Bikeshedding
Someone asked me earlier today why I hadnt blogged for a while, and the usual excuse of “too busy” came easily to my lips, and then I started reciting a long litany of taks, new job roles and other stuff that gave me a perfectly legitimate excuse for why I havent contributed to the global store of knowlege since last November. Frankly the too busy is just another way of saying that blogging just wasnt important to me any more, which is odd, because I like writing, even if only as a way to organise my thoughts and give myself clarity.
Then when piling through the 800 odd unread emails in my inbox (my very own “Big Data” problem), I stumbled across something Chirs Rutherford posted on linked-in about Parkinson’s Law of Triviality
It was at that time that I realised that this was the fundamental reason I’d stopped blogging, for the most part it was that I’d been blogging about increasingly trivial issues that werent worth either writing about and frankly I dont think many people really wanted to read it either.
Having said that there is stuff I do think is important, there are important issues that I believe need to be solved and that the smart dedicated people in IT can be a big part of helping to solve those problems. A lot of those people are people like my colleagues at NetApp, there are great guys in our reseller channel and our our customers and alliances and there are amazing, smart and admirable people working inside the companies against which I compete.
What I’d like to blog about from now on, is how we take what we do on a day to day basis and use that to make the world a better place. That was always what the “without borders” part of my blog title was meant to be about,
I still plan to write about some detailed tech stuff that NetApp does, (especially the stuff I think is geeky and cool), and I’ll probably still have the occasional rant about some of the more blatant misinformation, but if you see me getting into a rabid rant about trivialities, please remind me of this post, because information storage professionals are in many ways, the custodians of the worlds identities, and that, is way more important than arguing what colour the bike shed should be
John Martin
@life_no_borders
PS. The picture of the bikeshed I’ve used comes from http://www.thebikeshed.org.au/ an organisation who’s work I believe really is important. I havent obtained their permission for the picture yet, though I will do so ASAP or take the picture down. If you are based in Melbourne and are interested in people taking positive action to enable others to help themselves to improve the world they live in, please check them out
Movember …
It’s now the end Movember which for me was a time to focus on men’s health, and in particular mental health. Most of you will know that I can be a moody old thing from time to time and some of you will know the impact that depression has on young men in particular, and how that caused me to lose a number of good friends.
To show my commitment, I donated my face to the cause by growing a moustache for the entire month of November. My Mo sparked a number conversations, and no doubt generated some laughs; all in the name of raising vital awareness and funds for prostate cancer and male depression.
Why am I so passionate about men’s health?
- 1 in 9 men will be diagnosed with prostate cancer in their lifetime
- This year 20,000 new cases of the disease will be diagnosed
- 1 in 8 men will experience depression in their lifetime
I’m asking you to support my Movember campaign by making a donation by either:
- Donating online at: http://mobro.co/JohhMartin
- Writing a cheque payable to ‘Movember’, referencing my Registration ID: 2207632 and mailing it to: Movember, PO Box 60, East Melbourne, VIC, 8002
If you’d like to find out more about the type of work you’d be helping to fund by supporting Movember, take a look at the Programs We Fund section on the Movember website: http://au.movember.com/about
Thank you in advance for supporting my efforts to change the face of men’s health.
- Please donate to Movember
How mid-sized businesses can make smart decisions on technology
This post Originally appeared in the ABC Tech and Games blog
The IT transformation currently occurring in the market thanks to cloud computing and the wide adoption of shared IT infrastructure seems like it’s predominantly affecting the large enterprise sector. But while this big-business IT revolution is going on, there is a flow-on effect to the mid-size market which is also tackling unprecedented data growth whilst struggling to assess the benefits of a move to cloud computing. The technology challenge for MSEs is in understanding how best to optimise their IT environments for both efficiency and scale so they can be comfortable in the knowledge they’ve made the right decisions for their business in the longer term. What is a mid-size business? Generally 100-1000 staff 1-3 IT staff who are generalists, not specialists IT staff typically have responsibility for the entire IT infrastructure. For the most part, the IT Vendor community in Australia tends to focus on the big end of town with multinational organisations dominating the landscape. When you then consider that 73 per cent (source: Reckon 2010 Annual SMB Business Survey) of the Australian economy is based on small businesses it seems unsurprising that mid-size enterprises which fall between these two market segments often seem to be forgotten. This means many MSEs are either reaching the limits of technology designed for small business with the resulting reliability and management headaches, or they’re paying too much to use specialist driven IT solutions aimed at the big end of town in an effort to avoid the problems they’ve just escaped from trying to do too much with SMB focused technology. These high end technologies with dedicated and siloed functionality aren’t well suited to mid-sized enterprises, not only because of their inherently high costs and inefficiencies, but also because the IT employees in these organisations are usually generalists. They need to know how all the company’s systems work, and how to fix them if they break and they simply don’t have the time to gain the specialist expertise needed to get the most out of these solutions. All of this puts the purchaser of IT for mid-size enterprises in an unenviable position. With many vendors rapidly jumping onto the cloud bandwagon, the mid-sized enterprise who still needs internal IT, but not at the kind of scale that would allow “internal clouds” are seeing a lot of turbulence in the supplier marketplace, none of which seems to be helping them. Many vendors are changing the way they are going to market and moving their focus away from their products for mid-sized business towards “cloudy” futures, and no longer investing in the kind of innovation required by this challenging business environment. While this is a worry, many of the traditional solutions offered by technology vendors to mid-sized enterprises often never really met their specific needs and challenges effectively. In an effort to win the business in these very budget-conscious organisations, mid-size businesses are often offered commodity-based solutions with low upfront costs, without being fully informed that many of these solutions cannot continue to meet their business needs as their company grows. In the midst of this gloom, one piece of good news is that in general, unlike many other areas of the marketplace that are facing budget constraints, mid-size business budgets are still reasonably healthy, though not infinite. Over the last few years, many MSEs have successfully focused on cost containment. For example, a large percentage have already taken the virtualization path, in fact, the virtualization trend is moving faster now than ever before. MSEs have seen great savings from these efforts, but most are now seeing that they’ve saved about as much as they can from consolidating and virtualizing their compute infrastructure while at the same time they are seeing a steady increase in the amount of money and percentage of their IT budget they spend on data storage. As the focus moves towards optimising data storage costs, mid-size businesses are looking for ways to reproduce the cost savings benefits they have gained in virtualizing their compute capacity. They are also looking for ways to optimize their environments to achieve more, address the data growth they are seeing and gain competitive advantage. These factors have huge implications on a company’s IT and in particular their data storage infrastructure requirements. However, these issues also provide a great opportunity to enhance IT systems to poise the company for growth. By demanding solutions that solve important and difficult challenges without undue complexity, and are powerful and scalable enough for the future, MSEs can gain great return on investment and get more from their suppliers. The test for mid-size companies is to make smart decisions on technology that is genuinely efficient, provide simplicity and offer scalability to meet growth requirements. The goal of MSEs needs to be focused around a technology foundation that will maintain pace with the growing business demands and allow the company to do more with fewer resources. So, what’s the lesson? Mid-size businesses are poised to continue growth and dominate in the market. The things they need to look out for in the technology arena are: solutions that can scale with their business needs – up and down – to protect their initial IT investment simple technologies that can be managed by IT generalists, yet still provide good cost of ownership and advanced enterprise-level capabilities partner businesses who can help them make smart decisions about the longer-term IT strategy, so it aligns properly with business objectives
More Records ??
–This has been revised based on some comments I’ve received since the original posting, check the comment thread if you’re interested what/why–
I came in this morning with an unusually clear diary, and took the liberty to check the newsfeeds for NetApp and EMC, this is when I came across an EMC press release entitled ”EMC VNX SETS PERFORMANCE DENSITY RECORD WITH LUSTRE —SHOWCASES “NO COMPROMISE” HPC STORAGE“.
I’ve been doing some research on Lustre and HPC recently, and that claim surprised me more than a little, so I checked it out, maybe there’s a VNX sweetspot for HPC that I wasnt expecting. The one thing that stood out straight away was . “EMC® is announcing that the EMC® VNX7500 has set a performance density record with Lustre—delivering read performance density of 2GB/sec per rack” (highlight mine)
In the first revision of this I had some fun pointing out the lameness of that particular figure, (e.g. “From my perspective, measured on a GB/sec per rack, 2GB/sec/rack is pretty lackluster”) , but EMC aren’t stupid (or at least their engineers aren’t, though I’m not so sure about their PR agency at this point), so it turns out that this was one of those things where it seems that EMC’s PR people didn’t actually listen to what the engineers were saying, and it looks like they’ve missed out a small but important word, and that word is “unit”. This becomes apparent if you take a look at the other stuff in that press release “8 GB/s read and 5.3 GB/s write sustained performance, as measured by XDD benchmark performed on a 4U dual storage processor”. This gives us 2GB/sec/rack unit which actually sounds kind of impressive. So let’s dig a little deeper, what we’ve got is a 4U dual storage processor that gets some very good raw throughput numbers, about 1.5x, or 150% faster in fact on a “per controller” basis than the figures used on the E5400 press release I referenced earlier, so on that basis I think EMC has done a good job. But this is where the PR department starts stretching the truth again by leaving out some fairly crucial pieces of information. Notably that crucial information that the 2GB/sec/rack unit is for 4U controller is a 2U VNX7500SPE with 2U standby power supply which is required when the 60 drive dense shelves are used exclusively (which is the case for the VNX Lustre Proof of Concept information shown in their brochures), and this configuration doesn’t include any of the rack units required for the actual storage. Either that, or its a 2U VNX7500SPE with a 2U shelf , and no standby power supply that seems to be mandatory component of a VNX solution, and I cant quite believe that EMC would do that.
If we compare the VNX to the E5400, you’ll notice that controllers and standby power supplies alone consume 4U of rack space without adding any capacity, whereas the E5400 controllers are much much smaller, and they fit directly into a 2U or 4U disk shelf (or DAE’s in EMC terminology) which means a 4U E4500 based solution is something you can actually use, as the required disk capacity is already there in the 4U enclosure.
Lets go through some worked calculations, to show how this works. In order to add capacity in the densest possible EMC configuration, you’d need to add an additional 4RU shelf with 60 drives in it. Net result 8RU, 60 drives, and up to 8 GB/s read and 5.3 GB/s write (the press release doesn’t make it clear whether a VNX7500 can actually drive that much performance from only 60 drives, my suspicion is that it cannot, otherwise we would have seen something like that in the benchmark). Meausred on a GB/s per RU basis this ends up as only 1 GB/sec per Rack Unit, not the 2 GB/sec per Rack Unit which I believe was the point of the “record setting” configuration. And just for kicks as you add more storage to the solution that number goes down as shown for the “dual VNX7500/single rack solution that can deliver up to 16GB/s sustained read performance” to about 0.4 GB/sec per Rack Unit. Using the configurations mentioned in EMC’s proof of concept configuration you end up with around 0.666 GB/sec per Rack Unit, all of which are a lot less than the 2 GB/sec/RU claimed in the press release
If you wanted to have the highest performing configurations using a “DenseStak” solution within those 8RU with an E5400 based Lustre solution, you’d put in another e5400 unit with an additional 60 drives Net result 8RU, 120 drives, and 10 GB read and 7 GB/sec write (and yes we can prove that we can get this kind of performance from 120 drives). Meausured on a GB/s per RU basis this ends up as 1.25 GB/sec per Rack Unit. That’s good, but its still not the magic number mentioned in the EMC press release, however if you were to use a “FastStak” solution, those numbers would pretty much double (thanks to using 2RU disk shelves instead of 4RU disk shelves) which would give you controller performance density of around 2.5 GB/sec per Rack Unit.
Bottom line, for actual usable configurations a NetApp solution has much better performance density using the same measurements EMC used for their so called “Record Setting” benchmark result.
In case you think I’m making these numbers up, they are confirmed in the NetApp whitepaper wp-7142 which says
The FastStak reference configuration uses the NetApp E5400 scalable
storage system as a building block. The NetApp E5400 system is designed
to support up to 24 2.5-inch SAS drives, in a 2U form factor.
Up to 20 of these building blocks can be contained in an industry-standard
40U rack. A fully loaded rack delivers performance of up to 100GB/sec
sustained disk read throughput, 70GB/sec sustained disk write throughput,
and 1,500,000 sustained IOPS.
According to IDC, the average supercomputer produces 44GB/sec,
so a single FastStak rack is more than fast enough to meet the I/O
throughput needs of many installations.
While I’ll grant that this result is achieved with more hardware, it should be remembered that the key to good HPC performance is in part about the ability to efficiently throw hardware at a problem. From a storage point of view this means having the ability to scale performance with capacity. In this area the DenseStak and FastStak solutions are brilliantly matched to the requrements of, and the prevailing technology used, in High Performance Computing. Rather than measuring on a GB/sec/rack unit I think a better measure would be “additional sequential performance per additional gigabyte”. Measured on a full rack basis, the NetApp E5400 based solution ends up at around 27MB/sec/GB for the DenseStak, or 54MB/sec/GB for the FastStak. In comparison, the fastest EMC solution as referenced in the “record setting” press release comes in at about 10MB/sec of performance for every GB of provisioned capacity or about 22MB/sec/GB for the configuration in the proof of concept brochure . Any way you slice this, the VNX just doesn’t end up looking like a particularly viable or competetive option.
The key here is that Lustre is designed as a scale out architecture. The E5400 solution is built as a scale out solution by using Lustre to aggregate the performance of the multiple carfully matched E5400 controllers, whereas the VNX7500 used in the press release is relatively poorly matched scale-up configuration which is being shoe-horned into use case it wasn’t designed for.
In terms of performance per rack unit, or performance per GB there simply isn’t much out there that comes close to a E5400 based Lustre solution, certainly not from EMC, as even Isilon, their best Big Data offering, falls way behind. The only other significant questions that remain are how much do they cost to buy, and how much power do they consume ?
I’ve seen the pricing for EMC’s top of the range VNX 7500, and its not cheap, its not even a little bit cheap, and the ultra-dense stuff shown in the proof of concept documents is even less not cheap than their normal stuff. Now I’m not at liberty to discuss our pricing strategy in any detail on this blog, but I can say that in terms of “bang per buck”, the E5400 solutions are very very competetive, and the power impact of the E5400 controller inside of 60 drive dense shelf is pretty much negligible. I don’t have the specs for the power draw on a VNX7500 and its associated external power units , but I’m guessing it adds around as much as a shelf of disks, the power costs of which add up over the three year lifecycle typically seen in these kinds of environments.
From my perspective the VNX7500 is a good general purpose box, and EMC’s engineers have every right to be proud of the work they’ve done on it, but positioning this as a “record setting” controller for performance dense HPC workloads on Lustre, is stretching the truth just a little too far for my liking. While the 10GB/sec/rack mentioned in the press release might sound like a lot for those of us who’ve spent their lives around transaction processing systems, for HPC, 10GB/sec/rack simply doesnt cut it. I know this, the HPC community knows this, and I suspect most of the reputable HPC focussed engineers in EMC also know this.
It’s a pity though that EMC’s PR department is spinning this for all they’re worth ; I struggle with how they can possibly assert that they’ve set any kind of performance density record for any kind of realistic Lustre implementation, when the truth is that they are so very very far behind. Maybe their PR dept has been reading 1984, because claiming record setting performance in this context requires some of the most bizarre Orwellian doublespeak I’ve seen in some time.
Breaking Records … Revisited
So today I found out that we’d broken a few records of our own few days ago, which was, at least from my perspective associated with surprisingly little fanfare with the associated press release coming out late last night. I’d like to say that the results speak for themselves, and to an extent they do. NetApp now holds the top two spots, and four out of the top five results on the ranking ladder. If this were the olympics most people would agree that this represents a position of PURE DOMINATION. High fives all round, and much chest beating and downing of well deserved delicious amber beverages.
So, apart from having the biggest number (which is nice), what did we prove ?
Benchmarks are interesting to me because they are the almost perfect intersection of my interests in both technical storage performance and marketing and messaging. From a technical viewpoint, a benchmark can be really useful, but it only provides a relatively small number of proof points, and extrapolating beyond those, or making generalised conclusions is rarely a good idea.
For example, when NetApp released their SPC-1 benchmarks a few years ago, it proved a number of things
1. That under heavy load which involved a large number of random writes, a NetApp arrays performance remained steady over time
2. That this could be done while taking multiple snapshots, and more importantly while deleting and retiring them while under heavy load
3. That this could be done with RAID-6 and with a greater capacity efficiency as measured by RAW vs USED than any other submission
4. That this could be done at better levels of performance than an equivalently configured commonly used “traditional array” as exemplified by EMCs CX3-40
5. That the copy on write performance of the snapshots on an EMC array sucked under heavy load (and by implication similar copy on write snapshot implementations on other vendors arrays)
That’s a pretty good list of things to prove, especially in the face of considerable unfounded misinformation being put out at the time, and which, surprisingly is still bandied about despite the independently audited proof to the contrary. Having said that, this was not a “my number is the biggest”, exercise which generally proves nothing more than how much hardware you had available in your testing lab at the time.
A few months later we published another SPC-1 result which showed that we could pretty much doubl the numbers we’d achieved in the previous generation at a lower price per IOP with what was at the time a very competetive submission.
About two years after that we published yet another SPC-1 result with the direct replacement for the controller used in the previous test (3270 vs 3170). What this test didnt do was to show how much more load could be placed on the system, what it did do was to show that we could give our customers more IOPS at a lower latency with half the number of spindles . This was the first time we’d submitted an SPC-1e result which foucussed on energy efficiency. It showed, quite dramatically how effective our FlashCache technology was under a heavy random write workload. Its interesting to compare that submission with the previous one for a number of reasons, but for the most part, this benchmark was about Flashcache effectiveness.
We did a number of other benchmarks including Spec-SFS benchmarks that also proved the remarkable effectiveness of the Flashcache technology, showing how it could make SATA drives perform as better than Fibre channel drives, or dramatically reduce the number of fibre channel drives required to service a given workload. There were a couple of other benchmarks done which I’ll grant were “hey look at how fast our shiny new boxes can run”, but for the most part, these were all done with configurations we’d reasonably expect a decent number our customers to actually buy (no all SSD configurations).
In the mean time EMC released some “Lab Queen” benchmarks, at first I thought that EMC were trying to prove just how fast their new X-blades were for processing CIFS and NFS traffic. They did this by configuring the back end storage system in such a rediculously overengineered way as to remove any possibility that they could cause a bottleneck in any way, either that or EMC’s block storage devices are way slower than most people would assume. From an engineering perspective I think they guys in Hopkington who created those X-blades did a truly excellent job, almost 125,000 IOPS per X-Blade using 6 CPU cores is genuinely impressive to me, even if all they were doing was processing NFS/CIFS calls. You see, unlike the storage processors in a FAS or Isilon array, the X-Blade, much like the Network Processor in a SONAS system, or an Oceanspace N8500 relies on a back end block processing device to handle RAID , block checksums, write cache coherency and physical data movement to and from the disks, all of which is non-trivial work. What I find particularly interesting is that in all the benchmarks I looked at for these kinds of systems, the number of back end block storage systems was usually double that of the front end, which infers to me either that the load placed on back end systems by these benchmarks is higher than the load on the front end, or more likely that the front end / back end architecture is very sensitive to any latency on the back end systems which means the back end systems get overengineered for benchmarks. My guess is after seeing the “All Flash DMX” configuration is that Celerra’s performance is very adversly affected by even slight increases in latency in the back end and that we start seeing some nasty manifestations of little law in these architectures under heavy load.
A little while later after being present at a couple of EMC presentations (one at Cisco Live, the other at a SNIA event, where EMC staff were fully aware of my presence), it became clear to me exactly why EMC did these “my number is bigger than yours” benchmarks. Ther marketing staff at corporate created a slide that compared all of the current SPC benchmarks in a way that was accurate, compelling and completely misleading all at the same time, at least as far as the VNX portion goes. Part of this goes back to the way that vendors, including I might say Netapp, use an availability group as a point of aggregation when reporting peformance numbers, this is reasonably fair as adding Active/Active or Active/Passive availability generally slows things down due to the two phase commit nature of write caching in modular storage environments. However, the configuration of the EMC VNX VG8 Gateway/EMC VNX5700 actually involves 5 separate availability groups (1xVG8 Gateway system with 4+1 redundancy, and and 4x VNX5700 with 1+1 redundancy). Presenting this as one aggregated peformance number without any valid point of aggregation smacks of downright dishonesty to me. If NetApp had done the same thing, then, using only 4 availabilty groups, we could have claimed over 760,000 IOPS by combining 4 of our existing 6240 configurations, but we didnt, because frankly doing that is in my opinion on the other side of the fine line where marketing finesse falls off the precipice into the shadowy realm of deceptive practice.
Which brings me back to my original question, what did we prove with our most recent submissions, well three things come to mind
1. That Netapp’s Ontap 8.1 Cluster mode solution is real, and it performs briliiantly
2. It scales linearly as you add nodes (more so than the leading competitors)
3. That scaling with 24 big nodes gives you better performance and better efficiency than scaling with hundreds of smaller nodes (at least for the SPEC benchmark)
This is a valid configuration using a single vserver as a point of aggregation across the cluster, and trust me, this is only the beginning.
As always, comments and criticism is welcome.
Regards
John
Big Data – What does it mean to me
One Red Fly Green Dress with Puppy
There’s a lot of talk of “Big Data”, how it can help make businesses more efficient, uncover correlations between diet, income, location and and health outcomes, and advance science and human endeavor in thousands of ways. From a storage vendors perspective Big Data also changes some of the fundamental assumptions about the value of data storage and the architectures and asumptions of shared and network storage.
What I didn’t expect though was how relevant Big Data was to the small business owner. Last week my wife started Tivoli2moro, a new online fashion business for girls (yes this is blatant plug, if you have a daughter between the ages of 8 – 15 or know someone who does, check it out, I’m very proud of what she’s achieved) . While doing the market research for things like Google Adwords, Facebook ads, and demographic trends the quality of the information she had at her fingertips truly surprised me. All of this information she had relied on years of data gathering from millions of data points, the infrstructure she leveraged would have cost millions of dollars, the storage requirement I suspect would be measured in hundreds of Terabytes.
While this kind of big data may not save lives, it does help change the competitive business landscape by giving small business access to the kinds of research data that would have been unimaginable until a few years ago. Similar access to other kinds of datasets may also change the way social activism and politics is run in the future, which makes me believe that helping people build these Big Data infrastructures really can help make make my wife happier, and this planet a better place to live, and that, means the world to me.
How does capacity utilisation affect performance ?
A couple of days ago I saw an email asking “what is the recommendation for maximum capacity utilization that will not cause performance degradation”. On the one hand this kind of question annoys me because for the most part it’s borne out of some the usual FUD which gets thrown at NetApp on a regular basis, but on the other, even though correctly engineering storage for consistent performance rarely, if ever, boils down to any single metric, understanding capacity utilisation and its impact on performance is an important aspect of storage design.
Firstly, for the record, I’d like to reiterate that the performance characteristics of every storage technology I’m aware of that is based on spinning disks decreases in proportion to the amount of capacity consumed.
With that out of the way, I have to say that as usual, the answer to the question of how does capacity utilisation affect performance is, “it depends”, but for the most part, when this question is asked, it’s usually asked about high performance write intensive applications like VDI, and some kinds of online transaction processing, and email systems.
If you’re looking at that kind of workload, then you can always check out good old TR-3647 which talks specifically about a write intensive high performance workloads where it says
The Data ONTAP data layout engine, WAFL®, optimizes writes to disk to improve system performance and disk bandwidth utilization. WAFL optimization uses a small amount of free or reserve space within the aggregate. For write-intensive, high-performance workloads we recommend leaving available approximately 10% of the usable space for this optimization process. This space not only ensures high-performance writes but also functions as a buffer against unexpected demands of free space for applications that burst writes to disk
I’ve seen other benchmarks using synthetic workloads where a knee in the performance curve begins to be seen at between 98% and 100% of the usable capacity after WAFL reserve is taken away, I’ve also seen performance issues when people completely fill all the available space and then hit it with lots of small random overwrites (especially misaligned small random overwrites). This is not unique to WAFL, which is why it’s a bad idea generally to fill up all the space in any data structure which is subjected to heavy random write workloads.
Having said that for the vast majority of workloads you’ll get more IOPS per spindle out of a netapp array at all capacity points than you will out of any similarly priced/configured box from another vendor
Leaving the FUD aside, (the complete rebuttal of which requires a fairly deep understanding of ONTAP’s performance achitecture) when considering capacity and its effect on performance on a NetApp FAS array it’s worth keeping the following points in mind.
- For any given workload, and array type you’re only ever going to get a fairly limited number transactions per 15K RPM disk, usually less than 250
- Array performance is usually determined by how many disks you can throw at the workload
- Most array vendors bring more spindles to the workload by using RAID-10 which uses twice the amount of disks for the same capacity, NetApp uses RAID-DP which does not automatically double the spindle density
- In most benchmarks (check out SPC-1), NetApp uses all but 10% of the available space (in line with TR-3647) which allows the user to use approximately 60% of the RAW capacity while still achieving the same kinds of IOPS/drive that more other vendors are only able to do using 30% of the RAW capacity. i.e at the same performance per drive we offer 10% more usable capacity than the other vendors could theoretically attain using RAID-10.
The bottom line is, that even without dedupe or thin provisioning or anything else you can store twice as much information in a FAS array for the same level of performance as most competing solutions using RAID-10
While that is true, it’s worth mentioning it does have one drawback. While the IOPS/Spindle is more or less the same, the IOPS density measured in IOPS/GB on the NetApp SPC-1 results is about half that of the competing solutions, (same IOPS , 2x as much data = half the density). While that is actually harder to do because you have a lower cache:data ratio, if you have an application that requires very dense IOPS/GB (like some VDI deployments for example), then you might not be allocate all of that extra capacity to that workload. This in my view gives you three choices.
- Don’t use the extra capacity, just leave it as unused freespace in the aggregate which will make it easier to optimise writes
- Use that extra capacity for lower tier workloads such as storing snapshots or a mirror destination, or archives etc, and set those workloads to a low priority using FlexShare
- Put in a FlashCache card which will double the effective number of IOPS (depending on workload of course) per spindle, which is less expensive and resource consuming than doubling the number of disks
If you dont do this, then you may run into a situation I’ve heard of in a few cases where our storage efficiencies allowed the user to put too many hot workloads on not enough spindles, and unfortunately this is probably the basis for the “Anecdotal Evidence” that allows the Netapp Capacity / Performance FUD to be perpetuated. This is innacurate because it has less to do with the intricacies of ONTAP and WAFL, and far more to do with systems that were originally sized for a workload of X having a workload of 3X placed on them because there was still capacity available on Tier-1 disk capacity, long after all the performance had been squeezed out of the spindles by other workloads.
Keeping your storage users happy, means not only managing the available capacity, but also managing the available performance. More often than not, you will run out of one before you run out of the other and running an efficient IT infrastructure means balancing workloads between these two resources. Firstly this means you have to spend at least some time measuring, and monitor both the capacity and performance of your environment. Furthermore you should also set your system up to it’s easy to migrate and rebalance workloads across other resource pools, or be able to easily add performance to your existing workloads non disruptively which can be done via technologies such as Storage DRS in vSphere 5, or ONTAP’s Data motion and Virtual storage tiering features.
When it comes to measuring your environment so you can take action before the problems arise, NetApp has a number of excellent tools to monitor the performance of your storage environment. Performance Advisor gives you visualization and customised alerts and thresholds for the detailed inbuilt performance metrics available on every FAS Array, and OnCommand Insight Balance provides deeper reporting and predictive analysis of your entire virtualised infrastructure including non-NetApp hardware.
Whether you use NetApp’s tools or someone elses, the important thing is that you use them, and take a little time out of you day to find out which metrics are important and what you should do when thresholds or high watermarks are breached. If you’re not sure about this for your NetApp environment, feel free to ask me here, or better still open up a question in the Netapp communities which has a broader constituency than this blog.
While I appreciate that it’s tempting to just fall back to old practices, and overengineer Tier-1 storage so that there is little or no possibility of running out of IOPS before you run out of capacity, this is almost always incredibly wasteful and has in my experience resulted in storage utilisation rates of less than 20%, and drives the costs/GB for “Tier-1″ storage to unsustainable and uneconomically justifiable heights. The time may come when storage administrators are given the luxury of doing this again, or you may be in one of those rare industries where cost is no object, but unless you’ve got that luxury, it’s time to brush up on your monitoring and storage workload rebalancing and optimisation skills. Doing more with less is what it’s all about.
As always, comments and contrary views are welcomed.
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Storage Without Borders by Richard J Martin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
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