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Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

This was an email exchange with a reader, first published as ON ON-THE-FLY THINKING in March 2005.

“From a quick one-page marketing article:

"Relational databases are one to two orders of magnitude too slow."
--Michael Stonebreaker, quoted in Data on the Fly, Forbes

The quote is directly from the company's owner who "...created two well-known relational database systems, Ingres and Postgres." Further from the reporter:

"Unlike traditional database programs, Streambase analyzes data without storing it to disk, performing queries on data as it flows."

Hmmm... didn't know that the Relational Model of Data specifically proscribed in-memory implementations.”

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Note: In "Setting Matters Straight" posts I debunk online Q&As that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

“...The relational model organizes data through relations (aka tables). You then normalize it in one of six forms. By normalizing data you:
- Reduce redundancy
- Ensure consistency
- Optimize for atomic inserts, updates and deletes
The biggest drawback ... are keys that let you join different tables across multiple systems.”

                                                                      --LinkedIn.com

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(First published in 2006)

Note: "Then & Now" (T&N) is a new version of the old "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

(Nothing to add on re-publication.)

There is massive ignorance in data management, but the vociferous kind (VI) is the purview of a special breed that is characterized by one or more of the following:

1. Lack of knowledge and understanding of, and appreciation for data fundamentals in general and relational concepts, principles and methods in particular.
2. Unwillingness to let it stand in the way of pronouncing extensively on the subject.
3. Inability and/or unwillingness to respond to evidence of ignorance and/or to reason.
4. Lack of interest — often admitted — in truth and correctness.
5. Focus on self promotion and appeasement of the industry by riding fads, or telling (uninformed) audiences what they want to hear.

The combination of 1 and 2 characterizes the Unskilled and Unaware of It,  2 is the vociferous part. Frankfurt defined 4 as bullshit. 5 is just self-aggrandizing.

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by David McGoveran with Fabian Pascal

Note: In "Setting Matters Straight" posts I debunk online Q&As that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

“In a RDBMS, a table is columned rows, as in you treat individual rows as an actual entity while the columns are its attributes. In an excel tab, you can create a column, but it doesn't have to have all the same data types in that column, nor does one row have to represent one entity. It's more free form ... All in all, RDB is relational because it's column based rows and constrained to that format, while non relational can have free form like an excel. When you have rows that are uniform (constrained to what the column should be), you create entities as tables, and link them through columns to keep track of the relationships.”

--Quora.com

I posted this on LinkedIn as one of my "To Laugh or Cry?" items which, unlike "Setting Matters Right" posts, are beyond debunking. But the exchange that followed made me realize that there is, nevertheless, pedagogical value to it: it expresses something important, but poorly due to author's lack of foundation knowledge.

Follow @DBDebunk Follow @ThePostWest

Note: "Then & Now" (T&N) is a new version of what used to be the "Oldies but Goodies" (OBG) series. To demonstrate the superiority of a sound theoretical foundation relative to the industry's fad-driven "cookbook" practices, as well as the evolution/progress of RDM, I am re-visiting my 2000-06 debunkings, bringing them up to my with my knowledge and understanding of today. This will enable you to judge how well my arguments have held up and appreciate the increasing gap between scientific progress and the industry’s stagnation, if not outright regress.

On NULLs and Multi-Table Relvars

(first published 04/05/02)

"I had a question about the missing-values suggestion in PRACTICAL ISSUES IN DATABASE MANAGEMENT, page 234. You write:

"Table operations would have to be modified to yield results with as many tables as there are types of propositions with only known values."

How would this be represented in a language like Tutorial D, where relvars are required to be strongly typed? One possible idea is to make use of type inheritance. Suppose I had a domain of tuple values {x,a,b,c} (all integers, say) where x is not allowed to be missing but a, b, and c are allowed to be missing. Suppose we extended the domains of a, b, and c with an "imaginary" special value that we will never represent, which I will show for diagram purposes only as '?'. Then the domain can be split into parts:

XABC {x,a,b,c} possrep: {X: int, A: int, B: int, C: int}
XAB {x,a,b,'?'} possrep: {X: int, A: int, B: int}
XAC {x,a,'?',c} possrep: {X: int, A: int, C: int}
XBC {x,'?',b,c} possrep: {X: int, B: int, C: int}
XA {x,a,'?','?'} possrep: {X: int, A: int}
XB {x,'?',b,'?'} possrep: {X: int, B: int}
XC {x,'?','?',c} possrep: {X: int, C: int}
X {x,’?','?','?'} possrep: {X: int}

Using Mr. Date's specialization by constraint idea, we can inherit all the subtuple types from the main tuple type. Updates could make a tuple change type. A relation of relations of XABC type could be used to return results of a query. Each relation within the relation would contain one subtype.

However, the exponential explosion of possible subtypes would be very difficult to handle, practically speaking. As you admit in your book, a real DBMS might have to handle thousands of small subtables. This cannot be passed off as an "implementation detail" since table operations "yield results" at the user presentation level. No matter how efficient the underlying system might be, this seems unacceptable. Perhaps we have to fall back on default values after all."

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Note: In "Setting Matters Straight" posts I debunk online Q&As that involve fundamentals which I first post on LinkedIn. The purpose is to induce practitioners to test their foundation knowledge against our debunking, where we explain what is correct and what is fallacious. For in-depth treatments check out the POSTS and our PAPERS, LINKS and BOOKS (or organize one of our on-site/online SEMINARS, which can be customized to specific needs). Questions and comments are welcome here and on LinkedIn.

Q: “What is a relation in database?”

A: “Relational databases were so named in 1970 by computer scientist E. F. Codd because the tables are themselves relations, which is a mathematical term. What makes a relation (aka a table) a relation? Basically:

• A relation has a heading, which names a finite set of columns.
• Columns are defined by their name and their type.
• A relation has a finite set of tuples (aka rows), and every tuple has the same set of columns (i.e. same name and type) as those named in the heading.
• Being finite sets, both the set of columns in the heading and the set of tuples in the relation have no duplicates and no inherent order.

See? There’s nothing about relationships between tables in the definition of a relation. You could have a relational database that contains just one relation. If there’s any relationship described in a relation, it’s actually the relationship between the columns within a relation. That is, the value "Pittsburgh" goes with the value "Steelers" on a given row because the relation is defined as "pro football teams by city" and therefore there’s a linkage between some values in the set of football teams and the set of city names.”  --Quora.com

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