|By Dana Gardner||
|January 13, 2014 08:30 AM EST||
If, as the adage goes, you should fight fire with fire then perhaps its equally justified to fight Big Data optimization requirements with -- Big Data.
It turns out that high-performing, cost-effective Big-Data processing helps to make the best use of dynamic storage resources by taking in all the relevant storage activities data, analyzing it and then making the best real-time choices for dynamic hybrid storage optimization.
In other words, Big Data can be exploited to better manage complex data and storage. The concept, while tricky at first, is powerful and, I believe, a harbinger of what we're going to see more of, which is to bring high intelligence to bear on many more services, products and machines.
To explore how such Big Data analysis makes good on data storage efficiency, BriefingsDirect recently sat down with optimized hybrid storage provider Nimble Storage to hear their story on the use of HP Vertica as their data analysis platform of choice. Yes, it's the same Nimble that last month had a highly successful IPO. The expert is Larry Lancaster, Chief Data Scientist at Nimble Storage Inc. in San Jose, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: How do you use big data to support your hybrid storage optimization value?
Lancaster: At a high level, Nimble Storage recognized early, near the inception of the product, that if we were able to collect enough operational data about how our products are performing in the field, get it back home and analyze it, we'd be able to dramatically reduce support costs. Also, we can create a feedback loop that allows engineering to improve the product very quickly, according to the demands that are being placed on the product in the field.
Looking at it from that perspective, to get it right, you need to do it from the inception of the product. If you take a look at how much data we get back for every array we sell in the field, we could be receiving anywhere from 10,000 to 100,000 data points per minute from each array. Then, we bring those back home, we put them into a database, and we run a lot of intensive analytics on those data.
Once you're doing that, you realize that as soon as you do something, you have this data you're starting to leverage. You're making support recommendations and so on, but then you realize you could do a lot more with it. We can do dynamic cache sizing. We can figure out how much cache a customer needs based on an analysis of their real workloads.
We found that big data is really paying off for us. We want to continue to increase how much it's paying off for us, but to do that we need to be able to do bigger queries faster. We have a team of data scientists and we don't want them sitting here twiddling their thumbs. That’s what brought us to Vertica at Nimble.
Using Big Data
Gardner: It's an interesting juxtaposition that you're using big data in order to better manage data and storage. What better use of it? And what sort of efficiencies are we talking about here, when you are able to get that data in that massive scale and do these analytics and then go back out into the field and adjust? What does that get for you?
Lancaster: We have a very tight feedback loop. In one release we put out, we may make some changes in the way certain things happen on the back end, for example, the way NVRAM is drained. There are some very particular details around that, and we can observe very quickly how that performs under different workloads. We can make tweaks and do a lot of tuning.
Without the kind of data we have, we might have to have multiple cases being opened on performance in the field and escalations, looking at cores, and then simulating things in the lab.
It's a very labor-intensive, slow process with very little data to base the decision on. When you bring home operational data from all your products in the field, you're now talking about being able to figure out in near real-time the distribution of workloads in the field and how people access their storage. I think we have a better understanding of the way storage works in the real world than any other storage vendor, simply because we have the data.
Gardner: So it's an interesting combination of a product lifecycle approach to getting data -- but also combining a service with a product in such a way that you're adjusting in real time.
Lancaster: That’s right. We do a lot of neat things. We do capacity forecasting. We do a lot of predictive analytics to try to figure out when the storage administrator is going to need to purchase something, rather than having them just stumble into the fact that they need to provision for equipment because they've run out of space.
A lot of things that should have been done in storage from the very beginning that sound straightforward were simply never done. We're the first company to take a comprehensive approach to it. We open and close 80 percent of our cases automatically, 90 percent of them are automatically opened.
We have a suite of tools that run on this operational data, so we don't have to call people up and say, "Please gather this data for us. Please send us these log posts. Please send us these statistics." Now, we take a case that could have taken two or three days and we turn it into something that can be done in an hour.
That’s the kind of efficiency we gain that you can see, and the InfoSight service delivers that to our customers.
Gardner: Larry, just to be clear, you're supporting both flash and traditional disk storage, but you're able to exploit the hybrid relationship between them because of this data and analysis. Tell us a little bit about how the hybrid storage works.
Challenge for hard drives
Lancaster: At a high level, you have hard drives, which are inexpensive, but they're slow for random I/O. For sequential I/O, they are all right, but for random I/O performance, they're slow. It takes time to move the platter and the head. You're looking at 5 to 10 milliseconds seek time for random read.
That's been the challenge for hard drives. Flash drives have come out and they can dramatically improve on that. Now, you're talking about microsecond-order latencies, rather than milliseconds.
But the challenge there is that they're expensive. You could go buy all flash or you could go buy all hard drives and you can live with those downsides of each. Or, you can take the best of both worlds.
Then, there's a challenge. How do I keep the data that I need to access randomly in flash, but keep the rest of the data that I don't care so much about in a frequent random-read performance, keep that on the hard drives only, and in that way, optimize my use of flash. That's the way you can save money, but it's difficult to do that.
It comes down to having some understanding of the workloads that the customer is running and being able to anticipate the best algorithms and parameters for those algorithms to make sure that the right data is in flash.
We've built up an enormous dataset covering thousands of system-years of real-world usage to tell us exactly which approaches to caching are going to deliver the most benefit. It would be hard to be the best hybrid storage solution without the kind of analytics that we're doing.
Gardner: Then, to extrapolate a little bit higher, or maybe wider, for how this benefits an organization, the analysis that you're gathering also pertains to the data lifecycle, things like disaster recovery (DR), business continuity, backups, scheduling, and so forth. Tell us how the data gathering analytics has been applied to that larger data lifecycle equation.
Lancaster: You're absolutely right. One of the things that we do is make sure that we audit all of the storage that our customers have deployed to understand how much of it is protected with local snapshots, how much of it is replicated for disaster recovery, and how much incremental space is required to increase retention time and so on.
We have very efficient snapshots, but at the end of the day, if you're making changes, snapshots still do take some amount of space. So, learning exactly what is that overhead, and how can we help you achieve your disaster recovery goals.
We have a good understanding of that in the field. We go to customers with proactive service recommendations about what they could and should do. But we also take into account the fact that they may be doing DR when we forecast how much capacity they are going to need.
It is part of a larger lifecycle that we address, but at the end of the day, for my team it's still all about analytics. It's about looking to the data as the source of truth and as the source of recommendation.
We can tell you roughly how much space you're going to need to do disaster recovery on a given type of application, because we can look in our field and see the distribution of the extra space that would take and what kind of bandwidth you're going to need. We have all that information at our fingertips.
When you start to work this way, you realize that you can do things you couldn't do before. And the things you could do before, you can do orders of magnitude better. So we're a great case of actually applying data science to the product lifecycle, but also to front-line revenue and cost enhancement.
Gardner: How can you actually get that analysis in the speed, at the scale, and at the cost that you require?
Lancaster: To give you a brief history of my awareness of HP Vertica and my involvement around the product, I don’t remember the exact year, but it may have been eight years ago roughly. At some point, there was an announcement that Mike Stonebraker was involved in a group that was going to productize the C-Store Database, which was sort of an academic experiment at UC Berkeley, to understand the benefits and capabilities of real column store.
I was immediately interested and contacted them. I was working at another storage company at the time. I had a 20 terabyte (TB) data warehouse, which at the time was one of the largest Oracle on Linux data warehouses in the world.
They didn't want to touch that opportunity just yet, because they were just starting out in alpha mode. I hooked up with them again a few years later, when I was CTO at a company called Glassbeam, where we developed what's substantially an extract, transform, and load (ETL) platform.
By then, they were well along the road. They had a great product and it was solid. So we tried it out, and I have to tell you, I fell in love with Vertica because of the performance benefits that it provided.
When you start thinking about collecting as many different data points as we like to collect, you have to recognize that you’re going to end up with a couple choices on a row store. Either you're going to have very narrow tables and a lot of them or else you're going to be wasting a lot of I/O overhead, retrieving entire rows where you just need a couple fields.
That was what piqued my interest at first. But as I began to use it more and more at Glassbeam, I realized that the performance benefits you could gain by using HP Vertica properly were another order of magnitude beyond what you would expect just with the column-store efficiency.
That's because of certain features that Vertica allows, such as something called pre-join projections. We can drill into that sort of stuff more if you like, but, at a high-level, it lets you maintain the normalized logical integrity of your schema, while having under the hood, an optimized denormalized query performance physically on disk.
Now you might ask you can be efficient if you have a denormalized structure on disk. It's because Vertica allows you to do some very efficient types of encoding on your data. So all of the low cardinality columns that would have been wasting space in a row store end up taking almost no space at all.
What you find, at least it's been my impression, is that Vertica is the data warehouse that you would have wanted to have built 10 or 20 years ago, but nobody had done it yet.
Nowadays, when I'm evaluating other big data platforms, I always have to look at it from the perspective of it's great, we can get some parallelism here, and there are certain operations that we can do that might be difficult on other platforms, but I always have to compare it to Vertica. Frankly, I always find that Vertica comes out on top in terms of features, performance, and usability.
Gardner: When you arrived there at Nimble Storage, what were they using, and where are you now on your journey into a transition to Vertica?
Lancaster: I built the environment here from the ground up. When I got here, there were roughly 30 people. It's a very small company. We started with Postgres. We started with something free. We didn’t want to have a large budget dedicated to the backing infrastructure just yet. We weren’t ready to monetize it yet.
So, we started on Postgres and we've scaled up now to the point where we have about 100 TBs on Postgres. We get decent performance out of the database for the things that we absolutely need to do, which are micro-batch updates and transactional activity. We get that performance because the database lives on Nimble Storage.
I don't know what the largest unsharded Postgres instance is in the world, but I feel like I have one of them. It's a challenge to manage and leverage. Now, we've gotten to the point where we're really enjoying doing larger queries. We really want to understand the entire installed base of how we want to do analyses that extend across the entire base.
We want to understand the lifecycle of a volume. We want to understand how it grows, how it lives, what its performance characteristics are, and then how gradually it falls into senescence when people stop using it. It turns out there is a lot of really rich information that we now have access to to understand storage lifecycles in a way I don't think was possible before.
But to do that, we need to take our infrastructure to the next level. So we've been doing that and we've loaded a large number of our sensor data that’s the numerical data I have talked about into Vertica, started to compare the queries, and then started to use Vertica more and more for all the analysis we're doing.
Internally, we're using Vertica, just because of the performance benefits. I can give you an example. We had a particular query, a particularly large query. It was to look at certain aspects of latency over a month across the entire installed base to understand a little bit about the distribution, depending on different factors, and so on.
We ran that query in Postgres, and depending on how busy the server was, it took anywhere from 12 to 24 hours to run. On Vertica, to run the same query on the same data takes anywhere from three to seven seconds.
I anticipated that because we were aware upfront of the benefits we'd be getting. I've seen it before. We knew how to structure our projections to get that kind of performance. We knew what kind of infrastructure we'd need under it. I'm really excited. We're getting exactly what we wanted and better.
This is only a three node cluster. Look at the performance we're getting. On the smaller queries, we're getting sub-second latencies. On the big ones, we're getting sub-10 second latencies. It's absolutely amazing. It's game changing.
People can sit at their desktops now, manipulate data, come up with new ideas and iterate without having to run a batch and go home. It's a dramatic productivity increase. Data scientists tend to be fairly impatient. They're highly paid people, and you don’t want them sitting at their desk waiting to get an answer out of the database. It's not the best use of their time.
Gardner: Larry, is there another aspect to the HP Vertica value when it comes to the cloud model for deployment? It seems to me that if Nimble Storage continues to grow rapidly and scales that, bringing all that data back to a central single point might be problematic. Having it distributed or in different cloud deployment models might make sense. Is there something about the way Vertica works within a cloud services deployment that is of interest to you as well?
Lancaster: There's the ease of adding nodes without downtime, the fact that you can create a K-safe cluster. If my cluster is 16 nodes wide now, and I want two nodes redundancy, it's very similar to RAID. You can specify that, and the database will take care of that for you. You don’t have to worry about the database going down and losing data as a result of the node failure every time or two.
I love the fact that you don’t have to pay extra for that. If I want to put more cores or nodes on it or I want to put more redundancy into my design, I can do that without paying more for it. Wow! That’s kind of revolutionary in itself.
It's great to see a database company incented to give you great performance. They're incented to help you work better with more nodes and more cores. They don't have to worry about people not being able to pay the additional license fees to deploy more resources. In that sense, it's great.
We have our own private cloud -- that’s how I like to think of it -- at an offsite colocation facility. We do DR through Nimble Storage. At the same time, we have a K-safe cluster. We had a hardware glitch on one of the nodes last week, and the other two nodes stayed up, served data, and everything was fine.
Those kinds of features are critical, and that ability to be flexible and expand is critical for someone who is trying to build a large cloud infrastructure, because you're never going to know in advance exactly how much you're going to need.
If you do your job right as a cloud provider, people just want more and more and more. You want to get them hooked and you want to get them enjoying the experience. Vertica lets you do that.
You may also be interested in:
- MZI Healthcare Identifies Big Data Patient Productivity Gems Using HP Vertica
- Thought Leader Interview: HP's Global CISO Brett Wahlin on the future of Security and Risk
- Panel explains how CSC creates a tough cybersecurity posture against global threats
- Risk and complexity: Businesses need to get a grip
- HP Vertica General Manager Colin Mahony on the next generation of analytics platforms
- Advanced IT monitoring Delivers Predictive Diagnostics Focus to United Airlines
- CSC and HP team up to define the new state needed for comprehensive enterprise cybersecurity
- BYOD brings new security challenges for IT: Allowing greater access while protecting networks
- HP Vertica Architecture Gives Massive Performance Boost to Toughest BI Queries for Infinity Insurance
"We're a cybersecurity firm that specializes in engineering security solutions both at the software and hardware level. Security cannot be an after-the-fact afterthought, which is what it's become," stated Richard Blech, Chief Executive Officer at Secure Channels, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 3, 2016 08:30 AM EST Reads: 458
"Venafi has a platform that allows you to manage, centralize and automate the complete life cycle of keys and certificates within the organization," explained Gina Osmond, Sr. Field Marketing Manager at Venafi, in this SYS-CON.tv interview at DevOps at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 3, 2016 07:30 AM EST Reads: 688
"Qosmos has launched L7Viewer, a network traffic analysis tool, so it analyzes all the traffic between the virtual machine and the data center and the virtual machine and the external world," stated Sebastien Synold, Product Line Manager at Qosmos, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 3, 2016 07:00 AM EST Reads: 496
In addition to all the benefits, IoT is also bringing new kind of customer experience challenges - cars that unlock themselves, thermostats turning houses into saunas and baby video monitors broadcasting over the internet. This list can only increase because while IoT services should be intuitive and simple to use, the delivery ecosystem is a myriad of potential problems as IoT explodes complexity. So finding a performance issue is like finding the proverbial needle in the haystack.
Dec. 3, 2016 06:30 AM EST Reads: 5,992
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life sett...
Dec. 3, 2016 05:45 AM EST Reads: 6,918
Redis is not only the fastest database, but it has become the most popular among the new wave of applications running in containers. Redis speeds up just about every data interaction between your users or operational systems. In his session at 18th Cloud Expo, Dave Nielsen, Developer Relations at Redis Labs, shared the functions and data structures used to solve everyday use cases that are driving Redis' popularity.
Dec. 3, 2016 04:15 AM EST Reads: 3,421
20th Cloud Expo, taking place June 6-8, 2017, at the Javits Center in New York City, NY, will feature technical sessions from a rock star conference faculty and the leading industry players in the world. Cloud computing is now being embraced by a majority of enterprises of all sizes. Yesterday's debate about public vs. private has transformed into the reality of hybrid cloud: a recent survey shows that 74% of enterprises have a hybrid cloud strategy.
Dec. 3, 2016 04:15 AM EST Reads: 1,710
Internet-of-Things discussions can end up either going down the consumer gadget rabbit hole or focused on the sort of data logging that industrial manufacturers have been doing forever. However, in fact, companies today are already using IoT data both to optimize their operational technology and to improve the experience of customer interactions in novel ways. In his session at @ThingsExpo, Gordon Haff, Red Hat Technology Evangelist, will share examples from a wide range of industries – includin...
Dec. 3, 2016 02:30 AM EST Reads: 1,523
WebRTC is the future of browser-to-browser communications, and continues to make inroads into the traditional, difficult, plug-in web communications world. The 6th WebRTC Summit continues our tradition of delivering the latest and greatest presentations within the world of WebRTC. Topics include voice calling, video chat, P2P file sharing, and use cases that have already leveraged the power and convenience of WebRTC.
Dec. 3, 2016 02:15 AM EST Reads: 1,495
"We build IoT infrastructure products - when you have to integrate different devices, different systems and cloud you have to build an application to do that but we eliminate the need to build an application. Our products can integrate any device, any system, any cloud regardless of protocol," explained Peter Jung, Chief Product Officer at Pulzze Systems, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 3, 2016 01:45 AM EST Reads: 777
"We are the public cloud providers. We are currently providing 50% of the resources they need for doing e-commerce business in China and we are hosting about 60% of mobile gaming in China," explained Yi Zheng, CPO and VP of Engineering at CDS Global Cloud, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 3, 2016 01:15 AM EST Reads: 860
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at 20th Cloud Expo, Ed Featherston, director/senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
Dec. 3, 2016 12:30 AM EST Reads: 1,520
Between 2005 and 2020, data volumes will grow by a factor of 300 – enough data to stack CDs from the earth to the moon 162 times. This has come to be known as the ‘big data’ phenomenon. Unfortunately, traditional approaches to handling, storing and analyzing data aren’t adequate at this scale: they’re too costly, slow and physically cumbersome to keep up. Fortunately, in response a new breed of technology has emerged that is cheaper, faster and more scalable. Yet, in meeting these new needs they...
Dec. 3, 2016 12:15 AM EST Reads: 1,755
"Once customers get a year into their IoT deployments, they start to realize that they may have been shortsighted in the ways they built out their deployment and the key thing I see a lot of people looking at is - how can I take equipment data, pull it back in an IoT solution and show it in a dashboard," stated Dave McCarthy, Director of Products at Bsquare Corporation, in this SYS-CON.tv interview at @ThingsExpo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 2, 2016 11:15 PM EST Reads: 911
@DevOpsSummit taking place June 6-8, 2017 at Javits Center, New York City, is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. @DevOpsSummit at Cloud Expo New York Call for Papers is now open.
Dec. 2, 2016 10:30 PM EST Reads: 1,734
"We are an all-flash array storage provider but our focus has been on VM-aware storage specifically for virtualized applications," stated Dhiraj Sehgal of Tintri in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 2, 2016 08:30 PM EST Reads: 420
IoT is rapidly changing the way enterprises are using data to improve business decision-making. In order to derive business value, organizations must unlock insights from the data gathered and then act on these. In their session at @ThingsExpo, Eric Hoffman, Vice President at EastBanc Technologies, and Peter Shashkin, Head of Development Department at EastBanc Technologies, discussed how one organization leveraged IoT, cloud technology and data analysis to improve customer experiences and effici...
Dec. 2, 2016 08:30 PM EST Reads: 4,982
The cloud competition for database hosts is fierce. How do you evaluate a cloud provider for your database platform? In his session at 18th Cloud Expo, Chris Presley, a Solutions Architect at Pythian, gave users a checklist of considerations when choosing a provider. Chris Presley is a Solutions Architect at Pythian. He loves order – making him a premier Microsoft SQL Server expert. Not only has he programmed and administered SQL Server, but he has also shared his expertise and passion with b...
Dec. 2, 2016 07:00 PM EST Reads: 3,931
"IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications," stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Dec. 2, 2016 06:45 PM EST Reads: 3,996
As data explodes in quantity, importance and from new sources, the need for managing and protecting data residing across physical, virtual, and cloud environments grow with it. Managing data includes protecting it, indexing and classifying it for true, long-term management, compliance and E-Discovery. Commvault can ensure this with a single pane of glass solution – whether in a private cloud, a Service Provider delivered public cloud or a hybrid cloud environment – across the heterogeneous enter...
Dec. 2, 2016 06:30 PM EST Reads: 1,487