The lifeblood of any online retailer is the speed of its IT infrastructure. Shoppers arenâ€™t infinitely patient. Sluggish infrastructure performance can make shoppers wait precious seconds longer than they can stand, sending them fleeing to other sites for a faster purchase. Our federal governmentâ€™s halting rollout of the Health Insurance Marketplace website is a glaring example of what can happen when IT infrastructure isnâ€™t solid. A few bad user experiences that go viral can be damaging enough. Tens of thousands can be crippling. Â
In hyperscale datacenters, any number of problems including network issues, insufficient scaling and inconsistent management can undermine end usersâ€™ experience. But one that hits home for me is the impact of slow storage on the performance of databases, where the data sits. With the database at the heart of all those online transactions, retailers can ill afford to haveÂ their tier of database servers operating at anything less than peak performance.
Slow storage undermines database performance
Typically,Â Web 2.0 and e-commerce companies run relational databases (RDBs) on these massive server-centric infrastructures. (Take a look at my blog last week to get a feel for the size of these hyperscale datacenter infrastructures). If you are running that many servers to support millions of users, you are likely using some kind of open-sourced RDB such as MySQL or other variations. Keep in mind that Oracle 11gR2 likely retails around $30K per core but MSQL is free. But the performance of both, and most other relational databases, suffer immensely when transactions are retrieving data from storage (or disk). You can only throw so much RAM and CPU power at the performance problem â€¦ sooner rather than later you have to deal with slow storage.
Almost everyone in industry â€“ Web 2.0, cloud, hyperscale and other providers of massive database infrastructures â€“ is lining up to solve this problem the best way they can. How? By deploying flash as the sole storage for database servers and applications. But is low-latency flash enough? For sheer performance it beats rotational disk hands down. But â€¦ even flash storage has its limitations, most notably when you are trying to drive ultra-low latencies for write IOs. Most IO accesses by RDBs, which do the transactional processing, are a mix or read/writes to the storage. Specifically, the mix is 70%/30% reads/writes. These are also typically low q-depth accesses (less than 4). It is those writes that can really slow things down.
PCIe flash reduces write latencies
The good news is that the right PCIe flash technology in the mix can solve the slowdowns. Some interesting PCIe flash technologies designed to tackle this latency problem are on display atÂ AISÂ this week. DRAM and in particular NVDRAM are being deployed as a tier in front of flash to really tackle those nasty write latencies.
Among other demos, weâ€™re showing how a Nytroâ„˘ 6000 series PCIe flash cardÂ helps solve the MySQL database performance issues. The typical response time for a small data read (this is what the database will see for a Database IO) from an HDD is 5ms. Flash-based devices such as the Nytro WarpDriveÂ® card can complete the same read in less than 50ÎĽs on average during testing, an improvement of several orders-of-magnitude in response time. This response time translates to getting much higher transactions out of the same infrastructure â€“ but with less space (flash is denser) and a lot less power (flash consumes a lot lower power than HDDs).
Weâ€™re also showing the Nytro 7000 series PCIe flash cards. They reach even lower write latencies than the 6000 series and very low q-depths.Â The 7000 series cards also provide DRAM buffering while maintaining data-integrity even in the event of a power loss.
For online retailers and other businesses, higher database speeds mean more than just faster transactions. They canÂ help keep those cash registers ringing.
Tags: AIS, database, DRAM, e-commerce, flash, flash memory, hard disk drive, HDD, hyperscale datacenter, latency, MySQL, NVDRAM, Nytro 6000, Nytro 7000, Nytro WarpDrive, Oracle, PCIe flash, relational database, storage latency, web 2.0, write latency
You might be surprised to find out how big the infrastructure for cloud and Web 2.0 is. It is mind-blowing. Microsoft has acknowledged packing more than 1 million servers into its datacenters, and by some accounts that is fewer than Googleâ€™s massive server count but a bit more than Amazon. Â
Facebookâ€™s server count is said to have skyrocketed from 30,000 in 2012 to 180,000 just this past August, serving 900 million plus users. And the social media giant is even putting its considerable weight behind the Open Compute effort to make servers fit better in a rack and draw less power. The list of mega infrastructures also includes Tencent, Baidu and Alibaba and the roster goes on and on.
Even more jaw-dropping is that almost 99.9% of these hyperscale infrastructures are built with servers featuring direct-attached storage. Thatâ€™s right â€“ they do the computing and store the data. In other words, no special, dedicated storage gear. Yes, your Facebook photos, your Skydrive personal cloud and all the content you use for entertainment, on-demand video and gaming data are stored inside the server.
Direct-attached storage reigns supreme
Everything in these infrastructures â€“ compute and storage â€“ is built out of x-86 based servers with storage inside. Whatâ€™s more, growth of direct-attached storage is many folds bigger than any other storage deployments in IT. Rising deployments of cloud, or cloud-like, architectures are behind much of this expansion.
The prevalence of direct-attached storage is not unique to hyperscale deployments. Large IT organizations are looking to reap the rewards of creating similar on-premise infrastructures. The benefits are impressive: Build one kind of infrastructure (server racks), host anything you want (any of your properties), and scale if you need to very easily. TCO is much less than infrastructures relying on network storage or SANs.
With direct-attached you no longer need dedicated appliances for your database tier, your email tier, your analytics tier, your EDA tier. All of that can be hosted on scalable, share-nothing infrastructure. And just as with hyperscale, the storage is all in the server. No SAN storage required.
Open Compute, OpenStack and software-defined storage drive DAS growth
Open Compute is part of the picture. A recent Open Compute show I attended was mostly sponsored by hyperscale customers/suppliers. Many big-bank IT folks attended. Open Compute isnâ€™t the only initiative driving growing deployments of direct-attached storage. So is software-defined storage and OpenStack. Big application vendors such as Oracle, Microsoft, VMware and SAP are also on board, providing solutions that support server-based storage/compute platforms that are easy and cost-effective to deploy, maintain and scale and need no external storage (or SAN including all-flash arrays).
So if you are a network-storage or SAN manufacturer, you have to be doing some serious thinking (many have already) about how youâ€™re going to catch and ride this huge wave of growth.
Tags: Alibaba, Amazon, Baidu, cloud computing, DAS, direct attached storage, enterprise, enterprise IT, Google, hyperscale, Microsoft, Open Compute, OpenStack, Oracle, SAP, Tencent, VMware
Big data and Hadoop are all about exploiting new value and opportunities with data. In financial trading, business and some areas of science, itâ€™s all about being fastest or first to take advantage of the data. The bigger the data sets, the smarter the analytics. The next competitive edge with big data comes when you layer in flash acceleration. The challenge is scaling performance in Hadoop clusters.
The most cost-effective option emerging for breaking through disk-to-I/O bottlenecks to scale performance is to use high-performance read/write flash cache acceleration cards for caching. This is essentially a way to get more work for less cost, by bringing data closer to the processing. The LSIÂ® Nytroâ„˘ product has been shown during testing to improve the time it takes to complete Hadoop software framework jobs up to a 33%.
Combining flash cache acceleration cards with Hadoop software is a big opportunity for end users and suppliers. LSI estimates that less than 10% of Hadoop software installations today incorporate flash acceleration1. Â This will grow rapidly as companies see the increased productivity and ROI of flash to accelerate their systems.Â And use of Hadoop software is also growing fast. IDC predicts a CAGR of as much as 60% by 20162. Drivers include IT security, e-commerce, fraud detection and mobile data user management. Gartner predicts that Hadoop software will be in two-thirds of advanced analytics products by 20153. There are many thousands of Hadoop software clusters already employed.
Where flash makes the most immediate sense is with those who have smaller clusters doing lots of in-place batch processing. Hadoop is purpose-built for analyzing a variety of data, whether structured, semi-structured or unstructured, without the need to define a schema or otherwise anticipate results in advance. Hadoop enables scaling that allows an unprecedented volume of data to be analyzed quickly and cost-effectively on clusters of commodity servers. Speed gains are about data proximity. This is why flash cache acceleration typically delivers the highest performance gains when the card is placed directly in the server on the PCI ExpressÂ® (PCIe) bus.
PCIe flash cache cards are now available with multiple terabytes of NAND flash storage, which substantially increases the hit rate. We offer a solution with both onboard flash modules and Serial-Attached SCSI (SAS) interfaces to create high-performance direct-attached storage (DAS) configurations consisting of solid state and hard disk drive storage. This couples the low latency performance benefits of flash with the capacity and cost per gigabyte advantages of HDDs.
To keep the processor close to the data, Hadoop uses servers with DAS. And to get the data even closer to the processor, the servers are usually equipped with significant amounts of random access memory (RAM). An additional benefit, smart implementation of Hadoop and flash components can reduce the overall server footprint required. Scaling is simplified, with some solutions providing the ability to allow up to 128 devices which share a very high bandwidth interface. Most commodity servers provide 8 or less SATA ports for disks, reducing expandability.
Hadoop is great, but flash-accelerated Hadoop is best. Itâ€™s an effective way, as you work to extract full value from big data, to secure a competitive edge.