In an obtuse blend of incisive marketing strategy combined with hard-core database design, I will be speaking at the Jacksonville Developers User Group on November 5th at the Seashell room in Building 500, Bank of America Building, 9000 Southside Boulevard, Jacksonville, Florida.

The first seminar in my series, "Jedi Mind Tricks for Business", I will be exploring the database work that can be done in anticipation of marketing's demands for high speed, infallible reliability, and no cost solutions for SEO integration of websites.... it's just the tip of the iceberg folks...

For directions, please see this link:

http://www.jaxdug.com/Events/MeetingLocations/tabid/63/Default.aspx

A nice thank-you to the guys at CNN for running the story of my cubicle on their evening news and on their homepage.  Wasn't expecting it but it was a nice kudo:

http://www.ireport.com/docs/DOC-33852

Here's a fun article about the benefits of plush carpeting to complete my Extreme Cubicle Makeover:

 http://carpetology.blogspot.com/2008/08/carpet-for-your-tent-carpet-for-your.html

"Don't you agree that high design such as this in the workplace, particularly in a cubicle, can only elevate, enhance and improve?  I do have one suggestion. A larger rug. For that matter, what about wall-t0-wall carpet for this cubicle? It would add to the richness of the experience, help absorb unwanted noise from neighboring cubicle mates and generally enhance the work environment." - Christine Whittemore, Director of In-Store Innovation for Solutia's Wear-Dated carpet fiber.

Here's a blast from the past...

Barnett, Inc. Chooses 'AutoStock' Supply Chain And E-Commerce Management Software

http://findarticles.com/p/articles/mi_m0EIN/is_/ai_54534095

This is the second installment in a series that blends website architecture, data structures, and SEO marketing into a collaborative design pattern continuing from Part 1 - Best Business Practices for Product Catalog Data Structures - Atomic Data 

We've discussed some ways you can create highly discrete or "atomic" data for a product in the first article.  This article will delve into how to evaluate the choices involved in speed versus flexibility.

Any database administrator that works on a high volume, high production website will simply start to quiver uncontrollably however, because there are severe implications for accessing this type of data scattered throughout several tables in a production environment.  Pass him a mug of decaf and let's walk together through how we can tackle the thorny issue of speed related to product catalog data.

We can start with our sample product that we have now mapped into its discrete elements.

This data is fairly granular (or atomic) and is highly reusable within its domain ("Color" categorically means a similar thing to every product that is bound to it).  There are many considerations when it comes to allowing Speed to dictate your design, but I'll list some of the top ones:

• Static Edge Presentation vs. Dynamic Source Presentation
• Precomputation or Data Summarization
• Staged Caching or Static Publishing

Static Edge Presentation 

Static Edge Presentation refers to the concept that data that is requested through web pages goes through many stages.  One model that many people are familiar with is the following:

Generally when the first hit is generated for a distinct URL, such as http://www.domainname.com/?ID=5, the Data Server generates the data needed for the page, the Origin Web Server composes the data into a functional web page, and then the Edge Cache Server distributes that origin page into its "cache" where the unique page sits in "static" for all subsequent hits.  If the page is requested from hundreds of Client PCs after that, only the Edge Cache Server responds to the request (until its cache expires).  If a single Client PC hits refresh over and over again, depending on the Client PC settings, the page is instead served from the Client PC's Browser Cache, which is a local equivalent of server-based edge caching.  This is generally one of the more advanced methods of serving high volume pages in a fast manner (and in a way that the database is impacted the least).  This is the preferred shield which allows your data structures to be a bit more complex (read slow), because at the price of the initial render, the cost per page load is mitigated by the Edge Caching.

Take a page that requires 8 seconds to load.  This is generally considered "too heavy" of a page to be used in production environments.  However, this is only the Origin Page Render cost, meaning it only "costs" this much time for the very first load of that unique page.  If all subsequent page loads only take 0.5 seconds from the Edge Cache for all subsequent hits, then averaged over the numbers of hits, you can quickly see how the page load time continues to approximate the 0.5 seconds load time overall for the page.

Another model is the Dynamic Rendered Page which is far more common to most web developers and online businesses:

This model demonstrates the direct nature of the requests from the Client PC, straight to the Origin Web Server (which gets its data from the Data Server).  In this model, there is generally a one-to-one relationship between the "hit" and the "data request", so the load on the database server is relatively high.  There are tricks you can use to ameliorate this, including Origin Server Caching, SQL Dependency Caching, and other methods, but most implementations use this form of dynamic page delivery.  In this case, data structures that cause delays can severely impact the performance of the application.

 Take a page now, which due to its flatter data model, only costs a 3 second load time.  Because the Edge Cache has been removed from the architecture, your average page load time is going to remain 3 seconds (the page construction happens over and over again for each hit).  While you gain some flexibility by having constantly changing data available on the page, you pay in the overall load on your servers (up to six times more costly in time than an edge cached solution), and you also are forced into a far less flexible data model to compensate for the speed requirements of live rendered pages.

Precomputation

The concept of pre-computation is based on a similar concept as caching.  This means that pretty much anything your database is going to need to "think" about, can in many cases be "pre-thunk."  The art of pre-thinking things before they are needed involves storing what's been thought out and saving it somewhere.  You also have to factor in the speed of retrieving things... some methods of storage are faster than others.

The diagram below (Self Healing Data Retrieval) shows the "layers" that a data request goes through before a page can be rendered.  It's pretty clear that the fastest way to get data to the customer is when the customer asks for a webpage that has been "pre-thunk" already and is waiting in cache at the Edge Cache (Akamai for example).  Here's where the magic happens.  If the page is not available in cache, the Edge cache forwards the request to the Webserver.  The Webserver then can not only generate the page, but it "heals" the Edge Cache by delivering the new page so any subsequent hits to the same page are now "healed" and available on the Edge Cache again.

This type of failover I described above cascades all the way up to the top.  In the examples above, if the Edge Cache fails, the Webserver picks up the slack.  If the Webserver fails, then the Method Farm system checks to see if it has an XML representation of the data in memory (extremely fast).  If the Method Farm doesn't have it in memory, then the Edge Net Storage picks up the slack.  If the Edge Net Storage doesn't have the data, then the Method Farm checks to see if it has it saved in a file on the hard drive (pretty fast).  If the Method Farm doesn't have it written to disk, then the SQL Server attempts to pull a static, pre-generated copy from a static table.  If the static table doesn't have the data, then the SQL server regenerates the data.  In general the failover escalation follows this model:

1. Edge Cache Static Copy
2. Webserver XML
3. Method Farm Memory XML
4. Edge Net Storage XML
5. Method Farm XML from file
6. SQL Server Static Record
7. SQL Server Dynamic Generation from data

In any of these cases, each step is design to "repair" the previous caller that failed.  This ensures that over time, the vast majority of requests are being serviced by the Edge Cache Server and approaches near 100% availability. 

Static Publishing

The last method of high volume, high speed retrieval of web pages that can help reduce load on database systems is the Static Publishing technique.  This means that without waiting for for a user to request a page, the system is designed to "spit out" every single possible page and page combination that could possibly be hit and this entire pile of page data is dumped onto an edge cache somewhere.  There is certainly some value to this, particularly for legacy media archives and other non-dynamic, and non-live page data, but it's use is extremely limited in the e-Commerce arena. 

This highlights to some degree the ways in which network and publishing architecture can drive decisions of data structures in general.  If you choose a more normalized method of data structure, then you need to compensate on the performance side with effective edge caching.  If you choose a more dynamic method of page delivery, then you need to look more toward a flatter, more static form of data model that can deliver the performance that you need.  Many database administrators will tell you that the atomic data model listed above (Sample Product to Property Association) may be too normalized for high volume use, but if the data being accessed is used to serve up pages for an edge cache architecture, the negative is eliminated.

It is important to factor in all of the requirements of your web project before making final data architecture decisions, but it is important to note that deficiencies in one decision (choosing a more normalized data structure) can easily be offset in other ways (choosing edge caching over dynamic page construction).  This may give you more freedom as you make your data structure and architecture choices.

Now that you have evaluated your choices of data models and a highly normalized method is a good architectural choice for your situation, it's prudent to examine the benefits of what the data model will enable you to do.  We will examine some of these benefits in Part 3 - Best Business Practices for Product Catalog Data Structures - Customer Paths.

This is the fifth installment in a series that blends website architecture, data structures, and Search Engine Optimization (SEO) marketing into a collaborative design pattern continuing from Part 4 - Best Business Practices for Product Catalog Data Structures - SEO Path Aliasing.

We have discussed custom-tailoring a website's NOFOLLOW and Path Aliases to tightly tune the "stream" of natural search flow throughout your website.  By tuning what the search engines "see" you will be able to help your search engine rankings climb for the pages that you most care about.  In large scale e-Commerce platforms, it becomes an onerous task to keep up with all of these customizations.  Here is another case where your choice of an atomic data model will serve to automate some of these functions.

Let's examine the following model of SEO Weighted Auto-Mapping:

Here is a scenario where we assign "weights" to the various Property nodes that can be mapped to Products.  Once the weights are assigned, we can develop custom business rules that will help us "scale up" or "scale down" our Weighted Path's sensitivities to the search engines (through the use of NOFOLLOW tags).

We can roll back to the original case of a standard brick and mortar store that was the basis of our e-Business (for example).  In a traditional brick and mortar business, let's say that we determined that in general, segmenting our customers by Gender tended to be the most common and most popular means of diverting our customer traffic.  This could give us a clue on our e-Commerce website on what weight to assign to the Gender Property.  Since this Property holds primacy over the rest of the Properties in our categorization scheme, we could assign it with a high "weight" value.

Take our example above where we have decided that the Player Property is the highest ranking "Path" starting point in our categorization schema.  This is essentially because, in the cycling apparel business, Lance Armstrong (the keyword phrase) drives a significant portion of our prime traffic.  It also tends to be a highly competitive term that we would like a high search ranking for.  Additionally, it is a phrase that we would like to channel a lot of natural search traffic through, even to the exclusion of other lower performing phrases that have a significantly lower revenue opportunity.  For this, we assign the Player Property (regardless of the specific Player identified) a weight of 10.  This means that a customer that "lands" on the Lance Armstrong Player landing page who directly orders a product is defining the primary Customer Path that we are interested in promoting and that path gains a score of 10 / 1 (hop) which averages out to a 10 (no surprise).  Any links to this particular URL do NOT receive the NOFOLLOW parameter and the natural search engines will stream most of their energy through links like this.

We also have the option of defining our business logic for what rules we want to apply.  One example is how we set the threshold for NOFOLLOW parameter placements.  We have decided in the above example to set NOFOLLOW parameters on any Customer Paths that rank less than an average of 10.  Effectively we are deciding that we want the full "stream" of the natural search engines to flow through these highly weighted paths, which will tend to be very direct links through Products mapped to the Player Property.

We can layer in other business rules as well.  One business rule that we are using in the above model is the method of computing a multi-step Customer Path "weight".  In the example above, we simply decided to add the cumulative weights of all "hops" in each Customer Path and divide by the number of hops.  Take the Customer Path of "Tank Tops / Ladies / Cycling / Lance Armstrong".  Each "hop" as the customer steps through that path adds to our total and because there are four hops along the path, we divide the total (34) by the hops (4) and come up with an overall weight of 8.5.  This business rule may be subject to some review.  It seems that an alternative formula might be to reduce each hop's weight by the "distance" from the initial starting point.  This would then be 8 + (7-1) + (9-2) + (10-3) = 28 / 4 = 7.  However you decide to "compute" the average weight of any given Customer Path, it should make sense for your business while delivering some automation where possible for the NOFOLLOW mappings within your categorization scheme.

This demonstrates yet another possible use of blending the choice of data structure with your requirements for SEO initiatives.  We can explore more methods of integrating data models with Search Engine Optimization techniques in Part 6 - Best Business Practices for Product Catalog Data Structures - Search Optimization.