The introduction and technical-implementation part of this discussion was in Part 1.
It seems likely that widespread adoption of native XML storage is, at best, several years off, if for no other reason than that the DML (Data Manipulation Language) situation is still rather primitive. But looking beyond that nontrivial problem, it does seem as if there are broad classes of application that might go better in native XML. Here’s a survey.
First of all, there’s what might be called custom document composition – technical publishing, customized technical manuals, etc. If you make complex products, or sell information, this is obviously an important specialty application for you. Otherwise, it probably is rather peripheral, at least for now. If you do have an interest in this area, by the way, you shouldn’t only look at the big guys’ XML offerings; you should even talk to specialists like Mark Logic. (Mark Logic sells an XML-only DBMS with a strong text-search orientation.)
Second, there are complex documents with low update rates. Medical records are a prime example – and, by the way, may of those are stored in InterSystems’ OODBMS Cache rather than in a relational system. Other examples might include insurance claims, media assets, etc. – basically, the areas that have been thought of as the purview of document management systems. In many cases, these apps ain’t broke and shouldn’t be fixed, such as when they exist mainly to satisfy slow-changing regulatory requirements. Besides, it’s not obvious that native XML is particularly useful for these apps anyway. Often, the information is in a DBMS for three main reasons: General manageability (e.g., backup), ad-hoc searchability, and management of metadata. If the metadata is simple enough to fit comfortably into a tabular structure, extended-relational DBMS may be satisfactory as underpinnings for these apps indefinitely.
Third, and here’s where it really begins to get interesting, is complex transactional documents. One of the flagship apps in Viper’s alpha test was financial derivatives trading, with complex, number-laden, term-laden contracts being processed very quickly, and it’s easy to envision that kind of functionality spreading across the trading sector. Governments – wisely or not – may want to require new complex forms to be filled out, or to make older ones easier to process. (E.g., tax returns, or applications for various kinds of permits.) If privacy concerns allow, medical information might be collected and processed centrally by governments or large insurance providers. Complex service-level agreements could be negotiated for a broad variety of product and service categories. Customers might demand radically faster processing of insurance claims than has historically been necessary. Indeed, it’s hard to think of an industry sector where complex transactional documents might not gain a foothold. And if you’re looking for high performance access to portions of documents, native XML may well be the best storage choice.
Finally, there’s a fourth category, which I’ll give the trendy-looking name Profiles 2.0, in imitation of Web 2.0, Identity 2.0, and so on. Here’s what I mean by it. A number of the hottest buzzconcepts in computing focus on collecting, organizing, and using information about individual people – presence, identity, personalization/customization, narrowcasting/market-of-one, data mining/predictive analytics, weblog analysis, social software, and so on. Put all those together, and you have a humongous hairball of a user profile that no current systems come close to handling properly.
Let’s think about some characteristics of this data. Some of it is transient. Some of it is unreliable. Some of it indeed is guesswork – albeit educated guesswork – rather than fact (e.g., the results of data mining analyses). Much of it exists for some profilees but not others. Much of it is naturally tree- or graph-shaped (e.g., information about website traversal, product category interests, relationship networks, role-based authorizations, etc.) There are many kinds of it; pulling it all together relationally can lead to Joins From Hell.
And this isn’t just for individuals; similar kinds of stories can be told for information about organizations, battleships, and so on. Those are objects with rich internal structures. True, those can usually be modeled hierarchically – but at each node, some of the complications mentioned in the prior paragraph occur. Profiling an enterprise is even messier than profiling a single individual who shops or works there.
Applications using this kind of information are typically extremely primitive, even though the beginnings of the personalization hype are now 7-8 years in the past. I don’t think we’re going to get these systems kind right until we take a true, holistic view of individuals and their profiles – and until we learn how to think about apps whose fundamental objects keep changing in shape. But as hard as the problem is, it has to be worked on immediately, because what I’m talking about here are some of the major classes of competitive-advantage app.
So Profiles 2.0 isn’t something we can just ignore. And when we do pay attention to it, I don’t think we’ll find that it looks very natural dressed in rows and columns.