I am working on entirely new papers (not re-writes) in the PRACTICAL DATABASE FOUNDATIONS series. I have already published two:

• THE FIRST NORMAL FORM - A DEFINITIVE GUIDE
• PRIMARY KEYS - A NEW UNDERSTANDING

available for ordering from the PAPERS page, and two more:

• RELATIONAL DATABASE DOMAINS: A DEFINITIVE GUIDE
• DATABASE RELATIONS: A DEFINITIVE GUIDE

are in progress and forthcoming, respectively.

In the process I am coming across common and entrenched industry "pearls" that I am using for my "Setting Matters Straight" (SMS) and "To Laugh or Cry" (TLC) posts on Linkedin. I do those posts to enable the few thinking database professionals left realize how scarce foundation knowledge is, and to illustrate fallacies that abound in the industry, of which they are unaware, and which the papers are intended to dispel.

Time permitting, I may expose and dispel some of those fallacies, treated in more depth in the papers, such that those thinking professionals can test their knowledge and decide whether the papers are a worthy educational investment.

Here's one.

 “A domain in most SQL usage is essentially an alias name for an existing type + restrictions on an existing type that can be used in a column. As for an attribute, it's essentially a COLUMN in SQL, a field in other types of databases, etc.”

I am working on entirely new papers (not re-writes) in the PRACTICAL DATABASE FOUNDATIONS series. I have already published two:

• THE FIRST NORMAL FORM - A DEFINITIVE GUIDE
• PRIMARY KEYS - A NEW UNDERSTANDING

available for ordering from the PAPERS page, and two more:

• RELATIONAL DATABASE DOMAINS: A DEFINITIVE GUIDE
• DATABASE RELATIONS: A DEFINITIVE GUIDE

are in progress and forthcoming, respectively.

In the process I am coming across industry common and entrenched "pearls" that I am using for my "Setting Matters Straight" (SMS) and "To Laugh or Cry" (TLC) posts on Linkedin. I do those posts to enable the few thinking database professionals left realize how scarce foundation knowledge is, and to illustrate fallacies that abound in the industry, of which they are unaware, and which the papers are intended to dispel.

Time permitting, I may expose and dispell some of those fallacies, treated in more depth in the papers, such that those thinking professionals can test their knowledge and decide whether the papers are a worthy educational investment.

Here's one:

“There seams to be some confusion between what a Primary Key is, and what an Index is and how they are used. The Primary Key is a logical object. By that I mean that is simply defines a set of properties on one column or a set of columns to require that the columns which make up the primary key are unique and that none of them are null. Because they are unique and not null, these values (or value if your primary key is a single column) can then be used to identify a single row in the table every time. In most if not all database platforms the Primary Key will have an index created on it. An index on the other hand doesn’t define niqueness. An index is used to more quickly find rows in the table based on the values which are part of the index. When you create an index within the database, you are creating a physical object which is being saved to disk.”

 Can you identify the fallacies before you proceed?

Note: In "Setting Matters Straight" posts I debunk online pronouncements 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.

“As I have said many times, if the original relational model had been based on predicate logic and also the semantics and rules of definitions we'd all be better off now. It wasn't. Full stop.”

--Ronald Ross, LinkedIn.com

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 disregarded evolution/progress of RDM, I am re-visiting my old debunkings, bringing them up to the current state of knowledge. This will enable you to judge how well arguments have held up and realize the increasing gap between industry stagnation --  and scientific progress.

DENORMALIZATION, PERFORMANCE & INTEGRITY

(Email exchange with reader originally published August 2002)

Then ...

“I'd like to comment on your other recent articles: on denormalization. Of course you prove that denormalization does not improve performance, because you pay for it by maintaining integrity. But, when people say that de-normalization improves performance, they usually mean just on one side. For example, I can merge DEPT and EMP tables into a third table DE and achieve a better query performance by replacing a join by a simple select from the new table. If this is the most frequent and most important operation in my application (vs. updates, inserts, deletes), then my overall performance will be improved (and that's what usually happens in DW). But if the opposite is true, then performance will suffer. I didn't see these considerations in your articles ...

Many people, yes, but not nobody. I always considered the cost of denormalization. I know many people in this field that do the same; however, I do agree with you that many people, especially those "younger" ones learning from more "modern" books on database design, especially those in the OO field, are not aware, and what's worse, don't even want to be aware.

That's exactly how I always thought and when I had discussions with people, that's what I always said to them (not that it made a big difference in their thinking). However, when I read your articles on this topic, I had another thought. As you always say (and again, I fully agree with you on this), we must always separate logical and physical. I always considered denormalization as one of the things done at the physical level. So, denormalization shouldn't even be your concern, because it has nothing to do with the relational model. The rule I always follow is that whatever I do at the physical level, it should not destroy my logical model, which must stay normalized. If I denormalize to achieve some performance gains for a selected set of functions, then I do pay for it by writing additional logic to preserve the integrity and by creating views that represent the entities on my logical model, which I had to "destroy". So as long as I separate these two levels, I don't think I'm in any conflict with the relational model. Of course if DBMS gave me more options in physical design while protecting the integrity of my logical model, I wouldn't have to do this myself.

Theoretically, I think the way you do, and that's why I enjoy reading your columns. But I also have to deliver practical results to my users. Unfortunately, I can't go to my users and tell them that their response time is slow because of Oracle's technology. And I don't believe screaming at Oracle will do me any good either (and yes I know what you will say to this). So until that mysterious technology you mentioned many times is implemented, I have to do what I can.”

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.

Then ...

“I find [your article in DM Review] to contradict your stated devotion to scientific methods and the value of theory. You present a single example of denormalization, then proceed to draw a conclusion about denormalization in general. In addition, the example chosen is not typical of real world denormalizations.” In order to be half-way consistent with your own ideals, you would need to present at a minimum an exhaustive list of the types of denormalizations used in practice, along with an objective list of the pros and cons of each.  I would expect that if this were undertaken, you would end up with a more balanced view, and some exceptions to your black-and-white conclusions. Of course, to prove your point scientifically would require far more effort than this, if indeed it were at all possible to prove or disprove your statements. This brings me to my key point: if your contention is not falsifiable, it does not belong in the realm of true science at all, instead it belongs in the domain of mere opinion and belief. Please tell us how you have proved your propositions, or else refrain from claiming that you are working from a sound scientific foundation and everyone else is somehow misguided. Relational algebra has nothing to say about real-world performance.”

Note: In "Setting Matters Straight" posts I debunk online pronouncements 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.

“A conceptual data model usually just includes the main concepts (entities) required to store information and the relationships that exist between these entities. We don’t usually include any details about each piece of information. We can consider the conceptual stage as an initial model, without all the details required to create a database.

A logical data model is probably the most-used data model. It goes beyond the conceptual model; it includes entities, relationships, details on entities’ different attributes, and unique ways to identify entities (primary keys) and establish the relationships between them (foreign keys).

A physical data model is usually derived from a logical data model for a particular relational database management system (RDBMS), thus taking into account all technology-specific details. One big difference between logical and physical data models is that we now need to use table and column names rather than specifying entity and attribute names. This allows us to adapt to the limits and conventions of the desired database engine. We also provide the actual data types and constraints that allows us to store the desired information.”

--Vertabelo.com

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 is a re-published series of several DBDebunk 2002 posts on Simon Wlliams' Lazy Software so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

• Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that triggered the series.
• Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.
• Part 3 was my email exchange with Williams where he provided his definition of a data model on which I conditioned any discussion with him and I debunked it.
• Part 4 is my response to a reader's comments on my previous posts in the series.
• Part 5.1 provided the background for my critique of Edward Hurley's report on Simon Williams's Lazy Software and his so-called "Associative Model of Data" (AMD).

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 is a re-published series of several DBDebunk 2001 exchanges on Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was an email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series. Part 2 was my response to a reader's email questioning our dismissal of Williams's claims. Part 3 was my email exchange with Williams where he provided his definition of a data model on which I conditioned any discussion with him and I debunked it. Part 4 is my response to a reader's comments on my previous posts in the series.

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 is a re-published series of several DBDebunk 2001 exchanges on Simon Wlliams' so-called "Associative Model of Data" (AMD), academic claims of its superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 was the email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the series. Part 2 was my response to a reader's email questioning our dismissal of Williams's claims.  Part 3 is my email exchange with Williams: he provided his "definition" of a data model on which I conditioned any discussion with him and I proved my point by debunking it.

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.

“Codd's aim was to free programmers from having to know the physical structure of data. Our aim is to free them in addition from having to know its logical structure.”

                                                 --Simon Williams, LazySoft

This series is a re-publication of several DBDebunk 2001 posts in response to Simon Wlliams' so-called "Associative Model of Data", academic claims of superiority over RDM ("The Associative Data Model Versus the Relational model") and predictions of the demise of the latter ("The decline and eventual demise of the Relational Model of Data").

Part 1 is the email exchange among myself (FP), Chris Date (CJD) and Lee Fesperman (LF) in reaction to Williams' claims that started the whole thing.

Note: In "Setting Matters Straight" I post on LinkedIn online Q&As that involve fundamentals under the header "What's Right and Wrong with this Database Picture" and then debunk them here. 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: “I'm not sure what this means: "The order of the rows and columns is immaterial to the DBMS?" -- could anyone explain?”

A: “It means two things:
The engine is under no obligation to insert new rows immediately following the previously inserted row(s)... During processing of selects, the optimizer is free to use any index it finds efficient to use or none at all... For this reason, if the order of returned data is important to your processing, then you must include an ORDER BY clause.”

Q: “How do you reorder fields in the database?”

A: “Depends on how you define "reorder". What view of your data are you trying to set the order. Are you in Table Design view? ... Are you looking at form? The answer is different depending on what you are referring to.”

--Quora.com

Note: "Setting Matters Straight" is a new format: I post on LinkedIn an online Q&A involving data fundamentals that I subsequently debunk in a post here. This is to encourage readers to test their foundation knowledge against our debunking here, where we confirm what is correct and correct 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: “How do I avoid too many relations in databases?”

A: “You don’t. Every relation is there to store meaningful data, hopefully you do not define database relations for data that are not to be stored in your database.”

A: “By following proper design principles. Normalization, standard data patterns, and progressing from logical to physical always. Never denormalize (or avoid normalizing in the first place) because performance never trumps accuracy. It really doesn't matter how fast you get the wrong answer.”

--Quora.com

Note: "Setting Matters Straight" is a new format: I post on LinkedIn an online Q&A involving data fundamentals to encourage readers to test their foundation knowledge, which they can then compare with our debunking here, where we confirm what is correct and correct what is fallacious (with clarifications, wherever necessary). For in-depth treatment 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).

Q: “What is the relationship between an entity and a record?”

A: “In the context of a database design, an ‘entity’ is a type or category of persons, places, things or events. It’s a collectivisation of the nouns in a system about which you wish to keep data. For example, Employee might be the name of an entity in your system. A ‘record’ is a collection of data about a specific entity, a particular person or place, an identifiable thing, or a single event. For example, Name: ‘Dave Voorhis’, StartYear: 2019, Salary: £1,398,293 might be a record of one Employee entity in your system.”

A: “Database, file, and recordset are basically the same thing. They are collections of information or data. Each database or file or recordset typically has some sort of common purpose or definition. Like a database (relational, hierarchical, etc.) of data of a business process. A File is again a collection of data such as all transactions to be posted. A recordset is also basically a file.

Entity and table are basically the same thing. While you have the grouping of all the data, and entity (logical view) and a table (physical view) are the same. As Dave said, it is a logical grouping of a specific piece of data.

File, recordset, record, row or line are basically the same. A .csv file is a grouping of records. A file is a grouping of records. A row is an individual grouping of data from a relational database.

The last is element or attribute or field. This is the individual piece of data like Transaction_Amount or First Name.”

--Quora.com

One of the core objectives of this site (and my work) has been to demonstrate that there will not be progress in data management as long as the industry and trade media require and promote exclusively (mainly tool) experience in the absence of foundation knowledge. I have published and analyzed ample evidence that relational language and terminology are used without grasping what it actually means -- a good way to gauge lack of foundation knowledge.

Recently I posted a four part series titled "Nobody Understands the Relational Model" showing that even a practitioner steeped in the RDM does not really understand it. Consider now a practitioner's mistake at the beginning of career -- "a bad database schema and what it did to system performance" -- which, he claims, belatedly taught him a lesson. Hhhhmmm, did it, really?

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.

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).

In Part 1 we showed (yet again) how even those with their heart in the right (relational) place can't help being affected by the common and entrenched industry misconceptions, in this case about relationships, relations and tables. More often than not authors exhibit the very misconceptions they try to debunk.

We left the author distinguishing sets (with unordered, unique elements) from tables (lists of ordered, possibly duplicate rows). 

Note: To demonstrate the correctness and stability conferred by a sound theoretical foundation relative to the industry's fad-driven, ad-hoc "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 of then 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.

The following is an email exchange from 2001 that I recommend reading jointly with my Data Meaning and Mining post (itself a revision of an article originally published at the old "All Analytics" website). I have slightly touched my replies for pedagogical purposes and clarity. You can substitute any data structure for XML hierarchy.

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.

This is an email exchange with readers in response to my article Normalization and Performance: Never the Twain Shall Meet.

“To Normalize or not to Normalize? that really isn't a question. few things to consider:
Normalization is supposed to protect from data anomalies, but not prevent us from using data encapsulation is the magic trick that allows you to do what you want without breaking rules.what are your experiences with normalization?”

This is a question that at this time need -- and should -- not be asked anymore, and the fact that it still is is one confirmation -- among many -- that there is no progress in data management. According to the current understanding of the RDM:

• Database relations are both normalized (in 1NF) and fully normalized (in 5NF) by definition, otherwise they are not relations and the relational algebra (RA) does not work;
• Adherence to three database design principles produces 1NF and 5NF relational databases;
• Consequently, there should not be such a thing as "doing" normalization (to 1NF) and further normalization (to 5NF) except to repair databases that are non-relational due to failure to adhere to the principles.

Note: The three design principles are fundamental to SST/FOPL foundation of the RDM, but were never understood even by relational proponents. I do not know what encapsulation has to do with this.