"Well implemented true RDBMSs..." exist only in the weird fantasy world of relational purists. In that perfectly normalized world you just wave your magic wand and all performance problems disappear.
No one seems to talk about this but the "Moore's law has ended" meme does not apply to most database scenarios. Database servers are normally not CPU bound: they are bound by available memory and disk bandwidth and these are both still increasing.
Relational databases are relatively easy for anyone to start working with, but optimizing and tuning queries requires fair amount of domain knowledge. You'd be surprised to learn how little denormalization big financial company databasees have. They have people who are experts that do only databases.
For I/O intensive work you may need transaction rows where data written together is stored together, but it's not denormalization, it's optimizing for I/O bottleneck.
Query planners can do most of the work, if the person crafting queries has understanding of how they work. Carefully grafted queries and tuning memory layout for tables correctly is usually the right way to fix performance problems.
Because, unlike most products which only require training to work with, true RDBMSs require knowledge of data and relational fundamentals and such is scarce today, because education has been replaced by sheer training.
How many database "experts" do you know today who have any background in logic and math, particularly those designing DBMSs?
I've watched them for almost 50 years and they've become worse and worse, not better.
If users are so accepting of denormalization as a solution to performance and are unaware of all its costs, why should vendors bother to come up with true RDBMSs that obviate the need to denormalize?
As long as users do not understand the RDM and its benefits true RDBMSs will remain a fantasy. Ignorance has never been conducive to progress.
>kpmah: I think most are pragmatic and admit you _may_ have to do denormalise for performance
>calpaterson: So _rare_ to actually have to denormalise for performance today though.
The article's author didn't describe it as "may"/"rare" which leaves some wiggle room for the cases of denormalization required in real-world implementations. Instead he used absolute qualifiers such as "never", "no reason", "any":
- the "additional development costs" that Bolenok refers to -- but they would _never_ be justified:
- "consistency-performance tradeoff [...], there is _no_ reason to expect _any_."
The author does write his advice in abstract terms instead of discussing concrete "case studies" so we are left to speculate what mental model of the database world he holds in his mind when he's rigid with strict rules of normalization and relational purity. Based on the topics in his papers[1], I'm guessing his world consists of a single OLTP database. E.g, you develop a non-cloud restaurant reservation & POS system with a single-instance database. Yes, you don't need any denormalization hacks in that scenario.
But for other problems such as distributed databases, you can't do joins across 2 geographically separated data centers. (Well, you theoretically could do it but the slow nested-loop performance across the WAN would make it unusable for a real-world application.) Some duplication via data denormalization is required and no "sufficiently smart db engine"[2] can automatically optimize for it. An application architect has to manually make that design decision and live with the deliberate tradeoffs. (E.g., batch jobs now have to be run to periodically keep databases at different datacenters in sync.) There are many real-world scenarios that require denormalization which have nothing to do with a junior programmer's lack of SQL knowledge to join 20 tables to populate an "edit customer" data entry screen.
[1]http://www.dbdebunk.com/p/papers_3.html
[2]riff on the theoretical "Sufficiently Smart Compiler" to solve all performance optimizations so there's never a need to write performance-specific language syntax or switch to a "faster" language... because as we all know... "languages" are not fast or slow -- it's the "implementations" that are fast or slow. That Ruby is not as fast as C/C++ is an implementation detail (SSC) and not the issue of the language syntax.
But for other problems such as distributed databases, you can't do joins across 2 geographically separated data centers
I work with databases for, literally, decades and have never seen a successful implementation of two-phase-commits or joins on geographically distinct entities.
I don't blame poor performance on the mathematical purity of logical relations between entities.
>the article does imply that on rare occasion you may be forced to denormalize
You didn't read the article carefully enough. You overlooked how the author seemed to allow for denormalization but he immediately negated the followup dev work as "never justified":
" -- and performance is still unsatisfactory. You denormalize and, as Bolenok recognizes, introduce redundancy. [..] - but they would never be justified:"