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Note: Each "Test Your Foundation Knowledge" post presents one or more misconceptions about data fundamentals. To test your knowledge, first try to detect them, then proceed to read our debunking, reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date. If there isn't a match, you can review references -- reflecting the current understanding of the RDM, distinct from whatever has passed for it in the industry to date -- which explain and correct the misconceptions. You can acquire further knowledge by checking out our POSTS, BOOKS, PAPERS, LINKS (or, better, organize one of our on-site SEMINARS, which can be customized to specific needs).

“The key idea is "Parent-Child" relationship. Entities ~ Relations ~ Tables (tilde stands for "more or less like"). Concept of a Table resonates with most of the people just as everybody intuitively grasps a concept of "rows and columns” but might struggle with "tuples and attributes". Explain relations and relationships, 1:1, 1:N, N:N etc. Explain rationale for this way of collecting and storing data, touch upon data normalization, and tell a few anecdotes about cost of storage back in 1970 and Y2K problem it have caused; add that we have inadvertently created Y10K problem while fixing it (not exactly true but not wrong either). Show an ERD diagram, trace the relationships, introduce SQL, maybe run a few simple SELECT queries to help your listeners visualize it, including equijoin and ORDER BY. Save other JOIN types, data types and other, more advanced topics, and for the next encounter.”

--Quora.com

 An excellent example that validates my claim of lack of foundation knowledge in the industry: most "explainers" of RDM have acquired relational jargon, but do not know or understand it at all.

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(Title inspired by Richard Feynman)

In Parts 1 and 2 we provided some clarifications following a discussion on LinkedIn about our contention that, conventional wisdom notwithstanding, database relations -- distinct from mathematical relations -- are by definition not just in 1NF, but also in 5NF, as a consequence of which the relational algebra (RA), as currently defined for 1NF closure, produces update anomalies and, thus, is not a proper algebra. In this third part we will use that information to debunk some leftover misunderstandings in the discussion.

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Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may revise, break into parts, and/or add comments and/or references.

Remember XML DBMS? At one point it was the fad of the day, similar to today's NoSQL or the old new "knowledge graph" -- "the future that you ignored at the peril of being left behind". As I predicted, it went the way of all fads (ODBMS, Associative DBMS, you name them) together with their "data models" that were nothing of the sort. My prediction was grounded in the same sound foundations I rely on today -- unlike the industry we are progressing it -- that fads lack and which were and still are dismissed, evidence be damned.

Here's a typical example (comments on republication in square brackets).

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 with David McGoveran 

(Title inspired by Richard Feynman)

In Part 1 we explained that all database relations are, mathematically, relations, but not all relations are database relations, which are in both 1NF and 5NF and we agreed with a statement in a LinkedIn discussion ending as follows: "Update anomalies are not as big of a problem as an algebra where relations aren't closed under join". Unfortunately, update anomalies, closure, and how relational operators were defined are all interrelated and represent an even "bigger problem". Update anomalies are not "bugs", let alone irrelevant, but actually a reflection of  that much bigger problem.

In this second part we delve into that problem.

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Note: To demonstrate the correctness and stability due to a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, I am re-publishing as "Oldies But Goodies" material from the old DBDebunk.com (2000-06), so that you can judge for yourself how well my arguments hold up and whether the industry has progressed beyond the misconceptions those arguments were intended to dispel. I may slightly revise, break into parts, and/or add comments and/or references.

This is an email exchange with a reader responding to my third book.
(Originally posted on 06/21/2001)

“I'm presently reading your book PRACTICAL ISSUES IN DATABASE MANAGEMENT and there are a couple of points that I find a little confusing. I'll start first by saying that I have no formal database oriented education, and I'm attempting to familiarize myself with some of the underlying theories and practices, so that I can further my personal education and career prospects (but aren't we all!). My questions may sound a little bit ignorant, but that would be because I am! (Please note ignorant, not stupid!) I'll quote you directly from the book for this (possibly I'm taking you out of context or missing something important)

Chapter 3, A Matter of Identity: Keys, pg. 75: "Databases represent assertions of fact - propositions - about entities of interest in the real world. The representation must be correct - only true propositions (facts) must be represented."

Now, correct me if I'm wrong with a basic assumption here, but isn't a database simply a model of a "real world" data collection? I would've thought that the intention of a database would be to model real life effectively (and accurately) enough to provide useful data for interpretation. Now obviously this is not an easy process with complex data types, but would it even be possible to have a 100% true proposition with only atomic data types? (i.e. can a simplified model contain only facts?) In my understanding of modeling, any model that fits real life closely enough to be a good statistical representation is a usable model. e.g. Newton's Laws are accurate enough when applied on a local scale, but we need to use Einstein's model of space-time across larger scales. Wouldn't recording only "facts" (which I would presume you mean to be statements that are provable in the objective sense i.e. no interpretation, only investigation or calculation) possibly eliminate the utility of some aspects of the database? Or do we account for the interpretative aspect in the metadata or in some other way?

Essentially, I can see what you're saying, but not necessarily how you've reached the conclusion. Admittedly in an ideal world we should be able to record only facts in a database, but this is not an ideal world. As an example, in surveys we see such questions as "Are you happy with this product?" followed by a rating system of 1-5, or 'completely unhappy to completely happy'. This is an artificial enforcement of a quantitative measure on a qualitative property. How do we account for the fact that this is interpreted data and not calculated or measured?

My questions may have little relevance to database theory in general, but the concept fascinates me!”

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 with David McGoveran 

(Title inspired by Richard Feynman)

“As currently defined, relational algebra produces anomalies when applied to non-5NF relations. Since an algebra cannot have anomalies, they should have raised a red flag that RA was not defined quite right, especially defining "relation" as a 1NF table and claiming algebraic closure because 1NF was preserved. Being restricted to tabular representation as the "language" for relationships is like being restricted to arithmetic when doing higher mathematics like differential calculus -- you need more expressive power, not less! Defining RA operations in terms of table manipulations aided initial learning and implementations by making data management look simple and VISUAL. Unfortunately, it was never grasped how much was missing, let alone how much more "intelligent" the RA and the RDBMS needed to be made to fix the problems. And I can see that those oversights were, in part, probably due to having to spend so much time correcting the ignorance in the industry."

--David McGoveran

I recently posted the following Fundamental Truth of the Week on LinkedIn, together with links to more detailed discussions of 1NF and 5NF (see References):

“According to conventional understanding of the RDM (such as it is) [and I don't mean SQL], a relation is in at least first normal form (1NF) -- it has only attributes drawn from simple domains (i.e., no "nested relations") -- the formal way of saying that a relation represents at the logical level an entity group from the conceptual level that has only individual entities -- no groups thereof -- as members. 1NF is required for decidability of the data sublanguage.

However, correctness, namely (1) system-guaranteed logical validity (i.e., query results follow provably from the database) and (2) by-design semantic consistency (of query results with the conceptual model) requires that relations are in both 1NF and fifth normal form (5NF). Formally, the only dependencies that hold in a 5NF relation are functional dependencies of non-key attributes on the PK -- for each PK value there is exactly one value of every corresponding non-key attribute value. This is the formal way of saying that a relation represents facts about a group of entities of a single type.

Therefore we now contend that database relations are BY DEFINITION in both 1NF and 5NF, otherwise all bets are off.”

It triggered a discussion that raised some fundamental issues for which an online exchange is too limiting. This post offers further clarifications, including comments by David McGoveran, on whose interpretation of the RDM (LOGIC FOR SERIOUS DATABASE FOLKS, forthcoming) I rely on. The portions of my interlocutor in the discussion are in quotes.

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“Relational Data Integrity is like every other integrity constraint that checks that the relationships created between data using foreign keys has a consistency. This can be done by using ON UPDATE, ON DELETE constraints on the table.”

--Quora.com

I recently quoted this as one of my To Laugh or Cry? items on LinkedIn, which initiated an exchange triggered by the following question:

“You have a better definition? What is it?”

In the exchange the asker's interpretation seemed to be "referential constraints are constraints like any other constraints, so there is no problem".  It is hard to recognize misconceptions without proper understanding of the RDM. We ignore that the above is not really a definition and focus on debunking.

Decades ago I wrote an article in DATABASE PROGRAMMING AND DESIGN carrying the double-meaning title Integrity Is Not Only Referential, in which I debunked Borland's claim that its Paradox file manager supported referential integrity (at the time no PC product did). As one component of the RDM, database integrity is, of course, a DBMS function, but Pradox relegated it to applications. Then, as now, one of the most common and entrenched misconceptions was that relational comes from "relationships between tables" and so relational integrity amounts to referential integrity (RI). RI is, of course, but one of several components that comprise relational integrity -- it is necessary, but insufficient. While practitioners are familiar with referential and PK constraints, if asked what other constraints comprise relational integrity  very few know. Having enumerated them recently on LinkedIn, I asked this very question:

“... what other RELATIONAL constraints ARE there and what is their purpose? I recently posted a weekly truth and other items here that answer it.”

which went unanswered.

Data integrity is one of the three components of the RDM, together with data structure and manipulation. It consists of several categories of constraints which I detailed more than once, most recently in Understanding Relational Constraints, to which I referred the asker (can you give an example for each category?) Defining relational integrity means specifying all the constraint categories required by the RDM.

Consider now the above paragraph: it purports to define relational integrity, but it specifies functionality of referential integrity -- implying the old misconception I wrote about decades ago. The asker did not seem to comprehend the distinction:

“I can't see a problem here. Isn't it simply as follows? ... A *referential integrity constraint* ensures consistency between attributes of different entities - e.g. between primary and foreign keys of related entities (aka relational integrity). Isn't that what the definition says?"

Yes, it is the definition of referential integrity, but not of relational integrity -- there is more to the latter than the former. No matter in how many ways I tried to explain this, I was unable to convey it, because it's practically impossible in the absence of sufficient knowledge and understanding of the RDM.