Screaming IOP performance with StarWind’s new NVMeoF software & Optane SSDs

Was at SFD17 last week in San Jose and we heard from StarWind SAN (@starwindsan) and their latest NVMeoF storage system that they have been working on. Videos of their presentation are available here. Starwind is this amazing company from the Ukraine that have been developing software defined storage.

They have developed their own NVMe SPDK for Windows Server. Intel doesn’t currently offer SPDK for Windows today, so they developed their own. They also developed their own initiator (CentOS Linux) for NVMeoF. The target system was a multicore server running Windows Server with a single Optane SSD that they used to test their software.

Extreme IOP performance consumes cores

During their development activity they tested various configurations. At the start of their development they used a Windows Server with their NVMeoF target device driver. With this configuration and on a bare metal server, they found that they could max out the Optane SSD at 550K 4K random write IOPs at 0.6msec to a single Optane drive.

When they moved this code directly to run under a Hyper-V environment, they were able to come close to this performance at 518K 4K write IOPS at 0.6msec. However, this level of IO activity pegged 100% of 8 cores on their 40 core server.

More IOPs/core performance in user mode

Next they decided to optimize their driver code and move as much as possible into user space and out of kernel space, They continued to use Hyper-V. With this level off code, they were able to achieve the same performance as bare metal or ~551K 4K random write IOP performance at the 0.6msec RT and 2.26 GB/sec level. However, they were now able to perform only pegging 2 cores. They expect to release this initiator and target software in mid October 2018!

They converted this functionality to run under ESX/VMware and were able to see much the same 2 cores pegged, ~551K 4K random write IOPS at 0.6msec RT and 2.26 GB/sec. They will have the ESXi version of their target driver code available sometime later this year.

Their initiator was running CentOS on another server. When they decided to test how far they could push their initiator, they were able to drive 4 Optane SSDs at up to ~1.9M 4K random write IOP performance.

At SFD17, I asked what they could have done at 100 usec RT and Max said about 450K IOPs. This is still surprisingly good performance. With 4 Optane SSDs and consuming ~8 cores, you could achieve 1.8M IOPS and ~7.4GB/sec. Doubling the Optane SSDs one could achieve ~3.6M IOPS, with sufficient initiators and target cores with ~14.8GB/sec.

Optane based super computer?

ORNL Summit super computer, the current number one supercomputer in the world, has a sustained throughput of 2.5 TB/sec over 18.7K server nodes. You could do much the same with 337 CentOS initiator nodes, 337 Windows server nodes and ~1350 Optane SSDs.

This would assumes that Starwind’s initiator and target NVMeoF systems can scale but they’ve already shown they can do 1.8M IOPS across 4 Optane SSDs on a single initiator server. Aand I assume a single target server with 4 Optane SSDs and at least 8 cores to service the IO. Multiplying this by 4 or 400 shouldn’t be much of a concern except for the increasing networking bandwidth.

Of course, with Starwind’s Virtual SAN, there’s no data management, no data protection and probably very little in the way of logical volume management. And the ORNL Summit supercomputer is accessing data as files in a massive file system. The StarWind Virtual SAN is a block device.

But if I wanted to rule the supercomputing world, in a somewhat smallish data center, I might be tempted to put together 400 of StarWind NVMeoF target storage nodes with 4 Optane SSDs each. And convert their initiator code to work on IBM Spectrum Scale nodes and let her rip.

Comments?

Cloudlets at the edge

Read an article (Never heard of Edge Computing….) this week on ATT’s presentations at their Spark Conference.  Apparently, ATT is saying that the problem with AR, VR/immersive gaming, self-driving cars, drones, etc. has been two fold, lack of bandwidth and processing latency.

The long latency issue comes from having current processing  for these devices being done mostly at cloud data centers, 100s of miles away from the device doing the work.

The upcoming 5G rollout should hopefully solve the bandwidth problem (for now at least) but the processing latency issue can only be dealt with by moving compute closer to where it’s needed.

A couple of weeks back I was at VMworld and one of the big announcements there was vSphere supporting 64 bit ARM processors. Pat and others talked up the coming edge processing tsunami, that will overtake IT as we know it today and bring significant benefits to everything from traffic management, to infrastructure maintenance, to better security for all, etc. Windows Server has been ported to ARM for Azure apps  for a while now but I don’t know if it’s been slated for external release

The new edge

Up until this point, I had always considered edge devices as sensors and other equipment embedded in buildings, land, sea, air, machinery, etc., that provided useful, realworld information/status about their environments and when  somethings gone wrong, that has to be fixed. I hadn’t really saw AR and, VR immersive gaming as an edge issue.  However, drones and self-driving cars are edge devices.

AR seems to rely on smart phone levels of computation and VR today is usually tethered to a desktop PC or Mac. But to take AR and VR to the next level, processing requirements need to go up.

Self-driving cars have their own army of compute processing and sensors to deal with realtime road recognition and accident avoidance. Drones have smart phone levels of compute onboard and a nearby laptop for additional processing and control support. Not sure that edge processing requirements for these devices is increasing but I’m no expert.

But, they all need more low-latency computation to become more effective, they all require lot’s of bandwidth and some of them at least, can only perform well, if both of these requirements are solved.

CloudLets

ATT has been experimenting with neighborhood data centers, test zones or cloudlets to supply this new,  low-latency processing.

These are apparently local (edge) mini-datacenters that host edge electronics gear for to \ow latency latency processing. ATT has one current test zone (or cloudlet) set up in Silicon Valley and has plans to roll out more across the US.

Up until this point, I thought edge processing would be solved by moving AI and other compute resources out to the devices themselves (see my AI processing at the edge post). Moore’s law would allow today’s compute capabilities to be embedded in low-power edge devices in a decade or so.

But why wait. If you can setup a mini-(ARM based)-data center in a  neighborhood cell-phone/telephone/cable/electrical cabinet, running vSphere or Windows virtualization, with high speed networking data connections to edge devices and the cloud, you can get by with less compute processing at the edge devices, enjoy low-latency responsiveness and use less cloud resources to boot.

~~~~

Doesn’t this mean we need mini-racklets, to stack our mini cloudlets compute resources, something like 9.5″ wide and 0.5U shelving.

Just when I thought (edge) decentralization would take over compute again, cloudlets come to take it back again.

Photo Credit(s): L10000901-Edit|Guide van Nispen

Augmented Reality RFid Cup|JeanBaptisteParis

The Great Escape|Edward Webb

 

Photonic or Optical FPGAs on the horizon

Read an article this past week (Toward an optical FPGA – programable silicon photonics circuits) on a new technology that could underpin optical  FPGAs. The technology is based on implantable wave guides and uses silicon on insulator technology which is compatible with current chip fabrication.

How does the Optical FPGA work

Their Optical FPGA is based on an eraseable direct coupler (DC) built using GE (Germanium) ion implantation. A DC is used when two optical waveguides are placed close enough together such that optical energy (photons) on one wave guide is switched over to the other, nearby wave guide.

As can be seen in the figure, the red (eraseable, implantable) and blue (conventional) wave guides are fabricated on the FPGA. The red wave guide performs the function of DC between the two conventional wave guides. The diagram shows both a single stage and a dual stage DC.

By using imlantable (eraseable) DCs, one can change the path of a photonic circuit by just erasing the implantable wave guide(s).

The GE ion implantable wave guides are erased by passing a laser over it and thus annealing (melting) it.

Once erased, the implantable wave guide DC no longer works. The chart on the left of the figure above shows how long the implantable wave guide needs to be to work. As shown above once erased to be shorter than 4-5µm, it no longer acts as a DC.

It’s not clear how one directs the laser to the proper place on the Optical FPGA to anneal the implantable wave guide but that’s a question of servos and mirrors.

Previous attempts at optical FPGAs, required applying continuing voltage to maintain the switched photonics circuits. Once voltage was withdrawn the photonics reverted back to original configuration.

But once an implantable wave guide is erased (annealed) in their approach, the changes to the Optical FPGA are permanent.

FPGAs today

Electronic FPGAs have never gone out of favor with customers doing hardware innovation. By supplying Optical FPGAs, the techniques in the paper would allow for much more photonics innovation as well.

Optics are primarily used in communications and storage (CD-DVDs) today. But quantum computing could potentially use photonics and there’s been talk of a 100% optical computer for a long time. As more and more photonics circuitry comes online, the need for an optical FPGA grows. The fact that it’s able to be grown on today’s fab lines makes it even more appealing.

But an FPGA is more than just directional control over (electronic or photonic) energy. One needs to have other circuitry in place on the FPGA for it to do work.

For example, if this were an electronic FPGA, gates, adders, muxes, etc. would all be somewhere on the FPGA

However, once having placed additional optical componentry on the FPGA, photonic directional control would be the glue that makes the Optical FPGA programmable.

Comments?

Photo Credit(s): All photos from Toward an optical FPGA – programable silicon photonics circuits paper

 

Information flows everywhere – part 1

Read an article today from Scientific American (Sewage is helping cities flush out the opioid crisis) about how using chemical analysis of wastewater can be used to assess the extent of the opioid crisis in their city.

Wastewater information highway

There’s a lab at ASU (Arizona State University) that chemically analyzes samples of wastewater to determine the amount of drugs that a city’s population excretes. They can provide a near real-time assessment of the proportion of drugs in city sewage and thereby, in a city’s population.

The problem with public drug use surveys and hospital data gathering is that they take time.  Moreover, surveys and hospital data gathering typically come long after drugs problem have become a serious problem in a city’s population.

Wastewater sample drug analysis can be done in a matter of days and can be redone as often as needed. Such data could be used to track intervention activities and see if they have a real impact (positive or negative) on drug use in a population.

Neighborhood health

In addition, by sampling sewage at a neighborhood level, one can gain an assessment of drug problems at any sub-division of a city that’s needed.

The above article talks about an MIT program with Cary, NC (from Biobot.io)  that is designing robots to traverse sewer pipes and analyze wastewater chemical makeup in real time, reporting this back to ground stations around the city.

With such an approach, one could almost zero in (depending on sewer pipe networks) on any neighborhood in a city, target specific interventions at that level and measure impact in (digestion delayed) real time. Doing so, cities or states for that matter, could  experiment with different interventions on a neighborhood by neighborhood basis and gain statistical evidence on drug problem intervention effectiveness.

But, you can analyze wastewater for any number of variables, such as viruses, bacteria, enzymes, etc. Any of which can lead to a better understanding of a population’s health.

~~~~

Two things I want to leave you with:

First, public health has had a major impact on human health and has doubled our lifespan in 200 years. All modern cities have water treatment plants today to insure water quality and thereby, have reduced the incidence of cholera and other waterborne epidemics in their cities. Wastewater analysis has the potential for significant improvements in population health monitoring. Just like water treatment, wastewater analysis will someday become common public health practice in modern cities throughout the world.

Second, I was at a conference this week which presented a slide that there was no cold data anymore (Pure//Accelerate 2018). This was in reference to  re-analyzing old, cold data can often lead to insights and process improvements that were not obvious at first glance.

But it’s not just data anymore. Any activity done by man needs to be analyzed for (inherent & invisible) information flows that could be extracted to make the world a better place.

Photo Credit(s):

NetApp’s new NVMeoF/FC AFF & Cloud Data Volumes for every cloud

We attended a NetApp analyst event in their CA HQ last week and they had some interesting announcements as well other information to share. 1st up new faster ONTAP storage.

NVMeoF AFF

NetApp announced this week that their latest generation AFF (All Flash FAS) systems will support FC NVMeoF. We asked if this was just for NVMe SSDs or did it apply to all AFF media. The answer was it’s just another host interface which the customer can license for NVMe SSDs (available only on AFF F800) or SAS SSDs (A700S, A700, and A300). The only AFF not supporting the new host interface is their lowend AFF A220.

As for which NVMeoF, they only support FC at the moment, and it’s our belief that the FC NVMeoF spec is most well defined these days and the FC switch hardware (Brocade-Broadcom since Gen 5, now shipping Gen 6, Cisco not sure) already has NVMeoF support.

NetApp also mentioned support for 100GbE (A800 & A700S only) and 32Gbs FC hardware (all AFF systems but A220). So, presumably they offer NVMeoF for both 32Gbps and 16Gbps FC.

No word on when this will be available for Ethernet FCoE or iSCSI (iNVMe?) but with all the major storage vendors bar one, moving to NVMe SSDs it’s only a matter of time before they also support Ethernet NVMeoF.

As for AFF NVMeoF performance, the answer wasn’t entirely satisfactory. The indication was that the interface reduced response time by 10 usecs or so for NVMe SSDs over SAS SSDs. But I didn’t see any other performance information to substantiate that.

We did see on their AFF datasheet that with NVMe SSDs and NVMeoF FC, the AFF A800 response time was sub 200usec with throughput of 300GB/s (in a 24 node cluster, 12 HA pairs). This means they add only about 100usec for ONTAP data services, a decent trade off from our perspective. Later in their datasheet they say the A800 is capable of 1.3M IOPS and sub-500usec latencies. Unsure why they quoted both numbers.

Cloud Data Volumes

NetApp is taking storage to the cloud. They just announced that NetApp Cloud Data Volumes will be available as a native service under Google Cloud Platform (GCP). NetApp Cloud Data Volume is a storage-as-a-service offering that provides on demand ONTAP file services in the cloud.

For GCP,  both Google and NetApp will be offering the service. Dianne Green, GCP VP said Cloud Data Volumes are a bit like Kubernetes, disruption without disrupting. Customers can easily migrate their onprem file based applications to the cloud without having to worry about the performance of their data or data protection for that matter.

Getting the data there is another matter, but NetApp has other services like CloudSync and someday (maybe for Cloud Data Volumes), SnapMirror, which can help customers move data to and from the cloud.

Currently Cloud Data Volumes are in public preview as an Microsoft Azure Enterprise NFS (and SMB) service. It’s also in beta (I think) in AWS marketplace. And availability on GCP is still restricted. There’s a lot of emphasis at NetApp events on Cloud Data Volumes given its current status on public cloud providers but we think they are trying to gain some experience before they roll it out to the rest of the world.

However,  Jean English, NetApp CMO mentioned that NetApp’s Cloud Data Service business unit has over 1800 customers and currently supports a multi-PB storage footprint in various clouds. Note, this is not just Cloud Data Volumes but comprises all NetApp Cloud Data Services, which includes ONTAP Cloud, NPS, CloudSync, AltaVault, etc. Nonetheless, it’s an impressive indicator of just how far they have come in applying their storage magic to the public cloud in a short time. The hyperscalers (read public cloud providers) say NetApp is 2 or more years ahead of all the other competition and from what we can see, it’s true.

One of the key differentiators between NetApp Cloud Data Volumes and ONTAP Cloud is performance SLAs. Cloud Data Volume customers can select and purchase a specified performance SLA. We believe it comes at three levels and is normally purchased on a pay as you go, consumption based, service offering. However, it’s also available to be billed periodically, other purchase options may be available as well.

When asked what storage was behind the service, the only thing NetApp would confirm was that it was ONTAP storage, present in public cloud data centers in various regions. So Cloud Data Volumes is available in only specific regions but I would expect that to expand over time.

Data Visualization Center

They also christened their new Data Visualization Center (DVC) and we had a multi-course meal at the Bistro at the center. The DVC had a wrap around, 1.5 floor tall screen which showed some of NetApp customer success stories. Inside the screen was a more immersive setting and there was plenty of VR equipment in work spaces alongside customer conference rooms.

Full Disclosure: NetApp paid for all our travel, hotel and food during the analyst event and gave us all Google Home Minis as going away presents and NetApp is a long time customer of my firm.

Scratch file use in HPC @ORNL, a statistical analysis

Attended SC17 (Supercomputing Conference) this past week and I received a copy of the accompanying research proceedings. There are a number of interesting papers in the research and I came across one, Scientific User Behavior and Data Sharing Trends in a Peta Scale File System by Seung-Hwan Lim, et al from Oak Ridge National Laboratory (ORNL) and the use of files at the Oak Ridge Leadership Computing Facility (OLCF) which was very interesting.

The paper statistically describes the use of a Scratch files in a multi PB file system (Lustre) at OLCF from January 2015 to August 2016. The OLCF supports over 32PB of storage, has a peak aggregate of over 1TB/s and Spider II (current Lustre file system) consists of 288 Lustre Object Storage Servers, all interconnected and connected to all the supercomputing cluster of  servers via an InfiniBand network. Spider II supports all scratch storage requirements for active/queued jobs for the Titan (#4 in Top 500 [super computer clusters worldwide] list) and other clusters at ORNL.

ORNL uses an HPSS (High Performance Storage System) archive for permanent storage but uses the Spider II file system for all scratch files generated and used during supercomputing applications.  ORNL is expecting Spider III (2018-2023) to host 10 billion files.

Scratch files are purged from Spider II after 90 days of no access.The paper is based on metadata analysis captured during scratch purging process for 500 days of access.

The paper displays a number of statistics and metrics on the use of Spider II:

  • Less than 3% of projects have a directory depth >15, the maximum directory depth was recorded at 432, with most projects having a shallow (<10) directory depth.
  • A project typically has 10X the files that a specific researcher has and a median file count/researcher is 2000 files with a median project having 20,000 files.
  • Storage system performance is actively managed by many projects. For instance, 20 out of 35 science domains manually managed their Lustre cluster configuration to improve throughput.
  • File count continues to grow and reached a peak of 1B files during the time being analyzed.
  • On average only 3% of files were accessed readonly, 10% of files updated (read-write) and 76% of files were untouched during a week period. However, median and maximum file age was 138 and 214 days respectively, which means that these scratch files can continue to be accessed over the course of 200+ days.

There was more information in the paper but one item missing is statistics on scratch file size distribution a concern.

Nonetheless, in paints an interesting picture of scratch file use in HPC application/supercluster environments today.

Comments?

Axellio, next gen, IO intensive server for RT analytics by X-IO Technologies

We were at X-IO Technologies last week for SFD13 in Colorado Springs talking with the team and they showed us their new IO and storage intensive server, the Axellio. They want to sell Axellio to customers that need extreme IOPS, very high bandwidth, and large storage requirements. Videos of X-IO’s sessions at SFD13 are available here.

The hardware

Axellio comes in 2U appliance with two server nodes. Each server supports  2 sockets of Intel E5-26xx v4 CPUs (4 sockets total) supporting from 16 to 88 cores. Each server node can be configured with up to 1TB of DRAM or it also supports NVDIMMs.

There are two key differentiators to Axellio:

  1. The FabricExpress™, a PCIe based interconnect which allows both server nodes to access dual-ported,  2.5″ NVMe SSDs; and
  2. Dense drive trays, the Axellio supports up to 72 (6 trays with 12 drives each) 2.5″ NVMe SSDs offering up to 460TB of raw NVMe flash using 6.4TB NVMe SSDs. Higher capacity NVMe SSDS available soon will increase Axellio capacity to 1PB of raw NVMe flash.

They also probably spent a lot of time on packaging, cooling and power in order to make Axellio a reliable solution for edge computing. We asked if it was NEBs compliant and they told us not yet but they are working on it.

Axellio can also be configured to replace 2 drive trays with 2 processor offload modules such as 2x Intel Phi CPU extensions for parallel compute, 2X Nvidia K2 GPU modules for high end video or VDI processing or 2X Nvidia P100 Tesla modules for machine learning processing. Probably anything that fits into Axellio’s power, cooling and PCIe bus lane limitations would also probably work here.

At the frontend of the appliance there are 1x16PCIe lanes of server retained for networking that can support off the shelf NICs/HCAs/HBAs with HHHL or FHHL cards for Ethernet, Infiniband or FC access to the Axellio. This provides up to 2x100GbE per server node of network access.

Performance of Axellio

With Axellio using all NVMe SSDs, we expect high IO performance. Further, they are measuring IO performance from internal to the CPUs on the Axellio server nodes. X-IO says the Axellio can hit >12Million IO/sec with at 35µsec latencies with 72 NVMe SSDs.

Lab testing detailed in the chart above shows IO rates for an Axellio appliance with 48 NVMe SSDs. With that configuration the Axellio can do 7.8M 4KB random write IOPS at 90µsec average response times and 8.6M 4KB random read IOPS at 164µsec latencies. Don’t know why reads would take longer than writes in Axellio, but they are doing 10% more of them.

Furthermore, the difference between read and write IOP rates aren’t close to what we have seen with other AFAs. Typically, maximum write IOPs are much less than read IOPs. Why Axellio’s read and write IOP rates are so close to one another (~10%) is a significant mystery.

As for IO bandwitdh, Axellio it supports up to 60GB/sec sustained and in the 48 drive lax testing it generated 30.5GB/sec for random 4KB writes and 33.7GB/sec for random 4KB reads. Again much closer together than what we have seen for other AFAs.

Also noteworthy, given PCIe’s bi-directional capabilities, X-IO said that there’s no reason that the system couldn’t be doing a mixed IO workload of both random reads and writes at similar rates. Although, they didn’t present any test data to substantiate that claim.

Markets for Axellio

They really didn’t talk about the software for Axellio. We would guess this is up to the customer/vertical that uses it.

Aside from the obvious use case as a X-IO’s next generation ISE storage appliance, Axellio could easily be used as an edge processor for a massive fabric of IoT devices, analytics processor for large RT streaming data, and deep packet capture and analysis processing for cyber security/intelligence gathering, etc. X-IO seems to be focusing their current efforts on attacking these verticals and others with similar processing requirements.

X-IO Technologies’ sessions at SFD13

Other sessions at X-IO include: Richard Lary, CTO X-IO Technologies gave a very interesting presentation on an mathematically optimized way to do data dedupe (caution some math involved); Bill Miller, CEO X-IO Technologies presented on edge computing’s new requirements and Gavin McLaughlin, Strategy & Communications talked about X-IO’s history and new approach to take the company into more profitable business.

Again all the videos are available online (see link above). We were very impressed with Richard’s dedupe session and haven’t heard as much about bloom filters, since Andy Warfield, CTO and Co-founder Coho Data, talked at SFD8.

For more information, other SFD13 blogger posts on X-IO’s sessions:

Full Disclosure

X-IO paid for our presence at their sessions and they provided each blogger a shirt, lunch and a USB stick with their presentations on it.

 

There’s a new cluster filesystem on the block, Elastifile

At SFD12 last month we talked with the team from Elastifile. They are a new startup out of Israel working on a better cluster file system.

Elastifile was designed to support 1000s of nodes, 100,000 of users/client and 1000s of data containers (file systems/mount points), together with an infinite (64 bit) number of files and directories and up to Exabytes (10**18) in capacity. They also offer a 100% SSD file store capability. I encourage you to view the videos of their presentations at SFD12 to learn more.

Elastifile features

Elastifile supports data compression and optionally deduplication with NAND/Flash (e. g., low-/high-endurance) storage tiering, cloud storage tiering and multi-site storage. They also provide NFSv3/v4, SMB, AWS S3 and HDFS as native access protocols for their file storage.

They also offer non-disruptive hardware/software upgrades, n-way (2- or 3-way) data and metadata redundancy, self-healing capabilities, snapshots, and synchronous/asynchronous data replication or mirroring. Further, they provide multi-tenancy and QoS support.

Elastifile can be used in hyper converged mode as well as a dedicated storage server mode. For backend storage, they support heterogeneous, physical (block, I think?) storage systems as well as direct access storage in cluster nodes

Internals matter

Elastifile’s architecture supports accessor, owner and data nodes. But these can all be colocated on the same server or segregated across different servers.

Owner nodes, own all the metadata objects for a file or directory and caches the metadata working set in i’s memory. Ownership file or directory metadata may change in the case of hardware failures.

Elastifile supports a dynamic write data path, which means they determine, in real time, where to write file data rather than having the data locations identified before hand. They call this distributed write anywhere semantics.

Notably they don’t do data caching (with NVMe it doesn’t make sense) however, as noted above, they do use metadata caching

Internally, Elastifile uses variable length objects for both file data and metadata.

  • File data is composed of three object types: a file metadata (FileMD) object, mapping data objects, and file data objects. FileMD’s hold the normal file metadata (name, file size, create, access & modify ToDs, etc.) as well as pointing to all the Mapping Object (OIDs). Mapping objects exist for each 0.5MB of file data and consist of a 128 element table, each element mapping 4KB of file address space to a data object (OID). Each  data object holds the 4KB of compressed file data and journal log entries.
  • Director metadata is composed of directory metadata (DirMD) object and Directory listing objects. Directory listing objects maps file/directory names to FileMD or DirMD OIDs. Directory listing objects are accessed via an extensible hash table and contain a list of filenames/directory names within the directory

The Elastifile software architecture consists of three layers:

  • A protocol layer which terminates file system access protocols and translates requests into internal requests. The hashing and data compression of file data occur at this level.
  • A metadata layer which provides file system/directory name mapping to objects for owned files/directories and maintains file/directory metadata updates/journals/checkpoints.
  • A data layer which provides transaction consistency and a n-way redundant persistent data storage for (file or metadata) objects.

Metadata operations are persisted via journaled transactions and which are distributed across the cluster. For instance the journal entries for a mapping data object updates are written to the same file data object (OID) as the actual file data, the 4KB compressed data object.

There’s plenty of discussion on how they manage consistency for their metadata across cluster nodes. Elastifile invented and use Bizur, a key-value consensus based DB. Their chief architect Ezra Hoch (@EzraHoch) did a blog post and paper on Bizur for more information

~~~~

New file systems generally take many years to mature and get out into the market, cluster file systems even longer. Elastifile started in 2013, by some very smart engineers, is already on the market, just 4 years later. That’s impressive enough, but with their list of advanced functionality plus cloud storage tiering and multi-site operations all shipping in the current product is mind-blowing.

One lingering question is, does a market exist for another cluster file system? All flash is interesting but most of the current CFS’s do this and ship this today. Cloud storage tiering is interesting and a long term need but some CFSs already have this and others are no doubt implementing it as we speak. CFS’s use of objects for internal data and metadata management is not new and may make internals cleaner but don’t really provide a lot of customer benefit.

Exascale raw capacity, support for 100K users, 1000s of nodes, 1000s of file systems and an infinite # of files/directories is interesting. But most CFSs claim this level of support already, although this is more aspirational for some. And proving support at this scale is difficult, if not impossible.

On the other hand, Bizur is really neat. Its primary benefit is during recovery from hardware failures. For a CFS with 1000s of nodes, failures likely occur quite often. So Bizur’s advantage here may pay significant customer dividends.

Is that enough to to market a new CFS?

To see what other SFD12 bloggers have written on Elastifile, please see: