Up or Out? Probably Neither. Go Diagonal to the Future

January 27, 2008

I’ve been working on the introduction to Beyond Search. My fancy-dan monitor still makes my eyes tired. I’m the first to admit that next-generation technology is not without its weaknesses. To rest, I sat down and started flipping through the print magazines that accumulate each week.

Baseline is a Ziff Davis Enterprise magazine. I want you to know that I worked at Ziff Communications, and I have fond memories of Ziff Davis, one of Ziff’s most important units, at its peak. ZD’s products were good. Advertisers flocked to our trade shows, commercial online databases, and, of course, the magazines. I remember when Computer Shopper had so many pages, the printer complained because his binding unit wasn’t designed to do what he called “telephone books.” My recollection about that issue, which I saved for years, was a newsprint magazine with more than 600 pages that month. The Baseline I’m holding has 62 pages and editorial copy on the inside back cover, not an ad.

Baseline is a computer business magazine with the tagline “where leadership meets technology.” The Ziff of old was predicated on product reviews, product information, and product comparisons. This Baseline magazine doesn’t follow the old Ziff formula. Times change, and managers have to adapt. The original Ziff formula was right for the go-go years of the PC industry when ad money flowed to hard copy publications. It’s good that Baseline has a companion Web site. The information on the Web site is more timely than the articles in the print magazine, but maybe because of my monitor, I found the site difficult to read and confusing. Some of the news is timely and important; for example, Baseline carried the story about Google’s signing up the University of Phoenix, another educational scalp in its bag. That’s an important story largely unreported and not included in the Google News index. I like the idea of different, thoughtful approach to information technology. I also use the Baseline Web site.

The story in the January 2008 issue — “Scaling Up or Out” by David F. Carr — tackles an important subject. The question of how to scale to meet growing demand is one that many organizations now face. (I would provide a link to the article, but I could not locate it on the magazine’s Web site. The site lacks a key word search box, or I couldn’t find it. If you want to read the hard copy of this article, you will find it on pages 57, 58, 59, and 60.)

The subject addresses what IT options are available when systems get bogged down. The article correctly points out that you can buy bigger machines and consolidate activity. Traditional database bottlenecks can be reduced with big iron and big money. I think that’s scaling up. Another approach is to use more servers the way Google and many other Web sites do. I think that’s scaling out. The third option is to distribute the work over many commodity machines. But distributed processing brings some new headaches, and it is not a cure-all. There’s another option that walks a middle path. You “scale diagonally.” I think this means some “up” and some “out.” I’m sure some fancy Harvard MBA created apt terminology for this approach, but I think the phrase “technology grazing” fits the bill. The Baseline editors loved this story; the author loved it; and most readers of Baseline will love it. But when I read it, three points jabbed me in the snoot.

First, pages 58 and 59 feature pictures of three high-end servers. Most readers will not get their hands on these gizmos, but for techno-geeks these pix are better than a Sports Illustrated swimsuit issue. But no comparative data are offered. I don’t think anyone at ZD saw these super-hot computers or actually used them. With starting prices are in six figures and soaring effortlessly to $2 million or more for one server, some product analysis would be useful. It is clear from the article, that for really tough database jobs, you will need several of these fire breathers. The three servers are the HP Integrity Superdome, the Unisys ES7000/one, and the IBM p5 595. And page 60 has a photo of the Sun SPARC Enterprise M9000.From these graphics, I knew that the article was going to make the point that for my enterprise data center, I would have to have these machines. By the way, HP, IBM, and Sun are listed as advertisers on page 8. Do you think an ad sales professional at ZD will suggest to Unisys that it too should advertise in Baseline? The annoyance: product fluff presented as management meat.

Second, the reason to buy big, fast iron is the familiar RDBMS or relational database management system. The article sidesteps the ubiquitous Codd architecture. Today, Dr. Codd’s invention is asked to do more, faster. The problem is that big iron is a temporary fix. As the volume of data and transactions rise, today’s hot iron won’t be hot enough. I wasn’t reading about a solution; I was getting a dose of the hardware sales professional’s Holy Grail–guaranteed upgrades. I don’t think bigger iron will resolve transaction bottlenecks with big data. The annoyance: the IT folks embracing a return to the mainframe may be exacerbating the crisis.

Third, I may be too sensitive. I came away from the article with the sense that distributed, massively parallel systems are okay for lightweight applications. Forget it for the serious computing work. For real work, you need HP Integrity Superdome, Unisys ES7000/one, IBM p5 595, or the Sun SPARC Enterprise M9000. Baseline hasn’t suggested how to remove the RDBMS handcuffs limiting the freedom of some organizations. Annoyance: no solution presented.

Google’s enterprise boss is Dave Girouard. Not long ago, I heard him make reference to a coming “crisis in IT.” This Baseline article makes it clear that IT professionals who keep their eyes firmly on the past have to embrace tried and true solutions.

In my opinion here’s what’s coming and not mentioned or even hinted at in the Baseline article. The present financial downturn is going accelerate some signficant changes in the way organizations manage and crunch their data. Economics will start the snowball rolling. The need for speed will stimulate organizations’ appetite for a better way to handle mission-critical data management tasks.

Amazon and Google are embracing different approaches to “old” data problems. If I read the Baseline article correctly, lots of transactions in real time aren’t such a good idea with distributed, massively parallel architecture built on commodity hardware. What about Amazon and Google? Both run their respective $15 billion dollar annual turnover on this type of platform. And dozens of other companies are working like beavers to avoid a 1970s computer center using multi-core CPUs.

Finally, the problem is different from those referenced in the Baseline article. In Beyond Search, I profile one little-known Google initiative which may or may not become a product. But the research is forward looking and aims to solve the database problem. Not surprisingly, the Google research uses the commodity hardware and Googley distributed, massively parallel infrastructure. Could it be time for companies struggling with older technologies to look further ahead than having a big rack stuffed with several score multi-core CPUS? Could it be time to look for an alternative to the relational database? Could it be time to admit that the IT crisis has arrived?

Baseline seems unwilling to move beyond conventional wisdom. True, the article does advise me to “scale diagonally.” The problem is that I don’t know what this means. Do you?

Stephen Arnold, January 27, 2008

Comments

One Response to “Up or Out? Probably Neither. Go Diagonal to the Future”

  1. Reality on January 19th, 2009 1:14 pm

    Ah, yet another person touting Google’s distributed computing model without qualifying that google only uses this for point queries on chached URLs. All other Google services are hosted on big iron boxes. And did I mention that Google’s huge distributed model cost them an unnecessary fortune? We attempted to go from big iron machines to distributed computing on smaller, cheaper boxes. It worked okay. The problem was we went from a couple of big Sun boxes to over 300 nodes in a more “affordable” cluster. The result? Hardware costs: about the same. Power costs: significant increase. Data center footprint: OMG! Overall workload throughput: down 20%. Staff: increased from six sysadms to 16, for a personnel cost increase of $3.6M. Reliability: from 99.997% uptime to 99.2%. Why did we do this? Hardware vendors convinced the CIO that this is the new, cheapest and correct way to run a data center (without telling him their real motivation was ultimately to sell him much more hardware). So what are we doing now? Going back to big iron and consolidating those 300 nodes into two IBM p6-595s. Today’s big iron servers are not the cumbersome mainframes of the 1960s. If you have high transactional RDBMSs and heavy analytical work to do, you have to go big iron. If you have something to sell and a few thousand shopping carts to maintain, you maybe able to get away with clustering, but it’s a big maybe and you’d sure better hope that your transactional volume doesn’t suddently increase significantly.