, under the stewardship of Mr. Paul Talbut. Paul is a very seasoned veteran in the global circuit as an SNIA director of several initiatives. He has been immensely involved in the development of several SNIA chapters around the world, including South Asia, Malaysia, India, China, and even Brazil. He also leads by example with the SNIA Global Steering Committee (GSC); he is the SNIA Global Education Director and at one time, SNIA DPCO (Data Protection & Capacity Optimization) global proctor.
I have had the honour working with Paul for almost 8 years now, and I am sure he will lead the FlashForward Conference with valuable insights and experiences.
This is probably the greatest period for the industry and end users to get involved in the FlashForward Conference. For one, it is endorsed by SNIA, the vendor-neutral association which has been the growth beacon of the storage networking industry.
Secondly, it is the perfect opportunity for technology vendors to build their mindshare with end users and customers. And with the endorsement of the independent field experts and technology practitioners, end users would have a field day garnering approvals for their decisions, as well as learning the best practices to build upon the Flash technology they have implemented in their data center space.
The sponsorship packages are listed below, and I do encourage technology vendors, especially the All-Flash vendors to use the FlashForward conference as a platform to build their mindshare, and most of all, their branding. Continue reading →
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:
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 😉
The next all-Flash product in my review list is SolidFire. Immediately, the niche that SolidFire is trying to carve out is obvious. It’s not for regular commercial customers. It is meant for Cloud Service Providers, because the features and the technology that they have innovated are quite cloud-intended.
Are they solid (pun intended)? Well, if they have managed to secure a Series B funding of USD$25 million (total of USD$37 million overall) from VCs such as NEA and Valhalla, and also angel investors such as Frank Slootman (ex-Data Domain CEO) and Greg Papadopoulus(ex-Sun Microsystems CTO), then obviously there is something more than meets the eye.
The one thing I got while looking up SolidFire is there is probably a lot of technology and innovation behind their Nodes and their Element OS. They hold their cards very, very close to their chest, and I couldn’t not get much good technology related information from their website or in Google. But here’s a look of how the SolidFire is like:
The SolidFire only has one product model, and that is the 1U SF3010. The SF3010 has 10 x 2.5″ 300GB SSDs giving it a raw total of 3TB per 1U. The minimum configuration is 3 nodes, and it scales to 100 nodes. The reason for starting with 3 nodes is of course, for redundancy. Each SF3010 node has 8GB NVRAM and 72GB RAM and sports 2 x 10GbE ports for iSCSI connectivity, especially when the core engineering talents were from LeftHand Networks. LeftHand Networks product is now HP P4000. There is no Fibre Channel or NAS front end to the applications.
Each node runs 2 x Intel Xeon 2.4GHz 6-core CPUs. The 1U height is important to the cloud provider, as the price of floor space is an important consideration.
Aside from the SF3010 storage nodes, the other important ingredient is their SolidFire Element OS.
Cloud storage needs to be available. The SolidFire Helix Self-Healing data protection is a feature that is capable of handling multiple concurrent failures across all levels of their storage. Data blocks are replicated randomly but intelligently across all storage nodes to ensure that the failure or disruption of access to a particular data block is circumvented with another copy of the data block somewhere else within the cluster. The idea is not new, but effective because solutions such as EMC Centera and IBM XIV employ this idea in their data availability. But still, the ability for self-healing ensures a very highly available storage where data is always available.
To address the efficiency of storage, having 3TB raw in the SF3010 is definitely not sufficient. Therefore, the Element OS always have thin provision, real-time compression and in-line deduplication turned on. These features cannot be turned off and operate at a fine-grained 4K blocks. Also important is the intelligence to reclaim of zeroed blocks, no-reservation, and no data movement in these innovations. This means that there will be no I/O impact, as claimed by SolidFire.
But the one feature that differentiates SolidFire when targeting storage for Cloud Service Providers is their guaranteed volume level Quality of Service (QOS). This is important and SolidFire has positioned their QOS settings into an advantage. As best practice, Cloud Service Providers should always leverage the QOS functionality to improve their storage utilization
The QOS has:
Minimum IOPS – Lower IOPS means lower performance priority (makes good sense)
Burst IOPS – for those performance spikes moments
Maximum and Burst MB/sec
The combination of QOS and storage capacity efficiency gives SolidFire the edge when cloud providers can scale both performance and capacity in a more balanced manner, something that is not so simple with traditional storage vendors that relies on lots of spindles to achieve IOPS performance sacrificing capacity in the process. But then again, with SSDs, the IOPS are plenty (for now). SolidFire does not boast performance numbers of millions of IOPS or having throughput into the tens of Gigabytes like Violin, Virident or Kaminario, but what they want to be recognized as the cloud storage as it should be in a cloud service provider environment.
SolidFire calls this Performance Virtualization. Just as we would get to carve our storage volumes from a capacity pool, SolidFire allows different performance profiles to be carved out from the performance pool. This gives SolidFire the ability to mix storage capacity and storage performance in a seemingly independent manner, customizing the type of storage bundling required of cloud storage.
In fact, SolidFire only claims 50,000 IOPS per storage node (including the IOPS means for replicating data blocks). Together with their native multi-tenancy capability, the 50,000 or so IOPS will align well with many virtualized applications, rather than focusing on a 10x performance improvement on a single applications. Their approach is more about a more balanced and spread-out I/O architecture for cloud service providers and the applications that they service.
Their management is also targeted to the cloud. It has a REST API that integrates easily into OpenStack, Citrix CloudStack and VMware vCloud Director. This seamless and easy integration, is more relevant because the CSPs already have their own management tools. That is why SolidFire API is a REST-ready, integration ready to do just that.
The power of the SolidFire API is probably overlooked by storage professionals trained in the traditional manner. But what SolidFire API has done is to provide the full (I mean FULL) capability of the management and provisioning of the SolidFire storage. Fronting the API with REST means that it is real easy to integrate with existing CSP management interface.
Together with the Storage Nodes and the Element OS, the whole package is aimed towards a more significant storage platform for Cloud Service Providers(CSPs). Storage has always been a tricky component in Cloud Computing (despite what all the storage vendors might claim), but SolidFire touts that their solution focuses on what matters most for CSPs.
CSPs would want to maximize their investment without losing their edge in the cloud offerings to their customers. SolidFire lists their benefits in these 3 areas:
The edge in cloud storage is definitely solid for SolidFire. Their ability to leverage on their position and steering away from other all-Flash vendors’ battlezone could all make sense, as they aim to gain market share in the Cloud Service Provider space. I only wish they can share more about their technology online.
Fortunately, I found a video by SolidFire’s CEO, Dave Wright which gives a great insight about SolidFire’s technology. Have a look (it’s almost 2 hour long):
[2 hours later]: Phew, I just finished the video above and the technology is solid. Just to summarize,
No RAID (which is a Godsend for service providers)
Aiming for USD5.00 or less per Gigabyte (a good number!)
General availability in Q1 2012
Lots of confidence about the superiority of their technology, as portrayed by their CEO, Dave Wright.
Nowadays, the capacity of the hard disk drives (HDDs) are really big. 3TB is out and 4TB is in the horizon. What’s next?
For small-medium businesses in Malaysia, depending on their data requirements and applications, 3-10TB is pretty sufficient and with room to grow as well. Therefore, a 6TB requirement can be easily satisfied with 2 x 3TB HDDs.
If I were the customer, why would I buy a storage array, with the software licenses and other stuff that will not only increase my cost of equipment acquisition and data management, it will also increase the complexity of my IT infrastructure? I could just slot HDDs into my existing server, RAID it with RAID-0 (not a good idea but to save costs, most customers would do that) and I have a 6TB volume! It’s cheaper, easier to manage with Windows or Linux, and my system administrator doesn’t have to fuss about lack of storage experience.
And RAID isn’t really keeping up with the tremendous growth of HDD’s capacity as well. In fact, RAID is at risk. RAID (especially RAID 5/6) just cannot continue provide the LUN or volume reliability and data availability because it just takes too damn long to rebuild the volume after the failure of a disk.
Back in the days where HDDs were less than 500GB, RAID-5 would still hold up but after passing the 1TB mark, RAID-6 became more prevalent. But now, that 1TB has ballooned to 3TB and RAID-6 is on shaky ground. What’s next? RAID-7? ZFS has RAID-Z3, triple parity but come on, how many vendors have that? With triple parity or stronger RAID (is there one?), the price of the storage array is going to get too costly.
Experts have been speaking about parity-declustering, but that’s something that a few vendors have right now. Panasas, founded by one of forefathers of RAID, Garth Gibson, comes to mind. In fact, Garth Gibson and Mark Holland of Cargenie-Mellon University’s Parallel Data Lab (PDL) presented a paper about parity-declustering more than 10 years ago.
Let’s get back to our storage fatty. Yes, our storage is getting fat, obese, rotund or whatever you want to call it. And storage vendors have been pushing a concept in hope that storage administrators and customers can take advantage of it. It is called Storage Optimization or Storage Efficiency.
Here are a few ways you can consider to put your storage on a diet.
Tapes and SSDs
To me, compression has not taken the storage world by storm. But then again, there aren’t many vendors that tout compression as a feature for storage optimization. Most of them rather prefer to push the darling of data reduction, data deduplication, as the main feature for save more space. Theoretically, data deduplication makes more sense when the data is inactive, and has high occurrence of duplicated data. That is why secondary storage such as backup deduplication targets like Data Domain, HP StoreOnce, Quantum DXi can publish 20:1 rates and over time, that rate can get even higher.
NetApp also has been pushing their A-SIS data deduplication on primary storage. Yes, it helps with the storage savings in primary but when the need for higher data transfer rates and time to access “manipulated” data (deduped or compressed), it is likely that compression is a better choice for primary, active data.
So who has compression? NetApp ONTAP 8.0.1 has compression now and IBM with its Storewize V7000 started as a compression device. Read about IBM Storewize in my blog here. Dell has Ocarina Networks, which was recently unleashed. I am a big fan of Ocarina Networks and I wrote about the technology in my previous blog. EMC, during the Celerra days of DART has compression but I don’t hear much about it in their VNX. Compression is there, believe me, embedded all the loads of EMC marketing.
Thin Provisioning is now a must-have and standard feature of all storage vendors. What is Thin Provisioning? The diagram below shows you:
In the past, storage systems aren’t so intelligent. You ask for 10TB, you are given 10TB and that 10TB is “deducted” from the storage capacity. That leads to wastage and storage inefficiencies. Today, Thin Provisioning will give you 10TB but storage capacity is consumed as it is being used. The capacity is not pre-allocated as in the past. Thin provisioning is a great diet pill for bloated storage projects.
Another up and coming feature is storage tiering. Storage tiering, when associated to storage optimization, should include hierarchical storage management (HSM) and tape-out as well. Storage optimization solutions should not offer only in the storage array itself. Storage tiering within the storage array is available with most vendors – IBM EasyTier, EMC FAST2, Dell Fluid Data Management and many others. But what about data being moved out of the storage array? What about reducing the capacity of the data online or near-line? Why not put them offline if there isn’t a need for it?
I term this as Active Archiving, something I learned while I was at EMC. Here’s a look at EMC’s style of Active Archiving:
Active Archiving promotes the concept of data archiving and is not unique only to EMC. Almost all storage vendors, either natively or with 3rd party vendors, can perform fairly efficient data archiving in one way or another. One of the software that I liked (and not unique!) is Quantum Stornext. Here’s a video of how Quantum Stornext helps reduce the fat of the storage.
With the single-copy sharing feature of Quantum Stornext to multiple disparate OSes, there are lesser duplicate files in storage as well.
Tapes have been getting a bad name in the past few years. It has been repositioned and repurposed as an archive medium rather than a backup medium. But tape is the greenest and most powerful storage diet pill around. And we should not be discount tapes because tapes are fighting back. Pretty soon you will be hearing about Linear Tape File System (LTFS). In a nutshell, Linear Tape File System (LTFS) allows you to use the tape almost as if it were a hard disk. You can drag and drop files from your server to the tape, see the list of saved files using a standard operating system directory (no backup software catalog needed), and use point and click to restore. How cool is that!
And Solid State Drives (SSDs) makes sense as well.
There are times that we need IOPS and using spinning drives, we have to set up many disk spindles to achieve the IOPS that we want. For example, using the diagram below from the godfather of storage, Greg Schulz,
The set of 16 spinning HDD drives on the left can only deliver 3,520 IOPS. The problem is, we have wasted a lot of disk space, as seen in the diagram below. This design, which most customer would be accustomed to, may look cheaper but in actual fact, is NOT.
If the price of a Fibre Channel HDD is RM2,000, the total of 16 would make up RM32,000.00. That is not inclusive of additional power and cooling and rack space and also the data management costs. Assuming the SSDs costs 5 times more than the Fibre Channel HDD. SSDs are capable of delivering very high IOPS. Here I am putting a modest 5,000 IOPS per SSDs. With just 2 SSDs (as the right design suggests), the total costs is only RM20,000. It has greater performance room to grow, and also savings in data management, power and cooling.
Folks, consider SSDs as part of your storage diet plan.
All these features are available, in whole or in part, and they are part of the storage technology offerings that is out there. With all these being said, are you doing something about it? Get off your lazy bum and start managing your storage and put your storage on a diet!!!
One of the things that peeved at the HP D2D Workshop a few days ago was this heading in the HP PowerPoint slides – “Deduplication – a fancy form of Compression”. Somehow it bothered me.
I have always placed both deduplication and compression into a bucket I called “Data Reduction“. Some vendors might call it Storage Economics, spinning it in a cooler manner. Either way, both attempt and succeed to reduce the capacity required to store the amount of data and this translates into benefits in storage management and network. With a smaller data set, lesser processing and capacity are required, likely speeding up the performance of the storage array. At the same time, the primary data backup set (you know, the data that you back up every night?) becomes smaller, making backup and restore faster (not necessarily, but you have to rehydrate the data from its reduced state). Another obvious benefit is the ability to transfer the smaller data set over the network more efficiently, compared to its original state and size, making Disaster Recovery more possible and so on.
I have always known that deduplication works with data objects using a differential method. Whether the data object is a file or a chunk of the file, deduplication attempts to differentiate similarities (duplicates), and store one copy of that object and have others referencing to the single object. The differentiation methods commonly used are hashing and delta differential. In hashing, MD-5 and SHA-1 are the popular hashing algorithms used, while in delta differentials, the data objects are compared (usually in a scrutinizing manner) to find the differences. The duplicates or similarities are discarded.
There are many factors involved in deduplication. It could be the types of data, the processing power required to do the deduplication task, and throughput of processing and so on and resulting in the different deduplication ratio and time required to complete the process. I am not going to delve into that as there are many vendors who will be able to articulate this, such as EMC Data Domain, HP D2D/VLS with its StoreOnce technology, Exagrid, Sepaton, Dell Ocarina Networks, NetApp, EMC Centera, CommVault Simpana, Symantec PureDisk, Symantec NetBackup, EMC Avamar and many more.
Meanwhile, compression (especially most commercial compression technology) are based on dictionary coding, a lossless data reduction algorithm. Note that I am using the term encoding rather than compression because factually, encoding is the right word. You can’t squeeze the data into a smaller size like you do with a real life object.
The technique works like this.
When being encoded, a bit/byte or a set of bytes are compared to a “dictionary” which is a pool of “words” in a data structure maintained by the encoding technology
If a match is found, the bit/byte or set of bytes is substituted by an “word”, usually a much shorter (hence smaller size) representation form of the bytes being encoded.
As the encoding process continues, more “dictionary words” are built into the “dictionary” based on the bytes already encoded. This is popularly known as the sliding window implementation.
The end result is the data is highly encoded (heavily replaced) by “dictionary words” and of a much smaller size.
One of the heavily implemented compression technique is based on the theory and methodology introduced by Lempel-Ziv and further enhanced by the Lempel-Ziv-Welch trio. A very good explanation of LZ method can be found here.
Both deduplication and compression have the same objective – that is to reduce the data size for more efficient storage. But both approach it from a different angle but they are by no means, exclusive. Both can be used to complement each other and further reduce the capacity required to store the data.
Deduplication usually works with larger data objects (chunks, files etc) while compression works harder at the lower level (byte range level). Deduplication is heavily deployed in secondary data sets (or backup) because you can find plenty of duplicates while in primary data sets (the data in production), deduplication and compression are deployed, either in a singular fashion or one after another. Deduplication is usually run as Step 1 and then Compression is run in Step 2.
So far, the only one that has impressed me for the primary data reduction is Ocarina Networks, which uses a 3 step approach in dedupe, compress and using specialized compactors to reduce the data even more. I have seen the ability of Ocarina reducing Schlumberger Geoframe and Petrel seismic data to more than 50%. That was impressive!
Having my bothered state satisfied, I guess having the say of “Deduplication – a fancy form of Compression” is someone else’s cup of tea. I would rather say “Deduplication – a fancy form of Data Reduction Technology” but I am not complaining as much I did before.
Wow, after an entire week off with the holidays, I am back and excited about the many happenings in the storage world.
One of the more prominent news was the announcement of Pure Storage launching its enterprise storage array build entirely with flash-based solid state drives. In addition to that, there were other start-ups who were also offering SSDs storage arrays. The likes of Nimbus Data, Avere, Violin Memory Systems all made the news as well as the grand daddy of solid state storage arrays, Texas Memory Systems.
The first thing that came to my mind was, “Wow, this is great because this will push down the $/GB of SSDs closer to the range of $/GB for spinning disks”. But then skepticism crept in and I thought, “Do we really need an entire enterprise storage array of SSDs? That’s going to cost the world”.
At the same time, we in the storage industry knows that no piece of data are alike. They can be large, small, random, sequential, accessed frequently or infrequently and so on. It is obviously better to tier the storage, using SSDs for Tier 0, 10K/15K RPM spinning HDDs for Tier 1, SATA for Tier 2 and perhaps tape for the archive tier. I was already tempted to write my pessimism on Pure Storage when something interesting caught my attention.
Besides the usual marketing jive of sub-milliseconds, predictable latency, green messaging, global inline deduplication and compression and built-in data integrity into its Purity Operating Environment (POE), I was very surprised to find the team behind Pure Storage. Here’s their line-up
Scott Dietzen, CEO – starting from principal technologist of Transarc (sold to IBM), principal architect of Web Logic (sold to BEA Systems), CTO of BEA (sold to Oracle), CTO of Zimbra (sold to Yahoo! and then to VMware)
John “Coz” Colgrove, Founder & CTO – Veritas Fellow, CTO of Symantec Data Management group, principal architect of Veritas Volume Manager (VxVM) and Veritas File System (VxFS) and holder of 70 patents
John Hayes, Founder & Chief Architect – formerly of Yahoo! office of Chief Technologist
Bob Wood, VP of Engineering – Formerly NetApp’s VP of File System Engineering,
Michael Cornwell, Director of Technology & Strategy – formerly the lead technologist of Sun Microsystems’ Sun Storage F5100 Flash Array and also Quantum’s storage architect for their storage telemetry, VTL and DXi solutions
Ko Yamamoto, VP of System Engineering – previously NetApp’s director of platform engineering, Quantum DXi director of hardware engineering, and also key contributor to 4-generations of Tandem NonStop technology
In addition to that, there are 3 key individual investors worth mentioning
Diane Green – Founder of VMware and former CEO
Dr. Mendel Rosenblum – Founder and former Chief Scientist and creator of VMware
Frank Slootman – formerly CEO of Data Domain (acquired by EMC)
All these industry big guns are flocking to Pure Storage for a reason and it looks to me that Pure Storage ain’t your ordinary, run-of-the-mill enterprise storage company. There’s definitely more than meet the eye.
On top of the enterprise storage array platform is Pure Storage’s Purity Operating Environment (POE). POE focuses on 3 key storage services which are
High Performance Data Reduction
Mission Critical Reliability
Predictable Sub-millisecond Performance
After going through the deep-dive videos by Pure Storage’s CTO, John Colgrove, they are very much banking the success of their solution around SSDs. Everything that they have done is based on SSDs. For example, in order to achieve a larger capacity as well as a much cheaper $/GB, the data reduction techniques in global deduplication, high compression and also fine grained thin provision of 512 bytes are used. By trading off IOPS (which SSDs have plenty since they are several times faster than conventional spinning disks), a larger usable capacity is achieved.
In their RAID 3D, they also incorporated several high reliability techniques and data integrity algorithm that are specifically for SSDs. One note that was mentioned was that traditional RAID and especially the parity-based RAID levels were designed in the beginning to protect against an entire device failure. However, in SSDs, the failure does not necessarily occur in the entire device. Because of the way SSDs are built, the failure hotspots tend to happen at the much more granular bit level of the SSDs. The erase-then-write techniques that are inherent in NAND Flash SSDs causes the bit error rate (BER) of the SSD device to go up as the device ages. Therefore, it is more likely to get a read/write error from within the SSDs memory itself rather than having the entire SSD device failing. Pure Storage RAID 3D is meant to address such occurrences of bit errors.
I spoke a bit of storage tiering earlier in this article because every corporation employs storage tiering to be financially responsible. However, John Colgrove’s argument was why tier the storage when there’s plentiful of IOPS and the $/GB is comparable to spinning disks. That is true is when the $/GB of SSDs can match the $/GB of spinning disks. Factors we must also taken into account is the rack-space savings using the smaller profile disks of SSDs, the power-savings costs of SSDs versus conventional HDD-based enterprise storage arrays. In its entirety, there are strong indications that the $/GB of SSD-based systems to match or perhaps lower the $/GB of HDD-based systems. And since the IOPS requirement levels of present-day applications have not demanded super-high IOPS and multi-core processing is cheap, there’s plenty of head-room for Pure Storage and other similar enterprise storage array companies to grow.
The tides are changing for the storage industry and it is good to see a start-up like Pure Storage boldly coming forth to announce their backing for SSDs. It’s good for the consumer and good for the industry. But more importantly, they are driving innovations to rethink of how we build storage arrays. I am looking forward to more things to come.